system

Abstract

The system according to the embodiment aims to appropriately match the desired conditions of the customer with the market situation. [Solution] The system according to the embodiment includes a reception unit, a scoring unit, an explanation unit, and a provision unit. The reception unit receives the desired conditions of the client. The scoring unit scores the proposed property based on the desired conditions received by the reception unit. The explanation unit explains the differences between the desired conditions and the market based on the results of scoring by the scoring unit. The provision unit provides information on potential buyers to real estate businesses based on the information explained by the explanation 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] With conventional technology, it is difficult to properly match customer requirements with market conditions, and there is room for improvement.

[0005] The system according to the embodiment aims to appropriately match the desired conditions of the customer with the market situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a scoring unit, an explanation unit, and a provision unit. The reception unit receives the desired conditions of the customer. The scoring unit scores the proposed property based on the desired conditions received by the reception unit. The explanation unit explains the differences between the desired conditions and the market conditions based on the results of scoring by the scoring unit. The provision unit provides information on potential buyers to real estate businesses based on the information explained by the explanation unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately match the desired conditions of the customer with the market situation. [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 customer development system according to an embodiment of the present invention accepts customer requirements, has a generation AI score proposed properties, explains the discrepancy between the desired conditions and the market, and provides information on potential buyers to real estate businesses. The customer development system accepts customer requirements, has a generation AI score proposed properties, and explains the discrepancy between the desired conditions and the market. For example, the customer development system inputs the customer's desired conditions. For example, the customer development system analyzes the number of properties that meet the desired conditions and shows how much the number of candidates increases by changing conditions other than the "must" criteria. Next, the customer development system uses a generation AI to analyze the customer's desired conditions and data on the proposed properties and calculate a score. The input to the generation AI is the customer's desired conditions and data on the proposed properties, and the generation AI generates a score based on the input. For example, the generation AI receives a prompt such as "Please rate the suitability of this property" and quantifies the suitability of the proposed property. Next, the customer development system explains the discrepancy between the desired conditions analyzed by the generation AI and the market. For example, if there are few properties that meet the desired conditions, the generation AI shows how much the number of candidates increases by changing conditions other than the "must" criteria. Next, the customer development system provides real estate businesses with the information on potential buyers analyzed by the generation AI. For example, it provides information such as the customer's attributes, desired conditions, and score, and requests them to request information and arrange a viewing. This allows the customer development system to efficiently find properties that meet the customer's desired conditions, and real estate businesses to provide services tailored to the customer's needs. This allows the customer development system to efficiently find properties that meet the customer's desired conditions, and real estate businesses to provide services tailored to the customer's needs. For example, it can increase customer satisfaction by suggesting properties that fit the customer's desired area and price range, and arranging a viewing schedule.

[0029] A customer development system according to an embodiment includes a reception unit, a scoring unit, an explanation unit, and a provision unit. The reception unit receives customer desired conditions. The customer desired conditions include, but are not limited to, price, location, and floor plan. The reception unit, for example, stores the desired conditions entered by the customer in a database. The reception unit can also update the customer desired conditions in real time. For example, the reception unit immediately updates the database when the customer changes their desired conditions. The scoring unit uses a generation AI to score proposed properties based on the desired conditions received by the reception unit. Scoring, for example, quantifies the suitability of the proposed properties to the desired conditions, but is not limited to, this example. For example, the generation AI uses a text generation AI (e.g., LLM) to evaluate the suitability of the proposed properties. The scoring unit can also use a multimodal generation AI to evaluate the suitability of the proposed properties. The scoring unit can also use the generation AI to extract and score important features of the proposed properties. For example, the text generation AI has learned a large amount of property data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to select particularly important information from proposed properties and score them based on that information. The explanation unit explains the discrepancy between the desired conditions and the market based on the results scored by the scoring unit. The discrepancy may, for example, indicate how many properties meet the desired conditions, but is not limited to such an example. For example, if there are few properties that meet the desired conditions, the explanation unit may indicate how many candidates will increase by changing conditions other than the "must" criteria. The explanation unit can also visually display the discrepancy between the desired conditions and the market. For example, the explanation unit may show the discrepancy using graphs or charts. The explanation unit can also use the generation AI to analyze the discrepancy between the desired conditions and the market. For example, the explanation unit has the generation AI analyze the discrepancy between the desired conditions and the market and present the results to the customer. The provision unit provides information about potential buyers to real estate businesses based on the information explained by the explanation unit. The information provided includes, for example, but is not limited to, customer attributes, desired conditions, scores, and the like.The providing unit, for example, acquires customer attribute information from a database and provides it to a real estate business. The providing unit can also propose optimal properties based on the customer's desired conditions. The providing unit can also determine the priority of properties based on the customer's score information. For example, the providing unit preferentially proposes properties with high scores. As a result, the customer development system according to the embodiment can score proposed properties based on the customer's desired conditions, explain the discrepancy between the desired conditions and the market, and provide information on potential buyers to a real estate business. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input customer attribute information into a generation AI and cause the generation AI to propose optimal properties.

[0030] The scoring unit can analyze data on the customer's desired conditions and the proposed property and calculate a score. The scoring unit, for example, analyzes the data on the customer's desired conditions and the proposed property. Examples of the analysis include, but are not limited to, the type of data and the analysis method. The scoring unit, for example, compares the data on the customer's desired conditions and the proposed property and quantifies the degree of conformance. The scoring unit can also extract features of the proposed property and evaluate the conformance to the customer's desired conditions. For example, the scoring unit can analyze features such as the price, location, and layout of the proposed property and evaluate the conformance to the customer's desired conditions. The scoring unit can also use a generation AI to analyze the data on the customer's desired conditions and the proposed property. For example, the scoring unit uses a generation AI to analyze the data on the customer's desired conditions and the proposed property and calculate a score. By analyzing the data on the customer's desired conditions and the proposed property, the accuracy of scoring is improved. Some or all of the above-described processing in the scoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the scoring department can input the customer's desired conditions and data on the proposed property into the generation AI and have the generation AI calculate the score.

[0031] The explanation unit can analyze how many properties meet the desired conditions and indicate how much the number of candidates will increase by changing conditions other than the required conditions. The explanation unit, for example, analyzes how many properties meet the desired conditions. Examples of analysis include, but are not limited to, the type of data and the analysis method. The explanation unit, for example, quantifies the number of properties that meet the desired conditions and presents it to the customer. The explanation unit can also indicate how much the number of candidates will increase by changing conditions other than the required conditions. For example, the explanation unit can change the priority of the desired conditions to increase the number of candidate properties. The explanation unit can also use a generation AI to analyze the number of properties that meet the desired conditions. For example, the explanation unit can have the generation AI analyze the number of properties that meet the desired conditions and present the results to the customer. This provides the customer with realistic options by analyzing the number of properties that meet the desired conditions and indicating whether changing the conditions will increase the number of candidates. Some or all of the above-described processing in the explanation unit may be performed, for example, using AI or without AI. For example, the explanation section can input the number of properties that meet the desired conditions into the generation AI and have the generation AI execute the analysis results.

[0032] The providing unit can provide information such as customer attributes, desired conditions, and score to real estate businesses. For example, the providing unit retrieves customer attribute information from a database and provides it to real estate businesses. Customer attribute information includes, but is not limited to, age, occupation, and family composition. For example, the providing unit proposes optimal properties based on the customer's desired conditions. The providing unit can also determine the priority of properties based on the customer's score information. For example, the providing unit prioritizes properties with high scores. The providing unit can also analyze information such as customer attributes, desired conditions, and score using a generation AI. For example, the providing unit analyzes information such as customer attributes, desired conditions, and score using the generation AI and provides the results to real estate businesses. By providing information such as customer attributes, desired conditions, and score to real estate businesses, the real estate businesses can provide services tailored to the customer's needs. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input customer attribute information into a generation AI and have the generation AI suggest optimal properties.

[0033] The providing unit can request a request for information or arrange a viewing. The providing unit, for example, requests information based on the customer's desired conditions. The request for information includes, for example, detailed information and photos of the property, but is not limited to such examples. The providing unit, for example, requests information on a property that meets the customer's desired conditions from a real estate business. The providing unit can also request arrangements for a viewing. Arranging a viewing includes, for example, adjusting the date, time, and location of the viewing, but is not limited to such examples. The providing unit, for example, requests a real estate business to arrange a viewing of a property that meets the customer's desired conditions. This request for information or arrangements for a viewing facilitates communication between the customer and the real estate business. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the customer's desired conditions into a generation AI and have the generation AI execute a request for information or arrange a viewing.

[0034] The reception unit can analyze the customer's past history of desired conditions submission and select an appropriate reception method. The reception unit, for example, analyzes the customer's past history of desired conditions submission. The analysis includes, for example, the submission date and time and the submission content, but is not limited to these examples. The reception unit, for example, prioritizes and suggests reception methods (voice, text, etc.) that the customer has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the customer's past submission history. The reception unit can also select the most efficient reception method based on the customer's past history of desired conditions submission. For example, the reception unit analyzes the customer's past submission history and selects the optimal reception method. By analyzing the customer's past history of desired conditions submission, the optimal reception method is provided, improving customer convenience. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the customer's past submission history data into a generation AI and have the generation AI select the optimal reception method.

[0035] The reception unit may filter the desired conditions based on the customer's current living situation and areas of interest when receiving the desired conditions. The reception unit may, for example, consider the customer's current living situation. Examples of the living situation include, but are not limited to, family structure and occupation. The reception unit may, for example, preferentially receive related desired conditions based on the customer's current living situation. The reception unit may also consider the customer's areas of interest. Examples of the areas of interest include, but are not limited to, hobbies and lifestyle. The reception unit may, for example, filter the desired conditions based on the customer's areas of interest. The reception unit may also comprehensively consider the customer's current living situation and areas of interest to propose optimal desired conditions. For example, the reception unit may analyze the customer's living situation and areas of interest and propose optimal desired conditions. This allows for filtering based on the customer's current living situation and areas of interest, thereby accepting more appropriate desired conditions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception department can input the customer's living situation data and area of ​​interest data into the generation AI and have the generation AI perform filtering.

[0036] When receiving the desired conditions, the reception unit can select an appropriate reception means depending on the customer's input method. The reception unit, for example, considers the customer's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the customer desires voice input, the reception unit can receive the desired conditions using voice recognition technology. Furthermore, if the customer desires text input, the reception unit can provide a text input interface. Furthermore, if the customer desires image input, the reception unit can receive the desired conditions using image recognition technology. For example, the reception unit selects the optimal reception means depending on the customer's input method. This improves customer convenience by selecting the optimal reception means depending on the customer's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the customer's input method data into a generation AI and have the generation AI select the optimal reception means.

[0037] When accepting desired conditions, the reception unit can prioritize accepting highly relevant conditions by taking into account the customer's geographical location information. The reception unit, for example, considers the customer's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The reception unit, for example, prioritizes accepting desired conditions related to nearby properties based on the customer's current location. The reception unit can also prioritize accepting highly relevant desired conditions by referring to the customer's past travel history. The reception unit can also propose optimal desired conditions based on the customer's geographical location information. For example, the reception unit can analyze the customer's current location data and propose optimal desired conditions. This allows highly relevant desired conditions to be prioritized by taking the customer's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's geographical location information data into a generation AI and cause the generation AI to propose highly relevant desired conditions.

[0038] The reception unit may analyze the customer's social media activity and receive related conditions when receiving the desired conditions. The reception unit may, for example, analyze the customer's social media activity. Social media activity may include, but is not limited to, the content of posts and the number of followers. The reception unit may, for example, analyze the content of the customer's social media posts and receive related desired conditions. The reception unit may also suggest related desired conditions based on the activity of the customer's friends on social media. The reception unit may also accept related desired conditions based on the customer's check-in information on social media. For example, the reception unit may analyze the customer's social media activity and suggest optimal desired conditions. In this way, the related desired conditions can be accepted by analyzing the customer's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the customer's social media activity data into a generation AI and cause the generation AI to suggest related desired conditions.

[0039] The reception unit can customize the reception method by reflecting the customer's past feedback when receiving the desired conditions. The reception unit, for example, reflects the customer's past feedback. The feedback includes, but is not limited to, customer ratings and comments. The reception unit, for example, proposes an optimal reception method based on the customer's past feedback. The reception unit can also customize the reception interface by reflecting the customer's past feedback. The reception unit can also optimize the reception procedure for the desired conditions by referring to the customer's past feedback. For example, the reception unit can analyze the customer's past feedback and propose an optimal reception method. By reflecting the customer's past feedback, the optimal reception method is provided and customer satisfaction is improved. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's past feedback data into the generation AI and have the generation AI customize the reception method.

[0040] The scoring unit can adjust the accuracy of scoring based on the importance of the property during scoring. The scoring unit, for example, evaluates the importance of the property. The importance may include, but is not limited to, location, price, and facilities. The scoring unit, for example, performs detailed scoring for important properties. The scoring unit can also perform simplified scoring for properties with low importance. The scoring unit can also dynamically adjust the level of detail of scoring according to the importance of the property. For example, the scoring unit analyzes the importance of the property and adjusts the accuracy of the scoring. By adjusting the level of detail of scoring based on the importance of the property, detailed scoring can be performed for important properties. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input property importance data to a generation AI and cause the generation AI to adjust the accuracy of the scoring.

[0041] The scoring unit can apply different scoring algorithms depending on the property category during scoring. For example, the scoring unit classifies the property into categories. Categories include, but are not limited to, residential, commercial, and investment properties. For example, the scoring unit can apply a scoring algorithm specifically for residential properties to residential properties. Furthermore, the scoring unit can also apply a scoring algorithm specifically for commercial properties to commercial properties. Furthermore, the scoring unit can select an optimal scoring algorithm depending on the property category. For example, the scoring unit can analyze the property category and apply an optimal scoring algorithm. This improves the accuracy of scoring by applying the optimal scoring algorithm depending on the property category. Some or all of the above-described processing in the scoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the scoring unit can input property category data into the generation AI and cause the generation AI to apply the scoring algorithm.

[0042] The scoring unit can improve the accuracy of scoring by referring to the customer's past scoring results when scoring. The scoring unit, for example, refers to the customer's past scoring results. Past scoring results include, but are not limited to, score history and evaluation criteria. The scoring unit, for example, adjusts the scoring algorithm based on the customer's past scoring results. The scoring unit can also improve the accuracy of scoring by referring to the customer's past scoring results. The scoring unit can also analyze the customer's past scoring results and correct scoring bias. For example, the scoring unit analyzes the customer's past scoring results and improves the accuracy of scoring. As a result, the accuracy of scoring is improved by referring to the customer's past scoring results. Some or all of the above-described processing in the scoring unit may be performed, for example, using AI or without AI. For example, the scoring unit can input the customer's past scoring result data into the generation AI and cause the generation AI to improve the accuracy of scoring.

[0043] The scoring unit can determine the scoring order based on the time of submission of the property during scoring. The scoring unit, for example, considers the time of submission of the property. The submission time includes, but is not limited to, the submission date and the update date. For example, the scoring unit prioritizes scoring for recently submitted properties. The scoring unit can also lower the scoring priority for older submitted properties. The scoring unit can also dynamically adjust the scoring order based on the time of submission of the property. For example, the scoring unit analyzes the time of submission of the property and determines the scoring order. This allows the most recent property to be prioritized in scoring. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input property submission time data into the generation AI and have the generation AI determine the scoring order.

[0044] The scoring unit can adjust the scoring order based on the relevance of the properties during scoring. The scoring unit, for example, evaluates the relevance of the properties. Relevance includes, but is not limited to, the degree of match with the desired conditions and similar properties. For example, the scoring unit prioritizes scoring of properties that are most relevant to the customer's desired conditions. The scoring unit can also postpone the scoring order for properties with low relevance. The scoring unit can also dynamically adjust the scoring order based on the relevance of the properties. For example, the scoring unit analyzes the relevance of the properties and adjusts the scoring order. By adjusting the scoring order based on the relevance of the properties, it is possible to prioritize scoring of properties that are most relevant to the customer's desired conditions. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input property relevance data to a generation AI and cause the generation AI to adjust the scoring order.

[0045] The scoring unit can adjust the use of scoring terminology during scoring depending on the customer's level of expertise. The scoring unit, for example, evaluates the customer's level of expertise. Examples of expertise levels include, but are not limited to, beginner, intermediate, and expert. For example, if the customer has expertise, the scoring unit can provide a scoring result using detailed terminology. Alternatively, if the customer does not have expertise, the scoring unit can provide a scoring result using concise and easy-to-understand terminology. The scoring unit can also dynamically adjust the use of scoring terminology depending on the customer's level of expertise. For example, the scoring unit can analyze the customer's level of expertise and adjust the use of scoring terminology. By adjusting the use of scoring terminology depending on the customer's level of expertise, it is possible to provide a scoring result that is easy for the customer to understand. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without AI. For example, the scoring unit can input customer expertise level data into the generation AI and cause the generation AI to adjust the use of scoring terminology.

[0046] The explanation unit can optimize the current explanation by referring to past explanation data when providing an explanation. The explanation unit, for example, refers to past explanation data. Past explanation data includes, for example, explanation history and evaluation results, but is not limited to these examples. The explanation unit, for example, selects an optimal explanation method based on past explanation data. The explanation unit can also optimize the current explanation content by referring to past explanation data. The explanation unit can also analyze past explanation data and correct bias in the explanation. For example, the explanation unit analyzes past explanation data and optimizes the current explanation. By referring to the past explanation data, the current explanation can be optimized and an explanation that is easy for customers to understand can be provided. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input past explanation data to a generation AI and cause the generation AI to optimize the current explanation.

[0047] The explanation unit can apply different explanation methods to each property category when providing an explanation. For example, the explanation unit classifies the property into categories. Examples of categories include, but are not limited to, residential, commercial, and investment properties. For example, the explanation unit applies a residential explanation method to residential properties. Furthermore, the explanation unit can also apply a commercial explanation method to commercial properties. Furthermore, the explanation unit can select the optimal explanation method depending on the property category. For example, the explanation unit analyzes the property category and applies the optimal explanation method. By applying the optimal explanation method to each property category, it is possible to provide explanations that are easy for customers to understand. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input property category data into a generation AI and cause the generation AI to apply the explanation method.

[0048] The explanation unit can take customer information into consideration when providing an explanation. The explanation unit, for example, considers customer information. Customer information includes, but is not limited to, age, occupation, and family structure. The explanation unit selects the optimal explanation method based on, for example, the customer's age and gender. The explanation unit can also provide highly relevant information based on the customer's occupation and lifestyle. The explanation unit can also suggest optimal explanation content based on the customer's family structure and hobbies. For example, the explanation unit analyzes customer information and selects the optimal explanation method. This makes it possible to provide information that is highly relevant to the customer by taking the customer information into consideration. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input customer information data into a generation AI and have the generation AI select an explanation method.

[0049] The explanation unit can analyze changes in the explanation based on the time of submission of the property when providing the explanation. The explanation unit, for example, takes into account the time of submission of the property. The submission time includes, for example, the submission date and the update date, but is not limited to these examples. For example, the explanation unit provides an explanation based on the latest information for a recently submitted property. For a property that was submitted a long time ago, the explanation unit can also provide an explanation by referring to past data. The explanation unit can also dynamically adjust the content of the explanation based on the time of submission of the property. For example, the explanation unit analyzes the time of submission of the property and analyzes changes in the explanation. By analyzing changes in the explanation based on the time of submission of the property, it is possible to provide an explanation based on the latest information. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input property submission time data into the generation AI and cause the generation AI to analyze changes in the explanation.

[0050] The explanation unit can provide an explanation by referring to market data related to the property. The explanation unit, for example, references the market data related to the property. The related market data includes, but is not limited to, price trends and supply status. The explanation unit provides highly relevant information based on market data surrounding the property. The explanation unit can also analyze market trends for the property and explain the property to the customer. The explanation unit can also suggest optimal explanation content by referring to the market data related to the property. For example, the explanation unit analyzes market data for the property and suggests optimal explanation content. By referring to the market data related to the property, highly relevant information can be provided to the customer. Some or all of the above-described processing in the explanation unit can be performed, for example, using AI, or can be performed without using AI. For example, the explanation unit can input market data for the property into the generation AI and have the generation AI execute the suggestion of explanation content.

[0051] The explanation unit can take into account the technical condition of the property when providing the explanation. The explanation unit, for example, evaluates the technical condition of the property. The technical condition includes, but is not limited to, the newness of the equipment and technical features. For example, the explanation unit provides detailed technical information for a technically mature property. The explanation unit can also provide a concise explanation for a technically immature property. The explanation unit can also suggest optimal explanation content based on the technical condition of the property. For example, the explanation unit analyzes the technical condition of the property and suggests optimal explanation content. This makes it possible to provide an explanation that is easy for customers to understand by taking the technical condition of the property into consideration. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input technical condition data of the property into a generation AI and have the generation AI execute the suggested explanation content.

[0052] The providing unit can customize the information to be provided by taking into account customer attribute information when providing the information. The providing unit, for example, considers the customer attribute information. Customer attribute information includes, but is not limited to, age, occupation, and family structure. The providing unit, for example, provides optimal information based on the customer's age and gender. The providing unit can also provide highly relevant information based on the customer's occupation and lifestyle. The providing unit can also suggest optimal information based on the customer's family structure and hobbies. For example, the providing unit analyzes the customer's attribute information and provides optimal information. This makes it possible to provide information that is highly relevant to the customer by taking the customer's attribute information into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input customer attribute information data into a generation AI and cause the generation AI to customize the information.

[0053] The providing unit can improve the delivery method by reflecting the customer's past feedback when providing the service. The providing unit, for example, reflects the customer's past feedback. The feedback includes, for example, customer ratings and comments, but is not limited to such examples. The providing unit, for example, selects the optimal delivery method based on the customer's past feedback. The providing unit can also customize the delivery interface by reflecting the customer's past feedback. The providing unit can also optimize the delivery procedure by referring to the customer's past feedback. For example, the providing unit analyzes the customer's past feedback and selects the optimal delivery method. By reflecting the customer's past feedback, the optimal delivery method is provided and customer satisfaction is improved. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the customer's past feedback data into the generation AI and cause the generation AI to improve the delivery method.

[0054] The providing unit can adjust the information to be provided based on the customer's current living situation when providing the information. The providing unit, for example, considers the customer's current living situation. Examples of living situations include, but are not limited to, family structure and occupation. The providing unit, for example, provides relevant information based on the customer's current living situation. The providing unit also considers the customer's areas of interest. Examples of areas of interest include, but are not limited to, hobbies and lifestyle. The providing unit, for example, adjusts the information to be provided based on the customer's areas of interest. The providing unit can also provide optimal information by comprehensively considering the customer's current living situation and areas of interest. For example, the providing unit analyzes the customer's living situation and areas of interest and provides optimal information. By adjusting the information to be provided based on the customer's current living situation, it is possible to provide information that is highly relevant to the customer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the customer's living situation data and area of ​​interest data into a generation AI and cause the generation AI to adjust the information.

[0055] The providing unit can provide appropriate information by taking into account the customer's geographical location information. The providing unit, for example, considers the customer's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The providing unit, for example, provides information about nearby properties based on the customer's current location. The providing unit can also provide highly relevant information by referring to the customer's past travel history. The providing unit can also suggest optimal information based on the customer's geographical location information. For example, the providing unit analyzes the customer's current location data and suggests optimal information. This makes it possible to provide highly relevant information to the customer by taking into account the customer's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the customer's geographical location information data into a generation AI and cause the generation AI to provide information.

[0056] The providing unit may analyze the customer's social media activity and provide relevant information at the time of providing the information. The providing unit may, for example, analyze the customer's social media activity. Social media activity may include, but is not limited to, the content of posts and the number of followers. The providing unit may, for example, analyze the content of the customer's social media posts and provide relevant information. The providing unit may also suggest relevant information based on the activity of the customer's friends on social media. The providing unit may also provide relevant information based on the customer's social media check-in information. For example, the providing unit may analyze the customer's social media activity and provide optimal information. In this way, by analyzing the customer's social media activity, it is possible to provide information that is highly relevant to the customer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the customer's social media activity data into a generation AI and cause the generation AI to provide the information.

[0057] The providing unit can customize the delivery method by reflecting the customer's past feedback when providing the service. The providing unit, for example, reflects the customer's past feedback. The feedback includes, for example, but is not limited to, customer ratings and comments. The providing unit, for example, selects the optimal delivery method based on the customer's past feedback. The providing unit can also customize the delivery interface by reflecting the customer's past feedback. The providing unit can also optimize the delivery procedure by referring to the customer's past feedback. For example, the providing unit analyzes the customer's past feedback and selects the optimal delivery method. By reflecting the customer's past feedback, the optimal delivery method is provided and customer satisfaction is improved. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the customer's past feedback data into the generation AI and cause the generation AI to customize the delivery method.

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

[0059] When accepting a customer's desired conditions, the reception unit can refer to the customer's past purchase history and automatically suggest properties with similar desired conditions. For example, it can suggest properties that the customer is likely to like based on the characteristics of properties purchased in the past. The reception unit can also analyze the customer's past purchase history and prioritize displaying areas and price ranges that the customer prefers. Furthermore, the reception unit can suggest new properties that the customer may be interested in based on the customer's past purchase history. In this way, by utilizing the customer's past purchase history, more personalized suggestions can be made, thereby improving customer satisfaction.

[0060] When analyzing the customer's desired conditions and data on proposed properties, the scoring department can evaluate the energy efficiency of the property and reflect this in the score. For example, it can evaluate the property's energy consumption and the presence or absence of energy-saving equipment, and assign a high score to properties with high energy efficiency. The scoring department can also collect data on the energy efficiency of properties and provide it to customers. Furthermore, the scoring department can prioritize the proposal of properties with high energy efficiency. In this way, scoring that takes energy efficiency into account can support the selection of environmentally friendly properties.

[0061] The explanation unit can take into account future value predictions of properties when analyzing how many properties exist that meet the desired conditions. For example, it can identify properties that are likely to increase in value in the future based on changes in the property's location or surrounding environment, and provide that information to the customer. The explanation unit can also suggest property options based on future value predictions. Furthermore, the explanation unit can visually display future value predictions and explain them to customers in an easy-to-understand manner. This supports property selection that takes future value into consideration, and provides useful information to customers.

[0062] When providing information such as a customer's attributes, desired conditions, and score to real estate businesses, the offering unit can make suggestions based on the customer's lifestyle. For example, if a customer likes outdoor activities, the offering unit can suggest properties with plenty of parks and natural environments nearby. Also, if a customer prefers urban living, the offering unit can prioritize suggesting properties with good transportation access. Furthermore, the offering unit can emphasize and explain the features of properties that suit the customer's lifestyle. This makes it possible to suggest properties that suit the customer's lifestyle and improve customer satisfaction.

[0063] When requesting information requests or arranging a viewing, the information providing unit can suggest the optimal date and time based on the customer's schedule. For example, the information providing unit can refer to the customer's calendar information and arrange the viewing during an available time slot. The information providing unit can also efficiently combine viewings of multiple properties to suit the customer's schedule. Furthermore, the information providing unit can also adjust the timing of the information request based on the customer's schedule. This allows for flexible responses to suit the customer's schedule, improving customer convenience.

[0064] The reception unit can analyze the customer's past history of submitted desired conditions, learn the customer's preferences and tendencies, and make more personalized proposals. For example, it can analyze patterns in the desired conditions submitted in the past and identify the characteristics of properties that the customer prefers. The reception unit can also propose new properties that the customer may be interested in based on the customer's past history of submitted desired conditions. Furthermore, the reception unit can simplify the input of desired conditions based on the customer's preferences and tendencies. In this way, by utilizing the customer's past history of submitted desired conditions, more personalized proposals can be made, thereby improving customer satisfaction.

[0065] When receiving desired conditions, the reception department can take the customer's health condition into consideration when filtering based on the customer's current living situation and areas of interest. For example, if the customer has allergies, it can prioritize properties with low allergen environments. Also, if the customer is elderly, it can suggest properties with barrier-free access. Furthermore, the reception department can emphasize and explain the features of suitable properties based on the customer's health condition. This supports property selection that takes the customer's health condition into consideration, making it possible to provide a safe and comfortable living environment for the customer.

[0066] When accepting desired conditions, the reception unit can consider the type of device the customer is using when selecting an appropriate reception means according to the customer's input method. For example, a customer using a smartphone can be provided with an interface optimized for touch operation. A customer using a PC can also be provided with an interface suitable for keyboard input. Furthermore, the reception unit can adjust the interface layout and functions based on the type of device the customer is using. This allows for a more comfortable user experience by providing an interface optimized for the customer's device.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit accepts the customer's desired conditions. The customer's desired conditions include price, location, floor plan, etc. The reception unit saves the desired conditions entered by the customer in a database and can also update them in real time. For example, if the customer changes their desired conditions, the database is updated immediately. Step 2: The scoring unit uses the generation AI to score the proposed property based on the desired conditions received by the reception unit. Scoring quantifies the degree of suitability for the desired conditions. The generation AI uses text generation AI and multimodal generation AI to evaluate the suitability of the proposed property, extract important features, and score it. Step 3: The explanation section explains the discrepancy between the desired conditions and the market based on the results scored by the scoring section. The discrepancy indicates how many properties exist that meet the desired conditions, and how much the number of candidates will increase by changing conditions other than the "must" conditions. The explanation section visually displays the discrepancy using graphs and charts, and can also analyze it using generative AI. Step 4: The provision unit provides information about potential buyers to real estate businesses based on the information explained by the explanation unit. The information provided includes the customer's attributes, desired conditions, score, etc. The provision unit can also obtain customer attribute information from a database and provide it to real estate businesses to suggest optimal properties. It also prioritizes the suggestions of properties with high scores.

[0069] (Example 2) A customer development system according to an embodiment of the present invention accepts customer requirements, has a generation AI score proposed properties, explains the discrepancy between the desired conditions and the market, and provides information on potential buyers to real estate businesses. The customer development system accepts customer requirements, has a generation AI score proposed properties, and explains the discrepancy between the desired conditions and the market. For example, the customer development system inputs the customer's desired conditions. For example, the customer development system analyzes the number of properties that meet the desired conditions and shows how much the number of candidates increases by changing conditions other than the "must" criteria. Next, the customer development system uses a generation AI to analyze the customer's desired conditions and data on the proposed properties and calculate a score. The input to the generation AI is the customer's desired conditions and data on the proposed properties, and the generation AI generates a score based on the input. For example, the generation AI receives a prompt such as "Please rate the suitability of this property" and quantifies the suitability of the proposed property. Next, the customer development system explains the discrepancy between the desired conditions analyzed by the generation AI and the market. For example, if there are few properties that meet the desired conditions, the generation AI shows how much the number of candidates increases by changing conditions other than the "must" criteria. Next, the customer development system provides real estate businesses with the information on potential buyers analyzed by the generation AI. For example, it provides information such as the customer's attributes, desired conditions, and score, and requests them to request information and arrange a viewing. This allows the customer development system to efficiently find properties that meet the customer's desired conditions, and real estate businesses to provide services tailored to the customer's needs. This allows the customer development system to efficiently find properties that meet the customer's desired conditions, and real estate businesses to provide services tailored to the customer's needs. For example, it can increase customer satisfaction by suggesting properties that fit the customer's desired area and price range, and arranging a viewing schedule.

[0070] A customer development system according to an embodiment includes a reception unit, a scoring unit, an explanation unit, and a provision unit. The reception unit receives customer desired conditions. The customer desired conditions include, but are not limited to, price, location, and floor plan. The reception unit, for example, stores the desired conditions entered by the customer in a database. The reception unit can also update the customer desired conditions in real time. For example, the reception unit immediately updates the database when the customer changes their desired conditions. The scoring unit uses a generation AI to score proposed properties based on the desired conditions received by the reception unit. Scoring, for example, quantifies the suitability of the proposed properties to the desired conditions, but is not limited to, this example. For example, the generation AI uses a text generation AI (e.g., LLM) to evaluate the suitability of the proposed properties. The scoring unit can also use a multimodal generation AI to evaluate the suitability of the proposed properties. The scoring unit can also use the generation AI to extract and score important features of the proposed properties. For example, the text generation AI has learned a large amount of property data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to select particularly important information from proposed properties and score them based on that information. The explanation unit explains the discrepancy between the desired conditions and the market based on the results scored by the scoring unit. The discrepancy may, for example, indicate how many properties meet the desired conditions, but is not limited to such an example. For example, if there are few properties that meet the desired conditions, the explanation unit may indicate how many candidates will increase by changing conditions other than the "must" criteria. The explanation unit can also visually display the discrepancy between the desired conditions and the market. For example, the explanation unit may show the discrepancy using graphs or charts. The explanation unit can also use the generation AI to analyze the discrepancy between the desired conditions and the market. For example, the explanation unit has the generation AI analyze the discrepancy between the desired conditions and the market and present the results to the customer. The provision unit provides information about potential buyers to real estate businesses based on the information explained by the explanation unit. The information provided includes, for example, but is not limited to, customer attributes, desired conditions, scores, and the like.The providing unit, for example, acquires customer attribute information from a database and provides it to a real estate business. The providing unit can also propose optimal properties based on the customer's desired conditions. The providing unit can also determine the priority of properties based on the customer's score information. For example, the providing unit preferentially proposes properties with high scores. As a result, the customer development system according to the embodiment can score proposed properties based on the customer's desired conditions, explain the discrepancy between the desired conditions and the market, and provide information on potential buyers to a real estate business. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input customer attribute information into a generation AI and cause the generation AI to propose optimal properties.

[0071] The scoring unit can analyze data on the customer's desired conditions and the proposed property and calculate a score. The scoring unit, for example, analyzes the data on the customer's desired conditions and the proposed property. Examples of the analysis include, but are not limited to, the type of data and the analysis method. The scoring unit, for example, compares the data on the customer's desired conditions and the proposed property and quantifies the degree of conformance. The scoring unit can also extract features of the proposed property and evaluate the conformance to the customer's desired conditions. For example, the scoring unit can analyze features such as the price, location, and layout of the proposed property and evaluate the conformance to the customer's desired conditions. The scoring unit can also use a generation AI to analyze the data on the customer's desired conditions and the proposed property. For example, the scoring unit uses a generation AI to analyze the data on the customer's desired conditions and the proposed property and calculate a score. By analyzing the data on the customer's desired conditions and the proposed property, the accuracy of scoring is improved. Some or all of the above-described processing in the scoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the scoring department can input the customer's desired conditions and data on the proposed property into the generation AI and have the generation AI calculate the score.

[0072] The explanation unit can analyze how many properties meet the desired conditions and indicate how much the number of candidates will increase by changing conditions other than the required conditions. The explanation unit, for example, analyzes how many properties meet the desired conditions. Examples of analysis include, but are not limited to, the type of data and the analysis method. The explanation unit, for example, quantifies the number of properties that meet the desired conditions and presents it to the customer. The explanation unit can also indicate how much the number of candidates will increase by changing conditions other than the required conditions. For example, the explanation unit can change the priority of the desired conditions to increase the number of candidate properties. The explanation unit can also use a generation AI to analyze the number of properties that meet the desired conditions. For example, the explanation unit can have the generation AI analyze the number of properties that meet the desired conditions and present the results to the customer. This provides the customer with realistic options by analyzing the number of properties that meet the desired conditions and indicating whether changing the conditions will increase the number of candidates. Some or all of the above-described processing in the explanation unit may be performed, for example, using AI or without AI. For example, the explanation section can input the number of properties that meet the desired conditions into the generation AI and have the generation AI execute the analysis results.

[0073] The providing unit can provide information such as customer attributes, desired conditions, and score to real estate businesses. For example, the providing unit retrieves customer attribute information from a database and provides it to real estate businesses. Customer attribute information includes, but is not limited to, age, occupation, and family composition. For example, the providing unit proposes optimal properties based on the customer's desired conditions. The providing unit can also determine the priority of properties based on the customer's score information. For example, the providing unit prioritizes properties with high scores. The providing unit can also analyze information such as customer attributes, desired conditions, and score using a generation AI. For example, the providing unit analyzes information such as customer attributes, desired conditions, and score using the generation AI and provides the results to real estate businesses. By providing information such as customer attributes, desired conditions, and score to real estate businesses, the real estate businesses can provide services tailored to the customer's needs. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input customer attribute information into a generation AI and have the generation AI suggest optimal properties.

[0074] The providing unit can request a request for information or arrange a viewing. The providing unit, for example, requests information based on the customer's desired conditions. The request for information includes, for example, detailed information and photos of the property, but is not limited to such examples. The providing unit, for example, requests information on a property that meets the customer's desired conditions from a real estate business. The providing unit can also request arrangements for a viewing. Arranging a viewing includes, for example, adjusting the date, time, and location of the viewing, but is not limited to such examples. The providing unit, for example, requests a real estate business to arrange a viewing of a property that meets the customer's desired conditions. This request for information or arrangements for a viewing facilitates communication between the customer and the real estate business. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the customer's desired conditions into a generation AI and have the generation AI execute a request for information or arrange a viewing.

[0075] The reception unit can estimate the customer's emotions and adjust the timing of accepting the desired conditions based on the estimated customer emotions. The reception unit, for example, estimates the customer's emotions. Emotion estimation includes, but is not limited to, emotion classification and estimation algorithms. For example, if the customer is feeling stressed, the reception unit can delay the timing of accepting the desired conditions so that the customer can enter the desired conditions in a relaxed state. The reception unit can also accept the desired conditions immediately if the customer is relaxed. The reception unit can also provide a simplified interface for quickly accepting the desired conditions if the customer is in a hurry. For example, the reception unit can monitor the customer's emotions in real time and adjust the timing of accepting the desired conditions according to changes in emotions. This reduces customer stress and provides a smooth experience by adjusting the timing of accepting the desired conditions according to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input customer emotion data into the generation AI and have the generation AI estimate the emotion.

[0076] The reception unit can analyze the customer's past history of desired conditions submission and select an appropriate reception method. The reception unit, for example, analyzes the customer's past history of desired conditions submission. The analysis includes, for example, the submission date and time and the submission content, but is not limited to these examples. The reception unit, for example, prioritizes and suggests reception methods (voice, text, etc.) that the customer has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the customer's past submission history. The reception unit can also select the most efficient reception method based on the customer's past history of desired conditions submission. For example, the reception unit analyzes the customer's past submission history and selects the optimal reception method. By analyzing the customer's past history of desired conditions submission, the optimal reception method is provided, improving customer convenience. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the customer's past submission history data into a generation AI and have the generation AI select the optimal reception method.

[0077] The reception unit may filter the desired conditions based on the customer's current living situation and areas of interest when receiving the desired conditions. The reception unit may, for example, consider the customer's current living situation. Examples of the living situation include, but are not limited to, family structure and occupation. The reception unit may, for example, preferentially receive related desired conditions based on the customer's current living situation. The reception unit may also consider the customer's areas of interest. Examples of the areas of interest include, but are not limited to, hobbies and lifestyle. The reception unit may, for example, filter the desired conditions based on the customer's areas of interest. The reception unit may also comprehensively consider the customer's current living situation and areas of interest to propose optimal desired conditions. For example, the reception unit may analyze the customer's living situation and areas of interest and propose optimal desired conditions. This allows for filtering based on the customer's current living situation and areas of interest, thereby accepting more appropriate desired conditions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception department can input the customer's living situation data and area of ​​interest data into the generation AI and have the generation AI perform filtering.

[0078] When receiving the desired conditions, the reception unit can select an appropriate reception means depending on the customer's input method. The reception unit, for example, considers the customer's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the customer desires voice input, the reception unit can receive the desired conditions using voice recognition technology. Furthermore, if the customer desires text input, the reception unit can provide a text input interface. Furthermore, if the customer desires image input, the reception unit can receive the desired conditions using image recognition technology. For example, the reception unit selects the optimal reception means depending on the customer's input method. This improves customer convenience by selecting the optimal reception means depending on the customer's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the customer's input method data into a generation AI and have the generation AI select the optimal reception means.

[0079] The reception unit can estimate the customer's emotions and determine the priority of the desired conditions to be accepted based on the estimated customer emotions. The reception unit, for example, estimates the customer's emotions. Emotion estimation includes, but is not limited to, emotion classification and estimation algorithms. For example, when the customer is stressed, the reception unit can prioritize important desired conditions. Furthermore, when the customer is relaxed, the reception unit can equally accept all desired conditions. Furthermore, when the customer is in a hurry, the reception unit can prioritize the most important desired conditions. For example, the reception unit can monitor the customer's emotions in real time and determine the priority of the desired conditions according to changes in emotions. This enables responding to customer needs by determining the priority of the desired conditions according to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or without using an AI. For example, the reception unit can input customer emotion data into the generation AI and have the generation AI determine the priority of desired conditions.

[0080] When accepting desired conditions, the reception unit can prioritize accepting highly relevant conditions by taking into account the customer's geographical location information. The reception unit, for example, considers the customer's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The reception unit, for example, prioritizes accepting desired conditions related to nearby properties based on the customer's current location. The reception unit can also prioritize accepting highly relevant desired conditions by referring to the customer's past travel history. The reception unit can also propose optimal desired conditions based on the customer's geographical location information. For example, the reception unit can analyze the customer's current location data and propose optimal desired conditions. This allows highly relevant desired conditions to be prioritized by taking the customer's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the customer's geographical location information data into a generation AI and cause the generation AI to propose highly relevant desired conditions.

[0081] The reception unit may analyze the customer's social media activity and receive related conditions when receiving the desired conditions. The reception unit may, for example, analyze the customer's social media activity. Social media activity may include, but is not limited to, the content of posts and the number of followers. The reception unit may, for example, analyze the content of the customer's social media posts and receive related desired conditions. The reception unit may also suggest related desired conditions based on the activity of the customer's friends on social media. The reception unit may also accept related desired conditions based on the customer's check-in information on social media. For example, the reception unit may analyze the customer's social media activity and suggest optimal desired conditions. In this way, the related desired conditions can be accepted by analyzing the customer's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the customer's social media activity data into a generation AI and cause the generation AI to suggest related desired conditions.

[0082] The reception unit can customize the reception method by reflecting the customer's past feedback when receiving the desired conditions. The reception unit, for example, reflects the customer's past feedback. The feedback includes, but is not limited to, customer ratings and comments. The reception unit, for example, proposes an optimal reception method based on the customer's past feedback. The reception unit can also customize the reception interface by reflecting the customer's past feedback. The reception unit can also optimize the reception procedure for the desired conditions by referring to the customer's past feedback. For example, the reception unit can analyze the customer's past feedback and propose an optimal reception method. By reflecting the customer's past feedback, the optimal reception method is provided and customer satisfaction is improved. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's past feedback data into the generation AI and have the generation AI customize the reception method.

[0083] The scoring unit can estimate the customer's emotions and adjust the scoring expression method based on the estimated customer emotions. The scoring unit, for example, estimates the customer's emotions. Emotion estimation includes, but is not limited to, emotion classification and estimation algorithms. For example, the scoring unit provides a detailed scoring result when the customer is relaxed. The scoring unit can also provide a concise scoring result when the customer is in a hurry. The scoring unit can also provide a visually easy-to-understand scoring result when the customer is stressed. For example, the scoring unit monitors the customer's emotions in real time and adjusts the scoring expression method according to changes in emotions. This allows the scoring expression method to be adjusted according to the customer's emotions, thereby providing a scoring result that is easy for the customer to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or without AI. For example, the scoring unit can input customer emotion data into the generation AI and have the generation AI adjust the way the scoring is expressed.

[0084] The scoring unit can adjust the accuracy of scoring based on the importance of the property during scoring. The scoring unit, for example, evaluates the importance of the property. The importance may include, but is not limited to, location, price, and facilities. The scoring unit, for example, performs detailed scoring for important properties. The scoring unit can also perform simplified scoring for properties with low importance. The scoring unit can also dynamically adjust the level of detail of scoring according to the importance of the property. For example, the scoring unit analyzes the importance of the property and adjusts the accuracy of the scoring. By adjusting the level of detail of scoring based on the importance of the property, detailed scoring can be performed for important properties. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input property importance data to a generation AI and cause the generation AI to adjust the accuracy of the scoring.

[0085] The scoring unit can apply different scoring algorithms depending on the property category during scoring. For example, the scoring unit classifies the property into categories. Categories include, but are not limited to, residential, commercial, and investment properties. For example, the scoring unit can apply a scoring algorithm specifically for residential properties to residential properties. Furthermore, the scoring unit can also apply a scoring algorithm specifically for commercial properties to commercial properties. Furthermore, the scoring unit can select an optimal scoring algorithm depending on the property category. For example, the scoring unit can analyze the property category and apply an optimal scoring algorithm. This improves the accuracy of scoring by applying the optimal scoring algorithm depending on the property category. Some or all of the above-described processing in the scoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the scoring unit can input property category data into the generation AI and cause the generation AI to apply the scoring algorithm.

[0086] The scoring unit can improve the accuracy of scoring by referring to the customer's past scoring results when scoring. The scoring unit, for example, refers to the customer's past scoring results. Past scoring results include, but are not limited to, score history and evaluation criteria. The scoring unit, for example, adjusts the scoring algorithm based on the customer's past scoring results. The scoring unit can also improve the accuracy of scoring by referring to the customer's past scoring results. The scoring unit can also analyze the customer's past scoring results and correct scoring bias. For example, the scoring unit analyzes the customer's past scoring results and improves the accuracy of scoring. As a result, the accuracy of scoring is improved by referring to the customer's past scoring results. Some or all of the above-described processing in the scoring unit may be performed, for example, using AI or without AI. For example, the scoring unit can input the customer's past scoring result data into the generation AI and cause the generation AI to improve the accuracy of scoring.

[0087] The scoring unit can estimate the customer's emotions and adjust the length of scoring based on the estimated customer emotions. The scoring unit, for example, estimates the customer's emotions. Emotion estimation includes, but is not limited to, emotion classification and estimation algorithms. For example, the scoring unit provides a detailed scoring result when the customer is relaxed. The scoring unit can also provide a concise scoring result when the customer is in a hurry. The scoring unit can also provide a visually easy-to-understand scoring result when the customer is stressed. For example, the scoring unit monitors the customer's emotions in real time and adjusts the length of scoring according to changes in emotions. This adjusts the length of scoring according to the customer's emotions, thereby providing a scoring result appropriate for the customer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or without AI. For example, the scoring unit can input customer emotion data into the generation AI and have the generation AI adjust the length of the scoring.

[0088] The scoring unit can determine the scoring order based on the time of submission of the property during scoring. The scoring unit, for example, considers the time of submission of the property. The submission time includes, but is not limited to, the submission date and the update date. For example, the scoring unit prioritizes scoring for recently submitted properties. The scoring unit can also lower the scoring priority for older submitted properties. The scoring unit can also dynamically adjust the scoring order based on the time of submission of the property. For example, the scoring unit analyzes the time of submission of the property and determines the scoring order. This allows the most recent property to be prioritized in scoring. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input property submission time data into the generation AI and have the generation AI determine the scoring order.

[0089] The scoring unit can adjust the scoring order based on the relevance of the properties during scoring. The scoring unit, for example, evaluates the relevance of the properties. Relevance includes, but is not limited to, the degree of match with the desired conditions and similar properties. For example, the scoring unit prioritizes scoring of properties that are most relevant to the customer's desired conditions. The scoring unit can also postpone the scoring order for properties with low relevance. The scoring unit can also dynamically adjust the scoring order based on the relevance of the properties. For example, the scoring unit analyzes the relevance of the properties and adjusts the scoring order. By adjusting the scoring order based on the relevance of the properties, it is possible to prioritize scoring of properties that are most relevant to the customer's desired conditions. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input property relevance data to a generation AI and cause the generation AI to adjust the scoring order.

[0090] The scoring unit can adjust the use of scoring terminology during scoring depending on the customer's level of expertise. The scoring unit, for example, evaluates the customer's level of expertise. Examples of expertise levels include, but are not limited to, beginner, intermediate, and expert. For example, if the customer has expertise, the scoring unit can provide a scoring result using detailed terminology. Alternatively, if the customer does not have expertise, the scoring unit can provide a scoring result using concise and easy-to-understand terminology. The scoring unit can also dynamically adjust the use of scoring terminology depending on the customer's level of expertise. For example, the scoring unit can analyze the customer's level of expertise and adjust the use of scoring terminology. By adjusting the use of scoring terminology depending on the customer's level of expertise, it is possible to provide a scoring result that is easy for the customer to understand. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without AI. For example, the scoring unit can input customer expertise level data into the generation AI and cause the generation AI to adjust the use of scoring terminology.

[0091] The explanation unit can estimate the customer's emotions and adjust the display method of the explanation based on the estimated customer emotions. The explanation unit, for example, estimates the customer's emotions. Emotion estimation includes, but is not limited to, emotion classification and estimation algorithms. For example, if the customer is nervous, the explanation unit provides a simple, highly visible display method. Furthermore, if the customer is relaxed, the explanation unit can provide a display method that includes detailed information. Furthermore, if the customer is in a hurry, the explanation unit can provide a display method that focuses on the main points. For example, the explanation unit monitors the customer's emotions in real time and adjusts the display method of the explanation according to changes in emotions. This makes it possible to provide explanations that are easy for the customer to understand by adjusting the display method of the explanation according to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or without AI. For example, the explanation unit can input customer emotion data into the generation AI and have the generation AI adjust the way the explanation is displayed.

[0092] The explanation unit can optimize the current explanation by referring to past explanation data when providing an explanation. The explanation unit, for example, refers to past explanation data. Past explanation data includes, for example, explanation history and evaluation results, but is not limited to these examples. The explanation unit, for example, selects an optimal explanation method based on past explanation data. The explanation unit can also optimize the current explanation content by referring to past explanation data. The explanation unit can also analyze past explanation data and correct bias in the explanation. For example, the explanation unit analyzes past explanation data and optimizes the current explanation. By referring to the past explanation data, the current explanation can be optimized and an explanation that is easy for customers to understand can be provided. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input past explanation data to a generation AI and cause the generation AI to optimize the current explanation.

[0093] The explanation unit can apply different explanation methods to each property category when providing an explanation. For example, the explanation unit classifies the property into categories. Examples of categories include, but are not limited to, residential, commercial, and investment properties. For example, the explanation unit applies a residential explanation method to residential properties. Furthermore, the explanation unit can also apply a commercial explanation method to commercial properties. Furthermore, the explanation unit can select the optimal explanation method depending on the property category. For example, the explanation unit analyzes the property category and applies the optimal explanation method. By applying the optimal explanation method to each property category, it is possible to provide explanations that are easy for customers to understand. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input property category data into a generation AI and cause the generation AI to apply the explanation method.

[0094] The explanation unit can take customer information into consideration when providing an explanation. The explanation unit, for example, considers customer information. Customer information includes, but is not limited to, age, occupation, and family structure. The explanation unit selects the optimal explanation method based on, for example, the customer's age and gender. The explanation unit can also provide highly relevant information based on the customer's occupation and lifestyle. The explanation unit can also suggest optimal explanation content based on the customer's family structure and hobbies. For example, the explanation unit analyzes customer information and selects the optimal explanation method. This makes it possible to provide information that is highly relevant to the customer by taking the customer information into consideration. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input customer information data into a generation AI and have the generation AI select an explanation method.

[0095] The explanation unit can estimate the customer's emotions and adjust the importance of the explanation based on the estimated customer emotions. The explanation unit, for example, estimates the customer's emotions. Emotion estimation includes, but is not limited to, emotion classification and estimation algorithms. For example, if the customer is nervous, the explanation unit can prioritize providing important information. Furthermore, if the customer is relaxed, the explanation unit can provide an explanation with detailed information. Furthermore, if the customer is in a hurry, the explanation unit can provide an explanation that focuses on the main points. For example, the explanation unit monitors the customer's emotions in real time and adjusts the importance of the explanation according to changes in emotions. This allows the priority provision of information important to the customer by adjusting the importance of the explanation according to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or without AI. For example, the explanation unit can input customer emotional data into the generation AI and have the generation AI adjust the importance of the explanation.

[0096] The explanation unit can analyze changes in the explanation based on the time of submission of the property when providing the explanation. The explanation unit, for example, takes into account the time of submission of the property. The submission time includes, for example, the submission date and the update date, but is not limited to these examples. For example, the explanation unit provides an explanation based on the latest information for a recently submitted property. For a property that was submitted a long time ago, the explanation unit can also provide an explanation by referring to past data. The explanation unit can also dynamically adjust the content of the explanation based on the time of submission of the property. For example, the explanation unit analyzes the time of submission of the property and analyzes changes in the explanation. By analyzing changes in the explanation based on the time of submission of the property, it is possible to provide an explanation based on the latest information. Some or all of the above-described processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input property submission time data into the generation AI and cause the generation AI to analyze changes in the explanation.

[0097] The explanation unit can provide an explanation by referring to market data related to the property. The explanation unit, for example, references the market data related to the property. The related market data includes, but is not limited to, price trends and supply status. The explanation unit provides highly relevant information based on market data surrounding the property. The explanation unit can also analyze market trends for the property and explain the property to the customer. The explanation unit can also suggest optimal explanation content by referring to the market data related to the property. For example, the explanation unit analyzes market data for the property and suggests optimal explanation content. By referring to the market data related to the property, highly relevant information can be provided to the customer. Some or all of the above-described processing in the explanation unit can be performed, for example, using AI, or can be performed without using AI. For example, the explanation unit can input market data for the property into the generation AI and have the generation AI execute the suggestion of explanation content.

[0098] The explanation unit can take into account the technical condition of the property when providing the explanation. The explanation unit, for example, evaluates the technical condition of the property. The technical condition includes, but is not limited to, the newness of the equipment and technical features. For example, the explanation unit provides detailed technical information for a technically mature property. The explanation unit can also provide a concise explanation for a technically immature property. The explanation unit can also suggest optimal explanation content based on the technical condition of the property. For example, the explanation unit analyzes the technical condition of the property and suggests optimal explanation content. This makes it possible to provide an explanation that is easy for customers to understand by taking the technical condition of the property into consideration. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without using AI. For example, the explanation unit can input technical condition data of the property into a generation AI and have the generation AI execute the suggested explanation content.

[0099] The providing unit can estimate the customer's emotions and prioritize the information to be provided based on the estimated customer emotions. The providing unit, for example, estimates the customer's emotions. Emotion estimation includes, but is not limited to, emotion classification and estimation algorithms. For example, if the customer is stressed, the providing unit can prioritize providing important information. Furthermore, if the customer is relaxed, the providing unit can provide detailed information. Furthermore, if the customer is in a hurry, the providing unit can prioritize providing information that focuses on the main points. For example, the providing unit can monitor the customer's emotions in real time and prioritize the information to be provided based on changes in emotions. This allows the information to be prioritized based on the customer's emotions, thereby providing information that is important to the customer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input customer emotion data into the generating AI and have the generating AI determine the priority of the information.

[0100] The providing unit can customize the information to be provided by taking into account customer attribute information when providing the information. The providing unit, for example, considers the customer attribute information. Customer attribute information includes, but is not limited to, age, occupation, and family structure. The providing unit, for example, provides optimal information based on the customer's age and gender. The providing unit can also provide highly relevant information based on the customer's occupation and lifestyle. The providing unit can also suggest optimal information based on the customer's family structure and hobbies. For example, the providing unit analyzes the customer's attribute information and provides optimal information. This makes it possible to provide information that is highly relevant to the customer by taking the customer's attribute information into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input customer attribute information data into a generation AI and cause the generation AI to customize the information.

[0101] The providing unit can improve the delivery method by reflecting the customer's past feedback when providing the service. The providing unit, for example, reflects the customer's past feedback. The feedback includes, for example, customer ratings and comments, but is not limited to such examples. The providing unit, for example, selects the optimal delivery method based on the customer's past feedback. The providing unit can also customize the delivery interface by reflecting the customer's past feedback. The providing unit can also optimize the delivery procedure by referring to the customer's past feedback. For example, the providing unit analyzes the customer's past feedback and selects the optimal delivery method. By reflecting the customer's past feedback, the optimal delivery method is provided and customer satisfaction is improved. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the customer's past feedback data into the generation AI and cause the generation AI to improve the delivery method.

[0102] The providing unit can adjust the information to be provided based on the customer's current living situation when providing the information. The providing unit, for example, considers the customer's current living situation. Examples of living situations include, but are not limited to, family structure and occupation. The providing unit, for example, provides relevant information based on the customer's current living situation. The providing unit also considers the customer's areas of interest. Examples of areas of interest include, but are not limited to, hobbies and lifestyle. The providing unit, for example, adjusts the information to be provided based on the customer's areas of interest. The providing unit can also provide optimal information by comprehensively considering the customer's current living situation and areas of interest. For example, the providing unit analyzes the customer's living situation and areas of interest and provides optimal information. By adjusting the information to be provided based on the customer's current living situation, it is possible to provide information that is highly relevant to the customer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the customer's living situation data and area of ​​interest data into a generation AI and cause the generation AI to adjust the information.

[0103] The providing unit can estimate the customer's emotions and adjust the display method of the information to be provided based on the estimated customer emotions. The providing unit, for example, estimates the customer's emotions. Emotion estimation includes, but is not limited to, emotion classification and estimation algorithms. For example, if the customer is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the customer is relaxed, the providing unit can provide a display method that includes detailed information. Furthermore, if the customer is in a hurry, the providing unit can provide a display method that focuses on the main points. For example, the providing unit can monitor the customer's emotions in real time and adjust the display method of the information to be provided in response to changes in emotions. This allows the information to be provided in a way that is easy for the customer to understand by adjusting the display method of the information to be provided in response to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input customer emotion data into the generating AI and have the generating AI adjust the way information is displayed.

[0104] The providing unit can provide appropriate information by taking into account the customer's geographical location information. The providing unit, for example, considers the customer's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The providing unit, for example, provides information about nearby properties based on the customer's current location. The providing unit can also provide highly relevant information by referring to the customer's past travel history. The providing unit can also suggest optimal information based on the customer's geographical location information. For example, the providing unit analyzes the customer's current location data and suggests optimal information. This makes it possible to provide highly relevant information to the customer by taking into account the customer's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the customer's geographical location information data into a generation AI and cause the generation AI to provide information.

[0105] The providing unit may analyze the customer's social media activity and provide relevant information at the time of providing the information. The providing unit may, for example, analyze the customer's social media activity. Social media activity may include, but is not limited to, the content of posts and the number of followers. The providing unit may, for example, analyze the content of the customer's social media posts and provide relevant information. The providing unit may also suggest relevant information based on the activity of the customer's friends on social media. The providing unit may also provide relevant information based on the customer's social media check-in information. For example, the providing unit may analyze the customer's social media activity and provide optimal information. In this way, by analyzing the customer's social media activity, it is possible to provide information that is highly relevant to the customer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the customer's social media activity data into a generation AI and cause the generation AI to provide the information.

[0106] The providing unit can customize the delivery method by reflecting the customer's past feedback when providing the service. The providing unit, for example, reflects the customer's past feedback. The feedback includes, for example, but is not limited to, customer ratings and comments. The providing unit, for example, selects the optimal delivery method based on the customer's past feedback. The providing unit can also customize the delivery interface by reflecting the customer's past feedback. The providing unit can also optimize the delivery procedure by referring to the customer's past feedback. For example, the providing unit analyzes the customer's past feedback and selects the optimal delivery method. By reflecting the customer's past feedback, the optimal delivery method is provided and customer satisfaction is improved. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the customer's past feedback data into the generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, scoring unit, explanation unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives the customer's desired conditions using the reception device 38 of the smart device 14 and stores them in the database 24 of the data processing device 12. The scoring unit, for example, scores the proposed property using a generation AI by the specific processing unit 290 of the data processing device 12. The explanation unit, for example, analyzes the deviation between the desired conditions and the market conditions by the specific processing unit 290 of the data processing device 12 and visually displays the result by the output device 40 of the smart device 14. The providing unit, for example, provides information on the prospective purchaser to a real estate business operator by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, scoring unit, explanation unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives the customer's desired conditions using the microphone 238 of the smart glasses 214 and stores them in the database 24 of the data processing device 12. The scoring unit, for example, scores the proposed property using a generation AI by the specific processing unit 290 of the data processing device 12. The explanation unit, for example, analyzes the deviation between the desired conditions and the market conditions by the specific processing unit 290 of the data processing device 12 and provides an explanation by voice using the speaker 240 of the smart glasses 214. The providing unit, for example, provides information on the prospective purchaser to a real estate business operator by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, scoring unit, explanation unit, and providing unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit receives the customer's desired conditions using the microphone 238 of the headset terminal 314 and stores them in the database 24 of the data processing device 12. The scoring unit, for example, scores the proposed property using a generation AI by the specific processing unit 290 of the data processing device 12. The explanation unit, for example, analyzes the deviation between the desired conditions and the market conditions by the specific processing unit 290 of the data processing device 12 and visually displays the result by the display 343 of the headset terminal 314. The providing unit, for example, provides information on the prospective purchaser to the real estate business operator by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, scoring unit, explanation unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives the customer's desired conditions using the microphone 238 of the robot 414 and stores them in the database 24 of the data processing device 12. The scoring unit, for example, scores the proposed property using a generation AI by the specific processing unit 290 of the data processing device 12. The explanation unit, for example, analyzes the deviation between the desired conditions and the market conditions by the specific processing unit 290 of the data processing device 12 and provides an explanation by voice using the speaker 240 of the robot 414. The providing unit, for example, provides information on the prospective purchaser to a real estate business operator by the specific processing unit 290 of the data processing device 12.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] When accepting a customer's desired conditions, the reception unit can refer to the customer's past purchase history and automatically suggest properties with similar desired conditions. For example, it can suggest properties that the customer is likely to like based on the characteristics of properties purchased in the past. The reception unit can also analyze the customer's past purchase history and prioritize displaying areas and price ranges that the customer prefers. Furthermore, the reception unit can suggest new properties that the customer may be interested in based on the customer's past purchase history. In this way, by utilizing the customer's past purchase history, more personalized suggestions can be made, thereby improving customer satisfaction.

[0109] When analyzing the customer's desired conditions and data on proposed properties, the scoring department can evaluate the energy efficiency of the property and reflect this in the score. For example, it can evaluate the property's energy consumption and the presence or absence of energy-saving equipment, and assign a high score to properties with high energy efficiency. The scoring department can also collect data on the energy efficiency of properties and provide it to customers. Furthermore, the scoring department can prioritize the proposal of properties with high energy efficiency. In this way, scoring that takes energy efficiency into account can support the selection of environmentally friendly properties.

[0110] The explanation unit can take into account future value predictions of properties when analyzing how many properties exist that meet the desired conditions. For example, it can identify properties that are likely to increase in value in the future based on changes in the property's location or surrounding environment, and provide that information to the customer. The explanation unit can also suggest property options based on future value predictions. Furthermore, the explanation unit can visually display future value predictions and explain them to customers in an easy-to-understand manner. This supports property selection that takes future value into consideration, and provides useful information to customers.

[0111] When providing information such as a customer's attributes, desired conditions, and score to real estate businesses, the offering unit can make suggestions based on the customer's lifestyle. For example, if a customer likes outdoor activities, the offering unit can suggest properties with plenty of parks and natural environments nearby. Also, if a customer prefers urban living, the offering unit can prioritize suggesting properties with good transportation access. Furthermore, the offering unit can emphasize and explain the features of properties that suit the customer's lifestyle. This makes it possible to suggest properties that suit the customer's lifestyle and improve customer satisfaction.

[0112] When requesting information requests or arranging a viewing, the information providing unit can suggest the optimal date and time based on the customer's schedule. For example, the information providing unit can refer to the customer's calendar information and arrange the viewing during an available time slot. The information providing unit can also efficiently combine viewings of multiple properties to suit the customer's schedule. Furthermore, the information providing unit can also adjust the timing of the information request based on the customer's schedule. This allows for flexible responses to suit the customer's schedule, improving customer convenience.

[0113] The reception unit can estimate the customer's emotions and customize the reception interface to meet the customer's desired conditions based on the estimated customer emotions. For example, if the customer is feeling stressed, a simple and intuitive interface can be provided. Alternatively, if the customer is relaxed, an interface including detailed options can be provided. Furthermore, the reception unit can change the color and design of the interface depending on the customer's emotions. This makes it possible to provide a more comfortable user experience by providing an interface that suits the customer's emotions.

[0114] The reception unit can analyze the customer's past history of submitted desired conditions, learn the customer's preferences and tendencies, and make more personalized proposals. For example, it can analyze patterns in the desired conditions submitted in the past and identify the characteristics of properties that the customer prefers. The reception unit can also propose new properties that the customer may be interested in based on the customer's past history of submitted desired conditions. Furthermore, the reception unit can simplify the input of desired conditions based on the customer's preferences and tendencies. In this way, by utilizing the customer's past history of submitted desired conditions, more personalized proposals can be made, thereby improving customer satisfaction.

[0115] When receiving desired conditions, the reception department can take the customer's health condition into consideration when filtering based on the customer's current living situation and areas of interest. For example, if the customer has allergies, it can prioritize properties with low allergen environments. Also, if the customer is elderly, it can suggest properties with barrier-free access. Furthermore, the reception department can emphasize and explain the features of suitable properties based on the customer's health condition. This supports property selection that takes the customer's health condition into consideration, making it possible to provide a safe and comfortable living environment for the customer.

[0116] When accepting desired conditions, the reception unit can consider the type of device the customer is using when selecting an appropriate reception means according to the customer's input method. For example, a customer using a smartphone can be provided with an interface optimized for touch operation. A customer using a PC can also be provided with an interface suitable for keyboard input. Furthermore, the reception unit can adjust the interface layout and functions based on the type of device the customer is using. This allows for a more comfortable user experience by providing an interface optimized for the customer's device.

[0117] The reception unit can estimate the customer's emotions and take the customer's stress level into consideration when determining the priority of desired conditions to be accepted based on the estimated customer emotions. For example, if the customer is feeling highly stressed, the most important desired conditions can be accepted first. Alternatively, if the customer is relaxed, all desired conditions can be accepted equally. Furthermore, the reception unit can simplify the input of desired conditions according to the customer's stress level. This allows for flexible responses according to the customer's stress level, thereby improving customer satisfaction.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit accepts the customer's desired conditions. The customer's desired conditions include price, location, floor plan, etc. The reception unit saves the desired conditions entered by the customer in a database and can also update them in real time. For example, if the customer changes their desired conditions, the database is updated immediately. Step 2: The scoring unit uses the generation AI to score the proposed property based on the desired conditions received by the reception unit. Scoring quantifies the degree of suitability for the desired conditions. The generation AI uses text generation AI and multimodal generation AI to evaluate the suitability of the proposed property, extract important features, and score it. Step 3: The explanation section explains the discrepancy between the desired conditions and the market based on the results scored by the scoring section. The discrepancy indicates how many properties exist that meet the desired conditions, and how much the number of candidates will increase by changing conditions other than the "must" conditions. The explanation section visually displays the discrepancy using graphs and charts, and can also analyze it using generative AI. Step 4: The provision unit provides information about potential buyers to real estate businesses based on the information explained by the explanation unit. The information provided includes the customer's attributes, desired conditions, score, etc. The provision unit can also obtain customer attribute information from a database and provide it to real estate businesses to suggest optimal properties. It also prioritizes the suggestions of properties with high scores.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section that receives customer requirements; a scoring unit that scores the proposed property based on the desired conditions received by the receiving unit; an explanation unit that explains the difference between the desired conditions and the market conditions based on the results of the scoring by the scoring unit; a providing unit that provides information on the potential purchaser to a real estate business operator based on the information explained by the explanation unit; Equipped with A system characterized by:

2. The scoring unit Analyze the customer's desired conditions and proposed property data to calculate a score 2. The system of claim 1.

3. The explanation section Analyze how many properties exist that meet your desired criteria and show how much the number of candidates will increase by changing conditions other than the essential conditions.

2. The system of claim 1.

4. The providing unit Provide real estate companies with information such as customer attributes, desired conditions, and scores 2. The system of claim 1.

5. The providing unit Request information or arrange a viewing 2. The system of claim 1.

6. The reception unit Estimate customer sentiment and adjust the timing of accepting desired conditions based on the estimated sentiment 2. The system of claim 1.

7. The reception unit Analyze the customer's past request history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit When receiving requests, filter them based on the customer's current lifestyle and interests.

2. The system of claim 1.

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