System

Abstract

An object of a system according to an embodiment is to efficiently find an ideal property based on a condition desired by a user.SOLUTION: A system includes a condition input part, a condition analysis part, a property pickup part, and an application part. The condition input unit inputs a condition desired by the user in detail. The condition analysis unit analyzes the condition input by the condition input unit. The property pickup unit picks up a property based on the condition analyzed by the condition analysis unit. The application unit applies for the property picked up by the property pickup unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently find the ideal property even if desired conditions were entered.

[0005] The system according to the embodiment aims to enable a user to efficiently find an ideal property based on desired conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a condition input unit, a condition analysis unit, a property pick-up unit, and an application unit. The condition input unit inputs the user's desired conditions in detail. The condition analysis unit analyzes the conditions input by the condition input unit. The property pick-up unit picks up properties based on the conditions analyzed by the condition analysis unit. The application unit applies for the property picked up by the property pick-up unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to efficiently find an ideal property based on desired conditions. [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) The real estate search system according to an embodiment of the present invention is a system in which a user inputs desired conditions in detail, a generation AI analyzes the conditions, picks out properties that are close to their ideal, and the user applies to a real estate company. This allows the real estate search system to allow a user to input desired conditions in detail, a generation AI analyzes the conditions, picks out properties that are close to their ideal, and the user applies to a real estate company.

[0029] A real estate search system according to an embodiment includes a condition input unit, a condition analysis unit, a property selection unit, and an application unit. The condition input unit allows a user to input detailed desired conditions. For example, the user may input specific conditions such as "within a 10-minute walk from the station, 2LDK, pet-friendly, rent under 100,000 yen, and a room with a south-facing balcony." The condition analysis unit analyzes the conditions input by the condition input unit. For example, the generation AI analyzes the user's conditions using a text generation AI (e.g., LLM) and selects appropriate properties. The property selection unit selects properties based on the conditions analyzed by the condition analysis unit. For example, the generation AI lists properties that match the conditions from a database and presents them to the user. The application unit applies for the properties selected by the property selection unit. For example, the generation AI inputs user information and generates a form for submitting an application to a real estate company. As a result, the real estate search system according to an embodiment allows a user to input detailed desired conditions, the generation AI analyzes the conditions, selects properties that are close to their ideal, and submits an application to a real estate company.

[0030] The condition analysis unit can use a pre-fine-tuned model to understand the user's conditions and select an appropriate property. The condition analysis unit can use, for example, a pre-fine-tuned machine learning model to understand the user's conditions and select an appropriate property. For example, the generation AI learns the user's past search history and preferences and automatically completes more specific conditions. The generation AI also suggests detailed conditions related to the conditions entered by the user. For example, if "pets allowed" is entered, the generation AI automatically completes conditions according to the type and size of the pet. This allows the generation AI to use a pre-fine-tuned model to understand the user's conditions and select an appropriate property.

[0031] The property pick-up unit can present properties to the user in list format and display photos of the properties, floor plans, and information about the surrounding environment. The property pick-up unit, for example, presents properties to the user in list format and displays photos of the properties, floor plans, and information about the surrounding environment. For example, the generation AI automatically generates related questions based on the conditions entered by the user, and interactively narrows down the conditions for the user. For example, if the user enters "within a 10-minute walk from the station," it generates a question such as "Do you prefer a specific station name?" The generation AI also automatically generates detailed questions related to the conditions entered by the user, and interactively narrows down the conditions for the user. This makes it possible to present properties to the user in list format and display photos of the properties, floor plans, and information about the surrounding environment.

[0032] The condition analysis unit can make additional suggestions based on the conditions entered by the user. For example, the condition analysis unit uses an emotion estimation function to analyze the emotions the user is feeling when entering text, and makes suggestions to simplify the input if the user is feeling stressed. For example, if the user is feeling impatient or anxious, it presents simple options. It also analyzes the emotions the user is feeling when entering text in real time, and makes suggestions to simplify the input if the user is feeling stressed. This allows the generation AI to make additional suggestions based on the conditions entered by the user.

[0033] The condition input unit can accept the user's desired conditions by voice input. For example, the condition input unit can add a voice input function so that the user can input the desired conditions in detail just by speaking. For example, the conditions can be input by simply speaking, "within 10 minutes' walk from the station, 2LDK, pets allowed, rent less than 100,000 yen." In addition, using voice recognition technology, a function is provided that allows the user to input the desired conditions in detail just by speaking. This makes it possible to accept the user's desired conditions by voice input.

[0034] The condition input unit can provide an interface that visually displays the user's desired conditions and allows the user to adjust the conditions by drag and drop. The condition input unit, for example, visually displays the conditions entered by the user and provides an interface that allows the user to adjust the conditions by drag and drop. For example, the conditions can be displayed in card format and the order can be changed by drag and drop. A visual interface is also provided, allowing the user to adjust the conditions entered by drag and drop. This makes it possible to provide an interface that visually displays the user's desired conditions and allows the user to adjust the conditions by drag and drop.

[0035] When analyzing a user's conditions, the condition analysis unit refers to past success stories and user reviews, allowing it to pick out properties with greater accuracy. For example, when the generation AI analyzes a user's conditions, the condition analysis unit refers to past success stories and picks out properties with greater accuracy. For example, it prioritizes listing properties that have been successful under similar conditions in the past. It also refers to user reviews, allowing the generation AI to pick out properties with greater accuracy when analyzing the conditions. This allows it to refer to past success stories and user reviews and pick out properties with greater accuracy.

[0036] The condition analysis unit can take into account data on the surrounding environment when analyzing the conditions. For example, the condition analysis unit takes into account data on the surrounding environment when the generation AI analyzes the conditions. For example, it can prioritize listing properties in safe areas based on public safety data. In addition, by taking into account school evaluation data, the generation AI can pick out properties in areas with good educational environments when analyzing the conditions. This allows the conditions to be analyzed taking into account data on the surrounding environment.

[0037] The condition analysis unit can suggest properties based on the user's lifestyle. For example, when the generation AI analyzes the conditions, the condition analysis unit takes the user's lifestyle into consideration and suggests properties that suit their hobbies. For example, for a user who likes the outdoors, it can suggest properties with parks or nature nearby. In addition, when the generation AI analyzes the conditions, it can take work style into consideration and suggest properties that are suitable for remote work. This makes it possible to suggest properties based on the user's lifestyle.

[0038] The condition analysis unit can pick properties from a global perspective, including properties from different regions and countries. For example, when the generation AI analyzes the conditions, the condition analysis unit picks properties from a global perspective, including properties from different regions and countries. For example, if a user wishes to be transferred overseas, the unit will list properties in the transfer destination. In addition, to pick properties from a global perspective, the generation AI references databases from different countries. This allows the generation AI to pick properties from a global perspective, including properties from different regions and countries.

[0039] The property picking unit can present properties in an order customized based on the user's past browsing history and ratings. The property picking unit, for example, presents properties picked by the generation AI in an order customized based on the user's past browsing history. For example, properties similar to properties that have been highly rated in the past are preferentially displayed. In addition, properties picked by the generation AI are presented in a customized order based on the user's rating data. This makes it possible to present properties in an order customized based on the user's past browsing history and ratings.

[0040] The property pickup unit can display surrounding facilities that the user is likely to be interested in when presenting a property. For example, the property pickup unit displays surrounding facilities that the user is likely to be interested in when presenting a property. For example, information about cafes and restaurants is displayed along with the property information. Also, surrounding facilities that the user is likely to be interested in are displayed along with the property information. In this way, surrounding facilities that the user is likely to be interested in can be displayed together when presenting a property.

[0041] The property pick-up unit can display information about events and activities that the user is likely to be interested in when presenting a property. For example, the property pick-up unit displays information about events that the user is likely to be interested in when presenting a property. For example, information about festivals and concerts being held in the neighborhood is displayed along with the property information. Also, activity information that the user is likely to be interested in is displayed along with the property information. This makes it possible to display information about events and activities that the user is likely to be interested in when presenting a property.

[0042] The property pick-up unit can add a function that allows a user to discuss a property with other users when a property is presented. The property pick-up unit, for example, adds a function that allows a user to discuss a property with other users when a property is presented. For example, a comment section can be provided for each property, allowing users to exchange opinions with each other. In addition, a function that allows a user to discuss a property with other users can be provided. This makes it possible to add a function that allows a user to discuss a property with other users when a property is presented.

[0043] When the generation AI automatically inputs the user's information, the application unit can generate the optimal form by taking into consideration past application history and the user's preferences. For example, when the generation AI automatically inputs the user's information, the application unit references past application history to generate the optimal form. For example, it automatically completes information previously input. The generation AI also takes into consideration the user's preferences to generate the optimal form. This allows the generation AI to generate the optimal form by taking into consideration past application history and the user's preferences when automatically inputting the user's information.

[0044] The application unit can evaluate the user's credit information and repayment ability in advance when the generation AI goes through the application procedure, and suggest appropriate properties. For example, the application unit can evaluate the user's credit information in advance when the generation AI goes through the application procedure, and suggest appropriate properties. For example, expensive properties can be suggested to users with high credit scores. The user's repayment ability can also be evaluated in advance, and the generation AI can suggest appropriate properties. This allows the generation AI to evaluate the user's credit information and repayment ability in advance when the generation AI goes through the application procedure, and suggest appropriate properties.

[0045] The application unit can add a function that allows a user to apply jointly with other users when the generation AI performs the application procedure. The application unit, for example, adds a function that allows a user to apply jointly with other users when the generation AI performs the application procedure. For example, it allows users who wish to share a room to apply jointly. It also provides a function that allows a user to apply jointly with other users. This makes it possible to add a function that allows a user to apply jointly with other users when the generation AI performs the application procedure.

[0046] The application unit can add a function that allows the user to communicate directly with the real estate company via chat when the generation AI is carrying out the application procedure. The application unit, for example, adds a function that allows the user to communicate directly with the real estate company via chat when the generation AI is carrying out the application procedure. For example, it allows questions and confirmations about the property to be exchanged in real time. It also provides a function that allows the user to communicate directly with the real estate company via chat. This makes it possible to add a function that allows the user to communicate directly with the real estate company via chat when the generation AI is carrying out the application procedure.

[0047] When making additional suggestions based on the user's conditions, the condition analysis unit can refer to past success stories and user reviews to make more specific suggestions. For example, when the generation AI makes additional suggestions based on the user's conditions, the condition analysis unit can refer to past success stories and make more specific suggestions. For example, the proposal can be made based on properties that have been successful in the past under similar conditions. The generation AI can also refer to user reviews to make additional suggestions based on the conditions. This allows the generation AI to refer to past success stories and user reviews to make more specific suggestions when making additional suggestions based on the user's conditions.

[0048] The condition analysis unit can take into account the user's lifestyle and future plans when making additional suggestions. For example, when the generation AI makes additional suggestions, the condition analysis unit takes into account the user's lifestyle and suggests properties that suit their hobbies. For example, for a user who likes the outdoors, the unit suggests properties with parks or nature nearby. The generation AI also takes into account the user's future plans when making additional suggestions. This allows the user's lifestyle and future plans to be taken into account when making additional suggestions.

[0049] When making additional suggestions, the condition analysis unit can make the suggestions from a global perspective, including properties in different regions and countries. For example, when the generation AI makes additional suggestions, the condition analysis unit makes the suggestions from a global perspective, including properties in different regions and countries. For example, if a user wishes to be transferred overseas, the unit will suggest properties in the transfer destination. In addition, to make suggestions from a global perspective, the generation AI references databases of different countries. This allows the generation AI to make additional suggestions from a global perspective, including properties in different regions and countries.

[0050] The condition analysis unit can add a function that allows a user to discuss a proposal with other users when making an additional proposal. For example, when the generation AI makes an additional proposal, the condition analysis unit adds a function that allows a user to discuss the proposal with other users. For example, a comment field can be provided for each proposal so that users can exchange opinions with each other. In addition, a function that allows a user to discuss a proposal with other users is provided. This makes it possible to add a function that allows a user to discuss a proposal with other users when making an additional proposal.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The condition input unit can accept the user's desired conditions by voice input. For example, the user can input the conditions by simply saying, "within 10 minutes' walk from the station, 2LDK, pets allowed, rent under 100,000 yen." In addition, using voice recognition technology, a function is provided that allows the user to input the desired conditions in detail just by speaking. This allows the user's desired conditions to be accepted by voice input.

[0053] The condition analysis unit can suggest properties based on the user's lifestyle. For example, for a user who likes the outdoors, it can suggest properties with parks or nature nearby. It can also take into account the user's work style and suggest properties that are suitable for remote work. This makes it possible to suggest properties based on the user's lifestyle.

[0054] The property picking unit can present properties in a customized order based on the user's past browsing history and ratings. For example, it can prioritize displaying properties similar to properties that have been highly rated in the past. In addition, the generation AI presents properties picked up by the user in a customized order based on the user's rating data. This allows properties to be presented in a customized order based on the user's past browsing history and ratings.

[0055] The condition analysis unit can pick properties from a global perspective, including properties in different regions and countries. For example, if a user wishes to be transferred overseas, it will list properties in the transfer destination. In order to pick properties from a global perspective, the generation AI also references databases from different countries. This allows properties to be picked from a global perspective, including properties in different regions and countries.

[0056] The property pickup unit can display surrounding facilities that the user is likely to be interested in when presenting a property. For example, information about cafes and restaurants can be displayed along with the property information. Also, surrounding facilities that the user is likely to be interested in can be displayed along with the property information. This allows surrounding facilities that the user is likely to be interested in to be displayed together when presenting a property.

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

[0058] Step 1: The user enters the desired conditions in detail in the condition input section. For example, the user may enter specific conditions such as "within a 10-minute walk from the station, 2LDK, pet-friendly, rent under 100,000 yen, and a room with a balcony facing south." Step 2: The condition analysis unit analyzes the conditions entered by the condition input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the user's conditions and select appropriate properties. Step 3: The property selection unit selects properties based on the conditions analyzed by the condition analysis unit. For example, the generation AI lists properties that match the conditions from the database and presents them to the user. Step 4: The application unit applies for the property picked by the property picking unit. For example, the generation AI inputs the user's information and generates a form for submitting an application to a real estate company.

[0059] (Example 2) The real estate search system according to an embodiment of the present invention is a system in which a user inputs desired conditions in detail, a generation AI analyzes the conditions, picks out properties that are close to their ideal, and the user applies to a real estate company. This allows the real estate search system to allow a user to input desired conditions in detail, a generation AI analyzes the conditions, picks out properties that are close to their ideal, and the user applies to a real estate company.

[0060] A real estate search system according to an embodiment includes a condition input unit, a condition analysis unit, a property selection unit, and an application unit. The condition input unit allows a user to input detailed desired conditions. For example, the user may input specific conditions such as "within a 10-minute walk from the station, 2LDK, pet-friendly, rent under 100,000 yen, and a room with a south-facing balcony." The condition analysis unit analyzes the conditions input by the condition input unit. For example, the generation AI analyzes the user's conditions using a text generation AI (e.g., LLM) and selects appropriate properties. The property selection unit selects properties based on the conditions analyzed by the condition analysis unit. For example, the generation AI lists properties that match the conditions from a database and presents them to the user. The application unit applies for the properties selected by the property selection unit. For example, the generation AI inputs user information and generates a form for submitting an application to a real estate company. As a result, the real estate search system according to an embodiment allows a user to input detailed desired conditions, the generation AI analyzes the conditions, selects properties that are close to their ideal, and submits an application to a real estate company.

[0061] The condition analysis unit can use a pre-fine-tuned model to understand the user's conditions and select an appropriate property. The condition analysis unit can use, for example, a pre-fine-tuned machine learning model to understand the user's conditions and select an appropriate property. For example, the generation AI learns the user's past search history and preferences and automatically completes more specific conditions. The generation AI also suggests detailed conditions related to the conditions entered by the user. For example, if "pets allowed" is entered, the generation AI automatically completes conditions according to the type and size of the pet. This allows the generation AI to use a pre-fine-tuned model to understand the user's conditions and select an appropriate property.

[0062] The property pick-up unit can present properties to the user in list format and display photos of the properties, floor plans, and information about the surrounding environment. The property pick-up unit, for example, presents properties to the user in list format and displays photos of the properties, floor plans, and information about the surrounding environment. For example, the generation AI automatically generates related questions based on the conditions entered by the user, and interactively narrows down the conditions for the user. For example, if the user enters "within a 10-minute walk from the station," it generates a question such as "Do you prefer a specific station name?" The generation AI also automatically generates detailed questions related to the conditions entered by the user, and interactively narrows down the conditions for the user. This makes it possible to present properties to the user in list format and display photos of the properties, floor plans, and information about the surrounding environment.

[0063] The condition analysis unit can make additional suggestions based on the conditions entered by the user. For example, the condition analysis unit uses an emotion estimation function to analyze the emotions the user is feeling when entering text, and makes suggestions to simplify the input if the user is feeling stressed. For example, if the user is feeling impatient or anxious, it presents simple options. It also analyzes the emotions the user is feeling when entering text in real time, and makes suggestions to simplify the input if the user is feeling stressed. This allows the generation AI to make additional suggestions based on the conditions entered by the user.

[0064] The condition input unit can accept the user's desired conditions by voice input. For example, the condition input unit can add a voice input function so that the user can input the desired conditions in detail just by speaking. For example, the conditions can be input by simply speaking, "within 10 minutes' walk from the station, 2LDK, pets allowed, rent less than 100,000 yen." In addition, using voice recognition technology, a function is provided that allows the user to input the desired conditions in detail just by speaking. This makes it possible to accept the user's desired conditions by voice input.

[0065] The condition input unit can provide an interface that visually displays the user's desired conditions and allows the user to adjust the conditions by drag and drop. The condition input unit, for example, visually displays the conditions entered by the user and provides an interface that allows the user to adjust the conditions by drag and drop. For example, the conditions can be displayed in card format and the order can be changed by drag and drop. A visual interface is also provided, allowing the user to adjust the conditions entered by drag and drop. This makes it possible to provide an interface that visually displays the user's desired conditions and allows the user to adjust the conditions by drag and drop.

[0066] The condition input unit can use the emotion estimation function to analyze the emotion of the user when entering input in real time and propose an interface design that elicits positive emotions. The condition input unit can, for example, use the emotion estimation function to analyze the emotion of the user when entering input in real time and propose an interface design that elicits positive emotions. For example, it can provide colors and layouts that allow the user to relax. It can also analyze the emotion of the user in real time and propose an interface design that elicits positive emotions. This makes it possible to analyze the emotion of the user when entering input in real time and propose an interface design that elicits positive emotions.

[0067] When analyzing a user's conditions, the condition analysis unit refers to past success stories and user reviews, allowing it to pick out properties with greater accuracy. For example, when the generation AI analyzes a user's conditions, the condition analysis unit refers to past success stories and picks out properties with greater accuracy. For example, it prioritizes listing properties that have been successful under similar conditions in the past. It also refers to user reviews, allowing the generation AI to pick out properties with greater accuracy when analyzing the conditions. This allows it to refer to past success stories and user reviews and pick out properties with greater accuracy.

[0068] The condition analysis unit can take into account data on the surrounding environment when analyzing the conditions. For example, the condition analysis unit takes into account data on the surrounding environment when the generation AI analyzes the conditions. For example, it can prioritize listing properties in safe areas based on public safety data. In addition, by taking into account school evaluation data, the generation AI can pick out properties in areas with good educational environments when analyzing the conditions. This allows the conditions to be analyzed taking into account data on the surrounding environment.

[0069] The condition analysis unit can use the emotion estimation function to analyze the user's emotional response to properties viewed in the past, and prioritize picking out properties similar to properties to which the user responded positively. The condition analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response to properties viewed in the past, and prioritize picking out properties similar to properties to which the user responded positively. For example, the condition analysis unit selects similar properties based on the characteristics of properties to which the user responded favorably. The condition analysis unit also analyzes the user's emotional response data, and prioritizes picking out properties similar to properties to which the user responded positively. This makes it possible to analyze the user's emotional response to properties viewed in the past, and prioritize picking out properties similar to properties to which the user responded positively.

[0070] The condition analysis unit can suggest properties based on the user's lifestyle. For example, when the generation AI analyzes the conditions, the condition analysis unit takes the user's lifestyle into consideration and suggests properties that suit their hobbies. For example, for a user who likes the outdoors, it can suggest properties with parks or nature nearby. In addition, when the generation AI analyzes the conditions, it can take work style into consideration and suggest properties that are suitable for remote work. This makes it possible to suggest properties based on the user's lifestyle.

[0071] The condition analysis unit can pick properties from a global perspective, including properties from different regions and countries. For example, when the generation AI analyzes the conditions, the condition analysis unit picks properties from a global perspective, including properties from different regions and countries. For example, if a user wishes to be transferred overseas, the unit will list properties in the transfer destination. In addition, to pick properties from a global perspective, the generation AI references databases from different countries. This allows the generation AI to pick properties from a global perspective, including properties from different regions and countries.

[0072] The condition analysis unit can use the emotion estimation function to analyze the emotional response to the conditions entered by the user in real time and make property suggestions that will elicit positive emotions. The condition analysis unit can, for example, use the emotion estimation function to analyze the emotional response to the conditions entered by the user in real time and make property suggestions that will elicit positive emotions. For example, the condition analysis unit can preferentially suggest properties that make the user feel joyful or excited. The condition analysis unit can also analyze the emotional response of the user in real time and make property suggestions that will elicit positive emotions. This allows the condition analysis unit to analyze the emotional response to the conditions entered by the user in real time and make property suggestions that will elicit positive emotions.

[0073] The property picking unit can present properties in an order customized based on the user's past browsing history and ratings. The property picking unit, for example, presents properties picked by the generation AI in an order customized based on the user's past browsing history. For example, properties similar to properties that have been highly rated in the past are preferentially displayed. In addition, properties picked by the generation AI are presented in a customized order based on the user's rating data. This makes it possible to present properties in an order customized based on the user's past browsing history and ratings.

[0074] The property pickup unit can display surrounding facilities that the user is likely to be interested in when presenting a property. For example, the property pickup unit displays surrounding facilities that the user is likely to be interested in when presenting a property. For example, information about cafes and restaurants is displayed along with the property information. Also, surrounding facilities that the user is likely to be interested in are displayed along with the property information. In this way, surrounding facilities that the user is likely to be interested in can be displayed together when presenting a property.

[0075] The property pickup unit can use the emotion estimation function to analyze the emotion a user feels when viewing a property and adjust the display order of property information to elicit positive emotions. The property pickup unit, for example, uses the emotion estimation function to analyze the emotion a user feels when viewing a property and adjust the display order of property information to elicit positive emotions. For example, properties that make the user feel excited or happy are preferentially displayed. The property pickup unit also analyzes the user's emotion in real time and adjusts the display order of property information to elicit positive emotions. This makes it possible to analyze the emotion a user feels when viewing a property and adjust the display order of property information to elicit positive emotions.

[0076] The property pick-up unit can display information about events and activities that the user is likely to be interested in when presenting a property. For example, the property pick-up unit displays information about events that the user is likely to be interested in when presenting a property. For example, information about festivals and concerts being held in the neighborhood is displayed along with the property information. Also, activity information that the user is likely to be interested in is displayed along with the property information. This makes it possible to display information about events and activities that the user is likely to be interested in when presenting a property.

[0077] The property pick-up unit can add a function that allows a user to discuss a property with other users when a property is presented. The property pick-up unit, for example, adds a function that allows a user to discuss a property with other users when a property is presented. For example, a comment section can be provided for each property, allowing users to exchange opinions with each other. In addition, a function that allows a user to discuss a property with other users can be provided. This makes it possible to add a function that allows a user to discuss a property with other users when a property is presented.

[0078] The property pickup unit can use the emotion estimation function to analyze the emotions of a user when viewing a property in real time and propose a property information display method that elicits positive emotions. The property pickup unit, for example, uses the emotion estimation function to analyze the emotions of a user when viewing a property in real time and propose a property information display method that elicits positive emotions. For example, the property pickup unit prominently displays properties that make the user feel joyful or excited. The property pickup unit also analyzes the user's emotions in real time and proposes a property information display method that elicits positive emotions. This makes it possible to analyze the emotions of a user when viewing a property in real time and propose a property information display method that elicits positive emotions.

[0079] When the generation AI automatically inputs the user's information, the application unit can generate the optimal form by taking into consideration past application history and the user's preferences. For example, when the generation AI automatically inputs the user's information, the application unit references past application history to generate the optimal form. For example, it automatically completes information previously input. The generation AI also takes into consideration the user's preferences to generate the optimal form. This allows the generation AI to generate the optimal form by taking into consideration past application history and the user's preferences when automatically inputting the user's information.

[0080] The application unit can evaluate the user's credit information and repayment ability in advance when the generation AI goes through the application procedure, and suggest appropriate properties. For example, the application unit can evaluate the user's credit information in advance when the generation AI goes through the application procedure, and suggest appropriate properties. For example, expensive properties can be suggested to users with high credit scores. The user's repayment ability can also be evaluated in advance, and the generation AI can suggest appropriate properties. This allows the generation AI to evaluate the user's credit information and repayment ability in advance when the generation AI goes through the application procedure, and suggest appropriate properties.

[0081] The application unit can use the emotion estimation function to analyze the emotion of the user when performing the application procedure and provide an interface for reducing stress. The application unit, for example, uses the emotion estimation function to analyze the emotion of the user when performing the application procedure and provide an interface for reducing stress. For example, it provides colors and layouts that allow the user to relax. It also analyzes the user's emotion in real time and provides an interface for reducing stress. This makes it possible to analyze the emotion of the user when performing the application procedure and provide an interface for reducing stress.

[0082] The application unit can add a function that allows a user to apply jointly with other users when the generation AI performs the application procedure. The application unit, for example, adds a function that allows a user to apply jointly with other users when the generation AI performs the application procedure. For example, it allows users who wish to share a room to apply jointly. It also provides a function that allows a user to apply jointly with other users. This makes it possible to add a function that allows a user to apply jointly with other users when the generation AI performs the application procedure.

[0083] The application unit can add a function that allows the user to communicate directly with the real estate company via chat when the generation AI is carrying out the application procedure. The application unit, for example, adds a function that allows the user to communicate directly with the real estate company via chat when the generation AI is carrying out the application procedure. For example, it allows questions and confirmations about the property to be exchanged in real time. It also provides a function that allows the user to communicate directly with the real estate company via chat. This makes it possible to add a function that allows the user to communicate directly with the real estate company via chat when the generation AI is carrying out the application procedure.

[0084] The application unit can use the emotion estimation function to analyze the user's emotions in real time when performing the application procedure and propose an interface design that elicits positive emotions. The application unit, for example, uses the emotion estimation function to analyze the user's emotions in real time when performing the application procedure and propose an interface design that elicits positive emotions. For example, the application unit provides colors and layouts that allow the user to relax. The application unit also analyzes the user's emotions in real time and proposes an interface design that elicits positive emotions. This makes it possible to analyze the user's emotions in real time when performing the application procedure and propose an interface design that elicits positive emotions.

[0085] When making additional suggestions based on the user's conditions, the condition analysis unit can refer to past success stories and user reviews to make more specific suggestions. For example, when the generation AI makes additional suggestions based on the user's conditions, the condition analysis unit can refer to past success stories and make more specific suggestions. For example, the proposal can be made based on properties that have been successful in the past under similar conditions. The generation AI can also refer to user reviews to make additional suggestions based on the conditions. This allows the generation AI to refer to past success stories and user reviews to make more specific suggestions when making additional suggestions based on the user's conditions.

[0086] The condition analysis unit can take into account the user's lifestyle and future plans when making additional suggestions. For example, when the generation AI makes additional suggestions, the condition analysis unit takes into account the user's lifestyle and suggests properties that suit their hobbies. For example, for a user who likes the outdoors, the unit suggests properties with parks or nature nearby. The generation AI also takes into account the user's future plans when making additional suggestions. This allows the user's lifestyle and future plans to be taken into account when making additional suggestions.

[0087] The condition analysis unit uses the emotion estimation function to analyze the emotion of the user when accepting a proposal and make a proposal to elicit positive emotions. The condition analysis unit, for example, uses the emotion estimation function to analyze the emotion of the user when accepting a proposal and make a proposal to elicit positive emotions. For example, the condition analysis unit suggests properties that make the user feel joyful or excited. The condition analysis unit also analyzes the user's emotions in real time and makes a proposal to elicit positive emotions. This makes it possible to analyze the emotion of the user when accepting a proposal and make a proposal to elicit positive emotions.

[0088] When making additional suggestions, the condition analysis unit can make the suggestions from a global perspective, including properties in different regions and countries. For example, when the generation AI makes additional suggestions, the condition analysis unit makes the suggestions from a global perspective, including properties in different regions and countries. For example, if a user wishes to be transferred overseas, the unit will suggest properties in the transfer destination. In addition, to make suggestions from a global perspective, the generation AI references databases of different countries. This allows the generation AI to make additional suggestions from a global perspective, including properties in different regions and countries.

[0089] The condition analysis unit can add a function that allows a user to discuss a proposal with other users when making an additional proposal. For example, when the generation AI makes an additional proposal, the condition analysis unit adds a function that allows a user to discuss the proposal with other users. For example, a comment field can be provided for each proposal so that users can exchange opinions with each other. In addition, a function that allows a user to discuss a proposal with other users is provided. This makes it possible to add a function that allows a user to discuss a proposal with other users when making an additional proposal.

[0090] The condition analysis unit can use the emotion estimation function to analyze the emotion of the user when accepting a proposal in real time and propose a proposal method for eliciting positive emotions. The condition analysis unit, for example, uses the emotion estimation function to analyze the emotion of the user when accepting a proposal in real time and propose a proposal method for eliciting positive emotions. For example, a proposal method that makes the user feel joy or excitement is provided. The condition analysis unit can also analyze the emotion of the user in real time and propose a proposal method for eliciting positive emotions. This makes it possible to analyze the emotion of the user when accepting a proposal in real time and propose a proposal method for eliciting positive emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The condition input unit can accept the user's desired conditions by voice input. For example, the user can input the conditions by simply saying, "within 10 minutes' walk from the station, 2LDK, pets allowed, rent under 100,000 yen." In addition, using voice recognition technology, a function is provided that allows the user to input the desired conditions in detail just by speaking. This allows the user's desired conditions to be accepted by voice input.

[0093] The condition analysis unit can suggest properties based on the user's lifestyle. For example, for a user who likes the outdoors, it can suggest properties with parks or nature nearby. It can also take into account the user's work style and suggest properties that are suitable for remote work. This makes it possible to suggest properties based on the user's lifestyle.

[0094] The property picking unit can present properties in a customized order based on the user's past browsing history and ratings. For example, it can prioritize displaying properties similar to properties that have been highly rated in the past. In addition, the generation AI presents properties picked up by the user in a customized order based on the user's rating data. This allows properties to be presented in a customized order based on the user's past browsing history and ratings.

[0095] The condition analysis unit can pick properties from a global perspective, including properties in different regions and countries. For example, if a user wishes to be transferred overseas, it will list properties in the transfer destination. In order to pick properties from a global perspective, the generation AI also references databases from different countries. This allows properties to be picked from a global perspective, including properties in different regions and countries.

[0096] The property pickup unit can display surrounding facilities that the user is likely to be interested in when presenting a property. For example, information about cafes and restaurants can be displayed along with the property information. Also, surrounding facilities that the user is likely to be interested in can be displayed along with the property information. This allows surrounding facilities that the user is likely to be interested in to be displayed together when presenting a property.

[0097] The condition analysis unit uses the emotion estimation function to analyze the user's emotional response to properties viewed in the past, and can prioritize picking out properties similar to properties to which the user responded positively. For example, similar properties are selected based on the characteristics of properties to which the user responded favorably. The condition analysis unit also analyzes the user's emotional response data and prioritizes picking out properties similar to properties to which the user responded positively. This makes it possible to analyze the user's emotional response to properties viewed in the past, and prioritize picking out properties similar to properties to which the user responded positively.

[0098] The condition analysis unit uses the emotion estimation function to analyze the emotional response to the conditions entered by the user in real time and make property suggestions that will elicit positive emotions. For example, it prioritizes suggestions of properties that make the user feel joyful or excited. It also analyzes the emotional response of the user in real time and makes property suggestions that will elicit positive emotions. This makes it possible to analyze the emotional response to the conditions entered by the user in real time and make property suggestions that will elicit positive emotions.

[0099] The property pick-up unit uses the emotion estimation function to analyze the emotions of the user when viewing properties and adjust the display order of property information to elicit positive emotions. For example, properties that make the user feel excited or happy are preferentially displayed. The property pick-up unit also analyzes the user's emotions in real time and adjusts the display order of property information to elicit positive emotions. This makes it possible to analyze the emotions of the user when viewing properties and adjust the display order of property information to elicit positive emotions.

[0100] The application unit can use the emotion estimation function to analyze the user's emotions when performing the application procedure and provide an interface for reducing stress. For example, it can provide colors and layouts that allow the user to relax. It can also analyze the user's emotions in real time and provide an interface for reducing stress. This makes it possible to analyze the user's emotions when performing the application procedure and provide an interface for reducing stress.

[0101] The condition analysis unit uses the emotion estimation function to analyze the emotions of the user when accepting a proposal and make a proposal that elicits positive emotions. For example, it can suggest properties that make the user feel happy or excited. It can also analyze the user's emotions in real time and make a proposal that elicits positive emotions. This makes it possible to analyze the emotions of the user when accepting a proposal and make a proposal that elicits positive emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The user enters the desired conditions in detail in the condition input section. For example, the user may enter specific conditions such as "within a 10-minute walk from the station, 2LDK, pet-friendly, rent under 100,000 yen, and a room with a balcony facing south." Step 2: The condition analysis unit analyzes the conditions entered by the condition input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the user's conditions and select appropriate properties. Step 3: The property selection unit selects properties based on the conditions analyzed by the condition analysis unit. For example, the generation AI lists properties that match the conditions from the database and presents them to the user. Step 4: The application unit applies for the property picked by the property picking unit. For example, the generation AI inputs the user's information and generates a form for submitting an application to a real estate company.

[0104] 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.

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

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 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.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the 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.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0121] The data processing system 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 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 condition input section for the user to input desired conditions in detail; a condition analysis unit that analyzes the conditions input by the condition input unit; an object picking unit that picks up objects based on the conditions analyzed by the condition analyzing unit; an application unit that applies for the property picked up by the property pick-up unit; A system characterized by:

2. The condition analysis unit Uses a pre-fine-tuned model to understand the user's requirements and select the appropriate property 2. The system of claim 1.

3. The property pickup unit The properties are presented to the user in a list format, and photos, floor plans, and information about the surrounding environment of the properties are displayed.

2. The system of claim 1.

4. The condition analysis unit Providing additional suggestions based on the criteria entered by the user 2. The system of claim 1.

5. The condition input unit The user's desired conditions are received by voice input.

2. The system of claim 1.

6. The condition input unit Provide an interface that visually displays the user's desired conditions and allows them to adjust the conditions by drag and drop.

2. The system of claim 1.

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