system

Abstract

The system according to the embodiment aims to remove language barriers by allowing the entire process from property search to contract procedures to be carried out online. [Solution] A system according to an embodiment includes a reception unit, a proposal unit, a procedure unit, and a translation unit. The reception unit accepts desired conditions from a user. The proposal unit proposes suitable properties based on the conditions accepted by the reception unit. The procedure unit allows viewings of properties proposed by the proposal unit and handles contract procedures online. The translation unit translates the contract procedures carried out by the procedure unit into multiple languages.

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, property searches and contract procedures were often conducted face-to-face, which posed a challenge for foreigners in particular, as it meant that they had difficulty renting real estate due to the language barrier.

[0005] The system according to the embodiment aims to remove language barriers by allowing the entire process from property search to contract procedures to be carried out online. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a procedure unit, and a translation unit. The reception unit accepts desired conditions from a user. The proposal unit proposes suitable properties based on the conditions accepted by the reception unit. The procedure unit allows viewing of properties proposed by the proposal unit and handles contract procedures online. The translation unit translates the contract procedures carried out by the procedure unit into multiple languages. [Effects of the Invention]

[0007] The system according to the embodiment allows the entire process from property search to contract procedures to be carried out online, eliminating language barriers. [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) In an embodiment of the present invention, a property proposal system is a system in which a user inputs their desired conditions, and AI proposes optimal properties, allowing viewings and contract procedures online. The property proposal system allows users to input their desired conditions, and AI analyzes the conditions and proposes optimal properties. The proposed properties can be viewed and contract procedures conducted online, and AI translates languages ​​other than Japanese, making it easy for foreigners to complete the contract procedures. For example, the property proposal system allows users to input their desired conditions. For example, users can enter detailed conditions such as location, budget, and floor plan. This information is input into AI. The property proposal system then analyzes the input conditions and proposes optimal properties. The AI ​​searches a vast property database for properties that best meet the user's conditions. For example, it searches for 2LDK properties in urban areas within a user's budget and lists matching properties. The proposed properties can be viewed and contract procedures conducted online. Users can view detailed information about the proposed properties online, schedule viewings, and complete contract procedures. For example, they can view photos and videos of the properties and schedule viewings online. In addition, contract procedures are completed online, eliminating the need for face-to-face procedures or seals. Furthermore, AI can translate languages ​​other than Japanese, making it easy for foreigners to complete the contract process. For example, contracts translated into languages ​​such as English and Chinese can be viewed online and the contract process can be completed. This can improve the current situation in which it is difficult for foreigners to rent real estate in Japan. This allows the property recommendation system to suggest the most suitable property based on the user's desired conditions, and viewings and contract procedures can be completed online. For example, users can easily find properties that meet their desired conditions, and viewings and contract procedures can be completed online, saving time and effort. Furthermore, foreigners can complete the contract process smoothly without experiencing language barriers. For example, when foreigners are looking for a place to live in Japan, AI can suggest the most suitable property and complete the contract procedure online, allowing them to find a place to live with peace of mind.

[0029] A property proposal system according to an embodiment includes a reception unit, a proposal unit, a procedure unit, and a translation unit. The reception unit accepts desired conditions from a user. The desired conditions input by the user include, but are not limited to, detailed conditions such as location, budget, and floor plan. The reception unit accepts, for example, detailed conditions such as location, budget, and floor plan input by the user. The reception unit can also estimate the user's emotions and customize the input interface for the desired conditions based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided to minimize the input steps. The proposal unit proposes optimal properties based on the conditions accepted by the reception unit. The proposal unit searches for and proposes properties that best meet the user's conditions based on a massive property database. For example, the proposal unit searches for and proposes properties that best meet the user's conditions based on a massive property database. The proposal unit can also estimate the user's emotions and adjust the presentation of property proposals based on the estimated user emotions. For example, if the user is relaxed, detailed property information can be provided and proposals can be made at a leisurely pace. The procedure unit performs online viewings and contract procedures for properties proposed by the proposal unit. The procedure unit checks detailed information about the proposed property online and makes reservations for viewings and completes contract procedures. For example, the procedure unit checks detailed information about the proposed property online and completes reservations for viewings and completes contract procedures. The procedure unit can also estimate the user's emotions and adjust the way the procedures proceed based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible procedure is provided. The translation unit translates the contract procedures performed by the procedure unit into multiple languages. The translation unit translates contracts and property information into multiple languages, allowing foreign users to complete contract procedures without feeling a language barrier. For example, the translation unit translates contracts and property information into multiple languages, allowing foreign users to complete contract procedures without feeling a language barrier. The translation unit can also estimate the user's emotions and adjust the way the translation is expressed based on the estimated user emotions. For example, if the user is nervous, a simple and easy-to-understand expression is used in the translation.As a result, the property recommendation system according to the embodiment can propose optimal properties based on the user's desired conditions, and allow viewings and contract procedures to be completed online. For example, users can easily find properties that meet their desired conditions, and viewings and contract procedures can be completed online, saving time and effort. Furthermore, foreigners can smoothly complete contract procedures without experiencing language barriers. For example, when foreigners are looking for a place to live in Japan, AI can propose optimal properties and allow them to complete contract procedures online, allowing them to find a place to live with peace of mind.

[0030] The reception unit can accept detailed conditions, such as location, budget, and floor plan, entered by the user. The detailed conditions include, but are not limited to, location, budget, floor plan, and facilities. The reception unit can also accept detailed conditions, such as location, budget, and floor plan, entered by the user. The reception unit can also estimate the user's emotions and customize the input interface for desired conditions based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided, minimizing the input steps. This allows the user to enter detailed conditions, enabling more accurate property suggestions. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit inputs the detailed conditions entered by the user into AI, which then analyzes the conditions and suggests optimal properties.

[0031] The suggestion unit can search for and suggest properties that best meet the user's requirements based on a large-scale property database. The property database may, for example, include, but is not limited to, the size of the database and the types of properties included. The suggestion unit can, for example, search for and suggest properties that best meet the user's requirements based on the large-scale property database. The suggestion unit can also estimate the user's emotions and adjust the way property suggestions are presented based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide detailed property information and make suggestions at a leisurely pace. This allows the suggestion unit to utilize the large-scale property database to suggest properties that best meet the user's requirements. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit inputs the user's requirements into AI, which then analyzes the requirements and suggests optimal properties.

[0032] The procedure unit can check detailed information about the proposed property online and make reservations for viewings and contract procedures. Examples of detailed information include, but are not limited to, the property's floor plan, facilities, and surrounding environment. For example, the procedure unit can check detailed information about the proposed property online and make reservations for viewings and contract procedures. The procedure unit can also estimate the user's emotions and adjust the procedure based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible procedure can be provided. This allows the user to check detailed information about the property online and make reservations for viewings and contract procedures. Some or all of the above-described processing in the procedure unit can be performed using, or without, AI. For example, the procedure unit inputs detailed information about the proposed property into AI, which then analyzes the information and makes reservations for viewings and contract procedures.

[0033] The translation unit can translate contracts and property information into multiple languages, allowing foreign users to complete contract procedures without feeling a language barrier. Specific languages ​​and translation methods for translating into multiple languages ​​include, but are not limited to, the corresponding languages ​​and the accuracy of the translation. The translation unit can also translate contracts and property information into multiple languages, allowing foreign users to complete contract procedures without feeling a language barrier. The translation unit can also estimate the user's emotions and adjust the translation expression based on the estimated user emotions. For example, if the user is nervous, the translation unit can use simple, easy-to-understand expressions. This allows foreign users to complete contract procedures without feeling a language barrier. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs contracts and property information into AI, which then translates the information into multiple languages.

[0034] The reception unit can analyze the user's past property search history and suggest the optimal condition input method. The past property search history includes, but is not limited to, search date and time, search conditions, and properties viewed. The reception unit, for example, analyzes the user's past property search history and suggests the optimal condition input method. For example, the reception unit can automatically display conditions that the user frequently searched for in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest conditions to be used during a specific time period based on the user's past search history. By utilizing the past search history, the reception unit can suggest the optimal condition input method to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit inputs the user's past property search history into AI, which then analyzes the history and suggests the optimal condition input method.

[0035] The reception unit can filter the results based on the user's current living situation and future plans when the desired conditions are input. Examples of the current living situation and future plans include, but are not limited to, family composition, workplace location, commute time, planned job change, and children's educational plans. For example, the reception unit can filter the results based on the user's current living situation and future plans when the desired conditions are input. For example, if the user inputs their family composition, properties suitable for families can be preferentially suggested. Also, if the user inputs their future job change plans, properties convenient for commuting can be suggested. Also, if the user inputs their current living situation (such as whether or not they have pets), properties corresponding to that information can be suggested. This makes it possible to suggest more appropriate properties based on the user's living situation and future plans. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit inputs the user's living situation and future plans into AI, and the AI ​​analyzes the information and performs filtering.

[0036] When the user inputs desired conditions, the reception unit can select the optimal input means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when the user inputs desired conditions, the reception unit selects the optimal input means depending on the user's input method. For example, when the user inputs desired conditions by voice, the conditions can be automatically analyzed using voice recognition technology. Also, when the user inputs desired conditions by text, the entered text can be analyzed to extract conditions. Also, when the user uploads an image, image analysis technology can be used to extract property features and set conditions. This allows for the selection of the optimal input means depending on the user's input method, thereby providing a more user-friendly system. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit inputs the user's input method into AI, which then analyzes the method and selects the optimal input means.

[0037] The reception unit can prioritize input of highly relevant conditions based on the user's geographical location information when inputting desired conditions. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the reception unit can prioritize input of highly relevant conditions based on the user's geographical location information when inputting desired conditions. For example, when the user inputs their current location, properties in the vicinity of the user's current location can be prioritized. Furthermore, when the user specifies a specific area, conditions related to that area can be prioritized. Furthermore, when the user uses the app while on the move, the current location can be updated in real time and related conditions can be suggested. This allows more relevant properties to be suggested by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit inputs the user's geographical location information to AI, and the AI ​​analyzes the information and prioritizes input of highly relevant conditions.

[0038] The reception unit can analyze the user's social media activity when the desired conditions are input and suggest related conditions. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit can analyze the user's social media activity when the desired conditions are input and suggest related conditions. For example, the reception unit can suggest related properties based on the locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related conditions. The reception unit can also suggest related conditions based on the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to suggest related conditions to the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's social media activity into AI, and the AI ​​analyzes the activity and suggests related conditions.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when the desired conditions are input. Past feedback includes, but is not limited to, user ratings, comments, and survey results. The reception unit customizes the input method by reflecting the user's past feedback when the desired conditions are input. For example, the reception unit can suggest an optimal input method based on feedback previously provided by the user. It can also preferentially suggest a specific input method based on the user's past feedback. It can also analyze the user's past feedback and customize the input interface. In this way, it is possible to provide the user with an optimal input method by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past feedback to AI, and the AI ​​analyzes the feedback to customize the input method.

[0040] The suggestion unit can adjust the level of detail of the proposal based on the importance of the property when proposing a property. The importance of the property includes, but is not limited to, the price, location, and facilities of the property. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the property when proposing a property. For example, for important properties, detailed information can be provided to provide sufficient information to the user. For less important properties, concise information can be provided to save the user's time. The level of detail of the proposal can also be dynamically adjusted according to the importance of the property. By adjusting the level of detail of the proposal based on the importance of the property, optimal information can be provided to the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs property importance data into AI, and the AI ​​analyzes the data to adjust the level of detail of the proposal.

[0041] When proposing a property, the suggestion unit can apply different suggestion algorithms depending on the property category. Property categories include, but are not limited to, residential, commercial, and office properties. When proposing a property, the suggestion unit can apply different suggestion algorithms depending on the property category. For example, different suggestion algorithms can be applied to rental properties and condominium properties to make optimal suggestions. Different suggestion algorithms can also be applied to commercial properties and residential properties to make suggestions tailored to the user's needs. The suggestion algorithm can also be dynamically switched depending on the property category. This allows the optimal property to be proposed to the user by applying the optimal suggestion algorithm depending on the property category. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit inputs property category data into AI, which analyzes the data and applies the suggestion algorithm.

[0042] When proposing a property, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. Examples of past suggestion results include, but are not limited to, the success rate of the suggestion and the user's evaluation. For example, when proposing a property, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit can analyze the characteristics of properties previously selected by the user and suggest similar properties. It can also prioritize suggestions based on specific conditions from the user's past suggestion results. It can also adjust the suggestion algorithm based on the user's past suggestion results to improve accuracy. By referring to the past suggestion results, the accuracy of the suggestion can be improved. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit inputs the user's past suggestion result data into AI, and the AI ​​analyzes the data to improve the accuracy of the suggestion.

[0043] When proposing a property, the proposal unit can determine the priority of the proposal based on the date of registration of the property. The date of registration of the property includes, but is not limited to, for example, the registration date and the update date. When proposing a property, the proposal unit can determine the priority of the proposal based on the date of registration of the property. For example, the proposal unit can prioritize newly registered properties. Also, the proposal priority can be lowered for properties that were registered earlier. The proposal priority can also be dynamically adjusted depending on the date of registration of the property. In this way, by determining the priority of the proposal based on the date of registration of the property, the latest property information can be provided preferentially. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit inputs property registration date data into AI, and the AI ​​analyzes the data to determine the priority of the proposal.

[0044] The suggestion unit can adjust the order of proposals based on the relevance of properties when suggesting properties. Examples of property relevance include, but are not limited to, the degree of match with the user's desired conditions and past search history. For example, the suggestion unit can adjust the order of proposals based on the relevance of properties when suggesting properties. For example, the suggestion unit can prioritize suggesting properties that are most relevant to the user's conditions. Also, properties with low relevance can be postponed in the proposal order. The suggestion unit can also dynamically adjust the order of proposals based on the relevance of properties. By adjusting the order of proposals based on the relevance of properties, it is possible to prioritize suggesting properties that are most relevant to the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs property relevance data into AI, and the AI ​​analyzes the data to adjust the order of proposals.

[0045] When proposing a property, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past search history. For example, when proposing a property, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is knowledgeable about real estate, the suggestion unit can make a proposal that uses a lot of technical terminology. On the other hand, if the user is not knowledgeable about real estate, the suggestion unit can make a concise proposal that avoids technical terminology. The use of technical terminology in the proposal can also be dynamically adjusted according to the user's level of expertise. This allows the suggestion to be easily understood by the user by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit inputs the user's level of expertise data into AI, and the AI ​​analyzes the data to adjust the use of technical terminology.

[0046] The procedure unit can analyze the user's past procedure history and select the optimal procedure method during a procedure. The past procedure history includes, for example, the date and time of the procedure, the procedure content, etc., but is not limited to these examples. The procedure unit can, for example, analyze the user's past procedure history and select the optimal procedure method during a procedure. For example, the procedure unit can suggest the optimal procedure method based on the procedure methods the user has used in the past. It can also suggest an efficient procedure method from the user's past procedure history. It can also analyze the user's past procedure history and select the most efficient procedure method. In this way, by analyzing the past procedure history, it is possible to provide the user with the optimal procedure method. Some or all of the above-mentioned processing in the procedure unit can be performed, for example, using AI, or can be performed without using AI. For example, the procedure unit inputs the user's past procedure history data into AI, and the AI ​​analyzes the data and selects the optimal procedure method.

[0047] The procedure unit can customize the procedure means based on the user's current living situation during the procedure. Examples of the current living situation include, but are not limited to, family composition, workplace location, and commuting time. For example, the procedure unit can customize the procedure means based on the user's current living situation during the procedure. For example, if the user is busy, the procedure unit can suggest a way to quickly complete the procedure online. Also, if the user has time, the procedure unit can provide detailed procedure instructions. The procedure means can also be dynamically customized according to the user's living situation. This allows for more appropriate procedures by customizing the procedure means according to the user's living situation. Some or all of the above-described processing in the procedure unit may be performed using, for example, AI, or may be performed without AI. For example, the procedure unit inputs the user's living situation data into AI, which then analyzes the data and customizes the procedure means.

[0048] The procedure unit can improve the procedure method by reflecting user feedback during the procedure. Examples of feedback include, but are not limited to, user ratings, comments, and survey results. The procedure unit can improve the procedure method by reflecting user feedback during the procedure. For example, the procedure unit can improve the procedure method based on feedback previously provided by the user. It can also prioritize and suggest specific procedure methods based on user feedback. It can also analyze user feedback and customize the procedure interface. This allows the procedure method to be continuously improved by reflecting user feedback. Some or all of the above-described processing in the procedure unit can be performed using, for example, AI, or without AI. For example, the procedure unit inputs user feedback data into AI, which then analyzes the data to improve the procedure method.

[0049] The procedure unit can select the optimal procedure by taking into account the user's geographical location information during the procedure. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the procedure unit selects the optimal procedure by taking into account the user's geographical location information during the procedure. For example, when the user inputs their current location, procedures related to that area are prioritized. Furthermore, when the user specifies a specific area, procedures related to that area can also be suggested. Furthermore, when the user uses the app while on the move, the current location can be updated in real time and relevant procedures can be suggested. This allows for a more appropriate procedure by taking into account the user's geographical location information. Some or all of the above-described processing in the procedure unit may be performed using, or without, AI. For example, the procedure unit inputs the user's geographical location data into AI, which then analyzes the data to select the optimal procedure.

[0050] The procedure unit can analyze the user's social media activity during a procedure to suggest a procedure. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the procedure unit can analyze the user's social media activity during a procedure to suggest a procedure. For example, the procedure unit can suggest a procedure related to a place the user has checked in to on social media. The procedure unit can also analyze the content of the user's social media posts to suggest a related procedure. The procedure unit can also suggest a related procedure based on the activity of the user's friends on social media. In this way, by analyzing social media activity, the optimal procedure can be suggested for the user. Some or all of the above-described processing in the procedure unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure unit inputs the user's social media activity data into AI, which then analyzes the data and suggests a procedure.

[0051] The procedure unit can customize the procedure method by reflecting the user's past feedback during the procedure. Past feedback includes, but is not limited to, user ratings, comments, and survey results. The procedure unit, for example, customizes the procedure method by reflecting the user's past feedback during the procedure. For example, the procedure unit can suggest an optimal procedure method based on feedback provided by the user in the past. It can also preferentially suggest a specific procedure method based on the user's past feedback. It can also analyze the user's past feedback and customize the procedure interface. In this way, it is possible to provide the user with an optimal procedure method by reflecting the past feedback. Some or all of the above-described processing in the procedure unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure unit inputs the user's past feedback data into AI, and the AI ​​analyzes the data to customize the procedure method.

[0052] The translation unit can adjust the level of detail of the translation based on the importance of the contract during translation. Examples of the importance of the contract include, but are not limited to, the content and legal effect of the contract. The translation unit can adjust the level of detail of the translation based on the importance of the contract during translation. For example, a detailed translation can be provided for an important contract to provide sufficient information to the user. A concise translation can be provided for a less important contract to save the user's time. The level of detail of the translation can also be dynamically adjusted according to the importance of the contract. By adjusting the level of detail of the translation based on the importance of the contract, optimal information can be provided to the user. Some or all of the above-described processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs contract importance data into AI, which analyzes the data and adjusts the level of detail of the translation.

[0053] The translation unit can apply different translation algorithms depending on the category of the contract during translation. Contract categories include, but are not limited to, rental contracts and sales contracts. For example, the translation unit can apply different translation algorithms depending on the category of the contract during translation. For example, different translation algorithms can be applied to rental contracts and condominium contracts to provide optimal translations. Different translation algorithms can also be applied to commercial contracts and residential contracts to provide translations tailored to the user's needs. The translation algorithm can also be dynamically switched depending on the category of the contract. This allows the optimal translation to be applied depending on the category of the contract, thereby providing the user with the optimal translation. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs contract category data into AI, which analyzes the data and applies a translation algorithm.

[0054] The translation unit can improve the accuracy of translation by referring to the user's past translation results during translation. Past translation results include, but are not limited to, the translation success rate and user ratings. The translation unit can improve the accuracy of translation by referring to the user's past translation results during translation. For example, the translation unit can analyze the characteristics of translations selected by the user in the past and provide similar translations. It can also preferentially use specific expressions from the user's past translation results. It can also adjust the translation algorithm based on the user's past translation results to improve accuracy. In this way, the accuracy of translation can be improved by referring to the past translation results. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs the user's past translation result data into AI, and the AI ​​analyzes the data to improve the accuracy of the translation.

[0055] The translation unit can determine the translation priority based on the creation date of the contract during translation. The creation date of the contract includes, but is not limited to, the creation date and the update date. For example, the translation unit can determine the translation priority based on the creation date of the contract during translation. For example, newly created contracts can be translated with priority. Also, the translation priority can be lowered for older contracts. The translation priority can also be dynamically adjusted depending on the creation date of the contract. In this way, by determining the translation priority based on the creation date of the contract, the most recent contract can be translated with priority. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs data on the creation date of the contract into AI, and the AI ​​analyzes the data to determine the translation priority.

[0056] The translation unit can adjust the order of translation based on the relevance of the contract during translation. Examples of the relevance of the contract include, but are not limited to, the content of the contract and the user's interest. The translation unit can, for example, adjust the order of translation based on the relevance of the contract during translation. For example, the translation unit can prioritize translating contracts that are most relevant to the user's requirements. Furthermore, the translation order can be postponed for less relevant contracts. The translation order can also be dynamically adjusted according to the relevance of the contract. Thus, by adjusting the order of translation based on the relevance of the contract, the contract that is most relevant to the user can be prioritized for translation. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without AI. For example, the translation unit inputs contract relevance data into AI, and the AI ​​analyzes the data and adjusts the order of translation.

[0057] The translation unit can adjust the use of technical terms in the translation according to the user's level of expertise during translation. Examples of technical terms include, but are not limited to, the user's occupation and past translation history. For example, the translation unit can adjust the use of technical terms in the translation according to the user's level of expertise during translation. For example, if the user is knowledgeable about real estate, the translation unit can provide a translation that uses a lot of technical terms. On the other hand, if the user is not knowledgeable about real estate, the translation unit can provide a concise translation that avoids technical terms. The use of technical terms in the translation can also be dynamically adjusted according to the user's level of expertise. This allows for adjustment of the use of technical terms according to the user's level of expertise, thereby enabling a translation that is easy for the user to understand. Some or all of the above-described processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs the user's level of expertise data into AI, which then analyzes the data and adjusts the use of technical terms.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The property recommendation system can further include an energy management unit that optimizes the user's energy consumption. The energy management unit can evaluate the energy efficiency and environmental impact of a property and provide the results to the user. For example, it can provide information about the property's insulation performance and renewable energy usage status to recommend a highly energy-efficient property. It can also suggest optimal energy management methods based on the user's energy consumption patterns. Furthermore, the energy management unit can provide advice to the user on how to reduce energy consumption. This can optimize the user's energy consumption and support an environmentally conscious lifestyle.

[0060] The property recommendation system can further include a financial management unit that manages the user's financial situation. The financial management unit can analyze the user's income, expenses, and savings status and customize property recommendations based on this. For example, it can recommend properties that fit the user's budget and support the user in selecting a property within their means. It can also recommend optimal properties based on the user's financial goals (e.g., savings goals and investment plans). Furthermore, the financial management unit can provide advice to help the user create a financial plan after purchasing a property. This makes it possible to recommend properties that fit the user's financial situation, allowing the user to select a property with peace of mind.

[0061] The property recommendation system can further include a mobility management unit that takes into account the user's means of transportation. The mobility management unit can analyze the user's commute to work, school, and daily travel patterns, and customize property recommendations based on this. For example, if the user uses public transportation, it can recommend properties that are close to the nearest station or bus stop. If the user uses a car, it can also recommend properties with parking or good transportation access. Furthermore, the mobility management unit can provide advice to optimize the user's travel time. This makes it possible to recommend properties that suit the user's means of transportation, providing a more convenient living environment.

[0062] The property recommendation system can further include a dietary support unit that supports the user's diet. The dietary support unit can collect information about the user's diet and customize property recommendations based on this information. For example, if the user is health-conscious, the system can recommend properties with organic food stores or health food stores nearby. Also, if the user likes to eat out, the system can recommend properties with a variety of restaurants and cafes nearby. Furthermore, the dietary support unit can provide advice about the user's diet and support a healthy diet. This makes it possible to recommend properties that suit the user's diet, thereby providing a more satisfying living environment.

[0063] The property recommendation system can further include a learning support unit that supports the user's learning environment. The learning support unit can customize property recommendations based on the user's learning needs. For example, if the user is a student, the learning support unit can recommend properties with libraries and learning facilities nearby. Also, if the user works remotely, the learning support unit can recommend properties with a quiet environment and high-speed internet access. Furthermore, the learning support unit can provide advice to improve the user's learning efficiency. This makes it possible to recommend properties that suit the user's learning environment, thereby providing a more effective learning environment.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives desired conditions from the user. The desired conditions input by the user include detailed conditions such as location, budget, and floor plan. Furthermore, the reception unit can estimate the user's emotions and customize the input interface for desired conditions based on the estimated emotions. For example, if the user is feeling stressed, it can provide a simple and intuitive interface to minimize input steps. Step 2: The suggestion unit suggests the most suitable property based on the conditions received by the reception unit. The suggestion unit searches for and suggests the property that best suits the user's conditions based on a vast property database. The suggestion unit can also estimate the user's emotions and adjust the way property suggestions are presented based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed property information and make suggestions at a leisurely pace. Step 3: The procedure unit allows the user to view the property proposed by the proposal unit and complete the contract procedures online. The procedure unit checks the detailed information of the proposed property online, and makes reservations for viewings and completes the contract procedures. The procedure unit can also estimate the user's emotions and adjust the procedure based on the estimated emotions. For example, if the user is nervous, it provides a simple and easy-to-understand procedure. Step 4: The translation department translates the contract procedures carried out by the procedure department into multiple languages. The translation department translates contracts and property information into multiple languages, allowing foreign users to complete contract procedures without feeling a language barrier. The translation department can also estimate the user's emotions and adjust the way the translation is expressed based on the estimated emotions. For example, if the user is nervous, the translation will use simple, easy-to-understand expressions.

[0066] (Example 2) In an embodiment of the present invention, a property proposal system is a system in which a user inputs their desired conditions, and AI proposes optimal properties, allowing viewings and contract procedures online. The property proposal system allows users to input their desired conditions, and AI analyzes the conditions and proposes optimal properties. The proposed properties can be viewed and contract procedures conducted online, and AI translates languages ​​other than Japanese, making it easy for foreigners to complete the contract procedures. For example, the property proposal system allows users to input their desired conditions. For example, users can enter detailed conditions such as location, budget, and floor plan. This information is input into AI. The property proposal system then analyzes the input conditions and proposes optimal properties. The AI ​​searches a vast property database for properties that best meet the user's conditions. For example, it searches for 2LDK properties in urban areas within a user's budget and lists matching properties. The proposed properties can be viewed and contract procedures conducted online. Users can view detailed information about the proposed properties online, schedule viewings, and complete contract procedures. For example, they can view photos and videos of the properties and schedule viewings online. In addition, contract procedures are completed online, eliminating the need for face-to-face procedures or seals. Furthermore, AI can translate languages ​​other than Japanese, making it easy for foreigners to complete the contract process. For example, contracts translated into languages ​​such as English and Chinese can be viewed online and the contract process can be completed. This can improve the current situation in which it is difficult for foreigners to rent real estate in Japan. This allows the property recommendation system to suggest the most suitable property based on the user's desired conditions, and viewings and contract procedures can be completed online. For example, users can easily find properties that meet their desired conditions, and viewings and contract procedures can be completed online, saving time and effort. Furthermore, foreigners can complete the contract process smoothly without experiencing language barriers. For example, when foreigners are looking for a place to live in Japan, AI can suggest the most suitable property and complete the contract procedure online, allowing them to find a place to live with peace of mind.

[0067] A property proposal system according to an embodiment includes a reception unit, a proposal unit, a procedure unit, and a translation unit. The reception unit accepts desired conditions from a user. The desired conditions input by the user include, but are not limited to, detailed conditions such as location, budget, and floor plan. The reception unit accepts, for example, detailed conditions such as location, budget, and floor plan input by the user. The reception unit can also estimate the user's emotions and customize the input interface for the desired conditions based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided to minimize the input steps. The proposal unit proposes optimal properties based on the conditions accepted by the reception unit. The proposal unit searches for and proposes properties that best meet the user's conditions based on a massive property database. For example, the proposal unit searches for and proposes properties that best meet the user's conditions based on a massive property database. The proposal unit can also estimate the user's emotions and adjust the presentation of property proposals based on the estimated user emotions. For example, if the user is relaxed, detailed property information can be provided and proposals can be made at a leisurely pace. The procedure unit performs online viewings and contract procedures for properties proposed by the proposal unit. The procedure unit checks detailed information about the proposed property online and makes reservations for viewings and completes contract procedures. For example, the procedure unit checks detailed information about the proposed property online and completes reservations for viewings and completes contract procedures. The procedure unit can also estimate the user's emotions and adjust the way the procedures proceed based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible procedure is provided. The translation unit translates the contract procedures performed by the procedure unit into multiple languages. The translation unit translates contracts and property information into multiple languages, allowing foreign users to complete contract procedures without feeling a language barrier. For example, the translation unit translates contracts and property information into multiple languages, allowing foreign users to complete contract procedures without feeling a language barrier. The translation unit can also estimate the user's emotions and adjust the way the translation is expressed based on the estimated user emotions. For example, if the user is nervous, a simple and easy-to-understand expression is used in the translation.As a result, the property recommendation system according to the embodiment can propose optimal properties based on the user's desired conditions, and allow viewings and contract procedures to be completed online. For example, users can easily find properties that meet their desired conditions, and viewings and contract procedures can be completed online, saving time and effort. Furthermore, foreigners can smoothly complete contract procedures without experiencing language barriers. For example, when foreigners are looking for a place to live in Japan, AI can propose optimal properties and allow them to complete contract procedures online, allowing them to find a place to live with peace of mind.

[0068] The reception unit can accept detailed conditions, such as location, budget, and floor plan, entered by the user. The detailed conditions include, but are not limited to, location, budget, floor plan, and facilities. The reception unit can also accept detailed conditions, such as location, budget, and floor plan, entered by the user. The reception unit can also estimate the user's emotions and customize the input interface for desired conditions based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided, minimizing the input steps. This allows the user to enter detailed conditions, enabling more accurate property suggestions. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit inputs the detailed conditions entered by the user into AI, which then analyzes the conditions and suggests optimal properties.

[0069] The suggestion unit can search for and suggest properties that best meet the user's requirements based on a large-scale property database. The property database may, for example, include, but is not limited to, the size of the database and the types of properties included. The suggestion unit can, for example, search for and suggest properties that best meet the user's requirements based on the large-scale property database. The suggestion unit can also estimate the user's emotions and adjust the way property suggestions are presented based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide detailed property information and make suggestions at a leisurely pace. This allows the suggestion unit to utilize the large-scale property database to suggest properties that best meet the user's requirements. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit inputs the user's requirements into AI, which then analyzes the requirements and suggests optimal properties.

[0070] The procedure unit can check detailed information about the proposed property online and make reservations for viewings and contract procedures. Examples of detailed information include, but are not limited to, the property's floor plan, facilities, and surrounding environment. For example, the procedure unit can check detailed information about the proposed property online and make reservations for viewings and contract procedures. The procedure unit can also estimate the user's emotions and adjust the procedure based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible procedure can be provided. This allows the user to check detailed information about the property online and make reservations for viewings and contract procedures. Some or all of the above-described processing in the procedure unit can be performed using, or without, AI. For example, the procedure unit inputs detailed information about the proposed property into AI, which then analyzes the information and makes reservations for viewings and contract procedures.

[0071] The translation unit can translate contracts and property information into multiple languages, allowing foreign users to complete contract procedures without feeling a language barrier. Specific languages ​​and translation methods for translating into multiple languages ​​include, but are not limited to, the corresponding languages ​​and the accuracy of the translation. The translation unit can also translate contracts and property information into multiple languages, allowing foreign users to complete contract procedures without feeling a language barrier. The translation unit can also estimate the user's emotions and adjust the translation expression based on the estimated user emotions. For example, if the user is nervous, the translation unit can use simple, easy-to-understand expressions. This allows foreign users to complete contract procedures without feeling a language barrier. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs contracts and property information into AI, which then translates the information into multiple languages.

[0072] The reception unit can estimate the user's emotion and customize the input interface for the desired conditions based on the estimated user emotion. Specific methods and criteria for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. The reception unit, for example, estimates the user's emotion and customizes the input interface for the desired conditions based on the estimated user emotion. For example, if the user is stressed, a simple and intuitive interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Alternatively, if the user is in a hurry, voice input can be prioritized to enable quick input of the desired conditions. This allows the input interface to be customized according to the user's emotion, thereby providing a more user-friendly system. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit inputs the user's emotional data into the AI, which then analyzes the data and customizes the input interface for the desired conditions.

[0073] The reception unit can analyze the user's past property search history and suggest the optimal condition input method. The past property search history includes, but is not limited to, search date and time, search conditions, and properties viewed. The reception unit, for example, analyzes the user's past property search history and suggests the optimal condition input method. For example, the reception unit can automatically display conditions that the user frequently searched for in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest conditions to be used during a specific time period based on the user's past search history. By utilizing the past search history, the reception unit can suggest the optimal condition input method to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit inputs the user's past property search history into AI, which then analyzes the history and suggests the optimal condition input method.

[0074] The reception unit can filter the results based on the user's current living situation and future plans when the desired conditions are input. Examples of the current living situation and future plans include, but are not limited to, family composition, workplace location, commute time, planned job change, and children's educational plans. For example, the reception unit can filter the results based on the user's current living situation and future plans when the desired conditions are input. For example, if the user inputs their family composition, properties suitable for families can be preferentially suggested. Also, if the user inputs their future job change plans, properties convenient for commuting can be suggested. Also, if the user inputs their current living situation (such as whether or not they have pets), properties corresponding to that information can be suggested. This makes it possible to suggest more appropriate properties based on the user's living situation and future plans. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit inputs the user's living situation and future plans into AI, and the AI ​​analyzes the information and performs filtering.

[0075] When the user inputs desired conditions, the reception unit can select the optimal input means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when the user inputs desired conditions, the reception unit selects the optimal input means depending on the user's input method. For example, when the user inputs desired conditions by voice, the conditions can be automatically analyzed using voice recognition technology. Also, when the user inputs desired conditions by text, the entered text can be analyzed to extract conditions. Also, when the user uploads an image, image analysis technology can be used to extract property features and set conditions. This allows for the selection of the optimal input means depending on the user's input method, thereby providing a more user-friendly system. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit inputs the user's input method into AI, which then analyzes the method and selects the optimal input means.

[0076] The reception unit can estimate the user's emotions and prioritize the input conditions based on the estimated user emotions. Specific methods and criteria for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. The reception unit can estimate the user's emotions and prioritize the input conditions based on the estimated user emotions. For example, if the user is stressed, important conditions can be prioritized and suggestions can be made quickly. Also, if the user is relaxed, suggestions can be made taking into account detailed conditions. Also, if the user is in a hurry, suggestions can be made quickly and key conditions can be prioritized. This enables more appropriate property suggestions by prioritizing conditions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit inputs the user's emotional data into the AI, which then analyzes the data and determines the priority of the conditions.

[0077] The reception unit can prioritize input of highly relevant conditions based on the user's geographical location information when inputting desired conditions. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the reception unit can prioritize input of highly relevant conditions based on the user's geographical location information when inputting desired conditions. For example, when the user inputs their current location, properties in the vicinity of the user's current location can be prioritized. Furthermore, when the user specifies a specific area, conditions related to that area can be prioritized. Furthermore, when the user uses the app while on the move, the current location can be updated in real time and related conditions can be suggested. This allows more relevant properties to be suggested by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit inputs the user's geographical location information to AI, and the AI ​​analyzes the information and prioritizes input of highly relevant conditions.

[0078] The reception unit can analyze the user's social media activity when the desired conditions are input and suggest related conditions. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit can analyze the user's social media activity when the desired conditions are input and suggest related conditions. For example, the reception unit can suggest related properties based on the locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related conditions. The reception unit can also suggest related conditions based on the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to suggest related conditions to the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's social media activity into AI, and the AI ​​analyzes the activity and suggests related conditions.

[0079] The reception unit can customize the input method by reflecting the user's past feedback when the desired conditions are input. Past feedback includes, but is not limited to, user ratings, comments, and survey results. The reception unit customizes the input method by reflecting the user's past feedback when the desired conditions are input. For example, the reception unit can suggest an optimal input method based on feedback previously provided by the user. It can also preferentially suggest a specific input method based on the user's past feedback. It can also analyze the user's past feedback and customize the input interface. In this way, it is possible to provide the user with an optimal input method by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's past feedback to AI, and the AI ​​analyzes the feedback to customize the input method.

[0080] The suggestion unit can estimate the user's emotions and adjust the presentation of property proposals based on the estimated user emotions. Specific methods and criteria for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. The suggestion unit can estimate the user's emotions and adjust the presentation of property proposals based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed property information and make proposals at a leisurely pace. If the user is in a hurry, the suggestion unit can provide concise property information that focuses on the main points and make proposals quickly. If the user is excited, the suggestion unit can provide visually appealing property information and make interesting proposals. This enables more appropriate proposals by adjusting the presentation of property proposals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the proposal unit inputs the user's emotional data into the AI, which then analyzes the data and adjusts the way the property proposal is presented.

[0081] The suggestion unit can adjust the level of detail of the proposal based on the importance of the property when proposing a property. The importance of the property includes, but is not limited to, the price, location, and facilities of the property. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the property when proposing a property. For example, for important properties, detailed information can be provided to provide sufficient information to the user. For less important properties, concise information can be provided to save the user's time. The level of detail of the proposal can also be dynamically adjusted according to the importance of the property. By adjusting the level of detail of the proposal based on the importance of the property, optimal information can be provided to the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs property importance data into AI, and the AI ​​analyzes the data to adjust the level of detail of the proposal.

[0082] When proposing a property, the suggestion unit can apply different suggestion algorithms depending on the property category. Property categories include, but are not limited to, residential, commercial, and office properties. When proposing a property, the suggestion unit can apply different suggestion algorithms depending on the property category. For example, different suggestion algorithms can be applied to rental properties and condominium properties to make optimal suggestions. Different suggestion algorithms can also be applied to commercial properties and residential properties to make suggestions tailored to the user's needs. The suggestion algorithm can also be dynamically switched depending on the property category. This allows the optimal property to be proposed to the user by applying the optimal suggestion algorithm depending on the property category. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit inputs property category data into AI, which analyzes the data and applies the suggestion algorithm.

[0083] When proposing a property, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. Examples of past suggestion results include, but are not limited to, the success rate of the suggestion and the user's evaluation. For example, when proposing a property, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit can analyze the characteristics of properties previously selected by the user and suggest similar properties. It can also prioritize suggestions based on specific conditions from the user's past suggestion results. It can also adjust the suggestion algorithm based on the user's past suggestion results to improve accuracy. By referring to the past suggestion results, the accuracy of the suggestion can be improved. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit inputs the user's past suggestion result data into AI, and the AI ​​analyzes the data to improve the accuracy of the suggestion.

[0084] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. Specific methods and criteria for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed information. If the user is excited, the suggestion unit can provide a visually appealing suggestion. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the user's emotion. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit inputs the user's emotion data into an AI, and the AI ​​analyzes the data to adjust the length of the suggestion.

[0085] When proposing a property, the proposal unit can determine the priority of the proposal based on the date of registration of the property. The date of registration of the property includes, but is not limited to, for example, the registration date and the update date. When proposing a property, the proposal unit can determine the priority of the proposal based on the date of registration of the property. For example, the proposal unit can prioritize newly registered properties. Also, the proposal priority can be lowered for properties that were registered earlier. The proposal priority can also be dynamically adjusted depending on the date of registration of the property. In this way, by determining the priority of the proposal based on the date of registration of the property, the latest property information can be provided preferentially. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit inputs property registration date data into AI, and the AI ​​analyzes the data to determine the priority of the proposal.

[0086] The suggestion unit can adjust the order of proposals based on the relevance of properties when suggesting properties. Examples of property relevance include, but are not limited to, the degree of match with the user's desired conditions and past search history. For example, the suggestion unit can adjust the order of proposals based on the relevance of properties when suggesting properties. For example, the suggestion unit can prioritize suggesting properties that are most relevant to the user's conditions. Also, properties with low relevance can be postponed in the proposal order. The suggestion unit can also dynamically adjust the order of proposals based on the relevance of properties. By adjusting the order of proposals based on the relevance of properties, it is possible to prioritize suggesting properties that are most relevant to the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs property relevance data into AI, and the AI ​​analyzes the data to adjust the order of proposals.

[0087] When proposing a property, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and past search history. For example, when proposing a property, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is knowledgeable about real estate, the suggestion unit can make a proposal that uses a lot of technical terminology. On the other hand, if the user is not knowledgeable about real estate, the suggestion unit can make a concise proposal that avoids technical terminology. The use of technical terminology in the proposal can also be dynamically adjusted according to the user's level of expertise. This allows the suggestion to be easily understood by the user by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit inputs the user's level of expertise data into AI, and the AI ​​analyzes the data to adjust the use of technical terminology.

[0088] The procedure unit can estimate the user's emotions and adjust the procedure progress based on the estimated user emotions. Specific methods and criteria for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. The procedure unit can estimate the user's emotions and adjust the procedure progress based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible procedure can be provided. Alternatively, if the user is relaxed, a procedure that includes detailed information can be provided. Alternatively, if the user is in a hurry, a procedure that focuses on the main points can be provided. This allows the procedure progress to be adjusted according to the user's emotions, resulting in a smoother procedure. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the procedure unit can be performed using, for example, an AI, or without an AI. For example, the procedure unit inputs the user's emotion data into an AI, which analyzes the data and adjusts the procedure progress.

[0089] The procedure unit can analyze the user's past procedure history and select the optimal procedure method during a procedure. The past procedure history includes, for example, the date and time of the procedure, the procedure content, etc., but is not limited to these examples. The procedure unit can, for example, analyze the user's past procedure history and select the optimal procedure method during a procedure. For example, the procedure unit can suggest the optimal procedure method based on the procedure methods the user has used in the past. It can also suggest an efficient procedure method from the user's past procedure history. It can also analyze the user's past procedure history and select the most efficient procedure method. In this way, by analyzing the past procedure history, it is possible to provide the user with the optimal procedure method. Some or all of the above-mentioned processing in the procedure unit can be performed, for example, using AI, or can be performed without using AI. For example, the procedure unit inputs the user's past procedure history data into AI, and the AI ​​analyzes the data and selects the optimal procedure method.

[0090] The procedure unit can customize the procedure means based on the user's current living situation during the procedure. Examples of the current living situation include, but are not limited to, family composition, workplace location, and commuting time. For example, the procedure unit can customize the procedure means based on the user's current living situation during the procedure. For example, if the user is busy, the procedure unit can suggest a way to quickly complete the procedure online. Also, if the user has time, the procedure unit can provide detailed procedure instructions. The procedure means can also be dynamically customized according to the user's living situation. This allows for more appropriate procedures by customizing the procedure means according to the user's living situation. Some or all of the above-described processing in the procedure unit may be performed using, for example, AI, or may be performed without AI. For example, the procedure unit inputs the user's living situation data into AI, which then analyzes the data and customizes the procedure means.

[0091] The procedure unit can improve the procedure method by reflecting user feedback during the procedure. Examples of feedback include, but are not limited to, user ratings, comments, and survey results. The procedure unit can improve the procedure method by reflecting user feedback during the procedure. For example, the procedure unit can improve the procedure method based on feedback previously provided by the user. It can also prioritize and suggest specific procedure methods based on user feedback. It can also analyze user feedback and customize the procedure interface. This allows the procedure method to be continuously improved by reflecting user feedback. Some or all of the above-described processing in the procedure unit can be performed using, for example, AI, or without AI. For example, the procedure unit inputs user feedback data into AI, which then analyzes the data to improve the procedure method.

[0092] The procedure unit can estimate the user's emotions and determine the priority of procedures based on the estimated user emotions. Specific methods and criteria for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. The procedure unit can estimate the user's emotions and determine the priority of procedures based on the estimated user emotions. For example, if the user is stressed, important procedures can be prioritized and completed quickly. Also, if the user is relaxed, detailed procedures can be prioritized. Also, if the user is in a hurry, major procedures can be prioritized and completed quickly. This enables more efficient procedures by prioritizing procedures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the procedure unit can be performed using, for example, an AI. For example, the procedure unit inputs user emotion data into an AI, and the AI ​​analyzes the data to determine the priority of procedures.

[0093] The procedure unit can select the optimal procedure by taking into account the user's geographical location information during the procedure. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the procedure unit selects the optimal procedure by taking into account the user's geographical location information during the procedure. For example, when the user inputs their current location, procedures related to that area are prioritized. Furthermore, when the user specifies a specific area, procedures related to that area can also be suggested. Furthermore, when the user uses the app while on the move, the current location can be updated in real time and relevant procedures can be suggested. This allows for a more appropriate procedure by taking into account the user's geographical location information. Some or all of the above-described processing in the procedure unit may be performed using, or without, AI. For example, the procedure unit inputs the user's geographical location data into AI, which then analyzes the data to select the optimal procedure.

[0094] The procedure unit can analyze the user's social media activity during a procedure to suggest a procedure. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the procedure unit can analyze the user's social media activity during a procedure to suggest a procedure. For example, the procedure unit can suggest a procedure related to a place the user has checked in to on social media. The procedure unit can also analyze the content of the user's social media posts to suggest a related procedure. The procedure unit can also suggest a related procedure based on the activity of the user's friends on social media. In this way, by analyzing social media activity, the optimal procedure can be suggested for the user. Some or all of the above-described processing in the procedure unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure unit inputs the user's social media activity data into AI, which then analyzes the data and suggests a procedure.

[0095] The procedure unit can customize the procedure method by reflecting the user's past feedback during the procedure. Past feedback includes, but is not limited to, user ratings, comments, and survey results. The procedure unit, for example, customizes the procedure method by reflecting the user's past feedback during the procedure. For example, the procedure unit can suggest an optimal procedure method based on feedback provided by the user in the past. It can also preferentially suggest a specific procedure method based on the user's past feedback. It can also analyze the user's past feedback and customize the procedure interface. In this way, it is possible to provide the user with an optimal procedure method by reflecting the past feedback. Some or all of the above-described processing in the procedure unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure unit inputs the user's past feedback data into AI, and the AI ​​analyzes the data to customize the procedure method.

[0096] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. Specific methods and criteria for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. For example, if the user is nervous, the translation can be performed using simple and easy-to-understand expressions. Alternatively, if the user is relaxed, the translation can be provided with detailed information. Alternatively, if the user is in a hurry, the translation can be provided with a concise translation that focuses on the main points. This allows for more appropriate translation by adjusting the translation expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs the user's emotion data into AI, which analyzes the data and adjusts the translation expression.

[0097] The translation unit can adjust the level of detail of the translation based on the importance of the contract during translation. Examples of the importance of the contract include, but are not limited to, the content and legal effect of the contract. The translation unit can adjust the level of detail of the translation based on the importance of the contract during translation. For example, a detailed translation can be provided for an important contract to provide sufficient information to the user. A concise translation can be provided for a less important contract to save the user's time. The level of detail of the translation can also be dynamically adjusted according to the importance of the contract. By adjusting the level of detail of the translation based on the importance of the contract, optimal information can be provided to the user. Some or all of the above-described processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs contract importance data into AI, which analyzes the data and adjusts the level of detail of the translation.

[0098] The translation unit can apply different translation algorithms depending on the category of the contract during translation. Contract categories include, but are not limited to, rental contracts and sales contracts. For example, the translation unit can apply different translation algorithms depending on the category of the contract during translation. For example, different translation algorithms can be applied to rental contracts and condominium contracts to provide optimal translations. Different translation algorithms can also be applied to commercial contracts and residential contracts to provide translations tailored to the user's needs. The translation algorithm can also be dynamically switched depending on the category of the contract. This allows the optimal translation to be applied depending on the category of the contract, thereby providing the user with the optimal translation. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs contract category data into AI, which analyzes the data and applies a translation algorithm.

[0099] The translation unit can improve the accuracy of translation by referring to the user's past translation results during translation. Past translation results include, but are not limited to, the translation success rate and user ratings. The translation unit can improve the accuracy of translation by referring to the user's past translation results during translation. For example, the translation unit can analyze the characteristics of translations selected by the user in the past and provide similar translations. It can also preferentially use specific expressions from the user's past translation results. It can also adjust the translation algorithm based on the user's past translation results to improve accuracy. In this way, the accuracy of translation can be improved by referring to the past translation results. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs the user's past translation result data into AI, and the AI ​​analyzes the data to improve the accuracy of the translation.

[0100] The translation unit can estimate the user's emotion and adjust the length of the translation based on the estimated user emotion. Specific methods and criteria for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. The translation unit can estimate the user's emotion and adjust the length of the translation based on the estimated user emotion. For example, if the user is in a hurry, a short, concise translation can be provided. If the user is relaxed, a longer translation with more detailed information can be provided. If the user is excited, a visually appealing translation can be provided. This allows for more appropriate translation by adjusting the length of the translation according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs the user's emotion data into AI, and the AI ​​analyzes the data and adjusts the length of the translation.

[0101] The translation unit can determine the translation priority based on the creation date of the contract during translation. The creation date of the contract includes, but is not limited to, the creation date and the update date. For example, the translation unit can determine the translation priority based on the creation date of the contract during translation. For example, newly created contracts can be translated with priority. Also, the translation priority can be lowered for older contracts. The translation priority can also be dynamically adjusted depending on the creation date of the contract. In this way, by determining the translation priority based on the creation date of the contract, the most recent contract can be translated with priority. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs data on the creation date of the contract into AI, and the AI ​​analyzes the data to determine the translation priority.

[0102] The translation unit can adjust the order of translation based on the relevance of the contract during translation. Examples of the relevance of the contract include, but are not limited to, the content of the contract and the user's interest. The translation unit can, for example, adjust the order of translation based on the relevance of the contract during translation. For example, the translation unit can prioritize translating contracts that are most relevant to the user's requirements. Furthermore, the translation order can be postponed for less relevant contracts. The translation order can also be dynamically adjusted according to the relevance of the contract. Thus, by adjusting the order of translation based on the relevance of the contract, the contract that is most relevant to the user can be prioritized for translation. Some or all of the above-described processing in the translation unit may be performed using, for example, AI, or may be performed without AI. For example, the translation unit inputs contract relevance data into AI, and the AI ​​analyzes the data and adjusts the order of translation.

[0103] The translation unit can adjust the use of technical terms in the translation according to the user's level of expertise during translation. Examples of technical terms include, but are not limited to, the user's occupation and past translation history. For example, the translation unit can adjust the use of technical terms in the translation according to the user's level of expertise during translation. For example, if the user is knowledgeable about real estate, the translation unit can provide a translation that uses a lot of technical terms. On the other hand, if the user is not knowledgeable about real estate, the translation unit can provide a concise translation that avoids technical terms. The use of technical terms in the translation can also be dynamically adjusted according to the user's level of expertise. This allows for adjustment of the use of technical terms according to the user's level of expertise, thereby enabling a translation that is easy for the user to understand. Some or all of the above-described processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit inputs the user's level of expertise data into AI, which then analyzes the data and adjusts the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, procedure unit, and translation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and accepts desired conditions input by the user. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal properties based on the user's conditions. The procedure unit is realized, for example, by the control unit 46A of the smart device 14 and performs previews of proposed properties and contract procedures online. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates contract procedures into multiple languages. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, procedure unit, and translation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and accepts desired conditions input by the user. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal properties based on the user's conditions. The procedure unit is realized, for example, by the control unit 46A of the smart glasses 214 and performs previews of proposed properties and contract procedures online. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates contract procedures into multiple languages. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, procedure unit, and translation unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314 and accepts desired conditions input by the user. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal properties based on the user's conditions. The procedure unit is realized, for example, by the control unit 46A of the headset terminal 314 and performs previews of proposed properties and contract procedures online. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates contract procedures into multiple languages. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, procedure unit, and translation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts desired conditions input by the user. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal properties based on the user's conditions. The procedure unit is realized, for example, by the control unit 46A of the robot 414 and performs previews of proposed properties and contract procedures online. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates contract procedures into multiple languages.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The property recommendation system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's health data (e.g., heart rate, stress level, sleep patterns, etc.) and can adjust the content of property recommendations based on this. For example, if the user is in a high stress state, properties in quiet environments may be preferentially recommended. In addition, to help the user live a healthy lifestyle, properties with nearby fitness facilities or parks may be recommended. Furthermore, the health management unit may adjust the schedule for property viewings and contract procedures according to the user's health condition. This makes it possible to make property recommendations that take the user's health condition into consideration, thereby providing a more comfortable living environment.

[0106] The property recommendation system can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit can customize property recommendations based on the user's hobbies and interests. For example, if the user likes outdoor activities, properties with nearby nature parks or hiking trails can be recommended. Also, if the user likes cultural activities, properties with nearby art museums or theaters can be recommended. Furthermore, the hobby analysis unit can provide event information related to the user's hobbies to enhance the appeal of the property. This makes it possible to recommend properties that match the user's hobbies and interests, thereby providing a more satisfying living environment.

[0107] The property recommendation system can further include a lifestyle analysis unit that analyzes the user's lifestyle. The lifestyle analysis unit can customize property recommendations based on the user's lifestyle habits and daily behavior patterns. For example, if the user is a night owl, the system can recommend properties that are close to 24-hour supermarkets or convenience stores. Also, if the user has a pet, the system can recommend properties that allow pets or have ample pet-friendly facilities. Furthermore, the lifestyle analysis unit can adjust the schedule for property viewings and contract procedures to suit the user's lifestyle. This makes it possible to recommend properties that suit the user's lifestyle, providing a more comfortable living environment.

[0108] The property suggestion system may further include a community support unit that supports the user's community activities. The community support unit may provide information to make it easier for the user to participate in community activities in a new area. For example, the community support unit may provide information on nearby community centers and local events, making it easier for the user to integrate into the local community. The community support unit may also provide information on clubs and circles that the user can join based on the user's interests. Furthermore, the community support unit may provide a platform to support the user in making friends in the new area. This allows the user to smoothly start life in the new area and deepen their connections with the local community.

[0109] The property recommendation system can further include a safety management unit to ensure the safety of users. The safety management unit can evaluate the safety of the surrounding environment of a property and provide it to the user. For example, it can provide information on crime rates and disaster risks and recommend safe properties. It can also provide information on the property's security equipment (security cameras, auto-locks, etc.). Furthermore, the safety management unit can customize property recommendations based on the user's safety awareness. For example, if the user requires high safety, it will prioritize recommendations of properties with excellent security. This ensures the user's safety and makes it possible to provide properties where they can live with peace of mind.

[0110] The property recommendation system can further include an energy management unit that optimizes the user's energy consumption. The energy management unit can evaluate the energy efficiency and environmental impact of a property and provide the results to the user. For example, it can provide information about the property's insulation performance and renewable energy usage status to recommend a highly energy-efficient property. It can also suggest optimal energy management methods based on the user's energy consumption patterns. Furthermore, the energy management unit can provide advice to the user on how to reduce energy consumption. This can optimize the user's energy consumption and support an environmentally conscious lifestyle.

[0111] The property recommendation system can further include a financial management unit that manages the user's financial situation. The financial management unit can analyze the user's income, expenses, and savings status and customize property recommendations based on this. For example, it can recommend properties that fit the user's budget and support the user in selecting a property within their means. It can also recommend optimal properties based on the user's financial goals (e.g., savings goals and investment plans). Furthermore, the financial management unit can provide advice to help the user create a financial plan after purchasing a property. This makes it possible to recommend properties that fit the user's financial situation, allowing the user to select a property with peace of mind.

[0112] The property recommendation system can further include a mobility management unit that takes into account the user's means of transportation. The mobility management unit can analyze the user's commute to work, school, and daily travel patterns, and customize property recommendations based on this. For example, if the user uses public transportation, it can recommend properties that are close to the nearest station or bus stop. If the user uses a car, it can also recommend properties with parking or good transportation access. Furthermore, the mobility management unit can provide advice to optimize the user's travel time. This makes it possible to recommend properties that suit the user's means of transportation, providing a more convenient living environment.

[0113] The property recommendation system can further include a dietary support unit that supports the user's diet. The dietary support unit can collect information about the user's diet and customize property recommendations based on this information. For example, if the user is health-conscious, the system can recommend properties with organic food stores or health food stores nearby. Also, if the user likes to eat out, the system can recommend properties with a variety of restaurants and cafes nearby. Furthermore, the dietary support unit can provide advice about the user's diet and support a healthy diet. This makes it possible to recommend properties that suit the user's diet, thereby providing a more satisfying living environment.

[0114] The property recommendation system can further include a learning support unit that supports the user's learning environment. The learning support unit can customize property recommendations based on the user's learning needs. For example, if the user is a student, the learning support unit can recommend properties with libraries and learning facilities nearby. Also, if the user works remotely, the learning support unit can recommend properties with a quiet environment and high-speed internet access. Furthermore, the learning support unit can provide advice to improve the user's learning efficiency. This makes it possible to recommend properties that suit the user's learning environment, thereby providing a more effective learning environment.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives desired conditions from the user. The desired conditions input by the user include detailed conditions such as location, budget, and floor plan. Furthermore, the reception unit can estimate the user's emotions and customize the input interface for desired conditions based on the estimated emotions. For example, if the user is feeling stressed, it can provide a simple and intuitive interface to minimize input steps. Step 2: The suggestion unit suggests the most suitable property based on the conditions received by the reception unit. The suggestion unit searches for and suggests the property that best suits the user's conditions based on a vast property database. The suggestion unit can also estimate the user's emotions and adjust the way property suggestions are presented based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed property information and make suggestions at a leisurely pace. Step 3: The procedure unit allows the user to view the property proposed by the proposal unit and complete the contract procedures online. The procedure unit checks the detailed information of the proposed property online, and makes reservations for viewings and completes the contract procedures. The procedure unit can also estimate the user's emotions and adjust the procedure based on the estimated emotions. For example, if the user is nervous, it provides a simple and easy-to-understand procedure. Step 4: The translation department translates the contract procedures carried out by the procedure department into multiple languages. The translation department translates contracts and property information into multiple languages, allowing foreign users to complete contract procedures without feeling a language barrier. The translation department can also estimate the user's emotions and adjust the way the translation is expressed based on the estimated emotions. For example, if the user is nervous, the translation will use simple, easy-to-understand expressions.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0131] 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.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives desired conditions from a user; a proposal unit that proposes suitable properties based on the conditions received by the reception unit; a procedure section that performs online viewings of properties proposed by the proposal section and contract procedures; a translation unit that translates the contract procedures performed by the procedure unit into multiple languages; A system characterized by:

2. The reception unit Accepts detailed user input of location, budget, and layout 2. The system of claim 1.

3. The proposal unit Search and suggest properties that best suit the user's requirements based on a large property database 2. The system of claim 1.

4. The procedure division View the details of proposed properties online, schedule viewings and complete contract procedures 2. The system of claim 1.

5. The translation unit Translates contracts and property information into multiple languages, enabling foreign users to complete contract procedures without facing language barriers.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and customize the input interface for desired conditions based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past property search history and suggest the optimal way to enter search conditions 2. The system of claim 1.

8. The reception unit When entering your desired criteria, filters are performed based on the user's current living situation and future plans.

2. The system of claim 1.

Images