system

Abstract

The system according to the embodiment aims to grasp the appropriate market rent for a property based on the area and property conditions desired by the user, and to conduct effective negotiations. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a presentation unit, a lecture unit, and an agent unit. The collection unit inputs the user's desired area and property conditions. The analysis unit analyzes the information collected by the collection unit and calculates the market rent. The presentation unit presents the market rent calculated by the analysis unit. The lecture unit gives a lecture on negotiation methods based on the market rent presented by the presentation unit. The agent unit negotiates on behalf of the user based on the information lectured by the lecture unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to grasp appropriate rent prices based on the area and property conditions they desire and to negotiate effectively.

[0005] The system according to the embodiment aims to grasp the appropriate market rent for a property based on the area and property conditions desired by the user, and to conduct effective negotiations. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a presentation unit, a lecture unit, and an agent unit. The collection unit inputs the user's desired area and property conditions. The analysis unit analyzes the information collected by the collection unit and calculates the market rent. The presentation unit presents the market rent calculated by the analysis unit. The lecture unit gives a lecture on negotiation methods based on the market rent presented by the presentation unit. The agent unit negotiates on behalf of the user based on the information lectured by the lecture unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to grasp the appropriate market rent based on the desired area and property conditions, and to conduct effective negotiations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A rent negotiation support system according to an embodiment of the present invention is a system in which a user inputs the desired area and property conditions, and a generation AI calculates the optimal market rent, provides instruction on negotiation methods, and negotiates on the user's behalf. The rent negotiation support system allows a user to input the desired area and property conditions, and the generation AI analyzes past data and current market trends to present the optimal market rent. Furthermore, the generation AI provides instruction on how to negotiate to lower the rent and, if desired, negotiates on the user's behalf. For example, the rent negotiation support system allows a user to input the desired area and property conditions (e.g., floor plan, age of building, facilities, etc.). Next, the rent negotiation support system uses an analysis unit to analyze the information collected by the collection unit and calculates the optimal market rent. The presentation unit presents the analysis results to the user. Furthermore, the lecture unit provides instruction on how to negotiate to lower the rent based on the results of the presentation unit. Finally, the proxy unit negotiates on the user's behalf based on the information from the lecture unit. This allows the rent negotiation support system to easily lower the rent, thereby reducing the user's financial burden. This allows the rent negotiation support system to easily lower the rent, thereby reducing the user's financial burden. For example, users can input the desired area and property conditions, and the AI ​​generator will calculate the optimal market rent, provide instructions on how to negotiate, and negotiate on their behalf, allowing them to quickly and effectively lower their rent.

[0029] A rent negotiation support system according to an embodiment includes a collection unit, an analysis unit, a presentation unit, a lecture unit, and an agent unit. The collection unit inputs the user's desired area and property conditions. The user's desired area and property conditions include, but are not limited to, rent, floor plan, age of the building, and distance from the station. The collection unit, for example, stores the information input by the user in a database and provides it to the analysis unit. The analysis unit analyzes the information collected by the collection unit and calculates an optimal market rent. The analysis is performed using, for example, statistical techniques or machine learning algorithms. For example, the analysis unit analyzes rent data for similar properties in the same area based on past data and current market trends, and presents a market rent that reflects current market trends. The presentation unit presents the optimal market rent to the user based on the results of the analysis unit. The presentation is performed in the form of, for example, a graph or list. For example, the presentation unit displays the analysis results in a visually easy-to-understand format and provides them to the user. The lecture unit lectures on negotiating methods to lower rent based on the results of the presentation unit. The lecture provides specific advice, for example, on points to emphasize during negotiations and effective speaking techniques. For example, the lecture unit can provide text-based or video-based lectures. The proxy unit negotiates on behalf of the user based on the information from the lecture unit. The proxy negotiating can be performed, for example, by email or telephone. For example, the proxy unit can automatically negotiate using a generation AI to obtain optimal results. As a result, the rent negotiation support system according to the embodiment allows users to easily lower their rent and reduce their financial burden.

[0030] The collection unit can analyze the user's past property selection history and select a collection method. For example, the collection unit prioritizes collecting similar property conditions based on the conditions of properties previously selected by the user. For example, the collection unit retrieves the past property selection history from a database and extracts similar property conditions. The collection unit can also prioritize suggesting collection methods (voice, text, etc.) that the user has used in the past. For example, the collection unit analyzes past collection methods and selects an optimal collection method. The collection unit can also predict and suggest a collection method to be used in a specific time period based on the user's past selection history. For example, the collection unit suggests an optimal collection method for a specific time period based on the past selection history. In this way, the optimal collection method can be selected by analyzing the user's past property selection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past property selection history into a generation AI and cause the generation AI to select an optimal collection method.

[0031] When collecting property conditions, the collection unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user has a pet, the collection unit prioritizes collecting properties that allow pets. For example, the collection unit acquires the user's living situation from a database and extracts properties that allow pets. Furthermore, if the user is raising children, the collection unit can prioritize collecting properties with environments suitable for raising children. For example, the collection unit extracts properties suitable for raising children based on the user's living situation. Furthermore, if the user is working remotely, the collection unit can prioritize collecting properties suitable for remote work. For example, the collection unit extracts properties suitable for remote work based on the user's living situation. In this way, by filtering based on the user's current living situation and areas of interest, more appropriate property conditions can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's living situation data into a generation AI and cause the generation AI to perform filtering.

[0032] When collecting property conditions, the collection unit can select a collection means according to the user's input method. For example, if the user uses voice input, the collection unit collects property conditions using voice recognition technology. For example, the collection unit converts the user's voice into text using voice recognition software. Furthermore, if the user uses text input, the collection unit can also collect property conditions using text analysis technology. For example, the collection unit analyzes the user's input using text analysis software. Furthermore, if the user uses image input, the collection unit can also collect property conditions using image recognition technology. For example, the collection unit analyzes images uploaded by the user using image recognition software. This allows for efficient collection of property conditions by selecting the optimal collection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0033] When collecting property conditions, the collection unit can prioritize collecting highly relevant conditions by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting properties close to the user's current location. For example, the collection unit acquires the user's geographical location information from GPS data and extracts properties close to the user's current location. Furthermore, if the user is interested in a specific area, the collection unit can prioritize collecting properties in that area. For example, the collection unit extracts properties in a specific area based on the user's geographical location information. Furthermore, the collection unit can prioritize collecting properties along the user's commute route. For example, the collection unit extracts properties along the user's commute route based on the user's geographical location information. In this way, by taking the user's geographical location information into account, highly relevant property conditions can be prioritized. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information to a generation AI and cause the generation AI to collect highly relevant property conditions.

[0034] When collecting property conditions, the collection unit can analyze the user's social media activity and collect related conditions. For example, the collection unit collects properties near locations where the user has checked in on social media. For example, the collection unit obtains the user's social media activity through an API and extracts properties near the check-in locations. The collection unit can also analyze the user's social media posts to collect related property conditions. For example, the collection unit can analyze the user's posts using text analysis technology to extract related property conditions. The collection unit can also collect related property conditions by referring to the activities of the user's friends on social media. For example, the collection unit can analyze the activity data of the user's friends and extract related property conditions. In this way, related property conditions can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related property conditions.

[0035] The collection unit can customize the collection method by reflecting the user's past feedback when collecting property conditions. The collection unit, for example, improves the collection method based on feedback provided by the user in the past. For example, the collection unit obtains user feedback data from a database and adjusts the collection method. The collection unit can also preferentially collect specific property conditions from the user's past feedback. For example, the collection unit analyzes the user's feedback and extracts specific property conditions. The collection unit can also analyze the user's past feedback and propose an optimal collection method. For example, the collection unit proposes an optimal collection method based on the feedback data. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data to a generation AI and cause the generation AI to customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the property. For example, the analysis unit performs a detailed analysis of important properties. For example, the analysis unit generates detailed analysis results based on the importance of the property. The analysis unit can also perform a concise analysis of less important properties. For example, the analysis unit generates concise analysis results based on the importance of the property. The analysis unit can also gradually adjust the level of detail of the analysis according to the importance of the property. For example, the analysis unit adjusts the level of detail of the analysis based on the importance score of the property. In this way, by adjusting the level of detail of the analysis based on the importance of the property, important properties can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input property importance data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the property category. For example, for residential properties, the analysis unit applies an analysis algorithm dedicated to residential properties. For example, the analysis unit executes an analysis algorithm dedicated to residential properties based on data of residential properties. The analysis unit can also apply an analysis algorithm dedicated to commercial properties to commercial properties. For example, the analysis unit executes an analysis algorithm dedicated to commercial properties based on data of commercial properties. The analysis unit can also select an optimal analysis algorithm depending on the property category. For example, the analysis unit selects an optimal analysis algorithm based on property category data. This improves the accuracy of the analysis by applying an optimal analysis algorithm depending on the property category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input property category data to the generation AI and cause the generation AI to select an analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit retrieves past analysis result data from a database and adjusts the analysis algorithm. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. For example, the analysis unit analyzes past analysis results and extracts specific patterns. The analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. For example, the analysis unit proposes an optimal analysis method based on the past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data to the generation AI and have the generation AI adjust the analysis algorithm.

[0039] During analysis, the analysis unit can determine the analysis priority based on the submission time of the property. The analysis unit, for example, prioritizes analysis of properties submitted earlier. For example, the analysis unit extracts properties submitted earlier based on the property submission time data. The analysis unit can also postpone analysis of properties submitted later. For example, the analysis unit extracts properties submitted later based on the property submission time data. The analysis unit can also gradually adjust the analysis priority according to the submission time. For example, the analysis unit adjusts the analysis priority based on the property submission time score. In this way, by determining the analysis priority based on the property submission time, properties submitted earlier can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input property submission time data to the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the properties. The analysis unit, for example, prioritizes analysis of highly relevant properties. For example, the analysis unit extracts highly relevant properties based on property relevance data. The analysis unit can also postpone analysis of less relevant properties. For example, the analysis unit extracts less relevant properties based on property relevance data. The analysis unit can also gradually adjust the order of analysis according to the relevance of the properties. For example, the analysis unit adjusts the order of analysis based on the relevance score of the properties. In this way, by adjusting the order of analysis based on the relevance of the properties, highly relevant properties can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input property relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. For example, the analysis unit generates analysis results that use a lot of technical terms based on the user's level of expertise. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. For example, the analysis unit generates concise and easy-to-understand analysis results based on the user's level of expertise. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms based on the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0042] The presentation unit can adjust the level of detail of the presentation based on the importance of the property when presenting the property. The presentation unit, for example, provides a detailed presentation for important properties. For example, the presentation unit generates a detailed presentation result based on the importance of the property. The presentation unit can also provide a concise presentation for properties with low importance. For example, the presentation unit generates a concise presentation result based on the importance of the property. The presentation unit can also gradually adjust the level of detail of the presentation according to the importance of the property. For example, the presentation unit adjusts the level of detail of the presentation based on the importance score of the property. In this way, important properties can be presented in detail by adjusting the level of detail of the presentation based on the importance of the property. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input property importance data to a generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0043] The presentation unit can apply different presentation algorithms depending on the property category when presenting the property. For example, the presentation unit applies a presentation algorithm dedicated to residential properties to residential properties. For example, the presentation unit executes a presentation algorithm dedicated to residential properties based on residential property data. The presentation unit can also apply a presentation algorithm dedicated to commercial properties to commercial properties. For example, the presentation unit executes a presentation algorithm dedicated to commercial properties based on commercial property data. The presentation unit can also select an optimal presentation algorithm depending on the property category. For example, the presentation unit selects an optimal presentation algorithm based on property category data. This improves the accuracy of presentation by applying an optimal presentation algorithm depending on the property category. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input property category data to the generation AI and cause the generation AI to select a presentation algorithm.

[0044] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results. The presentation unit, for example, adjusts the presentation algorithm based on the user's past presentation results. For example, the presentation unit acquires past presentation result data from a database and adjusts the presentation algorithm. The presentation unit can also extract a specific pattern from the user's past presentation results to improve the accuracy of presentation. For example, the presentation unit analyzes past presentation results and extracts a specific pattern. The presentation unit can also analyze the user's past presentation results and propose an optimal presentation method. For example, the presentation unit proposes an optimal presentation method based on the past presentation results. This improves the accuracy of presentation by referring to the user's past presentation results. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input past presentation result data to the generation AI and cause the generation AI to adjust the presentation algorithm.

[0045] The presentation unit can determine the presentation priority based on the submission time of the property when presenting the property. The presentation unit, for example, prioritizes the presentation of properties submitted earlier. For example, the presentation unit extracts properties submitted earlier based on the submission time data of the property. The presentation unit can also postpone the presentation of properties submitted later. For example, the presentation unit extracts properties submitted later based on the submission time data of the property. The presentation unit can also gradually adjust the presentation priority according to the submission time. For example, the presentation unit adjusts the presentation priority based on the submission time score of the property. In this way, by determining the presentation priority based on the submission time of the property, properties submitted earlier can be presented preferentially. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the submission time data of the property to the generation AI and cause the generation AI to determine the presentation priority.

[0046] The presentation unit can adjust the presentation order based on the relevance of the properties when presenting them. The presentation unit, for example, prioritizes the presentation of highly relevant properties. For example, the presentation unit extracts highly relevant properties based on property relevance data. The presentation unit can also postpone the presentation of less relevant properties. For example, the presentation unit extracts less relevant properties based on property relevance data. The presentation unit can also gradually adjust the presentation order according to the property relevance. For example, the presentation unit adjusts the presentation order based on the property relevance score. In this way, by adjusting the presentation order based on the property relevance, highly relevant properties can be presented preferentially. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input property relevance data to a generation AI and cause the generation AI to adjust the presentation order.

[0047] The presentation unit can adjust the use of technical terms in the presentation according to the user's level of expertise. For example, if the user has technical expertise, the presentation unit provides a presentation result that uses a lot of technical terms. For example, the presentation unit generates a presentation result that uses a lot of technical terms based on the user's level of expertise. Furthermore, if the user does not have technical expertise, the presentation unit can provide a concise and easy-to-understand presentation result. For example, the presentation unit generates a concise and easy-to-understand presentation result based on the user's level of expertise. Furthermore, the presentation unit can gradually adjust the use of technical terms in the presentation according to the user's level of expertise. For example, the presentation unit adjusts the use of technical terms based on the user's level of expertise. In this way, by adjusting the use of technical terms in the presentation according to the user's level of expertise, more appropriate presentation results can be provided. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0048] The lecture unit can adjust the level of detail of the lecture based on the importance of the negotiation during the lecture. For example, the lecture unit provides a detailed lecture for important negotiations. For example, the lecture unit generates a detailed lecture based on the importance of the negotiation. The lecture unit can also provide a brief lecture for less important negotiations. For example, the lecture unit generates a brief lecture based on the importance of the negotiation. The lecture unit can also gradually adjust the level of detail of the lecture according to the importance of the negotiation. For example, the lecture unit adjusts the level of detail of the lecture based on the importance score of the negotiation. In this way, by adjusting the level of detail of the lecture based on the importance of the negotiation, a detailed lecture can be provided for important negotiations. Some or all of the above-described processing in the lecture unit may be performed using, for example, AI, or may be performed without using AI. For example, the lecture unit can input negotiation importance data to a generation AI and cause the generation AI to adjust the level of detail of the lecture.

[0049] The lecture unit can apply different lecture algorithms depending on the negotiation category during the lecture. For example, for rent negotiations, the lecture unit applies a lecture algorithm dedicated to rent negotiations. For example, the lecture unit executes a lecture algorithm dedicated to rent negotiations based on data on rent negotiations. The lecture unit can also apply a lecture algorithm dedicated to rental contract negotiations to rental contract negotiations. For example, the lecture unit executes a lecture algorithm dedicated to rental contract negotiations based on data on rental contract negotiations. The lecture unit can also select an optimal lecture algorithm depending on the negotiation category. For example, the lecture unit selects an optimal lecture algorithm based on negotiation category data. This improves the accuracy of the lecture by applying an optimal lecture algorithm depending on the negotiation category. Some or all of the above-mentioned processing in the lecture unit may be performed using, for example, AI, or may be performed without using AI. For example, the lecture unit can input negotiation category data into a generation AI and have the generation AI select a lecture algorithm.

[0050] The lecture unit can improve the accuracy of a lecture by referring to the user's past lecture results during the lecture. The lecture unit, for example, adjusts the lecture algorithm based on the user's past lecture results. For example, the lecture unit retrieves past lecture result data from a database and adjusts the lecture algorithm. The lecture unit can also extract specific patterns from the user's past lecture results to improve the accuracy of the lecture. For example, the lecture unit analyzes past lecture results and extracts specific patterns. The lecture unit can also analyze the user's past lecture results and suggest an optimal lecture method. For example, the lecture unit suggests an optimal lecture method based on the past lecture results. This improves the accuracy of the lecture by referring to the user's past lecture results. Some or all of the above-described processing in the lecture unit may be performed using, for example, AI, or may be performed without using AI. For example, the lecture unit can input past lecture result data into a generation AI and have the generation AI adjust the lecture algorithm.

[0051] During a lecture, the lecture section can determine the priority of the lecture based on the submission time of the negotiation. The lecture section, for example, prioritizes lecturing on negotiations submitted earlier. For example, the lecture section extracts negotiations submitted earlier based on negotiation submission time data. The lecture section can also postpone lecturing on negotiations submitted later. For example, the lecture section extracts negotiations submitted later based on negotiation submission time data. The lecture section can also gradually adjust the priority of the lecture depending on the submission time. For example, the lecture section adjusts the priority of the lecture based on the submission time score of the negotiation. In this way, by determining the priority of the lecture based on the submission time of the negotiation, negotiations submitted earlier can be lectured preferentially. Some or all of the above-described processing in the lecture section may be performed using, for example, AI, or may be performed without using AI. For example, the lecture section can input negotiation submission time data to a generation AI and have the generation AI determine the priority of the lecture.

[0052] During a lecture, the lecture section can adjust the order of the lectures based on the relevance of the negotiations. The lecture section, for example, prioritizes lecturing on highly relevant negotiations. For example, the lecture section extracts highly relevant negotiations based on negotiation relevance data. The lecture section can also postpone lecturing on less relevant negotiations. For example, the lecture section extracts less relevant negotiations based on negotiation relevance data. The lecture section can also gradually adjust the order of the lectures according to the relevance of the negotiations. For example, the lecture section adjusts the order of the lectures based on the relevance score of the negotiations. In this way, by adjusting the order of the lectures based on the relevance of the negotiations, highly relevant negotiations can be lectured on preferentially. Some or all of the above-described processing in the lecture section may be performed, for example, using AI, or may be performed without using AI. For example, the lecture section can input negotiation relevance data to a generation AI and cause the generation AI to adjust the order of the lectures.

[0053] During a lecture, the lecture unit can adjust the use of technical terms in the lecture according to the user's level of expertise. For example, if the user has technical expertise, the lecture unit provides a lecture that uses a lot of technical terms. For example, the lecture unit generates a lecture that uses a lot of technical terms based on the user's level of expertise. Furthermore, if the user does not have technical expertise, the lecture unit can provide a concise and easy-to-understand lecture. For example, the lecture unit generates a concise and easy-to-understand lecture based on the user's level of expertise. Furthermore, the lecture unit can gradually adjust the use of technical terms in the lecture according to the user's level of expertise. For example, the lecture unit adjusts the use of technical terms based on the user's level of expertise. This allows for the provision of a more appropriate lecture by adjusting the use of technical terms in the lecture according to the user's level of expertise. Some or all of the above-described processing in the lecture unit may be performed using, for example, AI, or may be performed without AI. For example, the lecture unit can input the user's level of expertise data into a generation AI and cause the generation AI to adjust the use of technical terms.

[0054] The proxy unit can adjust the level of detail of the proxy based on the importance of the negotiation during proxying. For example, the proxy unit performs detailed proxying for important negotiations. For example, the proxy unit generates a detailed proxy method based on the importance of the negotiation. The proxy unit can also perform simple proxying for negotiations with low importance. For example, the proxy unit generates a simple proxy method based on the importance of the negotiation. The proxy unit can also gradually adjust the level of detail of the proxy depending on the importance of the negotiation. For example, the proxy unit adjusts the level of detail of the proxy based on the importance score of the negotiation. In this way, detailed proxying can be performed for important negotiations by adjusting the level of detail of the proxy based on the importance of the negotiation. Some or all of the above-mentioned processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can input negotiation importance data to a generation AI and cause the generation AI to adjust the level of detail of the proxy.

[0055] The agent unit can apply different agent algorithms depending on the negotiation category during the proxy negotiation. For example, for rent negotiations, the agent unit applies an agent algorithm dedicated to rent negotiations. For example, the agent unit executes an agent algorithm dedicated to rent negotiations based on rent negotiation data. The agent unit can also apply an agent algorithm dedicated to rental contract negotiations for rental contract negotiations. For example, the agent unit executes an agent algorithm dedicated to rental contract negotiations based on rental contract negotiation data. The agent unit can also select an optimal agent algorithm depending on the negotiation category. For example, the agent unit selects an optimal agent algorithm based on negotiation category data. This improves the accuracy of the proxy by applying an optimal agent algorithm depending on the negotiation category. Some or all of the above-mentioned processing in the agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the agent unit can input negotiation category data into a generation AI and have the generation AI select an agent algorithm.

[0056] During proxy processing, the proxy unit can improve the accuracy of proxy processing by referring to the user's past proxy results. The proxy unit, for example, adjusts the proxy algorithm based on the user's past proxy results. For example, the proxy unit retrieves past proxy result data from a database and adjusts the proxy algorithm. The proxy unit can also extract specific patterns from the user's past proxy results to improve the accuracy of proxy processing. For example, the proxy unit analyzes past proxy results and extracts specific patterns. The proxy unit can also analyze the user's past proxy results and propose an optimal proxy method. For example, the proxy unit proposes an optimal proxy method based on the past proxy results. This improves the accuracy of proxy processing by referring to the user's past proxy results. Some or all of the above-described processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can input past proxy result data to the generation AI and cause the generation AI to adjust the proxy algorithm.

[0057] The proxy unit can determine the priority of proxy based on the submission timing of the negotiations when proxying. The proxy unit, for example, prioritizes proxying negotiations that are submitted early. For example, the proxy unit extracts negotiations that are submitted early based on negotiation submission timing data. The proxy unit can also postpone proxying negotiations that are submitted late. For example, the proxy unit extracts negotiations that are submitted late based on negotiation submission timing data. The proxy unit can also gradually adjust the priority of proxying depending on the submission timing. For example, the proxy unit adjusts the priority of proxying based on the submission timing score of the negotiations. In this way, by determining the priority of proxying based on the submission timing of the negotiations, it is possible to prioritize proxying negotiations that are submitted early. Some or all of the above-mentioned processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can input negotiation submission timing data into a generation AI and have the generation AI determine the priority of proxying.

[0058] The proxy unit can adjust the proxy order based on the relevance of the negotiations when proxying. The proxy unit, for example, prioritizes proxying highly relevant negotiations. For example, the proxy unit extracts highly relevant negotiations based on negotiation relevance data. The proxy unit can also postpone proxying for less relevant negotiations. For example, the proxy unit extracts less relevant negotiations based on negotiation relevance data. The proxy unit can also gradually adjust the proxy order according to the relevance of the negotiations. For example, the proxy unit adjusts the proxy order based on the relevance score of the negotiations. In this way, by adjusting the proxy order based on the relevance of the negotiations, it is possible to prioritize proxying for more relevant negotiations. Some or all of the above-described processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can input negotiation relevance data to a generation AI and cause the generation AI to adjust the proxy order.

[0059] The proxy unit can adjust the use of technical terms in the proxy depending on the user's level of expertise during proxying. For example, if the user has specialized knowledge, the proxy unit provides proxying that uses a lot of technical terms. For example, the proxy unit generates a proxy method that uses a lot of technical terms based on the user's level of expertise. Furthermore, if the user does not have specialized knowledge, the proxy unit can provide concise and easy-to-understand proxying. For example, the proxy unit generates a concise and easy-to-understand proxy method based on the user's level of expertise. Furthermore, the proxy unit can gradually adjust the use of technical terms in the proxy depending on the user's level of expertise. For example, the proxy unit adjusts the use of technical terms based on the user's level of expertise. In this way, by adjusting the use of technical terms in the proxy depending on the user's level of expertise, more appropriate proxying can be provided. Some or all of the above-described processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

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

[0061] The analysis unit can analyze the user's past negotiation results and provide feedback to improve the success rate of negotiations. For example, the analysis unit can extract successful negotiation patterns based on past negotiation data and provide specific advice to the user. The analysis unit can also analyze data on unsuccessful negotiations, identify areas for improvement, and provide feedback to the user. Furthermore, the analysis unit can analyze the user's negotiation style and tendencies and suggest optimal negotiation strategies. This allows the user to utilize their past experience to conduct more effective negotiations.

[0062] The presentation unit can analyze the user's past property viewing history and prioritize presenting properties that match the user's preferences. For example, the presentation unit can extract and present similar properties based on the characteristics of properties the user has viewed in the past. It can also prioritize presenting properties with similar conditions based on the conditions of properties that the user has previously given high ratings to. It can also prioritize presenting properties in areas in which the user has previously shown interest. This allows properties that match the user's preferences to be presented efficiently.

[0063] The proxy unit can propose strategies to improve the success rate of negotiations based on the user's past negotiation results. For example, the proxy unit can analyze data on past successful negotiations and propose similar strategies. It can also analyze data on past unsuccessful negotiations, identify areas for improvement, and propose new strategies. It can also analyze the user's negotiation style and tendencies and propose optimal negotiation methods. This allows the user to utilize past experience to conduct more effective negotiations.

[0064] The collection unit can dynamically adjust the property collection range based on the user's geographical location information. For example, if the user is in a specific area, properties in the vicinity of that area are collected preferentially. Also, if the user desires properties along their commute route, properties along that route can be collected. Furthermore, if the user is traveling, property information for the user's travel destination can be collected. This allows the system to provide more relevant property information based on the user's geographical location information.

[0065] The lecture section can analyze the user's past lecture history and provide a lecture method that matches the user's learning style. For example, if the user prefers visual learning, it can provide a lecture that makes extensive use of visual aids. If the user prefers auditory learning, it can provide an audio-based lecture. Furthermore, if the user prefers hands-on learning, it can provide an interactive lecture. This makes it possible to provide effective lectures that match the user's learning style.

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

[0067] Step 1: The collection unit inputs the user's desired area and property conditions. The user's desired area and property conditions include, for example, rent, floor plan, age of the building, distance from the station, etc. The collection unit saves the information input by the user in a database and provides it to the analysis unit. Step 2: The analysis unit analyzes the information collected by the collection unit and calculates the optimal market rent. The analysis is carried out using statistical methods and machine learning algorithms, and analyzes rent data for similar properties in the same area based on past data and current market trends. Step 3: The presentation unit presents the optimal market rent to the user based on the results of the analysis unit. The presentation is done in the form of a graph or list, and the analysis results are displayed visually in an easy-to-understand manner. Step 4: The lecture section provides guidance on how to negotiate to lower rent based on the results of the presentation section. The lecture is provided in text and video format, and provides specific advice on points to emphasize during negotiations and effective speaking techniques. Step 5: The proxy unit negotiates on behalf of the user based on the information from the lecture unit. The proxy unit negotiates via email or telephone, and uses generative AI to automatically negotiate and achieve the optimal result.

[0068] (Example 2) A rent negotiation support system according to an embodiment of the present invention is a system in which a user inputs the desired area and property conditions, and a generation AI calculates the optimal market rent, provides instruction on negotiation methods, and negotiates on the user's behalf. The rent negotiation support system allows a user to input the desired area and property conditions, and the generation AI analyzes past data and current market trends to present the optimal market rent. Furthermore, the generation AI provides instruction on how to negotiate to lower the rent and, if desired, negotiates on the user's behalf. For example, the rent negotiation support system allows a user to input the desired area and property conditions (e.g., floor plan, age of building, facilities, etc.). Next, the rent negotiation support system uses an analysis unit to analyze the information collected by the collection unit and calculates the optimal market rent. The presentation unit presents the analysis results to the user. Furthermore, the lecture unit provides instruction on how to negotiate to lower the rent based on the results of the presentation unit. Finally, the proxy unit negotiates on the user's behalf based on the information from the lecture unit. This allows the rent negotiation support system to easily lower the rent, thereby reducing the user's financial burden. This allows the rent negotiation support system to easily lower the rent, thereby reducing the user's financial burden. For example, users can input the desired area and property conditions, and the AI ​​generator will calculate the optimal market rent, provide instructions on how to negotiate, and negotiate on their behalf, allowing them to quickly and effectively lower their rent.

[0069] A rent negotiation support system according to an embodiment includes a collection unit, an analysis unit, a presentation unit, a lecture unit, and an agent unit. The collection unit inputs the user's desired area and property conditions. The user's desired area and property conditions include, but are not limited to, rent, floor plan, age of the building, and distance from the station. The collection unit, for example, stores the information input by the user in a database and provides it to the analysis unit. The analysis unit analyzes the information collected by the collection unit and calculates an optimal market rent. The analysis is performed using, for example, statistical techniques or machine learning algorithms. For example, the analysis unit analyzes rent data for similar properties in the same area based on past data and current market trends, and presents a market rent that reflects current market trends. The presentation unit presents the optimal market rent to the user based on the results of the analysis unit. The presentation is performed in the form of, for example, a graph or list. For example, the presentation unit displays the analysis results in a visually easy-to-understand format and provides them to the user. The lecture unit lectures on negotiating methods to lower rent based on the results of the presentation unit. The lecture provides specific advice, for example, on points to emphasize during negotiations and effective speaking techniques. For example, the lecture unit can provide text-based or video-based lectures. The proxy unit negotiates on behalf of the user based on the information from the lecture unit. The proxy negotiating can be performed, for example, by email or telephone. For example, the proxy unit can automatically negotiate using a generation AI to obtain optimal results. As a result, the rent negotiation support system according to the embodiment allows users to easily lower their rent and reduce their financial burden.

[0070] The collection unit estimates the user's emotions and adjusts the timing of collecting property conditions based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This allows the timing of collecting property conditions to be adjusted according to the user's emotions, thereby collecting information at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0071] The collection unit can analyze the user's past property selection history and select a collection method. For example, the collection unit prioritizes collecting similar property conditions based on the conditions of properties previously selected by the user. For example, the collection unit retrieves the past property selection history from a database and extracts similar property conditions. The collection unit can also prioritize suggesting collection methods (voice, text, etc.) that the user has used in the past. For example, the collection unit analyzes past collection methods and selects an optimal collection method. The collection unit can also predict and suggest a collection method to be used in a specific time period based on the user's past selection history. For example, the collection unit suggests an optimal collection method for a specific time period based on the past selection history. In this way, the optimal collection method can be selected by analyzing the user's past property selection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past property selection history into a generation AI and cause the generation AI to select an optimal collection method.

[0072] When collecting property conditions, the collection unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user has a pet, the collection unit prioritizes collecting properties that allow pets. For example, the collection unit acquires the user's living situation from a database and extracts properties that allow pets. Furthermore, if the user is raising children, the collection unit can prioritize collecting properties with environments suitable for raising children. For example, the collection unit extracts properties suitable for raising children based on the user's living situation. Furthermore, if the user is working remotely, the collection unit can prioritize collecting properties suitable for remote work. For example, the collection unit extracts properties suitable for remote work based on the user's living situation. In this way, by filtering based on the user's current living situation and areas of interest, more appropriate property conditions can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's living situation data into a generation AI and cause the generation AI to perform filtering.

[0073] When collecting property conditions, the collection unit can select a collection means according to the user's input method. For example, if the user uses voice input, the collection unit collects property conditions using voice recognition technology. For example, the collection unit converts the user's voice into text using voice recognition software. Furthermore, if the user uses text input, the collection unit can also collect property conditions using text analysis technology. For example, the collection unit analyzes the user's input using text analysis software. Furthermore, if the user uses image input, the collection unit can also collect property conditions using image recognition technology. For example, the collection unit analyzes images uploaded by the user using image recognition software. This allows for efficient collection of property conditions by selecting the optimal collection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0074] The collection unit can estimate the user's emotions and determine the priority of the property conditions to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting important property conditions. For example, the collection unit extracts important property conditions based on the user's emotion score. The collection unit can also collect detailed property conditions when the user is relaxed. For example, the collection unit extracts detailed property conditions based on the user's emotion score. The collection unit can also prioritize collecting the most important property conditions when the user is in a hurry. For example, the collection unit extracts the most important property conditions based on the user's emotion score. This allows the priority of property conditions to be determined according to the user's emotions, thereby prioritizing the collection of important property conditions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI determine the priorities of property conditions.

[0075] When collecting property conditions, the collection unit can prioritize collecting highly relevant conditions by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting properties close to the user's current location. For example, the collection unit acquires the user's geographical location information from GPS data and extracts properties close to the user's current location. Furthermore, if the user is interested in a specific area, the collection unit can prioritize collecting properties in that area. For example, the collection unit extracts properties in a specific area based on the user's geographical location information. Furthermore, the collection unit can prioritize collecting properties along the user's commute route. For example, the collection unit extracts properties along the user's commute route based on the user's geographical location information. In this way, by taking the user's geographical location information into account, highly relevant property conditions can be prioritized. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information to a generation AI and cause the generation AI to collect highly relevant property conditions.

[0076] When collecting property conditions, the collection unit can analyze the user's social media activity and collect related conditions. For example, the collection unit collects properties near locations where the user has checked in on social media. For example, the collection unit obtains the user's social media activity through an API and extracts properties near the check-in locations. The collection unit can also analyze the user's social media posts to collect related property conditions. For example, the collection unit can analyze the user's posts using text analysis technology to extract related property conditions. The collection unit can also collect related property conditions by referring to the activities of the user's friends on social media. For example, the collection unit can analyze the activity data of the user's friends and extract related property conditions. In this way, related property conditions can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related property conditions.

[0077] The collection unit can customize the collection method by reflecting the user's past feedback when collecting property conditions. The collection unit, for example, improves the collection method based on feedback provided by the user in the past. For example, the collection unit obtains user feedback data from a database and adjusts the collection method. The collection unit can also preferentially collect specific property conditions from the user's past feedback. For example, the collection unit analyzes the user's feedback and extracts specific property conditions. The collection unit can also analyze the user's past feedback and propose an optimal collection method. For example, the collection unit proposes an optimal collection method based on the feedback data. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data to a generation AI and cause the generation AI to customize the collection method.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit generates detailed analysis results based on the user's emotion score. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. For example, the analysis unit generates concise analysis results based on the user's emotion score. Furthermore, if the user is excited, the analysis unit can provide analysis results that add visually stimulating effects. For example, the analysis unit generates visually stimulating analysis results based on the user's emotion score. This allows for adjusting the presentation method of the analysis according to the user's emotions, thereby providing more appropriate analysis results. 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the property. For example, the analysis unit performs a detailed analysis of important properties. For example, the analysis unit generates detailed analysis results based on the importance of the property. The analysis unit can also perform a concise analysis of less important properties. For example, the analysis unit generates concise analysis results based on the importance of the property. The analysis unit can also gradually adjust the level of detail of the analysis according to the importance of the property. For example, the analysis unit adjusts the level of detail of the analysis based on the importance score of the property. In this way, by adjusting the level of detail of the analysis based on the importance of the property, important properties can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input property importance data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the property category. For example, for residential properties, the analysis unit applies an analysis algorithm dedicated to residential properties. For example, the analysis unit executes an analysis algorithm dedicated to residential properties based on data of residential properties. The analysis unit can also apply an analysis algorithm dedicated to commercial properties to commercial properties. For example, the analysis unit executes an analysis algorithm dedicated to commercial properties based on data of commercial properties. The analysis unit can also select an optimal analysis algorithm depending on the property category. For example, the analysis unit selects an optimal analysis algorithm based on property category data. This improves the accuracy of the analysis by applying an optimal analysis algorithm depending on the property category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input property category data to the generation AI and cause the generation AI to select an analysis algorithm.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit retrieves past analysis result data from a database and adjusts the analysis algorithm. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. For example, the analysis unit analyzes past analysis results and extracts specific patterns. The analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. For example, the analysis unit proposes an optimal analysis method based on the past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data to the generation AI and have the generation AI adjust the analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit generates a concise analysis result based on the user's emotion score. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit generates a detailed analysis result based on the user's emotion score. The analysis unit can also provide an analysis result with a visually stimulating effect if the user is excited. For example, the analysis unit generates a visually stimulating analysis result based on the user's emotion score. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the analysis.

[0083] During analysis, the analysis unit can determine the analysis priority based on the submission time of the property. The analysis unit, for example, prioritizes analysis of properties submitted earlier. For example, the analysis unit extracts properties submitted earlier based on the property submission time data. The analysis unit can also postpone analysis of properties submitted later. For example, the analysis unit extracts properties submitted later based on the property submission time data. The analysis unit can also gradually adjust the analysis priority according to the submission time. For example, the analysis unit adjusts the analysis priority based on the property submission time score. In this way, by determining the analysis priority based on the property submission time, properties submitted earlier can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input property submission time data to the generation AI and have the generation AI determine the analysis priority.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the properties. The analysis unit, for example, prioritizes analysis of highly relevant properties. For example, the analysis unit extracts highly relevant properties based on property relevance data. The analysis unit can also postpone analysis of less relevant properties. For example, the analysis unit extracts less relevant properties based on property relevance data. The analysis unit can also gradually adjust the order of analysis according to the relevance of the properties. For example, the analysis unit adjusts the order of analysis based on the relevance score of the properties. In this way, by adjusting the order of analysis based on the relevance of the properties, highly relevant properties can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input property relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0085] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. For example, the analysis unit generates analysis results that use a lot of technical terms based on the user's level of expertise. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. For example, the analysis unit generates concise and easy-to-understand analysis results based on the user's level of expertise. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms based on the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0086] The presentation unit can estimate the user's emotion and adjust the presentation expression method based on the estimated user's emotion. For example, when the user is relaxed, the presentation unit provides a detailed presentation result. For example, the presentation unit generates a detailed presentation result based on the user's emotion score. Furthermore, when the user is in a hurry, the presentation unit can provide a concise presentation result that focuses on the main points. For example, the presentation unit generates a concise presentation result based on the user's emotion score. Furthermore, when the user is excited, the presentation unit can provide a presentation result that adds a visually stimulating effect. For example, the presentation unit generates a visually stimulating presentation result based on the user's emotion score. This allows the presentation expression method to be adjusted according to the user's emotion, thereby providing a more appropriate presentation result. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit can be performed, for example, using AI or without AI. For example, the presentation unit can input the user's emotional data into the generation AI and have the generation AI adjust the presentation expression method.

[0087] The presentation unit can adjust the level of detail of the presentation based on the importance of the property when presenting the property. The presentation unit, for example, provides a detailed presentation for important properties. For example, the presentation unit generates a detailed presentation result based on the importance of the property. The presentation unit can also provide a concise presentation for properties with low importance. For example, the presentation unit generates a concise presentation result based on the importance of the property. The presentation unit can also gradually adjust the level of detail of the presentation according to the importance of the property. For example, the presentation unit adjusts the level of detail of the presentation based on the importance score of the property. In this way, important properties can be presented in detail by adjusting the level of detail of the presentation based on the importance of the property. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input property importance data to a generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0088] The presentation unit can apply different presentation algorithms depending on the property category when presenting the property. For example, the presentation unit applies a presentation algorithm dedicated to residential properties to residential properties. For example, the presentation unit executes a presentation algorithm dedicated to residential properties based on residential property data. The presentation unit can also apply a presentation algorithm dedicated to commercial properties to commercial properties. For example, the presentation unit executes a presentation algorithm dedicated to commercial properties based on commercial property data. The presentation unit can also select an optimal presentation algorithm depending on the property category. For example, the presentation unit selects an optimal presentation algorithm based on property category data. This improves the accuracy of presentation by applying an optimal presentation algorithm depending on the property category. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input property category data to the generation AI and cause the generation AI to select a presentation algorithm.

[0089] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results. The presentation unit, for example, adjusts the presentation algorithm based on the user's past presentation results. For example, the presentation unit acquires past presentation result data from a database and adjusts the presentation algorithm. The presentation unit can also extract a specific pattern from the user's past presentation results to improve the accuracy of presentation. For example, the presentation unit analyzes past presentation results and extracts a specific pattern. The presentation unit can also analyze the user's past presentation results and propose an optimal presentation method. For example, the presentation unit proposes an optimal presentation method based on the past presentation results. This improves the accuracy of presentation by referring to the user's past presentation results. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input past presentation result data to the generation AI and cause the generation AI to adjust the presentation algorithm.

[0090] The presentation unit can estimate the user's emotion and adjust the length of the presentation based on the estimated user's emotion. For example, if the user is in a hurry, the presentation unit provides a short, to-the-point presentation result. For example, the presentation unit generates a concise presentation result based on the user's emotion score. Furthermore, the presentation unit can provide a detailed presentation result when the user is relaxed. For example, the presentation unit generates a detailed presentation result based on the user's emotion score. Furthermore, if the user is excited, the presentation unit can provide a presentation result with a visually stimulating effect. For example, the presentation unit generates a visually stimulating presentation result based on the user's emotion score. This allows for adjusting the length of the presentation according to the user's emotion, thereby providing a more appropriate presentation result. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the presentation length.

[0091] The presentation unit can determine the presentation priority based on the submission time of the property when presenting the property. The presentation unit, for example, prioritizes the presentation of properties submitted earlier. For example, the presentation unit extracts properties submitted earlier based on the submission time data of the property. The presentation unit can also postpone the presentation of properties submitted later. For example, the presentation unit extracts properties submitted later based on the submission time data of the property. The presentation unit can also gradually adjust the presentation priority according to the submission time. For example, the presentation unit adjusts the presentation priority based on the submission time score of the property. In this way, by determining the presentation priority based on the submission time of the property, properties submitted earlier can be presented preferentially. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the submission time data of the property to the generation AI and cause the generation AI to determine the presentation priority.

[0092] The presentation unit can adjust the presentation order based on the relevance of the properties when presenting them. The presentation unit, for example, prioritizes the presentation of highly relevant properties. For example, the presentation unit extracts highly relevant properties based on property relevance data. The presentation unit can also postpone the presentation of less relevant properties. For example, the presentation unit extracts less relevant properties based on property relevance data. The presentation unit can also gradually adjust the presentation order according to the property relevance. For example, the presentation unit adjusts the presentation order based on the property relevance score. In this way, by adjusting the presentation order based on the property relevance, highly relevant properties can be presented preferentially. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input property relevance data to a generation AI and cause the generation AI to adjust the presentation order.

[0093] The presentation unit can adjust the use of technical terms in the presentation according to the user's level of expertise. For example, if the user has technical expertise, the presentation unit provides a presentation result that uses a lot of technical terms. For example, the presentation unit generates a presentation result that uses a lot of technical terms based on the user's level of expertise. Furthermore, if the user does not have technical expertise, the presentation unit can provide a concise and easy-to-understand presentation result. For example, the presentation unit generates a concise and easy-to-understand presentation result based on the user's level of expertise. Furthermore, the presentation unit can gradually adjust the use of technical terms in the presentation according to the user's level of expertise. For example, the presentation unit adjusts the use of technical terms based on the user's level of expertise. In this way, by adjusting the use of technical terms in the presentation according to the user's level of expertise, more appropriate presentation results can be provided. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0094] The lecture unit can estimate the user's emotions and adjust the presentation of the lecture based on the estimated user's emotions. For example, if the user is relaxed, the lecture unit can provide a detailed lecture. For example, the lecture unit can generate a detailed lecture based on the user's emotion score. Furthermore, if the user is in a hurry, the lecture unit can provide a concise lecture that focuses on the main points. For example, the lecture unit can generate a concise lecture based on the user's emotion score. Furthermore, if the user is excited, the lecture unit can provide a lecture with visually stimulating effects. For example, the lecture unit can generate a visually stimulating lecture based on the user's emotion score. This allows the presentation of the lecture to be adjusted according to the user's emotions, thereby providing a more appropriate lecture. 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 lecture unit can be performed using, for example, AI, or without AI. For example, the lecture department can input the user's emotional data into the generation AI and have the generation AI adjust the way the lecture is expressed.

[0095] The lecture unit can adjust the level of detail of the lecture based on the importance of the negotiation during the lecture. For example, the lecture unit provides a detailed lecture for important negotiations. For example, the lecture unit generates a detailed lecture based on the importance of the negotiation. The lecture unit can also provide a brief lecture for less important negotiations. For example, the lecture unit generates a brief lecture based on the importance of the negotiation. The lecture unit can also gradually adjust the level of detail of the lecture according to the importance of the negotiation. For example, the lecture unit adjusts the level of detail of the lecture based on the importance score of the negotiation. In this way, by adjusting the level of detail of the lecture based on the importance of the negotiation, a detailed lecture can be provided for important negotiations. Some or all of the above-described processing in the lecture unit may be performed using, for example, AI, or may be performed without using AI. For example, the lecture unit can input negotiation importance data to a generation AI and cause the generation AI to adjust the level of detail of the lecture.

[0096] The lecture unit can apply different lecture algorithms depending on the negotiation category during the lecture. For example, for rent negotiations, the lecture unit applies a lecture algorithm dedicated to rent negotiations. For example, the lecture unit executes a lecture algorithm dedicated to rent negotiations based on data on rent negotiations. The lecture unit can also apply a lecture algorithm dedicated to rental contract negotiations to rental contract negotiations. For example, the lecture unit executes a lecture algorithm dedicated to rental contract negotiations based on data on rental contract negotiations. The lecture unit can also select an optimal lecture algorithm depending on the negotiation category. For example, the lecture unit selects an optimal lecture algorithm based on negotiation category data. This improves the accuracy of the lecture by applying an optimal lecture algorithm depending on the negotiation category. Some or all of the above-mentioned processing in the lecture unit may be performed using, for example, AI, or may be performed without using AI. For example, the lecture unit can input negotiation category data into a generation AI and have the generation AI select a lecture algorithm.

[0097] The lecture unit can improve the accuracy of a lecture by referring to the user's past lecture results during the lecture. The lecture unit, for example, adjusts the lecture algorithm based on the user's past lecture results. For example, the lecture unit retrieves past lecture result data from a database and adjusts the lecture algorithm. The lecture unit can also extract specific patterns from the user's past lecture results to improve the accuracy of the lecture. For example, the lecture unit analyzes past lecture results and extracts specific patterns. The lecture unit can also analyze the user's past lecture results and suggest an optimal lecture method. For example, the lecture unit suggests an optimal lecture method based on the past lecture results. This improves the accuracy of the lecture by referring to the user's past lecture results. Some or all of the above-described processing in the lecture unit may be performed using, for example, AI, or may be performed without using AI. For example, the lecture unit can input past lecture result data into a generation AI and have the generation AI adjust the lecture algorithm.

[0098] The lecture unit can estimate the user's emotions and adjust the length of the lecture based on the estimated user emotions. For example, if the user is in a hurry, the lecture unit can provide a short, concise lecture. For example, the lecture unit can generate a concise lecture based on the user's emotion score. The lecture unit can also provide a detailed lecture if the user is relaxed. For example, the lecture unit can generate a detailed lecture based on the user's emotion score. The lecture unit can also provide a lecture with visually stimulating effects if the user is excited. For example, the lecture unit can generate a visually stimulating lecture based on the user's emotion score. This allows the length of the lecture to be adjusted according to the user's emotions, thereby providing a more appropriate lecture. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the lecture unit can be performed using, for example, AI, or without AI. For example, the lecture department can input the user's emotional data into the generation AI and have the generation AI adjust the length of the lecture.

[0099] During a lecture, the lecture section can determine the priority of the lecture based on the submission time of the negotiation. The lecture section, for example, prioritizes lecturing on negotiations submitted earlier. For example, the lecture section extracts negotiations submitted earlier based on negotiation submission time data. The lecture section can also postpone lecturing on negotiations submitted later. For example, the lecture section extracts negotiations submitted later based on negotiation submission time data. The lecture section can also gradually adjust the priority of the lecture depending on the submission time. For example, the lecture section adjusts the priority of the lecture based on the submission time score of the negotiation. In this way, by determining the priority of the lecture based on the submission time of the negotiation, negotiations submitted earlier can be lectured preferentially. Some or all of the above-described processing in the lecture section may be performed using, for example, AI, or may be performed without using AI. For example, the lecture section can input negotiation submission time data to a generation AI and have the generation AI determine the priority of the lecture.

[0100] During a lecture, the lecture section can adjust the order of the lectures based on the relevance of the negotiations. The lecture section, for example, prioritizes lecturing on highly relevant negotiations. For example, the lecture section extracts highly relevant negotiations based on negotiation relevance data. The lecture section can also postpone lecturing on less relevant negotiations. For example, the lecture section extracts less relevant negotiations based on negotiation relevance data. The lecture section can also gradually adjust the order of the lectures according to the relevance of the negotiations. For example, the lecture section adjusts the order of the lectures based on the relevance score of the negotiations. In this way, by adjusting the order of the lectures based on the relevance of the negotiations, highly relevant negotiations can be lectured on preferentially. Some or all of the above-described processing in the lecture section may be performed, for example, using AI, or may be performed without using AI. For example, the lecture section can input negotiation relevance data to a generation AI and cause the generation AI to adjust the order of the lectures.

[0101] During a lecture, the lecture unit can adjust the use of technical terms in the lecture according to the user's level of expertise. For example, if the user has technical expertise, the lecture unit provides a lecture that uses a lot of technical terms. For example, the lecture unit generates a lecture that uses a lot of technical terms based on the user's level of expertise. Furthermore, if the user does not have technical expertise, the lecture unit can provide a concise and easy-to-understand lecture. For example, the lecture unit generates a concise and easy-to-understand lecture based on the user's level of expertise. Furthermore, the lecture unit can gradually adjust the use of technical terms in the lecture according to the user's level of expertise. For example, the lecture unit adjusts the use of technical terms based on the user's level of expertise. This allows for the provision of a more appropriate lecture by adjusting the use of technical terms in the lecture according to the user's level of expertise. Some or all of the above-described processing in the lecture unit may be performed using, for example, AI, or may be performed without AI. For example, the lecture unit can input the user's level of expertise data into a generation AI and cause the generation AI to adjust the use of technical terms.

[0102] The proxy unit can estimate the user's emotion and adjust the proxy method based on the estimated user's emotion. For example, if the user is relaxed, the proxy unit provides a detailed proxy method. For example, the proxy unit generates a detailed proxy method based on the user's emotion score. Furthermore, if the user is in a hurry, the proxy unit can provide a concise proxy method that focuses on the main points. For example, the proxy unit generates a concise proxy method based on the user's emotion score. Furthermore, if the user is excited, the proxy unit can provide a proxy method that adds a visually stimulating effect. For example, the proxy unit generates a visually stimulating proxy method based on the user's emotion score. This allows the proxy method to be adjusted according to the user's emotion, thereby providing a more appropriate proxy. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit may input user emotion data to the generation AI and have the generation AI adjust the proxy method.

[0103] The proxy unit can adjust the level of detail of the proxy based on the importance of the negotiation during proxying. For example, the proxy unit performs detailed proxying for important negotiations. For example, the proxy unit generates a detailed proxy method based on the importance of the negotiation. The proxy unit can also perform simple proxying for negotiations with low importance. For example, the proxy unit generates a simple proxy method based on the importance of the negotiation. The proxy unit can also gradually adjust the level of detail of the proxy depending on the importance of the negotiation. For example, the proxy unit adjusts the level of detail of the proxy based on the importance score of the negotiation. In this way, detailed proxying can be performed for important negotiations by adjusting the level of detail of the proxy based on the importance of the negotiation. Some or all of the above-mentioned processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can input negotiation importance data to a generation AI and cause the generation AI to adjust the level of detail of the proxy.

[0104] The agent unit can apply different agent algorithms depending on the negotiation category during the proxy negotiation. For example, for rent negotiations, the agent unit applies an agent algorithm dedicated to rent negotiations. For example, the agent unit executes an agent algorithm dedicated to rent negotiations based on rent negotiation data. The agent unit can also apply an agent algorithm dedicated to rental contract negotiations for rental contract negotiations. For example, the agent unit executes an agent algorithm dedicated to rental contract negotiations based on rental contract negotiation data. The agent unit can also select an optimal agent algorithm depending on the negotiation category. For example, the agent unit selects an optimal agent algorithm based on negotiation category data. This improves the accuracy of the proxy by applying an optimal agent algorithm depending on the negotiation category. Some or all of the above-mentioned processing in the agent unit may be performed using, for example, AI, or may be performed without using AI. For example, the agent unit can input negotiation category data into a generation AI and have the generation AI select an agent algorithm.

[0105] During proxy processing, the proxy unit can improve the accuracy of proxy processing by referring to the user's past proxy results. The proxy unit, for example, adjusts the proxy algorithm based on the user's past proxy results. For example, the proxy unit retrieves past proxy result data from a database and adjusts the proxy algorithm. The proxy unit can also extract specific patterns from the user's past proxy results to improve the accuracy of proxy processing. For example, the proxy unit analyzes past proxy results and extracts specific patterns. The proxy unit can also analyze the user's past proxy results and propose an optimal proxy method. For example, the proxy unit proposes an optimal proxy method based on the past proxy results. This improves the accuracy of proxy processing by referring to the user's past proxy results. Some or all of the above-described processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can input past proxy result data to the generation AI and cause the generation AI to adjust the proxy algorithm.

[0106] The proxy unit can estimate the user's emotions and adjust the length of the proxy based on the estimated user's emotions. For example, if the user is in a hurry, the proxy unit provides a short, concise proxy. For example, the proxy unit generates a concise proxy method based on the user's emotion score. Furthermore, if the user is relaxed, the proxy unit can provide a detailed proxy. For example, the proxy unit generates a detailed proxy method based on the user's emotion score. Furthermore, if the user is excited, the proxy unit can provide a proxy with a visually stimulating effect. For example, the proxy unit generates a visually stimulating proxy method based on the user's emotion score. This allows the length of the proxy to be adjusted according to the user's emotions, thereby providing a more appropriate proxy. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the proxy unit can be performed using, for example, an AI, or without an AI. For example, the proxy unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the proxy.

[0107] The proxy unit can determine the priority of proxy based on the submission timing of the negotiations when proxying. The proxy unit, for example, prioritizes proxying negotiations that are submitted early. For example, the proxy unit extracts negotiations that are submitted early based on negotiation submission timing data. The proxy unit can also postpone proxying negotiations that are submitted late. For example, the proxy unit extracts negotiations that are submitted late based on negotiation submission timing data. The proxy unit can also gradually adjust the priority of proxying depending on the submission timing. For example, the proxy unit adjusts the priority of proxying based on the submission timing score of the negotiations. In this way, by determining the priority of proxying based on the submission timing of the negotiations, it is possible to prioritize proxying negotiations that are submitted early. Some or all of the above-mentioned processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can input negotiation submission timing data into a generation AI and have the generation AI determine the priority of proxying.

[0108] The proxy unit can adjust the proxy order based on the relevance of the negotiations when proxying. The proxy unit, for example, prioritizes proxying highly relevant negotiations. For example, the proxy unit extracts highly relevant negotiations based on negotiation relevance data. The proxy unit can also postpone proxying for less relevant negotiations. For example, the proxy unit extracts less relevant negotiations based on negotiation relevance data. The proxy unit can also gradually adjust the proxy order according to the relevance of the negotiations. For example, the proxy unit adjusts the proxy order based on the relevance score of the negotiations. In this way, by adjusting the proxy order based on the relevance of the negotiations, it is possible to prioritize proxying for more relevant negotiations. Some or all of the above-described processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can input negotiation relevance data to a generation AI and cause the generation AI to adjust the proxy order.

[0109] The proxy unit can adjust the use of technical terms in the proxy depending on the user's level of expertise during proxying. For example, if the user has specialized knowledge, the proxy unit provides proxying that uses a lot of technical terms. For example, the proxy unit generates a proxy method that uses a lot of technical terms based on the user's level of expertise. Furthermore, if the user does not have specialized knowledge, the proxy unit can provide concise and easy-to-understand proxying. For example, the proxy unit generates a concise and easy-to-understand proxy method based on the user's level of expertise. Furthermore, the proxy unit can gradually adjust the use of technical terms in the proxy depending on the user's level of expertise. For example, the proxy unit adjusts the use of technical terms based on the user's level of expertise. In this way, by adjusting the use of technical terms in the proxy depending on the user's level of expertise, more appropriate proxying can be provided. Some or all of the above-described processing in the proxy unit may be performed using, for example, AI, or may be performed without using AI. For example, the proxy unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, presentation unit, lecture unit, and proxy unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and estimates the user's emotions using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's emotions using the emotion identification model 59. The presentation unit displays the analysis results using, for example, the display 40A of the smart device 14. The lecture unit is realized, for example, by the control unit 46A of the smart device 14, and lectures the user on negotiation methods. The proxy unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically performs negotiations using a generation AI. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, presentation unit, lecture unit, and proxy unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214, and estimates the user's emotions using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's emotions using the emotion identification model 59. The presentation unit displays the analysis results using, for example, the display of the smart glasses 214. The lecture unit is realized, for example, by the control unit 46A of the smart glasses 214, and lectures the user on negotiation methods. The proxy unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically performs negotiations using a generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, presentation unit, lecture unit, and proxy unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the headset-type terminal 314, and estimates the user's emotions using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's emotions using an emotion identification model 59. The presentation unit displays the analysis results using, for example, the display 343 of the headset-type terminal 314. The lecture unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and lectures the user on negotiation methods. The proxy unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically performs negotiations using a generation AI. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, presentation unit, lecture unit, and proxy unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the robot 414, and estimates the user's emotions using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's emotions using an emotion identification model 59. The presentation unit displays the analysis results using, for example, the display of the robot 414. The lecture unit is realized, for example, by the control unit 46A of the robot 414, and lectures the user on negotiation methods. The proxy unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically performs negotiations using a generation AI.

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

[0111] The analysis unit can analyze the user's past negotiation results and provide feedback to improve the success rate of negotiations. For example, the analysis unit can extract successful negotiation patterns based on past negotiation data and provide specific advice to the user. The analysis unit can also analyze data on unsuccessful negotiations, identify areas for improvement, and provide feedback to the user. Furthermore, the analysis unit can analyze the user's negotiation style and tendencies and suggest optimal negotiation strategies. This allows the user to utilize their past experience to conduct more effective negotiations.

[0112] The collection unit can estimate the user's emotions and customize the property condition collection method based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting concise and to-the-point information. If the user is relaxed, the collection unit can also collect detailed information. Furthermore, if the user is excited, the collection unit can also collect visually appealing property information. In this way, by adjusting the collection method according to the user's emotions, more appropriate property information can be provided.

[0113] The presentation unit can analyze the user's past property viewing history and prioritize presenting properties that match the user's preferences. For example, the presentation unit can extract and present similar properties based on the characteristics of properties the user has viewed in the past. It can also prioritize presenting properties with similar conditions based on the conditions of properties that the user has previously given high ratings to. It can also prioritize presenting properties in areas in which the user has previously shown interest. This allows properties that match the user's preferences to be presented efficiently.

[0114] The lecture section can estimate the user's emotions and adjust the content of the lecture based on the estimated user emotions. For example, if the user is feeling anxious, the lecture section can provide content that gives the user a sense of security. If the user is feeling confident, the lecture section can provide more advanced negotiation techniques. Furthermore, if the user is excited, the lecture section can provide content that increases motivation. In this way, more effective support can be provided by adjusting the content of the lecture according to the user's emotions.

[0115] The proxy unit can propose strategies to improve the success rate of negotiations based on the user's past negotiation results. For example, the proxy unit can analyze data on past successful negotiations and propose similar strategies. It can also analyze data on past unsuccessful negotiations, identify areas for improvement, and propose new strategies. It can also analyze the user's negotiation style and tendencies and propose optimal negotiation methods. This allows the user to utilize past experience to conduct more effective negotiations.

[0116] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, analysis results with visually stimulating effects can be provided. In this way, by adjusting the way in which the analysis results are presented according to the user's emotions, more appropriate information can be provided.

[0117] The collection unit can dynamically adjust the property collection range based on the user's geographical location information. For example, if the user is in a specific area, properties in the vicinity of that area are collected preferentially. Also, if the user desires properties along their commute route, properties along that route can be collected. Furthermore, if the user is traveling, property information for the user's travel destination can be collected. This allows the system to provide more relevant property information based on the user's geographical location information.

[0118] The presentation unit can estimate the user's emotions and adjust the timing of presentation based on the estimated user emotions. For example, if the user is relaxed, detailed presentation can be provided. If the user is in a hurry, concise presentation that focuses on the main points can be provided. Furthermore, if the user is excited, presentation with visually stimulating effects can be provided. In this way, more appropriate information can be provided by adjusting the timing of presentation according to the user's emotions.

[0119] The lecture section can analyze the user's past lecture history and provide a lecture method that matches the user's learning style. For example, if the user prefers visual learning, it can provide a lecture that makes extensive use of visual aids. If the user prefers auditory learning, it can provide an audio-based lecture. Furthermore, if the user prefers hands-on learning, it can provide an interactive lecture. This makes it possible to provide effective lectures that match the user's learning style.

[0120] The proxy unit can estimate the user's emotions and adjust the proxy's progress speed based on the estimated user's emotions. For example, if the user is relaxed, the proxy unit can proceed with the proxy while providing detailed explanations. If the user is in a hurry, the proxy unit can proceed with the proxy quickly. Furthermore, if the user is excited, the proxy unit can perform the proxy with visually stimulating effects. In this way, more appropriate support can be provided by adjusting the proxy's progress speed according to the user's emotions.

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

[0122] Step 1: The collection unit inputs the user's desired area and property conditions. The user's desired area and property conditions include, for example, rent, floor plan, age of the building, distance from the station, etc. The collection unit saves the information input by the user in a database and provides it to the analysis unit. Step 2: The analysis unit analyzes the information collected by the collection unit and calculates the optimal market rent. The analysis is carried out using statistical methods and machine learning algorithms, and analyzes rent data for similar properties in the same area based on past data and current market trends. Step 3: The presentation unit presents the optimal market rent to the user based on the results of the analysis unit. The presentation is done in the form of a graph or list, and the analysis results are displayed visually in an easy-to-understand manner. Step 4: The lecture section provides guidance on how to negotiate to lower rent based on the results of the presentation section. The lecture is provided in text and video format, and provides specific advice on points to emphasize during negotiations and effective speaking techniques. Step 5: The proxy unit negotiates on behalf of the user based on the information from the lecture unit. The proxy unit negotiates via email or telephone, and uses generative AI to automatically negotiate and achieve the optimal result.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection section where users input their desired area and property conditions; an analysis unit that analyzes the information collected by the collection unit and calculates a market rent; a presentation unit that presents the market rent calculated by the analysis unit; a lecture unit that lectures on negotiation methods based on the market rent presented by the presentation unit; and a proxy unit that negotiates on behalf of the customer based on the information lectured by the lecture unit. A system characterized by:

2. The collecting unit Estimates user emotions and adjusts the timing of property condition collection based on the estimated user emotions 2. The system of claim 1.

3. The collecting unit Analyze the user's past property selection history and select the collection method 2. The system of claim 1.

4. The collecting unit When collecting property requirements, filter them based on the user's current living situation and interests.

2. The system of claim 1.

5. The collecting unit When collecting property conditions, select the collection method according to the user's input method.

2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and determine the priority of property conditions to be collected based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit When collecting property conditions, the system prioritizes collecting the most relevant conditions by taking into account the user's geographic location information.

2. The system of claim 1.

8. The collecting unit When collecting property conditions, analyze users' social media activity and collect related conditions.

2. The system of claim 1.

Images