System

Abstract

An object of the system according to the embodiment is to propose an optimal cleanup method in accordance with the personality of the user and the current state of the room.SOLUTION: A system includes a reception part, an analysis part, a specification part, a diagnosis reception part, and a proposal part. The reception unit receives an image indicating a current state of a room from a user. The analysis unit analyzes the image received by the reception unit and grasps a current state of the room. The specifying unit specifies a part that needs to be cleared up based on the current state of the room grasped by the analyzing unit. The diagnosis receiving unit receives a result of the personality diagnosis of the user. The proposing unit proposes an appropriate cleanup method to the user based on the location specified by the specifying unit and the result of the personality diagnosis received by the diagnosis receiving 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 techniques have a problem in that they provide a uniform method for tidying up a room, and are unable to suggest an optimal tidying method that suits the user's personality or the current state of the room.

[0005] The system according to the embodiment aims to propose an optimal tidying method that is suited to the user's personality and the current state of the room. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, an identification unit, a diagnosis receiving unit, and a suggestion unit. The receiving unit receives an image showing the current state of the room from the user. The analysis unit analyzes the image received by the receiving unit and determines the current state of the room. The identification unit identifies areas that need to be tidyed up based on the current state of the room determined by the analysis unit. The diagnosis receiving unit receives the results of a personality diagnosis of the user. The suggestion unit suggests an appropriate tidying method to the user based on the areas identified by the identification unit and the results of the personality diagnosis received by the diagnosis receiving unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal tidying method according to the user's personality and the current state of the room. [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 system according to an embodiment of the present invention utilizes a generative AI to analyze the current state of a room and propose a tidying method. By further incorporating a personality assessment, this system can propose more efficient tidying methods. For example, when a user takes a photo of the current state of the room and inputs the image into the generative AI, the generative AI analyzes the image to understand the current state of the room. Next, when the user takes a personality assessment, the generative AI analyzes the results of the personality assessment and understands the user's personality and behavioral patterns. Based on the current state of the room and the results of the personality assessment, the generative AI proposes the optimal tidying method for the user. For example, if the user is the type who likes to do things in a planned manner, the generative AI proposes a method to tidy up gradually. On the other hand, if the user prefers to finish tidying all at once, the system proposes a method to tidy up efficiently in a short amount of time. This allows the system to implement a tidying method tailored to the user's personality and behavioral patterns, resulting in efficient tidying. Furthermore, by analyzing the current state of the room, the generative AI can accurately identify areas that need tidying, thereby reducing unnecessary work.

[0029] A tidying suggestion system according to an embodiment includes a receiving unit, an analysis unit, an identification unit, a diagnosis receiving unit, and a suggestion unit. The reception unit receives an image showing the current state of a room from a user. For example, it can receive an image showing the current state of the room taken by the user. The analysis unit uses a generative AI to analyze the image received by the reception unit and grasp the current state of the room. For example, it can analyze the arrangement of objects and the level of clutter in the room using an object recognition algorithm. The identification unit identifies areas that need tidying up based on the current state of the room grasped by the analysis unit. For example, it can identify areas that need tidying up based on the analyzed current state of the room. The diagnosis receiving unit receives the results of a personality assessment of the user. For example, it can receive the results of the personality assessment in the form of answering questions to understand the user's personality and behavioral patterns. The suggestion unit proposes an optimal tidying method to the user based on the areas identified by the identification unit and the results of the personality assessment received by the diagnosis receiving unit. For example, it can propose an optimal tidying method to the user based on the identified areas and the results of the personality assessment. This allows the tidying suggestion system according to an embodiment to efficiently grasp the current state of a user's room and propose an optimal tidying method based on the results of the personality assessment.

[0030] The reception unit can analyze the user's past image submission history and select an appropriate reception method. For example, the reception unit can analyze time periods in which the user frequently submitted images in the past and prompt the user to submit images during those time periods. The reception unit can also preferentially suggest submission methods (email, app, etc.) that the user has used in the past. The reception unit can also analyze the user's tendency to submit images on specific days of the week based on the user's past submission history and prompt the user to submit images on those days. This makes it possible to select the optimal reception method based on the user's past submission history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0031] When receiving images, the reception unit can sort them based on the user's current living situation and areas of interest. For example, if the user is planning to move, the reception unit can preferentially receive tidying methods related to moving. Furthermore, if the user is interested in a particular hobby, the reception unit can also suggest tidying methods related to that hobby. Furthermore, if the user is raising children, the reception unit can preferentially receive tidying methods for families with children. This makes it possible to suggest tidying methods that suit the user's living situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0032] When accepting an image, the acceptance unit can select an appropriate acceptance means according to the user's input method. For example, if the user prefers voice input, the acceptance unit supports voice image acceptance. Furthermore, if the user prefers text input, the acceptance unit can also support text image acceptance. Furthermore, if the user prefers direct uploading of images, the acceptance unit can also provide an image upload function. This makes it possible to provide the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using AI, for example, or may be performed without using AI.

[0033] When receiving images, the reception unit can prioritize receiving highly relevant images taking into account the user's geographical location information. For example, if the user lives in an urban area, the reception unit can prioritize receiving tidying methods for urban areas. Furthermore, if the user lives in a suburban area, the reception unit can prioritize receiving tidying methods for suburban areas. Furthermore, if the user lives in a specific area, the reception unit can prioritize receiving tidying methods tailored to the characteristics of that area. This makes it possible to suggest the optimal tidying method based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0034] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. The reception unit can, for example, receive related tidying methods based on images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related tidying methods. The reception unit can also accept related tidying methods with reference to the activity of the user's friends on social media. This makes it possible to suggest an optimal tidying method based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0035] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an image. The reception unit can, for example, suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0036] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the room. For example, the analysis unit performs a detailed analysis of an important room such as a living room. The analysis unit can also perform a brief analysis of a less important room such as a closet. The analysis unit can also perform a detailed analysis of a frequently used room such as a kitchen. This makes it possible to adjust the level of detail of the analysis depending on the importance of the room. 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.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the room category. For example, the analysis unit applies an analysis algorithm that emphasizes the arrangement of furniture to a living room. The analysis unit can also apply an analysis algorithm that emphasizes the arrangement of cooking utensils to a kitchen. The analysis unit can also apply an analysis algorithm that emphasizes the arrangement of beds and storage to a bedroom. This makes it possible to apply the optimal analysis algorithm depending on the room category. 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.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can adjust the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. 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 can be performed, for example, using AI or without using AI.

[0039] During analysis, the analysis unit can determine the priority of analysis based on when the room was photographed. For example, the analysis unit prioritizes analysis of recently photographed images of the room. The analysis unit can also postpone analysis of older images. The analysis unit can also prioritize analysis of images photographed before or after a specific event (such as moving or redecorating). This makes it possible to determine the priority of analysis based on when the room was photographed. 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.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the rooms. For example, the analysis unit consecutively analyzes rooms with high relevance, such as the living room and dining room. The analysis unit can also separately analyze rooms with low relevance, such as the closet and bedroom. The analysis unit can also consecutively analyze rooms with high relevance, such as the kitchen and pantry. This makes it possible to adjust the order of analysis based on the relevance of the rooms. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0041] During analysis, the analysis unit can 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 uses detailed technical terminology. Furthermore, if the user is a beginner, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can select appropriate technical terminology according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise. 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.

[0042] The identification unit can improve the accuracy of identification based on the interrelationships between rooms during identification. For example, the identification unit performs identification taking into account the interrelationships between the living room and the dining room. The identification unit can also perform identification taking into account the interrelationships between the kitchen and the pantry. The identification unit can also perform identification taking into account the interrelationships between the bedroom and the closet. This makes it possible to improve the accuracy of identification by taking into account the interrelationships between rooms. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI.

[0043] The identification unit can perform identification by taking into consideration the attribute information of the submitter of the room. For example, if the submitter is a family member, the identification unit uses identification criteria for families. Furthermore, if the submitter lives alone, the identification unit can also use identification criteria for single people. Furthermore, if the submitter has a pet, the identification unit can also use identification criteria for pets. This allows optimal identification based on the submitter's attribute information. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0044] At the time of identification, the identification unit can assign a specific weight based on the frequency of submission of the room. For example, the identification unit assigns a high weight to images of a room that are frequently submitted. The identification unit can also assign a low weight to images of a room that are less frequently submitted. The identification unit can also assign a high weight to images submitted before or after a specific event (such as moving or redecorating). In this way, specific weights can be assigned based on the frequency of submission. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI.

[0045] The identification unit can perform identification based on the geographical distribution of the rooms during identification. For example, if the room is large, the identification unit can perform identification taking the geographical distribution into consideration. Furthermore, if the room is small, the identification unit can perform identification without taking the geographical distribution into consideration. Furthermore, if the room is divided into multiple floors, the identification unit can perform identification for each floor. This allows optimal identification based on the geographical distribution of the rooms. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI.

[0046] During identification, the identification unit can improve the accuracy of identification by referring to literature related to the room. For example, the identification unit performs identification by referring to literature related to tidying up a room. The identification unit can also perform identification by referring to literature related to organizing a room. The identification unit can also perform identification by referring to literature related to interior design of a room. In this way, the accuracy of identification can be improved by referring to the literature. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI.

[0047] At the time of identification, the identification unit can perform identification based on the market value of the room. For example, the identification unit can perform detailed identification of rooms that have expensive furniture or decorations. The identification unit can also perform simple identification of rooms with low market value. The identification unit can also determine the priority of identification based on the market value of the room. This allows optimal identification based on the market value of the room. Some or all of the above-described processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The diagnosis reception unit can analyze the user's past diagnostic history and select the optimal reception method. The diagnosis reception unit can, for example, analyze time periods in which the user frequently received diagnoses in the past and encourage the user to receive a diagnosis during those time periods. The diagnosis reception unit can also preferentially suggest diagnostic methods (online, face-to-face, etc.) that the user has used in the past. The diagnosis reception unit can also analyze the user's tendency to receive a diagnosis on a specific day of the week based on the user's past diagnostic history and encourage the user to receive a diagnosis on that day. This makes it possible to select the optimal reception method based on the user's past diagnostic history. Some or all of the above-mentioned processing in the diagnosis reception unit may be performed, for example, using AI, or may be performed without using AI.

[0049] When receiving a diagnosis, the diagnosis receiving unit can filter the diagnoses based on the user's current living situation and areas of interest. For example, if the user is planning to move, the diagnosis receiving unit can preferentially receive diagnoses related to moving. Furthermore, if the user is interested in a particular hobby, the diagnosis receiving unit can also suggest diagnoses related to that hobby. Furthermore, if the user is raising children, the diagnosis receiving unit can preferentially receive diagnoses for families with children. This makes it possible to provide an optimal diagnosis according to the user's living situation and areas of interest. Some or all of the above-mentioned processing in the diagnosis receiving unit may be performed, for example, using AI, or may be performed without using AI.

[0050] When accepting a diagnosis, the diagnosis acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user prefers voice input, the diagnosis acceptance unit can support voice-based diagnosis acceptance. Furthermore, if the user prefers text input, the diagnosis acceptance unit can also support text-based diagnosis acceptance. Furthermore, if the user prefers direct uploading of images, the diagnosis acceptance unit can also provide an image upload function. This makes it possible to provide the optimal diagnosis acceptance means depending on the user's input method. Some or all of the above-mentioned processing in the diagnosis acceptance unit may be performed using AI, for example, or may be performed without using AI.

[0051] When receiving a diagnosis, the diagnosis receiving unit can prioritize receiving a highly relevant diagnosis taking into account the user's geographical location information. For example, if the user lives in an urban area, the diagnosis receiving unit can prioritize receiving a diagnosis for urban areas. Furthermore, if the user lives in a suburban area, the diagnosis receiving unit can also prioritize receiving a diagnosis for suburban areas. Furthermore, if the user lives in a specific area, the diagnosis receiving unit can also prioritize receiving a diagnosis tailored to the characteristics of that area. This makes it possible to provide an optimal diagnosis based on the user's geographical location information. Some or all of the above-mentioned processing in the diagnosis receiving unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The diagnosis receiving unit can analyze the user's social media activity and receive a related diagnosis when receiving a diagnosis. The diagnosis receiving unit can receive a related diagnosis based on, for example, information shared by the user on social media. The diagnosis receiving unit can also analyze the content of the user's social media posts and suggest a related diagnosis. The diagnosis receiving unit can also receive a related diagnosis based on the activity of the user's friends on social media. This makes it possible to provide an optimal diagnosis based on the user's social media activity. Some or all of the above-mentioned processing in the diagnosis receiving unit can be performed, for example, using AI, or can be performed without using AI.

[0053] The diagnosis reception unit can customize the reception method by reflecting the user's past feedback when receiving a diagnosis. The diagnosis reception unit can, for example, propose an optimal reception method based on feedback provided by the user in the past. The diagnosis reception unit can also preferentially propose a specific reception method based on the user's past feedback. The diagnosis reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to provide an optimal diagnosis reception method based on the user's past feedback. Some or all of the above-mentioned processing in the diagnosis reception unit may be performed, for example, using AI, or may be performed without using AI.

[0054] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of tidying up. For example, the suggestion unit provides detailed suggestions for important rooms such as the living room. The suggestion unit can also provide brief suggestions for less important rooms such as the closet. The suggestion unit can also provide detailed suggestions for frequently used rooms such as the kitchen. This makes it possible to provide an optimal level of detail of the suggestion depending on the importance of tidying up. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0055] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the tidying category. For example, the suggestion unit can apply a suggestion algorithm that emphasizes furniture arrangement to the living room. The suggestion unit can also apply a suggestion algorithm that emphasizes cooking utensil arrangement to the kitchen. The suggestion unit can also apply a suggestion algorithm that emphasizes bed and storage arrangement to the bedroom. This makes it possible to provide an optimal suggestion algorithm depending on the tidying category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0056] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit can adjust the suggestion algorithm based on, for example, feedback provided by the user in the past. The suggestion unit can also learn specific patterns from the user's past suggestion results and improve the suggestion accuracy. The suggestion unit can also analyze the user's past suggestion results and optimize the suggestion algorithm. This can improve the accuracy of the suggestion based on the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI.

[0057] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on when the tidying up suggestions were submitted. For example, the suggestion unit can prioritize providing the most recently submitted tidying up suggestions. The suggestion unit can also prioritize providing older suggestions later. The suggestion unit can also prioritize providing suggestions submitted before or after a specific event (such as moving or redecorating). This makes it possible to provide the optimal priority of suggestions based on when the tidying up suggestions were submitted. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0058] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the tidying up. For example, the suggestion unit consecutively suggests highly relevant tidying up, such as for the living room and dining room. The suggestion unit can also separately suggest less relevant tidying up, such as for the closet and bedroom. The suggestion unit can also consecutively suggest highly relevant tidying up, such as for the kitchen and pantry. This makes it possible to provide an optimal suggestion order based on the relevance of the tidying up. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0059] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can use detailed technical terminology. Furthermore, if the user is a beginner, the suggestion unit can explain the suggestion in simple terms. Furthermore, the suggestion unit can select appropriate technical terminology according to the user's level of expertise. This makes it possible to provide an optimal suggestion according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the room. For example, an important room such as a living room can be analyzed in detail. A less important room such as a closet can be analyzed briefly. Furthermore, a frequently used room such as a kitchen can be analyzed in detail. This allows the level of detail of the analysis to be adjusted depending on the importance of the room. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0062] During analysis, the analysis unit can apply different analysis algorithms depending on the room category. For example, an analysis algorithm that emphasizes the arrangement of furniture can be applied to a living room. An analysis algorithm that emphasizes the arrangement of cooking utensils can also be applied to a kitchen. Furthermore, an analysis algorithm that emphasizes the arrangement of beds and storage can also be applied to a bedroom. This makes it possible to apply the optimal analysis algorithm depending on the room category. 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.

[0063] The identification unit can improve the accuracy of identification based on the interrelationships between rooms during identification. For example, identification can be performed taking into account the interrelationships between the living room and the dining room. Identification can also be performed taking into account the interrelationships between the kitchen and the pantry. Identification can also be performed taking into account the interrelationships between the bedroom and the closet. This makes it possible to improve the accuracy of identification by taking into account the interrelationships between rooms. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or without using AI.

[0064] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of tidying up. For example, detailed suggestions can be provided for important rooms such as the living room. Brief suggestions can also be provided for less important rooms such as the closet. Furthermore, detailed suggestions can be provided for frequently used rooms such as the kitchen. This makes it possible to provide the optimal level of detail of the suggestion depending on the importance of tidying up. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0065] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the tidying category. For example, a suggestion algorithm that emphasizes furniture placement can be applied to the living room. Also, a suggestion algorithm that emphasizes the placement of cooking utensils can be applied to the kitchen. Furthermore, a suggestion algorithm that emphasizes the placement of beds and storage can be applied to the bedroom. This makes it possible to provide the optimal suggestion algorithm depending on the tidying category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives an image showing the current state of the room from the user. For example, the reception unit can receive an image showing the current state of the room taken by the user. Step 2: The analysis unit uses the generation AI to analyze the image received by the reception unit and grasp the current state of the room. For example, it uses an object recognition algorithm to analyze the arrangement of objects in the room and the level of clutter. Step 3: The identifying unit identifies areas that need to be tidied up based on the current state of the room grasped by the analyzing unit. For example, areas that need to be tidied up can be identified based on the analyzed current state of the room. Step 4: The diagnosis receiving unit receives the results of the personality diagnosis of the user. For example, the results of the personality diagnosis can be received in the form of answering questions to understand the user's personality and behavioral patterns. Step 5: The suggestion unit suggests an optimal tidying method to the user based on the areas identified by the identification unit and the results of the personality diagnosis received by the diagnosis receiving unit. For example, the suggestion unit can suggest an optimal tidying method to the user based on the areas identified and the results of the personality diagnosis.

[0068] (Example 2) A system according to an embodiment of the present invention utilizes a generative AI to analyze the current state of a room and propose a tidying method. By further incorporating a personality assessment, this system can propose more efficient tidying methods. For example, when a user takes a photo of the current state of the room and inputs the image into the generative AI, the generative AI analyzes the image to understand the current state of the room. Next, when the user takes a personality assessment, the generative AI analyzes the results of the personality assessment and understands the user's personality and behavioral patterns. Based on the current state of the room and the results of the personality assessment, the generative AI proposes the optimal tidying method for the user. For example, if the user is the type who likes to do things in a planned manner, the generative AI proposes a method to tidy up gradually. On the other hand, if the user prefers to finish tidying all at once, the system proposes a method to tidy up efficiently in a short amount of time. This allows the system to implement a tidying method tailored to the user's personality and behavioral patterns, resulting in efficient tidying. Furthermore, by analyzing the current state of the room, the generative AI can accurately identify areas that need tidying, thereby reducing unnecessary work.

[0069] A tidying suggestion system according to an embodiment includes a receiving unit, an analysis unit, an identification unit, a diagnosis receiving unit, and a suggestion unit. The reception unit receives an image showing the current state of a room from a user. For example, it can receive an image showing the current state of the room taken by the user. The analysis unit uses a generative AI to analyze the image received by the reception unit and grasp the current state of the room. For example, it can analyze the arrangement of objects and the level of clutter in the room using an object recognition algorithm. The identification unit identifies areas that need tidying up based on the current state of the room grasped by the analysis unit. For example, it can identify areas that need tidying up based on the analyzed current state of the room. The diagnosis receiving unit receives the results of a personality assessment of the user. For example, it can receive the results of the personality assessment in the form of answering questions to understand the user's personality and behavioral patterns. The suggestion unit proposes an optimal tidying method to the user based on the areas identified by the identification unit and the results of the personality assessment received by the diagnosis receiving unit. For example, it can propose an optimal tidying method to the user based on the identified areas and the results of the personality assessment. This allows the tidying suggestion system according to an embodiment to efficiently grasp the current state of a user's room and propose an optimal tidying method based on the results of the personality assessment.

[0070] The reception unit can estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to receive images during a time when the user is able to relax. Furthermore, if the user is relaxed, the reception unit can also notify the user to receive images immediately. Furthermore, if the user is busy, the reception unit can send a reminder to submit images later. This allows images to be received at the optimal timing 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-described processing in the reception unit may be performed using, for example, AI, or without AI.

[0071] The reception unit can analyze the user's past image submission history and select an appropriate reception method. For example, the reception unit can analyze time periods in which the user frequently submitted images in the past and prompt the user to submit images during those time periods. The reception unit can also preferentially suggest submission methods (email, app, etc.) that the user has used in the past. The reception unit can also analyze the user's tendency to submit images on specific days of the week based on the user's past submission history and prompt the user to submit images on those days. This makes it possible to select the optimal reception method based on the user's past submission history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0072] When receiving images, the reception unit can sort them based on the user's current living situation and areas of interest. For example, if the user is planning to move, the reception unit can preferentially receive tidying methods related to moving. Furthermore, if the user is interested in a particular hobby, the reception unit can also suggest tidying methods related to that hobby. Furthermore, if the user is raising children, the reception unit can preferentially receive tidying methods for families with children. This makes it possible to suggest tidying methods that suit the user's living situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0073] When accepting an image, the acceptance unit can select an appropriate acceptance means according to the user's input method. For example, if the user prefers voice input, the acceptance unit supports voice image acceptance. Furthermore, if the user prefers text input, the acceptance unit can also support text image acceptance. Furthermore, if the user prefers direct uploading of images, the acceptance unit can also provide an image upload function. This makes it possible to provide the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using AI, for example, or may be performed without using AI.

[0074] The reception unit can estimate the user's emotions and determine the priority of images to be received based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can prioritize receiving tidying methods that help reduce stress. Furthermore, if the user is relaxed, the reception unit can also prioritize receiving regular tidying methods. Furthermore, if the user is in a hurry, the reception unit can also prioritize receiving tidying methods that can be completed in a short time. This makes it possible to determine the priority of images 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 reception unit may be performed, for example, using AI or without AI.

[0075] When receiving images, the reception unit can prioritize receiving highly relevant images taking into account the user's geographical location information. For example, if the user lives in an urban area, the reception unit can prioritize receiving tidying methods for urban areas. Furthermore, if the user lives in a suburban area, the reception unit can prioritize receiving tidying methods for suburban areas. Furthermore, if the user lives in a specific area, the reception unit can prioritize receiving tidying methods tailored to the characteristics of that area. This makes it possible to suggest the optimal tidying method based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0076] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. The reception unit can, for example, receive related tidying methods based on images shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and suggest related tidying methods. The reception unit can also accept related tidying methods with reference to the activity of the user's friends on social media. This makes it possible to suggest an optimal tidying method based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0077] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an image. The reception unit can, for example, suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to provide the optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0078] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise analysis results. If the user is in a hurry, the analysis unit can also provide analysis results that focus on the main points. This makes it possible to adjust the way the analysis results are presented 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 analysis unit may be performed using, for example, AI, or without AI.

[0079] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the room. For example, the analysis unit performs a detailed analysis of an important room such as a living room. The analysis unit can also perform a brief analysis of a less important room such as a closet. The analysis unit can also perform a detailed analysis of a frequently used room such as a kitchen. This makes it possible to adjust the level of detail of the analysis depending on the importance of the room. 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.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the room category. For example, the analysis unit applies an analysis algorithm that emphasizes the arrangement of furniture to a living room. The analysis unit can also apply an analysis algorithm that emphasizes the arrangement of cooking utensils to a kitchen. The analysis unit can also apply an analysis algorithm that emphasizes the arrangement of beds and storage to a bedroom. This makes it possible to apply the optimal analysis algorithm depending on the room category. 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.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can adjust the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. 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 can be performed, for example, using AI or without using AI.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can provide a detailed analysis when the user is relaxed. The analysis unit can also provide a concise analysis when the user is stressed. The analysis unit can also provide a short analysis that focuses on the main points when the user is in a hurry. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 analysis unit can be performed, for example, using AI or without AI.

[0083] During analysis, the analysis unit can determine the priority of analysis based on when the room was photographed. For example, the analysis unit prioritizes analysis of recently photographed images of the room. The analysis unit can also postpone analysis of older images. The analysis unit can also prioritize analysis of images photographed before or after a specific event (such as moving or redecorating). This makes it possible to determine the priority of analysis based on when the room was photographed. 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.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the rooms. For example, the analysis unit consecutively analyzes rooms with high relevance, such as the living room and dining room. The analysis unit can also separately analyze rooms with low relevance, such as the closet and bedroom. The analysis unit can also consecutively analyze rooms with high relevance, such as the kitchen and pantry. This makes it possible to adjust the order of analysis based on the relevance of the rooms. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0085] During analysis, the analysis unit can 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 uses detailed technical terminology. Furthermore, if the user is a beginner, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can select appropriate technical terminology according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise. 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.

[0086] The determination unit can estimate the user's emotion and adjust the specific criteria based on the estimated user's emotion. For example, the determination unit can use detailed specific criteria when the user is relaxed. The determination unit can also use concise specific criteria when the user is stressed. The determination unit can also use specific criteria that are concise when the user is in a hurry. This allows the specific criteria to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the determination unit can be performed, for example, using AI or without using AI.

[0087] The identification unit can improve the accuracy of identification based on the interrelationships between rooms during identification. For example, the identification unit performs identification taking into account the interrelationships between the living room and the dining room. The identification unit can also perform identification taking into account the interrelationships between the kitchen and the pantry. The identification unit can also perform identification taking into account the interrelationships between the bedroom and the closet. This makes it possible to improve the accuracy of identification by taking into account the interrelationships between rooms. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI.

[0088] The identification unit can perform identification by taking into consideration the attribute information of the submitter of the room. For example, if the submitter is a family member, the identification unit uses identification criteria for families. Furthermore, if the submitter lives alone, the identification unit can also use identification criteria for single people. Furthermore, if the submitter has a pet, the identification unit can also use identification criteria for pets. This allows optimal identification based on the submitter's attribute information. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0089] At the time of identification, the identification unit can assign a specific weight based on the frequency of submission of the room. For example, the identification unit assigns a high weight to images of a room that are frequently submitted. The identification unit can also assign a low weight to images of a room that are less frequently submitted. The identification unit can also assign a high weight to images submitted before or after a specific event (such as moving or redecorating). In this way, specific weights can be assigned based on the frequency of submission. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI.

[0090] The identification unit can estimate the user's emotions and adjust the order in which specific results are displayed based on the estimated user emotions. For example, if the user is relaxed, the identification unit can display detailed identified results first. Also, if the user is stressed, the identification unit can display concise identified results first. Also, if the user is in a hurry, the identification unit can display specific results that focus on the main points first. This makes it possible to adjust the order in which specific results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or without AI.

[0091] The identification unit can perform identification based on the geographical distribution of the rooms during identification. For example, if the room is large, the identification unit can perform identification taking the geographical distribution into consideration. Furthermore, if the room is small, the identification unit can perform identification without taking the geographical distribution into consideration. Furthermore, if the room is divided into multiple floors, the identification unit can perform identification for each floor. This allows optimal identification based on the geographical distribution of the rooms. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI.

[0092] During identification, the identification unit can improve the accuracy of identification by referring to literature related to the room. For example, the identification unit performs identification by referring to literature related to tidying up a room. The identification unit can also perform identification by referring to literature related to organizing a room. The identification unit can also perform identification by referring to literature related to interior design of a room. In this way, the accuracy of identification can be improved by referring to the literature. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI.

[0093] At the time of identification, the identification unit can perform identification based on the market value of the room. For example, the identification unit can perform detailed identification of rooms that have expensive furniture or decorations. The identification unit can also perform simple identification of rooms with low market value. The identification unit can also determine the priority of identification based on the market value of the room. This allows optimal identification based on the market value of the room. Some or all of the above-described processing in the identification unit may be performed, for example, using AI, or may be performed without using AI.

[0094] The diagnosis receiving unit can estimate the user's emotions and adjust the timing of receiving the diagnosis based on the estimated user emotions. For example, if the user is relaxed, the diagnosis receiving unit notifies the user to accept the diagnosis immediately. Furthermore, if the user is feeling stressed, the diagnosis receiving unit can also prompt the user to accept the diagnosis at a time when the user can relax. Furthermore, if the user is busy, the diagnosis receiving unit can also send a reminder to accept the diagnosis later. This allows the user to accept the diagnosis at the optimal time depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the diagnosis receiving unit may be performed, for example, using AI, or may be performed without using AI.

[0095] The diagnosis reception unit can analyze the user's past diagnostic history and select the optimal reception method. The diagnosis reception unit can, for example, analyze time periods in which the user frequently received diagnoses in the past and encourage the user to receive a diagnosis during those time periods. The diagnosis reception unit can also preferentially suggest diagnostic methods (online, face-to-face, etc.) that the user has used in the past. The diagnosis reception unit can also analyze the user's tendency to receive a diagnosis on a specific day of the week based on the user's past diagnostic history and encourage the user to receive a diagnosis on that day. This makes it possible to select the optimal reception method based on the user's past diagnostic history. Some or all of the above-mentioned processing in the diagnosis reception unit may be performed, for example, using AI, or may be performed without using AI.

[0096] When receiving a diagnosis, the diagnosis receiving unit can filter the diagnoses based on the user's current living situation and areas of interest. For example, if the user is planning to move, the diagnosis receiving unit can preferentially receive diagnoses related to moving. Furthermore, if the user is interested in a particular hobby, the diagnosis receiving unit can also suggest diagnoses related to that hobby. Furthermore, if the user is raising children, the diagnosis receiving unit can preferentially receive diagnoses for families with children. This makes it possible to provide an optimal diagnosis according to the user's living situation and areas of interest. Some or all of the above-mentioned processing in the diagnosis receiving unit may be performed, for example, using AI, or may be performed without using AI.

[0097] When accepting a diagnosis, the diagnosis acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user prefers voice input, the diagnosis acceptance unit can support voice-based diagnosis acceptance. Furthermore, if the user prefers text input, the diagnosis acceptance unit can also support text-based diagnosis acceptance. Furthermore, if the user prefers direct uploading of images, the diagnosis acceptance unit can also provide an image upload function. This makes it possible to provide the optimal diagnosis acceptance means depending on the user's input method. Some or all of the above-mentioned processing in the diagnosis acceptance unit may be performed using AI, for example, or may be performed without using AI.

[0098] The diagnosis receiving unit can estimate the user's emotions and determine the priority of diagnoses to be received based on the estimated user's emotions. For example, if the user is feeling stressed, the diagnosis receiving unit can preferentially receive diagnoses that are useful for stress reduction. Furthermore, if the user is relaxed, the diagnosis receiving unit can also preferentially receive diagnoses that can be completed in a short time if the user is in a hurry. This makes it possible to determine the priority of diagnoses 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 diagnosis receiving unit may be performed, for example, using AI, or may be performed without using AI.

[0099] When receiving a diagnosis, the diagnosis receiving unit can prioritize receiving a highly relevant diagnosis taking into account the user's geographical location information. For example, if the user lives in an urban area, the diagnosis receiving unit can prioritize receiving a diagnosis for urban areas. Furthermore, if the user lives in a suburban area, the diagnosis receiving unit can also prioritize receiving a diagnosis for suburban areas. Furthermore, if the user lives in a specific area, the diagnosis receiving unit can also prioritize receiving a diagnosis tailored to the characteristics of that area. This makes it possible to provide an optimal diagnosis based on the user's geographical location information. Some or all of the above-mentioned processing in the diagnosis receiving unit may be performed, for example, using AI, or may be performed without using AI.

[0100] The diagnosis receiving unit can analyze the user's social media activity and receive a related diagnosis when receiving a diagnosis. The diagnosis receiving unit can receive a related diagnosis based on, for example, information shared by the user on social media. The diagnosis receiving unit can also analyze the content of the user's social media posts and suggest a related diagnosis. The diagnosis receiving unit can also receive a related diagnosis based on the activity of the user's friends on social media. This makes it possible to provide an optimal diagnosis based on the user's social media activity. Some or all of the above-mentioned processing in the diagnosis receiving unit can be performed, for example, using AI, or can be performed without using AI.

[0101] The diagnosis reception unit can customize the reception method by reflecting the user's past feedback when receiving a diagnosis. The diagnosis reception unit can, for example, propose an optimal reception method based on feedback provided by the user in the past. The diagnosis reception unit can also preferentially propose a specific reception method based on the user's past feedback. The diagnosis reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to provide an optimal diagnosis reception method based on the user's past feedback. Some or all of the above-mentioned processing in the diagnosis reception unit may be performed, for example, using AI, or may be performed without using AI.

[0102] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can also provide concise suggestions. If the user is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. This makes it possible to provide an optimal way to express the suggestions depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0103] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of tidying up. For example, the suggestion unit provides detailed suggestions for important rooms such as the living room. The suggestion unit can also provide brief suggestions for less important rooms such as the closet. The suggestion unit can also provide detailed suggestions for frequently used rooms such as the kitchen. This makes it possible to provide an optimal level of detail of the suggestion depending on the importance of tidying up. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0104] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the tidying category. For example, the suggestion unit can apply a suggestion algorithm that emphasizes furniture arrangement to the living room. The suggestion unit can also apply a suggestion algorithm that emphasizes cooking utensil arrangement to the kitchen. The suggestion unit can also apply a suggestion algorithm that emphasizes bed and storage arrangement to the bedroom. This makes it possible to provide an optimal suggestion algorithm depending on the tidying category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0105] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit can adjust the suggestion algorithm based on, for example, feedback provided by the user in the past. The suggestion unit can also learn specific patterns from the user's past suggestion results and improve the suggestion accuracy. The suggestion unit can also analyze the user's past suggestion results and optimize the suggestion algorithm. This can improve the accuracy of the suggestion based on the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI.

[0106] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can also provide concise suggestions. If the user is in a hurry, the suggestion unit can also provide short suggestions that focus on the main points. This makes it possible to provide the optimal length of suggestions depending on 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, for example, using AI or without using AI.

[0107] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on when the tidying up suggestions were submitted. For example, the suggestion unit can prioritize providing the most recently submitted tidying up suggestions. The suggestion unit can also prioritize providing older suggestions later. The suggestion unit can also prioritize providing suggestions submitted before or after a specific event (such as moving or redecorating). This makes it possible to provide the optimal priority of suggestions based on when the tidying up suggestions were submitted. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0108] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the tidying up. For example, the suggestion unit consecutively suggests highly relevant tidying up, such as for the living room and dining room. The suggestion unit can also separately suggest less relevant tidying up, such as for the closet and bedroom. The suggestion unit can also consecutively suggest highly relevant tidying up, such as for the kitchen and pantry. This makes it possible to provide an optimal suggestion order based on the relevance of the tidying up. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0109] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can use detailed technical terminology. Furthermore, if the user is a beginner, the suggestion unit can explain the suggestion in simple terms. Furthermore, the suggestion unit can select appropriate technical terminology according to the user's level of expertise. This makes it possible to provide an optimal suggestion according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, diagnosis reception unit, and suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives an image of the current state of the room taken by the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the image using a generative AI to understand the current state of the room. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies areas that need to be tidyed up based on the analyzed current state of the room. The diagnosis reception unit is realized by the reception device 38 of the smart device 14 and receives the results of a personality diagnosis of the user. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests an optimal tidying method to the user based on the identified areas and the results of the personality diagnosis. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, diagnosis reception unit, and suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives an image of the current state of the room taken by the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the image using a generative AI to understand the current state of the room. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies areas that need to be tidyed up based on the analyzed current state of the room. The diagnosis reception unit is realized by the microphone 238 of the smart glasses 214 and receives the results of a personality diagnosis of the user. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests an optimal tidying method to the user based on the identified areas and the results of the personality diagnosis. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, diagnosis reception unit, and suggestion unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives an image of the current state of the room taken by the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the image using a generative AI to understand the current state of the room. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies areas that need to be tidyed up based on the analyzed current state of the room. The diagnosis reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives the results of a personality diagnosis of the user. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests an optimal tidying method to the user based on the identified areas and the results of the personality diagnosis. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, diagnosis reception unit, and suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives an image of the current state of the room taken by the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the image using a generative AI to understand the current state of the room. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies areas that need to be tidyed up based on the analyzed current state of the room. The diagnosis reception unit is realized by the microphone 238 of the robot 414 and receives the results of a personality diagnosis of the user. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests an optimal tidying method to the user based on the identified areas and the results of the personality diagnosis.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, detailed suggestions can be provided. If the user is stressed, concise suggestions can be provided. Furthermore, if the user is in a hurry, short suggestions that focus on the main points can be provided. This makes it possible to provide the optimal way to express the suggestions depending on 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 can be performed, for example, using AI, or can be performed without using AI.

[0112] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is stressed, concise analysis results can be provided. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be provided. This allows the way the analysis results are presented to be adjusted 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 analysis unit can be performed, for example, using AI, or can be performed without using AI.

[0113] The determination unit can estimate the user's emotion and adjust the specific criteria based on the estimated user's emotion. For example, if the user is relaxed, detailed specific criteria can be used. If the user is stressed, concise specific criteria can be used. Furthermore, if the user is in a hurry, specific criteria that focus on the main points can be used. This allows the specific criteria to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the determination unit may be performed, for example, using AI or without using AI.

[0114] The diagnosis receiving unit can estimate the user's emotions and adjust the timing of receiving the diagnosis based on the estimated user emotions. For example, if the user is relaxed, the unit can notify the user to accept the diagnosis immediately. Also, if the user is feeling stressed, the unit can encourage the user to accept the diagnosis at a time when the user is able to relax. Furthermore, if the user is busy, the unit can send a reminder to accept the diagnosis later. This allows the user to accept the diagnosis at the optimal time depending on 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 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 diagnosis receiving unit can be performed, for example, using AI, or can be performed without using AI.

[0115] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is relaxed, detailed suggestions can be provided. If the user is stressed, concise suggestions can be provided. Furthermore, if the user is in a hurry, short suggestions that focus on the main points can be provided. This makes it possible to provide the optimal length of suggestions depending on 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 can be performed, for example, using AI or without AI.

[0116] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the room. For example, an important room such as a living room can be analyzed in detail. A less important room such as a closet can be analyzed briefly. Furthermore, a frequently used room such as a kitchen can be analyzed in detail. This allows the level of detail of the analysis to be adjusted depending on the importance of the room. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0117] During analysis, the analysis unit can apply different analysis algorithms depending on the room category. For example, an analysis algorithm that emphasizes the arrangement of furniture can be applied to a living room. An analysis algorithm that emphasizes the arrangement of cooking utensils can also be applied to a kitchen. Furthermore, an analysis algorithm that emphasizes the arrangement of beds and storage can also be applied to a bedroom. This makes it possible to apply the optimal analysis algorithm depending on the room category. 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.

[0118] The identification unit can improve the accuracy of identification based on the interrelationships between rooms during identification. For example, identification can be performed taking into account the interrelationships between the living room and the dining room. Identification can also be performed taking into account the interrelationships between the kitchen and the pantry. Identification can also be performed taking into account the interrelationships between the bedroom and the closet. This makes it possible to improve the accuracy of identification by taking into account the interrelationships between rooms. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or without using AI.

[0119] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of tidying up. For example, detailed suggestions can be provided for important rooms such as the living room. Brief suggestions can also be provided for less important rooms such as the closet. Furthermore, detailed suggestions can be provided for frequently used rooms such as the kitchen. This makes it possible to provide the optimal level of detail of the suggestion depending on the importance of tidying up. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0120] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the tidying category. For example, a suggestion algorithm that emphasizes furniture placement can be applied to the living room. Also, a suggestion algorithm that emphasizes the placement of cooking utensils can be applied to the kitchen. Furthermore, a suggestion algorithm that emphasizes the placement of beds and storage can be applied to the bedroom. This makes it possible to provide the optimal suggestion algorithm depending on the tidying category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The reception unit receives an image showing the current state of the room from the user. For example, the reception unit can receive an image showing the current state of the room taken by the user. Step 2: The analysis unit uses the generation AI to analyze the image received by the reception unit and grasp the current state of the room. For example, it uses an object recognition algorithm to analyze the arrangement of objects in the room and the level of clutter. Step 3: The identifying unit identifies areas that need to be tidied up based on the current state of the room grasped by the analyzing unit. For example, areas that need to be tidied up can be identified based on the analyzed current state of the room. Step 4: The diagnosis receiving unit receives the results of the personality diagnosis of the user. For example, the results of the personality diagnosis can be received in the form of answering questions to understand the user's personality and behavioral patterns. Step 5: The suggestion unit suggests an optimal tidying method to the user based on the areas identified by the identification unit and the results of the personality diagnosis received by the diagnosis receiving unit. For example, the suggestion unit can suggest an optimal tidying method to the user based on the areas identified and the results of the personality diagnosis.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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 reception unit that receives an image showing the current state of the room from a user; an analysis unit that analyzes the image received by the reception unit and grasps the current state of the room; an identification unit that identifies areas that need to be tidied up based on the current state of the room grasped by the analysis unit; a diagnosis receiving unit that receives the results of a personality diagnosis of the user; a suggestion unit that suggests an appropriate tidying method to the user based on the location identified by the identification unit and the results of the personality diagnosis received by the diagnosis receiving unit. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past image submission history and select the appropriate reception method 2. The system of claim 1.

4. The reception unit When accepting images, they are filtered based on the user's current lifestyle and interests.

2. The system of claim 1.

5. The reception unit When accepting an image, select an appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the images to be accepted based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When accepting images, prioritize images that are highly relevant based on the user's geographic location information.

2. The system of claim 1.

8. The reception unit When accepting images, analyze the user's social media activity and accept related images.

2. The system of claim 1.

Images