System

Abstract

An object of a system according to an embodiment is to propose an optimal maintenance schedule in consideration of a maintenance history and building material characteristics of a house.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data of a maintenance history and building material characteristics of a house. The analysis unit analyzes the data collected by the collection unit and predicts deterioration and problems. The proposal unit proposes an appropriate maintenance schedule based on the analysis result obtained 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 technologies do not adequately propose optimal maintenance schedules that take into account the maintenance history of a home and the characteristics of building materials, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal maintenance schedule by taking into consideration the maintenance history of a house and the characteristics of building materials. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data on the maintenance history and building material characteristics of a home. The analysis unit analyzes the data collected by the collection unit and predicts deterioration and problems. The proposal unit proposes an appropriate maintenance schedule based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal maintenance schedule by taking into consideration the maintenance history of a house and the characteristics of building materials. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A maintenance proposal system according to an embodiment of the present invention proposes an optimal maintenance schedule by taking into account the maintenance history and building material characteristics of a home. The maintenance proposal system analyzes the maintenance history and building material characteristics of a home, predicts deterioration and problems, and proposes an optimal schedule to ensure continued safety and durability. For example, the maintenance proposal system collects data on the home's maintenance history and building material characteristics. For example, it collects information such as past repair history, the types of building materials used, and the durability of the building materials. This information is input into a generating AI. The maintenance proposal system then analyzes the collected data using the generating AI. The generating AI analyzes the maintenance history and building material characteristics to predict deterioration and problems. For example, it predicts how long it will take for a specific building material to deteriorate and which parts will need to be repaired again based on past repair history. The generating AI then proposes an optimal maintenance schedule based on the analysis results. For example, it proposes a schedule to repair specific building materials before they deteriorate and a schedule to perform regular inspections based on past repair history. This schedule is provided to the owner or manager. This allows the maintenance proposal system to ensure the safety and lifespan of the home. This allows the maintenance suggestion system to perform home maintenance efficiently and effectively, ensuring the safety and lifespan of the building. For example, owners and managers can prevent deterioration and problems by following the maintenance schedule suggested by the generative AI. Furthermore, the ability to easily create maintenance plans makes home management easier.

[0029] A maintenance proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data on the maintenance history and building material characteristics of a home. The collection unit collects information such as past repair history, the types of building materials used, and the durability of the building materials. The collection unit can also collect data using sensors, manual input, an external database, or the like. For example, the collection unit measures the durability of building materials using a sensor and collects the data. The collection unit can also collect past repair history through manual input. The collection unit can also acquire building material characteristic data from an external database. The analysis unit analyzes the data collected by the collection unit and predicts deterioration and problems. The analysis unit analyzes the data using, for example, statistical analysis or a machine learning algorithm. For example, the analysis unit can predict how long it will take for a specific building material to deteriorate using statistical analysis. The analysis unit can also predict which parts will require further repair based on past repair history using a machine learning algorithm. The analysis unit can also predict the progression of deterioration using a deterioration progression model. The proposal unit proposes an appropriate maintenance schedule based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes a schedule for repairs to be performed before a specific building material deteriorates. The proposal unit can also propose a schedule for periodic inspections based on past repair history. The proposal unit can also propose an appropriate maintenance schedule based on the timing and priority of repairs. As a result, the maintenance proposal system according to the embodiment can propose an optimal maintenance schedule taking into account the maintenance history of a house and the characteristics of building materials.

[0030] The collection unit can collect data on past repair history, the types of building materials used, and the durability of the building materials. The collection unit, for example, collects past repair history. For example, the collection unit collects information such as the type of repair, the frequency of repairs, and the repair history. The collection unit can also collect the types of building materials used. For example, the collection unit collects information such as the types of building materials, such as wood, concrete, and metal. The collection unit can also collect the durability of the building materials. For example, the collection unit collects information such as the number of years the building materials have been in use, the rate of deterioration, and the load-bearing capacity of the building materials. This makes it possible to collect data that takes into account the past repair history and the characteristics of the building materials. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input past repair history and characteristic data of the building materials into the generation AI and cause the generation AI to collect data.

[0031] The analysis unit can analyze the collected data and predict how long it will take for a specific building material to deteriorate. The analysis unit, for example, analyzes the collected data and predicts how long it will take for a specific building material to deteriorate. For example, the analysis unit can predict how long it will take for a specific building material to deteriorate using statistical analysis. The analysis unit can also predict how long it will take for a specific building material to deteriorate using a machine learning algorithm. The analysis unit can also predict how long it will take for a specific building material to deteriorate using a deterioration progression model. This makes it possible to predict the deterioration of a specific building material. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI perform a deterioration prediction.

[0032] The analysis unit can predict which parts will need repair again based on past repair history. The analysis unit, for example, analyzes past repair history and predicts which parts will need repair again. For example, the analysis unit predicts the need for repair based on past repair history. The analysis unit can also analyze the progression of deterioration and predict which parts will need repair again. The analysis unit can also predict signs of deterioration based on past repair history. This makes it possible to predict repairs based on past repair history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past repair history into a generation AI and have the generation AI execute a repair prediction.

[0033] The proposal unit can propose an appropriate schedule for repairing specific building materials before they deteriorate. The proposal unit, for example, proposes a schedule for repairing specific building materials before they deteriorate. For example, the proposal unit proposes a schedule for repairing specific building materials before they deteriorate based on a deterioration progression model. The proposal unit can also propose a schedule for repairing specific building materials before they deteriorate based on signs of deterioration. The proposal unit can also propose a schedule for repairing specific building materials before they deteriorate based on the timing and priority of the repairs. This makes it possible to propose a repair schedule before deterioration occurs. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input deterioration prediction data to a generation AI and cause the generation AI to propose a repair schedule.

[0034] The proposal unit can propose an appropriate schedule for periodic inspections based on past repair history. The proposal unit, for example, proposes a schedule for periodic inspections based on past repair history. For example, the proposal unit proposes a schedule for periodic inspections based on past repair history. The proposal unit can also propose a schedule for periodic inspections based on the frequency and history of repairs. The proposal unit can also propose a schedule for periodic inspections based on the timing and priority of the inspections. This makes it possible to propose a periodic inspection schedule. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past repair history data into a generation AI and cause the generation AI to propose an inspection schedule.

[0035] The collection unit can analyze past maintenance history and select an appropriate data collection method. The collection unit, for example, analyzes past maintenance history and selects the optimal data collection method. For example, the collection unit identifies areas that frequently require repair from the past maintenance history and prioritizes data collection. The collection unit can also prioritize data collection of building materials that are prone to deterioration based on the past maintenance history. The collection unit can also analyze past maintenance history and determine the optimal timing for data collection. This makes it possible to select the optimal data collection method based on the past maintenance history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past maintenance history data into a generation AI and have the generation AI select a data collection method.

[0036] The collection unit can perform filtering based on the current state and environmental conditions of the home when collecting data. The collection unit, for example, monitors the current state of the home in real time and collects data when an abnormality is detected. For example, the collection unit monitors the progress of deterioration of the home and collects data when an abnormality is detected. The collection unit can also collect data in conditions where deterioration is likely to progress, taking into account environmental conditions (temperature, humidity, etc.). The collection unit can also adjust the type and amount of data to be collected based on the state and environmental conditions of the home. This enables filtering of data collection based on the state and environmental conditions of the home. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input home state data and environmental condition data to a generation AI and have the generation AI perform filtering of data collection.

[0037] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects data using voice recognition technology. For example, the collection unit converts voice data into text data using voice recognition software. Furthermore, if the user uses text input, the collection unit can also collect data using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect data using image analysis technology. This makes it possible to select the optimal data collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the data collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data based on the geographical location information of the house. For example, if the house is located in a humid area, the collection unit prioritizes collecting data related to humidity. For example, the collection unit collects humidity data using a humidity sensor. Furthermore, if the house is located in an earthquake-prone area, the collection unit can prioritize collecting data related to earthquake resistance. Furthermore, if the house is located in a cold area, the collection unit can prioritize collecting data related to insulation performance. This enables data collection based on the geographical location information of the house. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data of the house to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can analyze the user's social media activities during data collection and collect relevant data. For example, the collection unit can analyze photos of the home shared by the user on social media and identify areas of deterioration. For example, the collection unit can analyze the photos of the home using image analysis technology and identify areas of deterioration. The collection unit can also analyze the content of the user's social media posts to determine the need for maintenance. The collection unit can also collect relevant data by referring to the activities of the user's friends on social media. This makes it possible to collect data based on the user's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect relevant data.

[0040] When collecting data, the collection unit can adjust the collection method based on the user's past feedback. The collection unit, for example, adjusts the type of data to be collected based on feedback provided by the user in the past. For example, the collection unit adjusts the frequency of data collection by referring to the user's past feedback. The collection unit can also customize the collection means (audio, text, image, etc.) by reflecting the user's past feedback. This makes it possible to customize the data collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the collection method.

[0041] During analysis, the analysis unit can appropriately adjust the level of detail of the analysis based on the importance of the building material. For example, the analysis unit performs a detailed analysis of building materials with high importance to accurately grasp the progress of deterioration. For example, the analysis unit performs a detailed analysis of the progress of deterioration for building materials with high importance. The analysis unit can also perform a simple analysis for building materials with low importance to grasp the overall deterioration state. The analysis unit can also adjust the frequency of analysis according to the importance of the building material. This makes it possible to adjust the level of detail of the analysis according to the importance of the building material. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input building material importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the building material. For example, the analysis unit applies a deterioration analysis algorithm dedicated to wood to wood. For example, the analysis unit analyzes the deterioration status of wood using a deterioration analysis algorithm dedicated to wood. The analysis unit can also apply a deterioration analysis algorithm dedicated to concrete to concrete. The analysis unit can also apply a deterioration analysis algorithm dedicated to metal to metal. This makes it possible to apply an analysis algorithm depending on the category of the building material. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input building material category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, learns the deterioration progression pattern based on the user's past analysis results and improves the accuracy of the analysis. For example, the analysis unit learns the deterioration progression pattern based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. The analysis unit can also adjust the frequency and timing of analysis based on the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can appropriately determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of recently collected data to understand the latest deterioration status. For example, the analysis unit prioritizes analysis of recently collected data. The analysis unit can also analyze the progress of long-term deterioration based on data collected in the past. The analysis unit can also adjust the frequency and timing of analysis depending on the time when the data was collected. This makes it possible to determine the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the time when the data was collected to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can appropriately adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high importance to grasp the progress of deterioration. For example, the analysis unit prioritizes analysis of data with high importance. The analysis unit can also analyze highly relevant data collectively to grasp the overall deterioration status. The analysis unit can also adjust the order and frequency of analysis according to the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] During analysis, the analysis unit can appropriately adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides the analysis result using detailed technical terminology. For example, if the user has technical expertise, the analysis unit can provide the analysis result using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis result using simple terminology. The analysis unit can also adjust the way the analysis result is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology in the analysis.

[0047] The suggestion unit can appropriately adjust the level of detail of the suggestion based on the importance of the maintenance when making a suggestion. For example, the suggestion unit provides a detailed suggestion for highly important maintenance. For example, the suggestion unit provides a detailed suggestion for highly important maintenance. The suggestion unit can also provide a simple suggestion for less important maintenance. The suggestion unit can also adjust the frequency and timing of the suggestion based on the importance of the maintenance. This makes it possible to adjust the level of detail of the suggestion based on the importance of the maintenance. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input maintenance importance data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0048] When making a proposal, the proposal unit can apply different proposal algorithms depending on the maintenance category. For example, the proposal unit applies a proposal algorithm dedicated to wood to wood maintenance. For example, the proposal unit proposes wood maintenance using a proposal algorithm dedicated to wood. The proposal unit can also apply a proposal algorithm dedicated to concrete to concrete maintenance. The proposal unit can also apply a proposal algorithm dedicated to metal to metal maintenance. This makes it possible to apply a proposal algorithm depending on the maintenance category. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input maintenance category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal based on the user's past proposal results. The suggestion unit, for example, learns the deterioration progression pattern based on the user's past proposal results and improves the accuracy of the proposal. For example, the suggestion unit learns the deterioration progression pattern based on the user's past proposal results. The suggestion unit can also optimize the proposal algorithm by referring to the user's past proposal results. The suggestion unit can also adjust the frequency and timing of proposals based on the user's past proposal results. This makes it possible to improve the accuracy of the proposal based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0050] When making a proposal, the proposal unit can appropriately determine the priority of the proposal based on the timing of maintenance. For example, the proposal unit prioritizes the proposal of maintenance that needs to be performed soon. For example, the proposal unit prioritizes the proposal of maintenance that needs to be performed soon. The proposal unit can also systematically propose maintenance that is required over the long term. The proposal unit can also adjust the frequency and timing of the proposals depending on the timing of the maintenance. This makes it possible to determine the priority of the proposals based on the timing of the maintenance. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input maintenance timing data into the generation AI and cause the generation AI to determine the priority of the proposals.

[0051] When making a proposal, the proposal unit can appropriately adjust the order of proposals based on the relevance of the maintenance. For example, the proposal unit prioritizes the proposal of maintenance with high importance. For example, the proposal unit prioritizes the proposal of maintenance with high importance. The proposal unit can also collectively propose maintenance with high relevance. The proposal unit can also adjust the order and frequency of proposals according to the relevance of the maintenance. This makes it possible to adjust the order of proposals based on the relevance of the maintenance. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input maintenance relevance data into the generation AI and cause the generation AI to adjust the order of proposals.

[0052] When making a proposal, the suggestion unit can appropriately adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides the proposal using detailed technical terminology. For example, if the user has technical expertise, the suggestion unit can provide the proposal using detailed technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide the proposal using simple terminology. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology in the proposal.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can collect data on the surrounding environment in addition to the home's maintenance history. For example, the collection unit collects meteorological data (temperature, humidity, precipitation, etc.) around the home to identify factors that affect the deterioration of building materials. The collection unit can also collect traffic data around the home to evaluate the impact of vibrations and noise on building materials. Furthermore, the collection unit can collect vegetation data around the home to analyze the impact of plant growth on building materials. This makes it possible to propose maintenance that takes surrounding environmental data into account.

[0055] In addition to predicting the deterioration of building materials, the analysis unit can also predict repair costs. For example, the analysis unit predicts repair costs based on past repair history and market price data. The analysis unit can also make suggestions to optimize the timing of repairs and minimize costs depending on the progress of deterioration. Furthermore, the analysis unit can compare the costs of different repair methods and propose the most economical option. This makes it possible to make maintenance suggestions that take costs into consideration.

[0056] In addition to making maintenance proposals, the proposal unit can also make proposals for improving energy efficiency. For example, the proposal unit may evaluate the insulation performance of a home and make proposals such as adding insulation or replacing windows. The proposal unit may also analyze the energy consumption of lighting and home appliances and propose replacing them with energy-efficient products. Furthermore, the proposal unit may propose the introduction of a solar power generation system to reduce energy costs. This makes it possible to make comprehensive maintenance proposals that take energy efficiency into consideration.

[0057] In addition to the home maintenance history, the collection unit can also collect data on residents' lifestyle patterns. For example, the collection unit collects data on the amount of time residents are at home and which rooms are frequently used, and determines maintenance priorities. The collection unit can also optimize the timing of maintenance based on the residents' lifestyle patterns. Furthermore, the collection unit can make suggestions for optimizing energy consumption based on the residents' lifestyle pattern data. This makes it possible to make maintenance suggestions that take residents' lifestyle patterns into consideration.

[0058] In addition to making maintenance proposals, the proposal unit can also make proposals that take into account the health status of residents. For example, the proposal unit can analyze air quality data within the home and propose installing an air purifier or improving the ventilation system. The proposal unit can also propose measures to prevent mold growth based on humidity data within the home. Furthermore, the proposal unit can also consider residents' allergy information and propose allergen countermeasures. This makes it possible to make comprehensive maintenance proposals that take into account the residents' health status.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects data on the home's maintenance history and building material characteristics. The collection unit collects information such as past repair history, the types of building materials used, and the durability of the building materials. The collection unit can also collect data using sensors, manual input, an external database, etc. For example, the collection unit measures the durability of building materials using a sensor and collects data. The collection unit can also collect past repair history through manual input. The collection unit can also obtain building material characteristic data from an external database. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts deterioration and problems. The analysis unit analyzes the data using statistical analysis and machine learning algorithms. For example, the analysis unit uses statistical analysis to predict how long it will take for a specific building material to deteriorate. The analysis unit also uses machine learning algorithms to predict which parts will need to be repaired again based on past repair history. The analysis unit can also predict the progression of deterioration using a deterioration progression model. Step 3: The proposal unit proposes an appropriate maintenance schedule based on the analysis results obtained by the analysis unit. The proposal unit proposes a schedule for repairs to be carried out before specific building materials deteriorate. The proposal unit can also propose a schedule for periodic inspections based on past repair history. The proposal unit can also propose an appropriate maintenance schedule based on the timing and priority of repairs.

[0061] (Example 2) A maintenance proposal system according to an embodiment of the present invention proposes an optimal maintenance schedule by taking into account the maintenance history and building material characteristics of a home. The maintenance proposal system analyzes the maintenance history and building material characteristics of a home, predicts deterioration and problems, and proposes an optimal schedule to ensure continued safety and durability. For example, the maintenance proposal system collects data on the home's maintenance history and building material characteristics. For example, it collects information such as past repair history, the types of building materials used, and the durability of the building materials. This information is input into a generating AI. The maintenance proposal system then analyzes the collected data using the generating AI. The generating AI analyzes the maintenance history and building material characteristics to predict deterioration and problems. For example, it predicts how long it will take for a specific building material to deteriorate and which parts will need to be repaired again based on past repair history. The generating AI then proposes an optimal maintenance schedule based on the analysis results. For example, it proposes a schedule to repair specific building materials before they deteriorate and a schedule to perform regular inspections based on past repair history. This schedule is provided to the owner or manager. This allows the maintenance proposal system to ensure the safety and lifespan of the home. This allows the maintenance suggestion system to perform home maintenance efficiently and effectively, ensuring the safety and lifespan of the building. For example, owners and managers can prevent deterioration and problems by following the maintenance schedule suggested by the generative AI. Furthermore, the ability to easily create maintenance plans makes home management easier.

[0062] A maintenance proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data on the maintenance history and building material characteristics of a home. The collection unit collects information such as past repair history, the types of building materials used, and the durability of the building materials. The collection unit can also collect data using sensors, manual input, an external database, or the like. For example, the collection unit measures the durability of building materials using a sensor and collects the data. The collection unit can also collect past repair history through manual input. The collection unit can also acquire building material characteristic data from an external database. The analysis unit analyzes the data collected by the collection unit and predicts deterioration and problems. The analysis unit analyzes the data using, for example, statistical analysis or a machine learning algorithm. For example, the analysis unit can predict how long it will take for a specific building material to deteriorate using statistical analysis. The analysis unit can also predict which parts will require further repair based on past repair history using a machine learning algorithm. The analysis unit can also predict the progression of deterioration using a deterioration progression model. The proposal unit proposes an appropriate maintenance schedule based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes a schedule for repairs to be performed before a specific building material deteriorates. The proposal unit can also propose a schedule for periodic inspections based on past repair history. The proposal unit can also propose an appropriate maintenance schedule based on the timing and priority of repairs. As a result, the maintenance proposal system according to the embodiment can propose an optimal maintenance schedule taking into account the maintenance history of a house and the characteristics of building materials.

[0063] The collection unit can collect data on past repair history, the types of building materials used, and the durability of the building materials. The collection unit, for example, collects past repair history. For example, the collection unit collects information such as the type of repair, the frequency of repairs, and the repair history. The collection unit can also collect the types of building materials used. For example, the collection unit collects information such as the types of building materials, such as wood, concrete, and metal. The collection unit can also collect the durability of the building materials. For example, the collection unit collects information such as the number of years the building materials have been in use, the rate of deterioration, and the load-bearing capacity of the building materials. This makes it possible to collect data that takes into account the past repair history and the characteristics of the building materials. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input past repair history and characteristic data of the building materials into the generation AI and cause the generation AI to collect data.

[0064] The analysis unit can analyze the collected data and predict how long it will take for a specific building material to deteriorate. The analysis unit, for example, analyzes the collected data and predicts how long it will take for a specific building material to deteriorate. For example, the analysis unit can predict how long it will take for a specific building material to deteriorate using statistical analysis. The analysis unit can also predict how long it will take for a specific building material to deteriorate using a machine learning algorithm. The analysis unit can also predict how long it will take for a specific building material to deteriorate using a deterioration progression model. This makes it possible to predict the deterioration of a specific building material. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI perform a deterioration prediction.

[0065] The analysis unit can predict which parts will need repair again based on past repair history. The analysis unit, for example, analyzes past repair history and predicts which parts will need repair again. For example, the analysis unit predicts the need for repair based on past repair history. The analysis unit can also analyze the progression of deterioration and predict which parts will need repair again. The analysis unit can also predict signs of deterioration based on past repair history. This makes it possible to predict repairs based on past repair history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past repair history into a generation AI and have the generation AI execute a repair prediction.

[0066] The proposal unit can propose an appropriate schedule for repairing specific building materials before they deteriorate. The proposal unit, for example, proposes a schedule for repairing specific building materials before they deteriorate. For example, the proposal unit proposes a schedule for repairing specific building materials before they deteriorate based on a deterioration progression model. The proposal unit can also propose a schedule for repairing specific building materials before they deteriorate based on signs of deterioration. The proposal unit can also propose a schedule for repairing specific building materials before they deteriorate based on the timing and priority of the repairs. This makes it possible to propose a repair schedule before deterioration occurs. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input deterioration prediction data to a generation AI and cause the generation AI to propose a repair schedule.

[0067] The proposal unit can propose an appropriate schedule for periodic inspections based on past repair history. The proposal unit, for example, proposes a schedule for periodic inspections based on past repair history. For example, the proposal unit proposes a schedule for periodic inspections based on past repair history. The proposal unit can also propose a schedule for periodic inspections based on the frequency and history of repairs. The proposal unit can also propose a schedule for periodic inspections based on the timing and priority of the inspections. This makes it possible to propose a periodic inspection schedule. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past repair history data into a generation AI and cause the generation AI to propose an inspection schedule.

[0068] The collection unit can estimate the user's emotions and adjust the appropriate timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can collect detailed data to obtain more accurate information. Furthermore, if the user is busy, the collection unit can automate data collection to reduce the user's effort. This enables the timing of data collection to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0069] The collection unit can analyze past maintenance history and select an appropriate data collection method. The collection unit, for example, analyzes past maintenance history and selects the optimal data collection method. For example, the collection unit identifies areas that frequently require repair from the past maintenance history and prioritizes data collection. The collection unit can also prioritize data collection of building materials that are prone to deterioration based on the past maintenance history. The collection unit can also analyze past maintenance history and determine the optimal timing for data collection. This makes it possible to select the optimal data collection method based on the past maintenance history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past maintenance history data into a generation AI and have the generation AI select a data collection method.

[0070] The collection unit can perform filtering based on the current state and environmental conditions of the home when collecting data. The collection unit, for example, monitors the current state of the home in real time and collects data when an abnormality is detected. For example, the collection unit monitors the progress of deterioration of the home and collects data when an abnormality is detected. The collection unit can also collect data in conditions where deterioration is likely to progress, taking into account environmental conditions (temperature, humidity, etc.). The collection unit can also adjust the type and amount of data to be collected based on the state and environmental conditions of the home. This enables filtering of data collection based on the state and environmental conditions of the home. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input home state data and environmental condition data to a generation AI and have the generation AI perform filtering of data collection.

[0071] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects data using voice recognition technology. For example, the collection unit converts voice data into text data using voice recognition software. Furthermore, if the user uses text input, the collection unit can also collect data using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect data using image analysis technology. This makes it possible to select the optimal data collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the data collection means.

[0072] The collection unit can estimate the user's emotions and determine an appropriate priority for the data to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority for the data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data of high importance. Furthermore, when the user is relaxed, the collection unit can collect detailed data to improve accuracy. Furthermore, when the user is busy, the collection unit can collect the minimum amount of data necessary to reduce the user's burden. This enables data prioritization according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0073] When collecting data, the collection unit can prioritize collecting highly relevant data based on the geographical location information of the house. For example, if the house is located in a humid area, the collection unit prioritizes collecting data related to humidity. For example, the collection unit collects humidity data using a humidity sensor. Furthermore, if the house is located in an earthquake-prone area, the collection unit can prioritize collecting data related to earthquake resistance. Furthermore, if the house is located in a cold area, the collection unit can prioritize collecting data related to insulation performance. This enables data collection based on the geographical location information of the house. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data of the house to the generation AI and cause the generation AI to collect highly relevant data.

[0074] The collection unit can analyze the user's social media activities during data collection and collect relevant data. For example, the collection unit can analyze photos of the home shared by the user on social media and identify areas of deterioration. For example, the collection unit can analyze the photos of the home using image analysis technology and identify areas of deterioration. The collection unit can also analyze the content of the user's social media posts to determine the need for maintenance. The collection unit can also collect relevant data by referring to the activities of the user's friends on social media. This makes it possible to collect data based on the user's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect relevant data.

[0075] When collecting data, the collection unit can adjust the collection method based on the user's past feedback. The collection unit, for example, adjusts the type of data to be collected based on feedback provided by the user in the past. For example, the collection unit adjusts the frequency of data collection by referring to the user's past feedback. The collection unit can also customize the collection means (audio, text, image, etc.) by reflecting the user's past feedback. This makes it possible to customize the data collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the collection method.

[0076] The analysis unit can estimate the user's emotions and appropriately adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This enables the presentation method of the analysis to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0077] During analysis, the analysis unit can appropriately adjust the level of detail of the analysis based on the importance of the building material. For example, the analysis unit performs a detailed analysis of building materials with high importance to accurately grasp the progress of deterioration. For example, the analysis unit performs a detailed analysis of the progress of deterioration for building materials with high importance. The analysis unit can also perform a simple analysis for building materials with low importance to grasp the overall deterioration state. The analysis unit can also adjust the frequency of analysis according to the importance of the building material. This makes it possible to adjust the level of detail of the analysis according to the importance of the building material. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input building material importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the building material. For example, the analysis unit applies a deterioration analysis algorithm dedicated to wood to wood. For example, the analysis unit analyzes the deterioration status of wood using a deterioration analysis algorithm dedicated to wood. The analysis unit can also apply a deterioration analysis algorithm dedicated to concrete to concrete. The analysis unit can also apply a deterioration analysis algorithm dedicated to metal to metal. This makes it possible to apply an analysis algorithm depending on the category of the building material. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input building material category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, learns the deterioration progression pattern based on the user's past analysis results and improves the accuracy of the analysis. For example, the analysis unit learns the deterioration progression pattern based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. The analysis unit can also adjust the frequency and timing of analysis based on the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotions and appropriately adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide an analysis result with a visually stimulating effect if the user is excited. This makes it possible to adjust the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0081] During analysis, the analysis unit can appropriately determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of recently collected data to understand the latest deterioration status. For example, the analysis unit prioritizes analysis of recently collected data. The analysis unit can also analyze the progress of long-term deterioration based on data collected in the past. The analysis unit can also adjust the frequency and timing of analysis depending on the time when the data was collected. This makes it possible to determine the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the time when the data was collected to the generation AI and have the generation AI determine the analysis priority.

[0082] During analysis, the analysis unit can appropriately adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high importance to grasp the progress of deterioration. For example, the analysis unit prioritizes analysis of data with high importance. The analysis unit can also analyze highly relevant data collectively to grasp the overall deterioration status. The analysis unit can also adjust the order and frequency of analysis according to the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0083] During analysis, the analysis unit can appropriately adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides the analysis result using detailed technical terminology. For example, if the user has technical expertise, the analysis unit can provide the analysis result using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis result using simple terminology. The analysis unit can also adjust the way the analysis result is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology in the analysis.

[0084] The suggestion unit can estimate the user's emotions and appropriately adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit provides simple, highly visible suggestions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. The suggestion unit can also provide suggestions that focus on the main points if the user is in a hurry. This makes it possible to adjust the way suggestions are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0085] The suggestion unit can appropriately adjust the level of detail of the suggestion based on the importance of the maintenance when making a suggestion. For example, the suggestion unit provides a detailed suggestion for highly important maintenance. For example, the suggestion unit provides a detailed suggestion for highly important maintenance. The suggestion unit can also provide a simple suggestion for less important maintenance. The suggestion unit can also adjust the frequency and timing of the suggestion based on the importance of the maintenance. This makes it possible to adjust the level of detail of the suggestion based on the importance of the maintenance. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input maintenance importance data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0086] When making a proposal, the proposal unit can apply different proposal algorithms depending on the maintenance category. For example, the proposal unit applies a proposal algorithm dedicated to wood to wood maintenance. For example, the proposal unit proposes wood maintenance using a proposal algorithm dedicated to wood. The proposal unit can also apply a proposal algorithm dedicated to concrete to concrete maintenance. The proposal unit can also apply a proposal algorithm dedicated to metal to metal maintenance. This makes it possible to apply a proposal algorithm depending on the maintenance category. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input maintenance category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0087] When making a proposal, the suggestion unit can improve the accuracy of the proposal based on the user's past proposal results. The suggestion unit, for example, learns the deterioration progression pattern based on the user's past proposal results and improves the accuracy of the proposal. For example, the suggestion unit learns the deterioration progression pattern based on the user's past proposal results. The suggestion unit can also optimize the proposal algorithm by referring to the user's past proposal results. The suggestion unit can also adjust the frequency and timing of proposals based on the user's past proposal results. This makes it possible to improve the accuracy of the proposal based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0088] The suggestion unit can estimate the user's emotions and appropriately adjust the length of suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit provides short and to-the-point suggestions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. The suggestion unit can also provide suggestions with visually stimulating effects if the user is excited. This makes it possible to adjust the length of suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0089] When making a proposal, the proposal unit can appropriately determine the priority of the proposal based on the timing of maintenance. For example, the proposal unit prioritizes the proposal of maintenance that needs to be performed soon. For example, the proposal unit prioritizes the proposal of maintenance that needs to be performed soon. The proposal unit can also systematically propose maintenance that is required over the long term. The proposal unit can also adjust the frequency and timing of the proposals depending on the timing of the maintenance. This makes it possible to determine the priority of the proposals based on the timing of the maintenance. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input maintenance timing data into the generation AI and cause the generation AI to determine the priority of the proposals.

[0090] When making a proposal, the proposal unit can appropriately adjust the order of proposals based on the relevance of the maintenance. For example, the proposal unit prioritizes the proposal of maintenance with high importance. For example, the proposal unit prioritizes the proposal of maintenance with high importance. The proposal unit can also collectively propose maintenance with high relevance. The proposal unit can also adjust the order and frequency of proposals according to the relevance of the maintenance. This makes it possible to adjust the order of proposals based on the relevance of the maintenance. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input maintenance relevance data into the generation AI and cause the generation AI to adjust the order of proposals.

[0091] When making a proposal, the suggestion unit can appropriately adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides the proposal using detailed technical terminology. For example, if the user has technical expertise, the suggestion unit can provide the proposal using detailed technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide the proposal using simple terminology. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology in the proposal. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and proposal unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data on the maintenance history of the home and building material characteristics using the camera 42 and sensors of the smart device 14. The collection unit can also acquire building material characteristic data from an external database using the specific processing unit 290 of the data processing device 12. The analysis unit, for example, uses statistical analysis or a machine learning algorithm to analyze the collected data and predict deterioration and problems using the specific processing unit 290 of the data processing device 12. The proposal unit, for example, uses the specific processing unit 290 of the data processing device 12 to propose an appropriate maintenance schedule based on the analysis results. The proposal unit, for example, uses the control unit 46A of the smart device 14 to provide the owner or manager with a maintenance schedule based on the timing and priority of repairs. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and proposal unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data on the maintenance history of the home and building material characteristics using the camera 42 and sensors of the smart glasses 214. The collection unit can also acquire building material characteristic data from an external database using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected data using statistical analysis or machine learning algorithms to predict deterioration and problems using the specific processing unit 290 of the data processing device 12. For example, the proposal unit proposes an appropriate maintenance schedule based on the analysis results using the specific processing unit 290 of the data processing device 12. For example, the proposal unit provides the owner or manager with a maintenance schedule based on the timing and priority of repairs using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and proposal unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect data on the maintenance history of the home and building material characteristics using the camera 42 or sensors of the headset terminal 314. The collection unit can also acquire building material characteristic data from an external database using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected data using statistical analysis or machine learning algorithms to predict deterioration and problems using the specific processing unit 290 of the data processing device 12. For example, the proposal unit proposes an appropriate maintenance schedule based on the analysis results using the specific processing unit 290 of the data processing device 12. For example, the proposal unit provides the owner or manager with a maintenance schedule based on the timing and priority of repairs using the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and proposal unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data on the maintenance history of the home and building material characteristics using the camera 42 and sensors of the robot 414. The collection unit can also acquire building material characteristic data from an external database using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected data using statistical analysis or machine learning algorithms to predict deterioration and problems using the specific processing unit 290 of the data processing device 12. For example, the proposal unit proposes an appropriate maintenance schedule based on the analysis results using the specific processing unit 290 of the data processing device 12. For example, the proposal unit provides the owner or manager with a maintenance schedule based on the timing and priority of repairs using the control unit 46A of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit will prioritize analyzing important data and provide results quickly. If the user is relaxed, the analysis unit can perform a detailed analysis and provide comprehensive results. Furthermore, if the user is busy, the analysis unit can analyze the minimum amount of data necessary to reduce the user's burden. This makes it possible to adjust the priority of analysis according to the user's emotions.

[0094] The collection unit can collect data on the surrounding environment in addition to the home's maintenance history. For example, the collection unit collects meteorological data (temperature, humidity, precipitation, etc.) around the home to identify factors that affect the deterioration of building materials. The collection unit can also collect traffic data around the home to evaluate the impact of vibrations and noise on building materials. Furthermore, the collection unit can collect vegetation data around the home to analyze the impact of plant growth on building materials. This makes it possible to propose maintenance that takes surrounding environmental data into account.

[0095] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit reduces the frequency of suggestions to reduce the user's burden. Also, if the user is relaxed, the suggestion unit can make detailed suggestions to provide the user with sufficient information. Furthermore, if the user is busy, the suggestion unit can make concise suggestions to save the user's time. This makes it possible to adjust the timing of suggestions according to the user's emotions.

[0096] In addition to predicting the deterioration of building materials, the analysis unit can also predict repair costs. For example, the analysis unit predicts repair costs based on past repair history and market price data. The analysis unit can also make suggestions to optimize the timing of repairs and minimize costs depending on the progress of deterioration. Furthermore, the analysis unit can compare the costs of different repair methods and propose the most economical option. This makes it possible to make maintenance suggestions that take costs into consideration.

[0097] The collection unit can also estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes an automated data collection method to reduce the user's effort. Alternatively, if the user is relaxed, the collection unit can prompt detailed manual input to collect more accurate data. Furthermore, if the user is busy, the collection unit can select a simple data collection method to reduce the user's burden. This makes it possible to adjust the data collection method according to the user's emotions.

[0098] In addition to making maintenance proposals, the proposal unit can also make proposals for improving energy efficiency. For example, the proposal unit may evaluate the insulation performance of a home and make proposals such as adding insulation or replacing windows. The proposal unit may also analyze the energy consumption of lighting and home appliances and propose replacing them with energy-efficient products. Furthermore, the proposal unit may propose the introduction of a solar power generation system to reduce energy costs. This makes it possible to make comprehensive maintenance proposals that take energy efficiency into consideration.

[0099] The analysis unit can also estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a brief notification to reduce the user's burden. If the user is relaxed, the analysis unit can provide a detailed notification to provide the user with sufficient information. Furthermore, if the user is busy, the analysis unit can provide a notification that focuses on the main points, saving the user's time. This makes it possible to adjust the notification method of the analysis results according to the user's emotions.

[0100] In addition to the home maintenance history, the collection unit can also collect data on residents' lifestyle patterns. For example, the collection unit collects data on the amount of time residents are at home and which rooms are frequently used, and determines maintenance priorities. The collection unit can also optimize the timing of maintenance based on the residents' lifestyle patterns. Furthermore, the collection unit can make suggestions for optimizing energy consumption based on the residents' lifestyle pattern data. This makes it possible to make maintenance suggestions that take residents' lifestyle patterns into consideration.

[0101] In addition to making maintenance proposals, the proposal unit can also make proposals that take into account the health status of residents. For example, the proposal unit can analyze air quality data within the home and propose installing an air purifier or improving the ventilation system. The proposal unit can also propose measures to prevent mold growth based on humidity data within the home. Furthermore, the proposal unit can also consider residents' allergy information and propose allergen countermeasures. This makes it possible to make comprehensive maintenance proposals that take into account the residents' health status.

[0102] The analysis unit can also estimate the user's emotions and adjust the level of analysis detail based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a concise and to-the-point analysis result. On the other hand, if the user is relaxed, the analysis unit provides a detailed analysis result, allowing the user to receive sufficient information. Furthermore, if the user is busy, the analysis unit provides the minimum necessary analysis result, reducing the user's burden. This makes it possible to adjust the level of analysis detail according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects data on the home's maintenance history and building material characteristics. The collection unit collects information such as past repair history, the types of building materials used, and the durability of the building materials. The collection unit can also collect data using sensors, manual input, an external database, etc. For example, the collection unit measures the durability of building materials using a sensor and collects data. The collection unit can also collect past repair history through manual input. The collection unit can also obtain building material characteristic data from an external database. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts deterioration and problems. The analysis unit analyzes the data using statistical analysis and machine learning algorithms. For example, the analysis unit uses statistical analysis to predict how long it will take for a specific building material to deteriorate. The analysis unit also uses machine learning algorithms to predict which parts will need to be repaired again based on past repair history. The analysis unit can also predict the progression of deterioration using a deterioration progression model. Step 3: The proposal unit proposes an appropriate maintenance schedule based on the analysis results obtained by the analysis unit. The proposal unit proposes a schedule for repairs to be carried out before specific building materials deteriorate. The proposal unit can also propose a schedule for periodic inspections based on past repair history. The proposal unit can also propose an appropriate maintenance schedule based on the timing and priority of repairs.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0107] 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.

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

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[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 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.

[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. 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.

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

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

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 AI 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 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.

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

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

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

[0128] The 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.

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

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0132] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

[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 AI 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 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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 AI 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data on the maintenance history and building material characteristics of the house; an analysis unit that analyzes the data collected by the collection unit and predicts deterioration and problems; a proposal unit that proposes an appropriate maintenance schedule based on the analysis results obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect data on past repair history, types of building materials used, and durability of building materials 2. The system of claim 1.

3. The analysis unit Analyzing the collected data to predict how long it will take for a specific building material to deteriorate 2. The system of claim 1.

4. The analysis unit Predict which parts will need repairs again based on past repair history 2. The system of claim 1.

5. The proposal unit Propose appropriate repair schedules before specific building materials deteriorate 2. The system of claim 1.

6. The proposal unit Propose an appropriate schedule for regular inspections based on past repair history 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the appropriate timing of data collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze past maintenance history and select the appropriate data collection method 2. The system of claim 1.

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