System

Abstract

To provide a system capable of proposing an appropriate property to a customer and consistently offering a purchase procedure and legal advice.SOLUTION: A system according to an embodiment comprises an acceptance unit, a proposal unit, a purchase procedure unit, and an analysis unit. The acceptance unit inputs customer information. The proposal unit proposes the property to be purchased to the customer on the basis of the information input by the acceptance unit. The purchase procedure unit provides a purchase procedure, loan consultation, and legal advice on the basis of the property proposed by the proposal unit. The analysis unit analyzes the market price trends, nearby facilities, and transportation accessibility of the property to be purchased.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to recommend suitable properties to customers and provide them with consistent purchasing procedures and legal advice.

[0005] The system according to the embodiment aims to propose suitable properties to customers and provide them with consistent purchasing procedures and legal advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a purchase procedure unit, and an analysis unit. The reception unit inputs customer information. The proposal unit proposes a property to be purchased to the customer based on the information input by the reception unit. The purchase procedure unit provides purchase procedures, loan consultations, and legal advice based on the property proposed by the proposal unit. The analysis unit analyzes market price trends, nearby facilities, and transportation accessibility of the property to be purchased. Effect of the Invention

[0007] The system according to the embodiment can propose suitable properties to customers and provide them with consistent purchasing procedures and legal advice. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The real estate purchase consultation support AI assistant according to the embodiment of the present invention is a system that proposes an appropriate property based on information such as a customer's budget, the type of property desired, and the area. This system starts when the customer inputs information such as the budget, the type of property desired, and the area. This information is input to the reception unit. Next, based on the information input by the reception unit, the proposal unit proposes a property to be purchased. Based on the proposed property, the provision unit provides purchase procedures, loan consultation, and legal advice. Furthermore, the analysis unit analyzes market price trends, nearby facilities, and transportation convenience, and the provision unit provides the results to the customer. For example, the system supports the purchase decision by analyzing the market price trends of the property, information on nearby facilities, transportation convenience, etc., and providing them to the customer. In addition, the provision unit is provided with a simulation unit that simulates a loan, so that the customer can simulate a loan. Furthermore, the provision unit is provided with a check unit that provides a checklist of the contract, so that the customer can check the contents of the contract. The provision unit is provided with an interface that visualizes the progress of the purchase procedure, so that the customer can check the progress of the purchase procedure. The analysis unit calculates a safety score, including the crime rate of the neighborhood of the property to be purchased, and the provision unit provides the result to the customer. The simulation unit has a prediction unit that predicts the future market price of the property to be purchased, so that the customer can predict the future market price. The provision unit provides 24-hour support using an AI chatbot, providing an environment where customers can consult at any time. In addition, the provision unit also has a function to propose renovation plans after the purchase of the property to be purchased, so that customers can consult about renovations after the purchase. As a result, the real estate purchase consultation support AI assistant can support the purchase decision by suggesting appropriate properties based on the customer's information and providing purchase procedures and advice.

[0029] The real estate purchase consultation support AI assistant according to the embodiment includes a reception unit, a proposal unit, a provision unit, and an analysis unit. The reception unit inputs customer information. The customer information includes, for example, name, address, age, desired purchase conditions, etc., but is not limited to such examples. The reception unit, for example, stores the information input by the customer in a database and uses it for subsequent processing. The proposal unit proposes a property to be purchased to the customer based on the information input by the reception unit. The proposal unit, for example, searches a database for properties that match the customer's desired conditions and generates a proposal list. The proposal unit can also use a generation AI to select a property that best suits the customer's desired conditions. For example, the generation AI receives the customer's desired conditions as input and outputs the optimal property. The provision unit provides purchase procedures, loan consultations, and legal advice based on the property proposed by the proposal unit. For example, the provision unit guides the steps of the purchase procedure and supports the preparation of necessary documents. The provision unit can also use AI to generate answers to customer questions. For example, the provision unit receives a customer's question as input and outputs an appropriate answer. The analysis unit analyzes the market price trends, nearby facilities, and transportation convenience of the property to be purchased. For example, the analysis unit predicts the current market price based on past market data. The analysis unit can also use AI to evaluate the property. For example, the analysis unit receives property data as input and outputs an evaluation score. As a result, the real estate purchase consultation support AI assistant according to the embodiment can propose an appropriate property based on the customer's information and provide purchase procedures and advice. For example, the reception unit inputs the customer's information. The proposal unit proposes a property based on the customer's desired conditions. The provision unit provides purchase procedures and advice. The analysis unit analyzes market price trends and nearby facilities. As a result, the customer can select an appropriate property and smoothly proceed with the purchase procedure.

[0030] The reception unit inputs customer information. The customer information includes, for example, name, address, age, desired purchase conditions, etc., but is not limited to such examples. The reception unit, for example, stores the information input by the customer in a database and uses it for subsequent processing. Specifically, the reception unit receives information input by the customer through a web form or application in real time and automatically stores it in a database. This allows the customer information to be managed centrally and allows the subsequent proposal unit and provision unit to quickly access it. Furthermore, the reception unit has a validation function for confirming the accuracy of the input information, for example, checking the address format and age range. This makes it possible to prevent input errors and registration of inaccurate information. Furthermore, the reception unit also has a security function for protecting the privacy of the customer, and performs data encryption and access control. This allows the customer's personal information to be managed safely. Furthermore, the reception unit also has a function for periodically updating the customer information, and for example, the customer can add new desired conditions or change existing information. This allows proposals and provision to always be made based on the latest information. The reception department plays an important role in ensuring smooth initial contact with customers and the smooth running of subsequent processes.

[0031] The proposal unit proposes properties to be purchased to the customer based on the information input by the reception unit. For example, the proposal unit searches a database for properties that meet the customer's desired conditions and generates a proposal list. The proposal unit can also use the generation AI to select a property that best suits the customer's desired conditions. For example, the generation AI receives the customer's desired conditions as input and outputs the optimal property. Specifically, the generation AI receives conditions such as the customer's desired area, budget, floor plan, and age as prompts, and extracts properties that best suit these conditions from the database. The generation AI learns past data and trends, and can propose optimal properties with high accuracy based on the customer's conditions. In addition, the proposal unit provides an interface for displaying the proposal list to the customer in a visually easy-to-understand manner. For example, detailed information including photos of the property, floor plan, and information on the surrounding environment can be displayed to make it easier for the customer to compare and consider. The proposal unit also has a function of receiving customer feedback and continuously improving the proposal content. For example, if a customer shows interest in a specific property, other properties related to that property can also be proposed, providing more options. This enables the proposal department to quickly and accurately propose properties that best suit the customer's needs, thereby increasing customer satisfaction.

[0032] The provision unit provides purchase procedures, loan consultations, and legal advice based on the property proposed by the proposal unit. For example, the provision unit guides the steps of the purchase procedure and supports the preparation of necessary documents. The provision unit can also generate answers to customer questions using AI. For example, the provision unit receives customer questions as input and outputs appropriate answers. Specifically, the provision unit has a function to explain each step of the purchase procedure in detail and remind customers of the deadlines for necessary documents and procedures. This makes it easier for customers to understand the progress of the procedure and allows them to proceed with the purchase procedure smoothly. In addition, the provision unit also proposes the optimal loan plan for loan consultations based on the customer's income, expenses, and credit information. AI analyzes the customer's financial situation and presents the optimal loan options to help the customer create a reasonable repayment plan. In addition, the provision unit also provides legal advice and generates appropriate answers to questions regarding the contents of the contract and legal procedures. This allows the customer to proceed with the purchase procedure with peace of mind. The provision unit plays an important role in resolving customers' doubts and concerns and smoothly progressing with the purchase procedure.

[0033] The analysis unit analyzes the market price trends, nearby facilities, and transportation convenience of the property to be purchased. For example, the analysis unit predicts the current market price based on past market data. The analysis unit can also use AI to evaluate the property. For example, the analysis unit receives property data as input and outputs an evaluation score. Specifically, the analysis unit collects market data from the past few years and analyzes price fluctuations and trends. This makes it possible to predict the current market price and future price trends. In addition, information on nearby facilities and transportation convenience is also collected to evaluate the convenience of the property. For example, a comprehensive evaluation of the property is performed taking into account the accessibility of nearby schools, hospitals, shopping centers, and public transportation. The AI ​​analyzes these data and calculates the evaluation score of the property. The evaluation score is an important indicator for customers when selecting a property and serves as a standard for objectively evaluating the value of the property. In addition, the analysis unit can also evaluate future risks and opportunities based on the customer's desired conditions and market trends. This allows the customer to comprehensively grasp information about the purchase of the property and make a wiser decision. The Analysis Department plays an important role in objectively evaluating property value and helping customers make the best choices.

[0034] The providing unit can provide the analysis results by the analysis unit to the customer. The analysis results include, for example, market price trends, nearby facilities, and transportation convenience, but are not limited to such examples. The providing unit, for example, provides the analysis results to the customer in a report format. The report can provide information in a visually easy-to-understand format using graphs and charts. The providing unit can also update the analysis results in real time and provide the latest information to the customer. For example, the providing unit obtains the latest market data through the Internet and updates the analysis results. This allows the customer to grasp the latest market trends and support purchasing decisions. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the analysis results using an AI model that receives the analysis results generated by the analysis unit as input and provides them to the customer.

[0035] The provision unit may include a simulation unit that performs a simulation of a loan. The simulation unit performs a simulation of a loan by, for example, setting an interest rate and a repayment period. The simulation unit creates a repayment plan based on, for example, the loan conditions input by the customer. The simulation unit may also perform a simulation of a loan using AI. For example, the simulation unit may perform a simulation using an AI model that receives the customer's loan conditions as input and outputs a repayment plan. This makes it easier for the customer to make a loan plan and supports the purchase decision. A part or all of the above-mentioned processing in the simulation unit may be performed, for example, using AI, or may be performed without using AI. For example, the simulation unit may input the loan conditions input by the customer to a generation AI and cause the generation AI to generate a repayment plan.

[0036] The providing unit may include a checking unit that provides a checklist for the contract. The checking unit, for example, checks important clauses in the contract and explains risks. The checking unit, for example, displays the contents of the contract in a list format to make it easier for the customer to check. The checking unit may also check the contract using AI. For example, the checking unit may receive the contents of the contract as input and generate a checklist using an AI model that extracts important clauses. This allows the customer to check the contents of the contract and proceed with the contract with peace of mind. Some or all of the above-mentioned processing in the checking unit may be performed, for example, using AI or may be performed without using AI. For example, the checking unit may input the contents of the contract to a generating AI and cause the generating AI to generate a checklist.

[0037] The providing unit may include an interface that visualizes the progress of the purchase procedure. The interface visually displays the progress of the purchase procedure, for example, using a progress bar or a status display. The interface, for example, displays a list of each step of the purchase procedure, so that the customer can grasp the current progress at a glance. The interface may also visualize the progress using AI. For example, the interface may receive data of the purchase procedure as input and perform visualization using an AI model that displays the progress. This makes it easier for the customer to grasp the progress of the purchase procedure, and the procedure can be carried out smoothly. A part or all of the above-mentioned processing in the interface may be performed, for example, using AI, or may be performed without using AI. For example, the interface may input data of the purchase procedure to a generation AI and cause the generation AI to execute visualization of the progress.

[0038] The analysis unit can calculate a safety score including a crime rate next to the property to be purchased. The safety score includes, for example, crime rate data, security evaluation, etc., but is not limited to such examples. The analysis unit can calculate the safety score, for example, based on past crime data. The analysis unit can also calculate the safety score using AI. For example, the analysis unit can calculate the score using an AI model that receives crime data as input and outputs a safety score. This allows customers to evaluate the safety of properties and select properties with peace of mind. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input crime data to the generation AI and cause the generation AI to calculate the safety score.

[0039] The simulation unit may include a prediction unit that predicts the future market price of the property to be purchased. The prediction unit predicts the future market price, for example, by using past data analysis, a prediction algorithm, etc. The prediction unit predicts the future market price, for example, based on the property conditions input by the customer. The prediction unit may also predict the future market price using AI. For example, the prediction unit may make a prediction using an AI model that receives property data as input and outputs the future market price. This makes it easier for the customer to determine the future investment value. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit may input property data to a generation AI and cause the generation AI to execute a prediction of the future market price.

[0040] The providing unit can provide 24-hour support using an AI chatbot. The AI ​​chatbot, for example, automatically generates answers to customer questions and provides 24-hour support. The AI ​​chatbot, for example, analyzes questions entered by customers and generates appropriate answers. The AI ​​chatbot can also generate answers to customer questions using a generation AI. For example, the AI ​​chatbot can generate answers using a generation AI model that receives customer questions as input and outputs answers. This provides an environment in which customers can consult at any time and receive prompt support. Some or all of the above-mentioned processing in the AI ​​chatbot may be performed, for example, using AI or may be performed without using AI. For example, the AI ​​chatbot can input customer questions to a generation AI and have the generation AI generate answers.

[0041] The providing unit can propose a renovation plan after the purchase of the property to be purchased. The renovation plan includes, for example, the scope of renovation, an estimate of costs, and the like, but is not limited to such examples. The providing unit proposes a renovation plan, for example, based on the customer's wishes. The providing unit can also propose a renovation plan using AI. For example, the providing unit can propose a plan using an AI model that receives the customer's desired conditions as input and outputs a renovation plan. This allows the customer to consult about renovations after purchase, making it easier to make plans after purchasing the property. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the customer's desired conditions into the generating AI and cause the generating AI to execute a proposal for a renovation plan.

[0042] The reception unit can analyze the customer's past property search history and select an information input method. For example, the reception unit can prioritize displaying property types that the customer has frequently searched for in the past, simplifying input. For example, the reception unit can automatically suggest a specific area or price range from the customer's past search history. For example, the reception unit can prioritize providing an input method (such as voice or text) that the customer has used in the past. This can reduce the input burden on the customer by selecting the optimal information input method based on the past search history. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the customer's past search history data to the generation AI and cause the generation AI to select the optimal information input method.

[0043] The reception unit can customize the input items based on the customer's current living situation and areas of interest when inputting information. For example, when the customer inputs a family structure, the reception unit preferentially displays properties for families. For example, when the customer has a pet, the reception unit preferentially displays properties that allow pets. For example, when the customer has a specific hobby or interest, the reception unit proposes properties that are close to facilities related to the hobby. This allows more appropriate property proposals by customizing the input items based on the customer's living situation and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the customer's living situation and areas of interest to the generation AI and cause the generation AI to customize the input items.

[0044] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the customer when inputting information. For example, when the customer inputs the current location, the reception unit preferentially displays property information in the vicinity. For example, when the customer specifies a specific area, the reception unit preferentially displays property information in that area. For example, when the customer is traveling, the reception unit preferentially displays property information in the travel destination. In this way, by taking the geographical location information into consideration, information highly relevant to the customer can be preferentially input. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the customer to the generation AI and cause the generation AI to execute the preferential input of highly relevant information.

[0045] The reception unit can analyze the social media activity of the customer at the time of information input and input relevant information. The reception unit, for example, suggests related property information based on the location shared by the customer on social media. The reception unit, for example, estimates and suggests a property type of interest from the content of the customer's social media posts. The reception unit, for example, preferentially displays property information in an area where the customer's social media friends live. This allows information that is highly relevant to the customer to be input by analyzing social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media activity data of the customer to the generation AI and cause the generation AI to input related information.

[0046] The suggestion unit can adjust the level of detail of the proposal based on the importance of the property when making a proposal. For example, the suggestion unit provides detailed information for important properties. For example, the suggestion unit provides simplified information for properties that are not so important. For example, the suggestion unit makes additional proposals along with detailed information for properties in which the customer is particularly interested. This allows the provision of optimal information for the customer by adjusting the level of detail of the proposal based on the importance of the property. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input property importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0047] The suggestion unit can apply different suggestion algorithms depending on the property category when making a suggestion. For example, in the case of an apartment, the suggestion unit emphasizes information on surrounding facilities and management fees. For example, in the case of a detached house, the suggestion unit emphasizes information on the land size and building structure. For example, in the case of a commercial property, the suggestion unit emphasizes information on location conditions and transportation convenience. This enables more appropriate suggestions by applying different suggestion algorithms depending on the property category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input property category data to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0048] The proposal unit can determine the priority of the proposal based on the submission time of the property at the time of proposal. The proposal unit, for example, prioritizes the proposal of new properties. For example, for properties that have been on the market for a long time, the proposal unit proposes that there is room for price negotiation. For example, the proposal unit makes a proposal promptly for properties whose submission time is approaching. In this way, by determining the priority of the proposal based on the submission time of the property, it is possible to make an optimal proposal for the customer. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input the submission time data of the property to the generation AI and cause the generation AI to determine the priority of the proposal.

[0049] The proposal unit can adjust the order of proposals based on the relevance of properties when making proposals. For example, the proposal unit first proposes a property that best matches the desired conditions of the customer. For example, the proposal unit next proposes a property that partially matches the desired conditions of the customer. For example, the proposal unit last proposes a property that does not match the desired conditions of the customer. In this way, by adjusting the order of proposals based on the relevance of properties, it is possible to make an optimal proposal for the customer. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit can input property relevance data to the generation AI and cause the generation AI to adjust the order of proposals.

[0050] At the time of providing, the providing unit can select the optimal advice method by analyzing the past consumption behavior of the customer. For example, the providing unit suggests similar properties based on information on properties previously purchased by the customer. For example, the providing unit suggests properties that fit the budget from the past consumption behavior of the customer. For example, the providing unit analyzes the past consumption behavior of the customer and selects the most efficient advice method. In this way, the optimal advice method for the customer can be selected by analyzing the past consumption behavior. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the past consumption behavior data of the customer to the generating AI and cause the generating AI to select the optimal advice method.

[0051] The providing unit can customize the means of advice based on the customer's current living situation at the time of providing. For example, when the customer inputs the family composition, the providing unit provides advice for families. For example, when the customer has a pet, the providing unit provides advice on properties that allow pets. For example, when the customer has a specific hobby or interest, the providing unit provides advice on properties that are close to facilities related to the hobby. This enables more appropriate advice by customizing the means of advice based on the customer's living situation. Part or all of the above-mentioned 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 living situation data of the customer to the generating AI and cause the generating AI to customize the means of advice.

[0052] The providing unit can select the optimal advice method by considering the geographical location information of the customer at the time of providing. For example, when the customer inputs the current location, the providing unit provides advice on properties in the vicinity. For example, when the customer specifies a specific area, the providing unit provides advice on properties in the area. For example, when the customer is traveling, the providing unit provides advice on properties at the travel destination. In this way, by considering the geographical location information, the optimal advice method for the customer can be selected. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the customer to the generating AI and cause the generating AI to select the optimal advice method.

[0053] The providing unit can analyze the social media activity of the customer at the time of providing and suggest a means of advice. The providing unit provides advice on related properties based on, for example, a location shared by the customer on social media. The providing unit estimates a property type of interest from, for example, the content of the customer's social media posts and provides the advice. The providing unit provides advice on properties in an area where the customer's social media friends live. In this way, by analyzing social media activity, it is possible to suggest the optimal means of advice for the customer. A part or all of the above-mentioned 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 social media activity data of the customer to the generating AI and cause the generating AI to execute the suggestion of the means of advice.

[0054] During the analysis, the analysis unit can predict current market trends by referring to past market data. The analysis unit predicts current market trends based on market data from the past 10 years, for example. The analysis unit predicts seasonal market trends from past market data, for example. The analysis unit analyzes past market data, for example, and predicts market trends in a specific region. This makes it possible to accurately predict current market trends by referring to past market data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input past market data to the generation AI and cause the generation AI to execute a prediction of current market trends.

[0055] The analysis unit can apply different analysis methods to each property category during analysis. For example, in the case of an apartment, the analysis unit focuses on data related to surrounding facilities and management fees. For example, in the case of a detached house, the analysis unit focuses on data related to the land size and building structure. For example, in the case of a commercial property, the analysis unit focuses on data related to location conditions and transportation access. This allows for more accurate analysis by applying different analysis methods to each property category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input property category data to the generation AI and cause the generation AI to apply the analysis method.

[0056] During the analysis, the analysis unit can analyze changes in market trends based on the submission time of the property. The analysis unit, for example, prioritizes the analysis of market trends of newly arrived properties. The analysis unit, for example, analyzes the market trends of properties that have been on the market for a long time. The analysis unit, for example, analyzes the market trends of properties that are close to the submission time. This enables more accurate market forecasting by analyzing changes in market trends based on the submission time of the property. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input property submission time data to the generation AI and cause the generation AI to perform an analysis of changes in market trends.

[0057] During the analysis, the analysis unit can analyze market trends by referring to market data related to the property. The analysis unit analyzes current market trends, for example, based on market data of similar properties. The analysis unit analyzes current market trends, for example, based on market data of neighboring areas. The analysis unit analyzes current market trends, for example, based on past market data. This allows for more accurate analysis of market trends by referring to the related market data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input related market data to the generation AI and cause the generation AI to perform an analysis of market trends.

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

[0059] The suggestion unit can analyze the customer's past purchase history and prioritize suggesting similar properties. For example, it can suggest properties with similar characteristics based on the characteristics of properties the customer has previously purchased. Furthermore, the suggestion unit can use data on properties the customer has shown interest in in the past to suggest properties that match the customer's preferences. This makes it possible to make more personalized suggestions by utilizing the customer's past purchase history.

[0060] The provision unit can provide customized advice based on the customer's lifestyle. For example, if the customer enjoys outdoor activities, the provision unit can suggest properties with plenty of parks and natural environments nearby. Furthermore, if the customer prefers urban living, the provision unit can suggest properties with good transportation access or properties close to commercial facilities. This makes it possible to provide advice tailored to the customer's lifestyle and provide suggestions that will increase customer satisfaction.

[0061] The analysis unit can evaluate the energy efficiency of a property, and the provision unit can provide the results to the customer. For example, the analysis unit can evaluate the insulation performance and energy consumption of a property and recommend a property with high energy efficiency. Furthermore, the analysis unit can predict future utility costs based on the energy efficiency of the property. This allows the customer to reduce costs in the long term by choosing a property with high energy efficiency.

[0062] The provision unit can suggest properties based on the customer's health condition. For example, if the customer has allergies, the provision unit can suggest properties with air purification functions or properties that provide an environment with fewer allergens. Furthermore, if the customer likes to exercise, the provision unit can suggest properties that are close to fitness facilities. This makes it possible to suggest properties that take into consideration the customer's health condition.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit inputs customer information. Customer information includes name, address, age, desired purchase conditions, etc. The reception unit stores the information input by the customer in a database and uses it for subsequent processing. Step 2: The proposal unit proposes properties to the customer based on the information entered by the reception unit. The proposal unit searches the database for properties that match the customer's desired conditions and generates a proposal list. The generation AI can also be used to select the property that best suits the customer's desired conditions. Step 3: The Providing Department provides purchasing procedures, loan consultations, and legal advice based on the properties proposed by the Proposal Department. The Providing Department guides the customer through the steps of the purchasing procedure and assists in preparing the necessary documents. It can also use AI to generate answers to customer questions. Step 4: The analysis department analyzes the market price trends, nearby facilities, and transportation accessibility of the property to be purchased. The analysis department can also predict the current market price based on past market data and evaluate the property using AI.

[0065] (Example 2) The real estate purchase consultation support AI assistant according to the embodiment of the present invention is a system that proposes an appropriate property based on information such as a customer's budget, the type of property desired, and the area. This system starts when the customer inputs information such as the budget, the type of property desired, and the area. This information is input to the reception unit. Next, based on the information input by the reception unit, the proposal unit proposes a property to be purchased. Based on the proposed property, the provision unit provides purchase procedures, loan consultation, and legal advice. Furthermore, the analysis unit analyzes market price trends, nearby facilities, and transportation convenience, and the provision unit provides the results to the customer. For example, the system supports the purchase decision by analyzing the market price trends of the property, information on nearby facilities, transportation convenience, etc., and providing them to the customer. In addition, the provision unit is provided with a simulation unit that simulates a loan, so that the customer can simulate a loan. Furthermore, the provision unit is provided with a check unit that provides a checklist of the contract, so that the customer can check the contents of the contract. The provision unit is provided with an interface that visualizes the progress of the purchase procedure, so that the customer can check the progress of the purchase procedure. The analysis unit calculates a safety score, including the crime rate of the neighborhood of the property to be purchased, and the provision unit provides the result to the customer. The simulation unit has a prediction unit that predicts the future market price of the property to be purchased, so that the customer can predict the future market price. The provision unit provides 24-hour support using an AI chatbot, providing an environment where customers can consult at any time. In addition, the provision unit also has a function to propose renovation plans after the purchase of the property to be purchased, so that customers can consult about renovations after the purchase. As a result, the real estate purchase consultation support AI assistant can support the purchase decision by suggesting appropriate properties based on the customer's information and providing purchase procedures and advice.

[0066] The real estate purchase consultation support AI assistant according to the embodiment includes a reception unit, a proposal unit, a provision unit, and an analysis unit. The reception unit inputs customer information. The customer information includes, for example, name, address, age, desired purchase conditions, etc., but is not limited to such examples. The reception unit, for example, stores the information input by the customer in a database and uses it for subsequent processing. The proposal unit proposes a property to be purchased to the customer based on the information input by the reception unit. The proposal unit, for example, searches a database for properties that match the customer's desired conditions and generates a proposal list. The proposal unit can also use a generation AI to select a property that best suits the customer's desired conditions. For example, the generation AI receives the customer's desired conditions as input and outputs the optimal property. The provision unit provides purchase procedures, loan consultations, and legal advice based on the property proposed by the proposal unit. For example, the provision unit guides the steps of the purchase procedure and supports the preparation of necessary documents. The provision unit can also use AI to generate answers to customer questions. For example, the provision unit receives a customer's question as input and outputs an appropriate answer. The analysis unit analyzes the market price trends, nearby facilities, and transportation convenience of the property to be purchased. For example, the analysis unit predicts the current market price based on past market data. The analysis unit can also use AI to evaluate the property. For example, the analysis unit receives property data as input and outputs an evaluation score. As a result, the real estate purchase consultation support AI assistant according to the embodiment can propose an appropriate property based on the customer's information and provide purchase procedures and advice. For example, the reception unit inputs the customer's information. The proposal unit proposes a property based on the customer's desired conditions. The provision unit provides purchase procedures and advice. The analysis unit analyzes market price trends and nearby facilities. As a result, the customer can select an appropriate property and smoothly proceed with the purchase procedure.

[0067] The reception unit inputs customer information. The customer information includes, for example, name, address, age, desired purchase conditions, etc., but is not limited to such examples. The reception unit, for example, stores the information input by the customer in a database and uses it for subsequent processing. Specifically, the reception unit receives information input by the customer through a web form or application in real time and automatically stores it in a database. This allows the customer information to be managed centrally and allows the subsequent proposal unit and provision unit to quickly access it. Furthermore, the reception unit has a validation function for confirming the accuracy of the input information, for example, checking the address format and age range. This makes it possible to prevent input errors and registration of inaccurate information. Furthermore, the reception unit also has a security function for protecting the privacy of the customer, and performs data encryption and access control. This allows the customer's personal information to be managed safely. Furthermore, the reception unit also has a function for periodically updating the customer information, and for example, the customer can add new desired conditions or change existing information. This allows proposals and provision to always be made based on the latest information. The reception department plays an important role in ensuring smooth initial contact with customers and the smooth running of subsequent processes.

[0068] The proposal unit proposes properties to be purchased to the customer based on the information input by the reception unit. For example, the proposal unit searches a database for properties that meet the customer's desired conditions and generates a proposal list. The proposal unit can also use the generation AI to select a property that best suits the customer's desired conditions. For example, the generation AI receives the customer's desired conditions as input and outputs the optimal property. Specifically, the generation AI receives conditions such as the customer's desired area, budget, floor plan, and age as prompts, and extracts properties that best suit these conditions from the database. The generation AI learns past data and trends, and can propose optimal properties with high accuracy based on the customer's conditions. In addition, the proposal unit provides an interface for displaying the proposal list to the customer in a visually easy-to-understand manner. For example, detailed information including photos of the property, floor plan, and information on the surrounding environment can be displayed to make it easier for the customer to compare and consider. The proposal unit also has a function of receiving customer feedback and continuously improving the proposal content. For example, if a customer shows interest in a specific property, other properties related to that property can also be proposed, providing more options. This enables the proposal department to quickly and accurately propose properties that best suit the customer's needs, thereby increasing customer satisfaction.

[0069] The provision unit provides purchase procedures, loan consultations, and legal advice based on the property proposed by the proposal unit. For example, the provision unit guides the steps of the purchase procedure and supports the preparation of necessary documents. The provision unit can also generate answers to customer questions using AI. For example, the provision unit receives customer questions as input and outputs appropriate answers. Specifically, the provision unit has a function to explain each step of the purchase procedure in detail and remind customers of the deadlines for necessary documents and procedures. This makes it easier for customers to understand the progress of the procedure and allows them to proceed with the purchase procedure smoothly. In addition, the provision unit also proposes the optimal loan plan for loan consultations based on the customer's income, expenses, and credit information. AI analyzes the customer's financial situation and presents the optimal loan options to help the customer create a reasonable repayment plan. In addition, the provision unit also provides legal advice and generates appropriate answers to questions regarding the contents of the contract and legal procedures. This allows the customer to proceed with the purchase procedure with peace of mind. The provision unit plays an important role in resolving customers' doubts and concerns and smoothly progressing with the purchase procedure.

[0070] The analysis unit analyzes the market price trends, nearby facilities, and transportation convenience of the property to be purchased. For example, the analysis unit predicts the current market price based on past market data. The analysis unit can also use AI to evaluate the property. For example, the analysis unit receives property data as input and outputs an evaluation score. Specifically, the analysis unit collects market data from the past few years and analyzes price fluctuations and trends. This makes it possible to predict the current market price and future price trends. In addition, information on nearby facilities and transportation convenience is also collected to evaluate the convenience of the property. For example, a comprehensive evaluation of the property is performed taking into account the accessibility of nearby schools, hospitals, shopping centers, and public transportation. The AI ​​analyzes these data and calculates the evaluation score of the property. The evaluation score is an important indicator for customers when selecting a property and serves as a standard for objectively evaluating the value of the property. In addition, the analysis unit can also evaluate future risks and opportunities based on the customer's desired conditions and market trends. This allows the customer to comprehensively grasp information about the purchase of the property and make a wiser decision. The Analysis Department plays an important role in objectively evaluating property value and helping customers make the best choices.

[0071] The providing unit can provide the analysis results by the analysis unit to the customer. The analysis results include, for example, market price trends, nearby facilities, and transportation convenience, but are not limited to such examples. The providing unit, for example, provides the analysis results to the customer in a report format. The report can provide information in a visually easy-to-understand format using graphs and charts. The providing unit can also update the analysis results in real time and provide the latest information to the customer. For example, the providing unit obtains the latest market data through the Internet and updates the analysis results. This allows the customer to grasp the latest market trends and support purchasing decisions. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the analysis results using an AI model that receives the analysis results generated by the analysis unit as input and provides them to the customer.

[0072] The provision unit may include a simulation unit that performs a simulation of a loan. The simulation unit performs a simulation of a loan by, for example, setting an interest rate and a repayment period. The simulation unit creates a repayment plan based on, for example, the loan conditions input by the customer. The simulation unit may also perform a simulation of a loan using AI. For example, the simulation unit may perform a simulation using an AI model that receives the customer's loan conditions as input and outputs a repayment plan. This makes it easier for the customer to make a loan plan and supports the purchase decision. A part or all of the above-mentioned processing in the simulation unit may be performed, for example, using AI, or may be performed without using AI. For example, the simulation unit may input the loan conditions input by the customer to a generation AI and cause the generation AI to generate a repayment plan.

[0073] The providing unit may include a checking unit that provides a checklist for the contract. The checking unit, for example, checks important clauses in the contract and explains risks. The checking unit, for example, displays the contents of the contract in a list format to make it easier for the customer to check. The checking unit may also check the contract using AI. For example, the checking unit may receive the contents of the contract as input and generate a checklist using an AI model that extracts important clauses. This allows the customer to check the contents of the contract and proceed with the contract with peace of mind. Some or all of the above-mentioned processing in the checking unit may be performed, for example, using AI or may be performed without using AI. For example, the checking unit may input the contents of the contract to a generating AI and cause the generating AI to generate a checklist.

[0074] The providing unit may include an interface that visualizes the progress of the purchase procedure. The interface visually displays the progress of the purchase procedure, for example, using a progress bar or a status display. The interface, for example, displays a list of each step of the purchase procedure, so that the customer can grasp the current progress at a glance. The interface may also visualize the progress using AI. For example, the interface may receive data of the purchase procedure as input and perform visualization using an AI model that displays the progress. This makes it easier for the customer to grasp the progress of the purchase procedure, and the procedure can be carried out smoothly. A part or all of the above-mentioned processing in the interface may be performed, for example, using AI, or may be performed without using AI. For example, the interface may input data of the purchase procedure to a generation AI and cause the generation AI to execute visualization of the progress.

[0075] The analysis unit can calculate a safety score including a crime rate next to the property to be purchased. The safety score includes, for example, crime rate data, security evaluation, etc., but is not limited to such examples. The analysis unit can calculate the safety score, for example, based on past crime data. The analysis unit can also calculate the safety score using AI. For example, the analysis unit can calculate the score using an AI model that receives crime data as input and outputs a safety score. This allows customers to evaluate the safety of properties and select properties with peace of mind. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input crime data to the generation AI and cause the generation AI to calculate the safety score.

[0076] The simulation unit may include a prediction unit that predicts the future market price of the property to be purchased. The prediction unit predicts the future market price, for example, by using past data analysis, a prediction algorithm, etc. The prediction unit predicts the future market price, for example, based on the property conditions input by the customer. The prediction unit may also predict the future market price using AI. For example, the prediction unit may make a prediction using an AI model that receives property data as input and outputs the future market price. This makes it easier for the customer to determine the future investment value. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit may input property data to a generation AI and cause the generation AI to execute a prediction of the future market price.

[0077] The providing unit can provide 24-hour support using an AI chatbot. The AI ​​chatbot, for example, automatically generates answers to customer questions and provides 24-hour support. The AI ​​chatbot, for example, analyzes questions entered by customers and generates appropriate answers. The AI ​​chatbot can also generate answers to customer questions using a generation AI. For example, the AI ​​chatbot can generate answers using a generation AI model that receives customer questions as input and outputs answers. This provides an environment in which customers can consult at any time and receive prompt support. Some or all of the above-mentioned processing in the AI ​​chatbot may be performed, for example, using AI or may be performed without using AI. For example, the AI ​​chatbot can input customer questions to a generation AI and have the generation AI generate answers.

[0078] The providing unit can propose a renovation plan after the purchase of the property to be purchased. The renovation plan includes, for example, the scope of renovation, an estimate of costs, and the like, but is not limited to such examples. The providing unit proposes a renovation plan, for example, based on the customer's wishes. The providing unit can also propose a renovation plan using AI. For example, the providing unit can propose a plan using an AI model that receives the customer's desired conditions as input and outputs a renovation plan. This allows the customer to consult about renovations after purchase, making it easier to make plans after purchasing the property. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the customer's desired conditions into the generating AI and cause the generating AI to execute a proposal for a renovation plan.

[0079] The reception unit can estimate the customer's emotion and adjust the timing of information input based on the estimated customer's emotion. For example, when the customer is stressed, the reception unit pauses input and displays an interface that allows the customer to relax. For example, when the customer is relaxed, the reception unit prompts the customer to input more information and collects more information. For example, when the customer is in a hurry, the reception unit displays a simplified input form to allow the customer to input information quickly. This allows the customer to reduce stress and input information efficiently by adjusting the timing of information input according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the customer's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0080] The reception unit can analyze the customer's past property search history and select an information input method. For example, the reception unit can prioritize displaying property types that the customer has frequently searched for in the past, simplifying input. For example, the reception unit can automatically suggest a specific area or price range from the customer's past search history. For example, the reception unit can prioritize providing an input method (such as voice or text) that the customer has used in the past. This can reduce the input burden on the customer by selecting the optimal information input method based on the past search history. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the customer's past search history data to the generation AI and cause the generation AI to select the optimal information input method.

[0081] The reception unit can customize the input items based on the customer's current living situation and areas of interest when inputting information. For example, when the customer inputs a family structure, the reception unit preferentially displays properties for families. For example, when the customer has a pet, the reception unit preferentially displays properties that allow pets. For example, when the customer has a specific hobby or interest, the reception unit proposes properties that are close to facilities related to the hobby. This allows more appropriate property proposals by customizing the input items based on the customer's living situation and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the customer's living situation and areas of interest to the generation AI and cause the generation AI to customize the input items.

[0082] The reception unit can estimate the customer's emotion and determine the priority of information to be input based on the estimated customer's emotion. For example, when the customer is stressed, the reception unit allows the customer to input only the most important information with priority. For example, when the customer is relaxed, the reception unit allows the customer to input detailed information. For example, when the customer is in a hurry, the reception unit allows the customer to input only the minimum amount of information. This allows efficient information input by determining the priority of information to be input according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the customer's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0083] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the customer when inputting information. For example, when the customer inputs the current location, the reception unit preferentially displays property information in the vicinity. For example, when the customer specifies a specific area, the reception unit preferentially displays property information in that area. For example, when the customer is traveling, the reception unit preferentially displays property information in the travel destination. In this way, by taking the geographical location information into consideration, information highly relevant to the customer can be preferentially input. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the customer to the generation AI and cause the generation AI to execute the preferential input of highly relevant information.

[0084] The reception unit can analyze the social media activity of the customer at the time of information input and input relevant information. The reception unit, for example, suggests related property information based on the location shared by the customer on social media. The reception unit, for example, estimates and suggests a property type of interest from the content of the customer's social media posts. The reception unit, for example, preferentially displays property information in an area where the customer's social media friends live. This allows information that is highly relevant to the customer to be input by analyzing social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media activity data of the customer to the generation AI and cause the generation AI to input related information.

[0085] The suggestion unit can estimate the customer's emotion and adjust the way of expressing the proposal based on the estimated emotion of the customer. For example, when the customer is nervous, the suggestion unit makes a simple and highly visible proposal. For example, when the customer is relaxed, the suggestion unit makes a proposal including detailed information. For example, when the customer is excited, the suggestion unit makes a visually attractive proposal. This enables a more effective proposal by adjusting the way of expressing the proposal according to the emotion of the customer. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the emotion data of the customer to the generation AI and cause the generation AI to adjust the way of expressing the proposal.

[0086] The suggestion unit can adjust the level of detail of the proposal based on the importance of the property when making a proposal. For example, the suggestion unit provides detailed information for important properties. For example, the suggestion unit provides simplified information for properties that are not so important. For example, the suggestion unit makes additional proposals along with detailed information for properties in which the customer is particularly interested. This allows the provision of optimal information for the customer by adjusting the level of detail of the proposal based on the importance of the property. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input property importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0087] The suggestion unit can apply different suggestion algorithms depending on the property category when making a suggestion. For example, in the case of an apartment, the suggestion unit emphasizes information on surrounding facilities and management fees. For example, in the case of a detached house, the suggestion unit emphasizes information on the land size and building structure. For example, in the case of a commercial property, the suggestion unit emphasizes information on location conditions and transportation convenience. This enables more appropriate suggestions by applying different suggestion algorithms depending on the property category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input property category data to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0088] The suggestion unit can estimate the customer's emotion and adjust the length of the suggestion based on the estimated customer's emotion. For example, when the customer is in a hurry, the suggestion unit makes a short and to the point suggestion. For example, when the customer is relaxed, the suggestion unit makes a longer suggestion including a detailed explanation. For example, when the customer is excited, the suggestion unit makes a suggestion with a visually stimulating effect. This allows for a more effective suggestion by adjusting the length of the suggestion according to the customer's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input customer emotion data to the generation AI and cause the generation AI to adjust the length of the suggestion.

[0089] The proposal unit can determine the priority of the proposal based on the submission time of the property at the time of proposal. The proposal unit, for example, prioritizes the proposal of new properties. For example, for properties that have been on the market for a long time, the proposal unit proposes that there is room for price negotiation. For example, the proposal unit makes a proposal promptly for properties whose submission time is approaching. In this way, by determining the priority of the proposal based on the submission time of the property, it is possible to make an optimal proposal for the customer. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input the submission time data of the property to the generation AI and cause the generation AI to determine the priority of the proposal.

[0090] The proposal unit can adjust the order of proposals based on the relevance of properties when making proposals. For example, the proposal unit first proposes a property that best matches the desired conditions of the customer. For example, the proposal unit next proposes a property that partially matches the desired conditions of the customer. For example, the proposal unit last proposes a property that does not match the desired conditions of the customer. In this way, by adjusting the order of proposals based on the relevance of properties, it is possible to make an optimal proposal for the customer. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit can input property relevance data to the generation AI and cause the generation AI to adjust the order of proposals.

[0091] The providing unit can estimate the emotion of the customer and adjust the method of providing advice based on the estimated emotion of the customer. For example, when the customer is nervous, the providing unit provides advice in a calm voice. For example, when the customer is relaxed, the providing unit provides advice in a bright voice. For example, when the customer is in a hurry, the providing unit provides quick and concise advice. This allows more effective advice by adjusting the method of advice according to the emotion of the customer. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the emotion data of the customer to the generation AI and cause the generation AI to adjust the method of advice.

[0092] At the time of providing, the providing unit can select the optimal advice method by analyzing the past consumption behavior of the customer. For example, the providing unit suggests similar properties based on information on properties previously purchased by the customer. For example, the providing unit suggests properties that fit the budget from the past consumption behavior of the customer. For example, the providing unit analyzes the past consumption behavior of the customer and selects the most efficient advice method. In this way, the optimal advice method for the customer can be selected by analyzing the past consumption behavior. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the past consumption behavior data of the customer to the generating AI and cause the generating AI to select the optimal advice method.

[0093] The providing unit can customize the means of advice based on the customer's current living situation at the time of providing. For example, when the customer inputs the family composition, the providing unit provides advice for families. For example, when the customer has a pet, the providing unit provides advice on properties that allow pets. For example, when the customer has a specific hobby or interest, the providing unit provides advice on properties that are close to facilities related to the hobby. This enables more appropriate advice by customizing the means of advice based on the customer's living situation. Part or all of the above-mentioned 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 living situation data of the customer to the generating AI and cause the generating AI to customize the means of advice.

[0094] The providing unit can estimate the emotion of the customer and determine the priority of advice to be provided based on the estimated emotion of the customer. For example, when the customer is stressed, the providing unit provides the most important advice with priority. For example, when the customer is relaxed, the providing unit provides detailed advice. For example, when the customer is in a hurry, the providing unit provides concise advice. This enables more effective advice by determining the priority of advice according to the emotion of the customer. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the emotion data of the customer to the generation AI and cause the generation AI to execute the determination of the priority of advice.

[0095] The providing unit can select the optimal advice method by considering the geographical location information of the customer at the time of providing. For example, when the customer inputs the current location, the providing unit provides advice on properties in the vicinity. For example, when the customer specifies a specific area, the providing unit provides advice on properties in the area. For example, when the customer is traveling, the providing unit provides advice on properties at the travel destination. In this way, by considering the geographical location information, the optimal advice method for the customer can be selected. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the customer to the generating AI and cause the generating AI to select the optimal advice method.

[0096] The providing unit can analyze the social media activity of the customer at the time of providing and suggest a means of advice. The providing unit provides advice on related properties based on, for example, a location shared by the customer on social media. The providing unit estimates a property type of interest from, for example, the content of the customer's social media posts and provides the advice. The providing unit provides advice on properties in an area where the customer's social media friends live. In this way, by analyzing social media activity, it is possible to suggest the optimal means of advice for the customer. A part or all of the above-mentioned 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 social media activity data of the customer to the generating AI and cause the generating AI to execute the suggestion of the means of advice.

[0097] The analysis unit can estimate the customer's emotions and adjust the display method of the analysis results based on the estimated customer's emotions. For example, when the customer is nervous, the analysis unit provides a simple and highly visible display method. For example, when the customer is relaxed, the analysis unit provides a display method including detailed information. For example, when the customer is in a hurry, the analysis unit provides a display method that focuses on the main points. This makes it possible to provide more effective information by adjusting the display method of the analysis results according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input customer emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0098] During the analysis, the analysis unit can predict current market trends by referring to past market data. The analysis unit predicts current market trends based on market data from the past 10 years, for example. The analysis unit predicts seasonal market trends from past market data, for example. The analysis unit analyzes past market data, for example, and predicts market trends in a specific region. This makes it possible to accurately predict current market trends by referring to past market data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input past market data to the generation AI and cause the generation AI to execute a prediction of current market trends.

[0099] The analysis unit can apply different analysis methods to each property category during analysis. For example, in the case of an apartment, the analysis unit focuses on data related to surrounding facilities and management fees. For example, in the case of a detached house, the analysis unit focuses on data related to the land size and building structure. For example, in the case of a commercial property, the analysis unit focuses on data related to location conditions and transportation access. This allows for more accurate analysis by applying different analysis methods to each property category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input property category data to the generation AI and cause the generation AI to apply the analysis method.

[0100] The analysis unit can estimate the customer's emotions and adjust the importance of the analysis results based on the estimated customer's emotions. For example, when the customer is nervous, the analysis unit displays only the most important analysis results. For example, when the customer is relaxed, the analysis unit displays detailed analysis results. For example, when the customer is in a hurry, the analysis unit displays analysis results that focus on the main points. This allows for more effective information provision by adjusting the importance of the analysis results according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input customer emotion data to the generation AI and cause the generation AI to adjust the importance of the analysis results.

[0101] During the analysis, the analysis unit can analyze changes in market trends based on the submission time of the property. The analysis unit, for example, prioritizes the analysis of market trends of newly arrived properties. The analysis unit, for example, analyzes the market trends of properties that have been on the market for a long time. The analysis unit, for example, analyzes the market trends of properties that are close to the submission time. This enables more accurate market forecasting by analyzing changes in market trends based on the submission time of the property. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input property submission time data to the generation AI and cause the generation AI to perform an analysis of changes in market trends.

[0102] During the analysis, the analysis unit can analyze market trends by referring to market data related to the property. The analysis unit analyzes current market trends, for example, based on market data of similar properties. The analysis unit analyzes current market trends, for example, based on market data of neighboring areas. The analysis unit analyzes current market trends, for example, based on past market data. This allows for more accurate analysis of market trends by referring to the related market data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input related market data to the generation AI and cause the generation AI to perform an analysis of market trends.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The suggestion unit can analyze the customer's past purchase history and prioritize suggesting similar properties. For example, it can suggest properties with similar characteristics based on the characteristics of properties the customer has previously purchased. Furthermore, the suggestion unit can use data on properties the customer has shown interest in in the past to suggest properties that match the customer's preferences. This makes it possible to make more personalized suggestions by utilizing the customer's past purchase history.

[0105] The provision unit can provide customized advice based on the customer's lifestyle. For example, if the customer enjoys outdoor activities, the provision unit can suggest properties with plenty of parks and natural environments nearby. Furthermore, if the customer prefers urban living, the provision unit can suggest properties with good transportation access or properties close to commercial facilities. This makes it possible to provide advice tailored to the customer's lifestyle and provide suggestions that will increase customer satisfaction.

[0106] The analysis unit can evaluate the energy efficiency of a property, and the provision unit can provide the results to the customer. For example, the analysis unit can evaluate the insulation performance and energy consumption of a property and recommend a property with high energy efficiency. Furthermore, the analysis unit can predict future utility costs based on the energy efficiency of the property. This allows the customer to reduce costs in the long term by choosing a property with high energy efficiency.

[0107] The provision unit can suggest properties based on the customer's health condition. For example, if the customer has allergies, the provision unit can suggest properties with air purification functions or properties that provide an environment with fewer allergens. Furthermore, if the customer likes to exercise, the provision unit can suggest properties that are close to fitness facilities. This makes it possible to suggest properties that take into consideration the customer's health condition.

[0108] The suggestion unit can estimate the customer's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the customer is feeling stressed, the suggestion unit can temporarily refrain from making suggestions and provide relaxing content. Furthermore, if the customer is excited, the suggestion unit can proactively make suggestions to support a quick decision. This makes it possible to make more effective suggestions by adjusting the timing of suggestions according to the customer's emotions.

[0109] The providing unit can estimate the customer's emotions and adjust the content of the advice based on the estimated emotions. For example, if the customer is feeling anxious, the providing unit can provide advice that gives the customer a sense of security. Furthermore, if the customer is confident, the providing unit can also suggest a specific action plan. This makes it possible to provide more effective support by providing advice according to the customer's emotions.

[0110] The analysis unit can estimate the customer's emotions and adjust the way in which the analysis results are presented based on the estimated emotions. For example, if the customer is nervous, the results can be presented in a simple, visually easy-to-understand format. Furthermore, if the customer is relaxed, the results can be presented together with detailed data. This makes it possible to provide information more effectively by adjusting the way in which the analysis results are presented according to the customer's emotions.

[0111] The providing unit can estimate the customer's emotions and determine the priority order of information to be provided based on the estimated emotions. For example, if the customer is feeling stressed, the most important information can be provided with priority. Furthermore, if the customer is relaxed, more detailed information can be provided. This allows for more effective support by determining the priority order of information provision according to the customer's emotions.

[0112] The proposal unit can estimate the customer's emotions and adjust the content of the proposal based on the estimated emotions. For example, if the customer is feeling anxious, it can propose a property with low risk. Furthermore, if the customer is excited, it can also propose a property with high investment value. In this way, by adjusting the content of the proposal according to the customer's emotions, it becomes possible to make more effective proposals.

[0113] The providing unit can estimate the customer's emotions and adjust the way in which advice is provided based on the estimated emotions. For example, if the customer is nervous, the advice can be provided in a calm voice. Furthermore, if the customer is relaxed, the advice can be provided in a bright voice. In this way, more effective support can be provided by adjusting the way advice is provided according to the customer's emotions.

[0114] The process flow of the second embodiment will be briefly described below.

[0115] Step 1: The reception unit inputs customer information. Customer information includes name, address, age, desired purchase conditions, etc. The reception unit stores the information input by the customer in a database and uses it for subsequent processing. Step 2: The proposal unit proposes properties to the customer based on the information entered by the reception unit. The proposal unit searches the database for properties that match the customer's desired conditions and generates a proposal list. The generation AI can also be used to select the property that best suits the customer's desired conditions. Step 3: The Providing Department provides purchasing procedures, loan consultations, and legal advice based on the properties proposed by the Proposal Department. The Providing Department guides the customer through the steps of the purchasing procedure and assists in preparing the necessary documents. It can also use AI to generate answers to customer questions. Step 4: The analysis department analyzes the market price trends, nearby facilities, and transportation accessibility of the property to be purchased. The analysis department can also predict the current market price based on past market data and evaluate the property using AI.

[0116] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0117] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0118] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0119] For example, the reception unit can input customer information using the reception device 38 of the smart device 14. The proposal unit is realized by the specific processing unit 290 of the data processing device 12, and proposes properties based on the customer's desired conditions. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides purchase procedures and advice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes market price trends and nearby facilities. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] 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, the RAM 30, and the 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.

[0123] 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.

[0124] 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 the voice according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0126] 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 performed in a secure state.

[0127] Fig. 4 shows an example of 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.

[0128] 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.

[0129] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0132] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0133] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0134] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0135] For example, the reception unit can input customer information using the microphone 238 of the smart glasses 214. The proposal unit is realized by the specific processing unit 290 of the data processing device 12, and proposes properties based on the customer's desired conditions. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides purchase procedures and advice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes market price trends and nearby facilities. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0138] 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, the RAM 30, and the 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.

[0139] 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.

[0140] 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 the voice according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0142] 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 performed in a secure state.

[0143] 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.

[0144] 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.

[0145] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0147] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] 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 voice indicating a user input for 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.

[0149] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0151] For example, the reception unit can input customer information using the microphone 238 of the headset terminal 314. The proposal unit is realized by the specific processing unit 290 of the data processing device 12, and proposes properties based on the customer's desired conditions. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides purchasing procedures and advice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes market price trends and nearby facilities. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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, the RAM 30, and the 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.

[0155] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0156] 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 the voice according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0158] 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 performed in a secure state.

[0159] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0160] 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.

[0161] 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.

[0162] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0163] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0164] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0165] 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 a voice indicating a user input for 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.

[0166] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0167] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0168] For example, the reception unit can input customer information using the microphone 238 of the robot 414. The proposal unit is realized by the specific processing unit 290 of the data processing device 12, and proposes properties based on the customer's desired conditions. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides purchasing procedures and advice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes market price trends and nearby facilities. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0169] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0170] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0172] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0174] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0176] 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.

[0177] 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 Universal Serial Bus (USB) 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.

[0178] In addition, 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 upon request from the data processing device 12.

[0179] 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.

[0180] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0181] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0182] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0183] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0185] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0187] (Appendix 1) A reception section for inputting customer information; a proposal unit that proposes properties to be purchased to the customer based on the information input by the reception unit; A department that provides purchase procedures, loan consultations, and legal advice based on the properties proposed by the proposal department; and a unit for analyzing market price trends, nearby facilities, and transportation accessibility regarding the property to be purchased. A system characterized by: (Appendix 2) The providing unit is The analysis results by the analysis unit are provided to the customer. 2. The system of claim 1, (Appendix 3) The providing unit is Equipped with a simulation department that performs loan simulations 2. The system of claim 1, (Appendix 4) The providing unit is Equipping a check section that provides a checklist for contracts 2. The system of claim 1, (Appendix 5) The providing unit is Equipping the user with an interface that visualizes the progress of the purchase procedure 2. The system of claim 1, (Appendix 6) The analysis unit includes: Calculate a safety score, including crime rates, for the neighborhoods you're considering buying 2. The system of claim 1, (Appendix 7) The simulation unit is Equipped with a prediction unit that predicts the future market price of the property to be purchased 4. The system of claim 3. (Appendix 8) The providing unit is Providing 24 / 7 support with AI chatbots 2. The system of claim 1, (Appendix 9) The providing unit is Propose a renovation plan for the property you are considering purchasing. 2. The system of claim 1, (Appendix 10) The reception unit is Estimate customer sentiment and adjust the timing of information input based on the estimated customer sentiment 2. The system of claim 1, (Appendix 11) The reception unit is Analyze customers' past property search history and choose the method of entering information 2. The system of claim 1, (Appendix 12) The reception unit is As information is entered, customise it based on the customer's current life situation and interests 2. The system of claim 1, (Appendix 13) The reception unit is Estimate customer sentiment and prioritize input information based on the estimated customer sentiment 2. The system of claim 1, (Appendix 14) The reception unit is When entering information, consider the customer's geographic location to prioritize the most relevant information 2. The system of claim 1, (Appendix 15) The reception unit is Analyze customer social media activity and enter relevant information when entering information 2. The system of claim 1, (Appendix 16) The suggestion unit, Infer customer sentiment and tailor the wording of offers based on the inferred customer sentiment 2. The system of claim 1, (Appendix 17) The suggestion unit, When making a proposal, adjust the level of detail in the proposal based on the importance of the property 2. The system of claim 1, (Appendix 18) The suggestion unit, When making suggestions, apply different suggestion algorithms depending on the property category 2. The system of claim 1, (Appendix 19) The suggestion unit, Estimate customer sentiment and adjust the length of the offer based on the estimated customer sentiment 2. The system of claim 1, (Appendix 20) The suggestion unit, When making proposals, prioritize proposals based on when the property was submitted 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of the property 2. The system of claim 1, (Appendix 22) The providing unit is Estimate customer sentiment and tailor advice delivery based on estimated customer sentiment 2. The system of claim 1, (Appendix 23) The providing unit is When providing advice, the system analyzes the customer's past consumption behavior to select the most appropriate advice method. 2. The system of claim 1, (Appendix 24) The providing unit is At the time of delivery, tailor advice delivery based on the customer's current life situation 2. The system of claim 1, (Appendix 25) The providing unit is Estimate customer sentiment and prioritize advice based on the estimated customer sentiment 2. The system of claim 1, (Appendix 26) The providing unit is At the time of delivery, the geographic location of the customer is taken into account to determine the best advice method 2. The system of claim 1, (Appendix 27) The providing unit is At the time of delivery, analyze the customer's social media activity to suggest advice measures 2. The system of claim 1, (Appendix 28) The analysis unit includes: Estimate customer sentiment and adjust how analytics results are presented based on the estimated customer sentiment 2. The system of claim 1, (Appendix 29) The analysis unit includes: During analysis, historical market data is referenced to predict current market trends 2. The system of claim 1, (Appendix 30) The analysis unit includes: During analysis, apply different analytical methods to different property categories 2. The system of claim 1, (Appendix 31) The analysis unit includes: Infer customer sentiment and adjust the importance of analysis results based on the inferred customer sentiment 2. The system of claim 1, (Appendix 32) The analysis unit includes: During the analysis, analyze changes in market trends based on when the property was submitted 2. The system of claim 1, (Appendix 33) The analysis unit includes: During the analysis, we refer to the property’s relevant market data to analyze market trends. 2. The system of claim 1, [Explanation of symbols]

[0188] 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 for inputting customer information; a proposal unit that proposes properties to be purchased to the customer based on the information input by the reception unit; a provision unit that provides purchase procedures, loan consultations, and legal advice based on the properties proposed by the proposal unit; and an analysis unit that analyzes market price trends, nearby facilities, and transportation accessibility of the property to be purchased. A system characterized by:

2. The providing unit is The analysis results by the analysis unit are provided to the customer.

2. The system of claim 1 .

3. The providing unit is Equipped with a simulation department that performs loan simulations 2. The system of claim 1 .

4. The providing unit is Equipping the contract checking department with a contract checklist 2. The system of claim 1 .

5. The providing unit is Equipping the user with an interface that visualizes the progress of the purchase procedure 2. The system of claim 1 .

6. The analysis unit includes: Calculate a safety score, including the crime rate, for the neighborhood of the property to be purchased 2. The system of claim 1 .

7. The simulation unit is A prediction unit for predicting the future market price of the property to be purchased 4. The system of claim 3.

8. The providing unit is Providing 24 / 7 support using AI chatbots 2. The system of claim 1 .

9. The providing unit is Propose a renovation plan after the purchase of the property to be purchased 2. The system of claim 1 .

10. The reception unit is The customer's feelings are estimated, and the timing of information input is adjusted based on the estimated feelings of the customer.

2. The system of claim 1 .

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