system

Abstract

We provide the system. [Solution] Means of obtaining geographic information from users, Methods for collecting rental market information from external databases, A method for calculating appropriate rents that take regional characteristics into account, based on acquired geographic information and collected rental market information, using a generative artificial intelligence model. A means of presenting the calculated appropriate rent to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In current rental contracts, negotiations regarding rent fluctuations and appropriateness at the time of contract renewal are left to the users themselves, and as a result, there may be a situation where the users continue to pay an inappropriate rent. In addition, there is a lack of means to know the appropriate rent, and since the rental market information in the region is not fully utilized, it is difficult for users to conduct negotiations based on appropriate information.

Means for Solving the Problems

[0005] This invention provides a system that collects the latest rental market information from an external database based on geographic information input by the user, and calculates an appropriate rent that takes regional characteristics into account using a generated artificial intelligence model. It also includes means to present the calculated appropriate rent to the user, automatically negotiate rental conditions as needed, and generate and provide evidence regarding the rent to the user, thereby enabling a rental agreement at an appropriate rent.

[0006] "User" refers to an individual or legal entity that uses this system to obtain information regarding rental agreements.

[0007] "Geographic information" refers to address and location information used to identify a region specified by the user.

[0008] An "external database" refers to a third-party data storage system that holds the latest information on the rental market.

[0009] "Rental market information" refers to data on the prices and conditions of rental properties in each region.

[0010] A "generative artificial intelligence model" refers to an AI system that uses machine learning to analyze data and generate specific conclusions or information.

[0011] "Appropriate rent" refers to a fair price for a rental property, calculated by taking into account collected data and regional characteristics.

[0012] "Means of presentation" refers to methods or devices that visually or audibly present calculated information or results to the user.

[0013] "Methods for automating negotiations" refers to technologies or programs that use AI to perform negotiations on behalf of users to adjust rental terms.

[0014] "Evidence" refers to documents or data that compile the basis and related materials for the calculated fair rent. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered 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), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention provides a system that calculates appropriate rent in rental agreements and assists users in easily negotiating rent. An embodiment of the system according to the present invention is shown below.

[0037] First, the user inputs geographical information about their residential area via a terminal and sends it to the system. Based on this information, the server automatically collects the latest rental market information related to the specified area from an external database.

[0038] Next, the server uses a generative artificial intelligence model to analyze the collected data. This AI model calculates an appropriate rent based on the acquired rental market information and regional characteristics. Through this process, rents that reflect local rent market rates and trends in real time are calculated.

[0039] The calculated appropriate rent is transmitted to the terminal via the server and presented to the user. This presentation includes supporting documents as the basis for the rent, which the user can use to help negotiate rental terms.

[0040] For example, if a user inputs information about a specific area in Tokyo, the server calculates a fair rent based on the average rent and past trends in that area, and displays the result via the terminal. Furthermore, if necessary, the server can provide an automated negotiation function via an AI agent, negotiating rent with the landlord on behalf of the user.

[0041] This system allows users to easily obtain accurate rent information and facilitate smoother rental negotiations. By implementing this system, users can enjoy fairer and more transparent terms in their rental agreements.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user uses a terminal to enter their address and geographical information about their residential area. Once the input is complete, this information is sent to the server.

[0045] Step 2:

[0046] Based on the geographical information it receives, the server selects an appropriate external database and collects the latest rental market information for the specified area. This includes rental property prices and trends for each region.

[0047] Step 3:

[0048] The server organizes the collected rental market information and analyzes the data using a generative artificial intelligence model. This analysis calculates an appropriate rent based on current market conditions, taking regional characteristics into account.

[0049] Step 4:

[0050] The calculated appropriate rent information and supporting documents are transmitted from the server to the terminal. The terminal presents this information to the user in a format that makes it easy to compare with the current rent.

[0051] Step 5:

[0052] Users can choose whether or not to negotiate rent based on the presented fair rent. Alternatively, users can request the server to handle automated negotiations via an AI agent.

[0053] Step 6:

[0054] Based on the user's selection, the server automatically initiates negotiations with the landlord via an AI agent as needed. The negotiation results are reported to the terminal via the server and presented to the user.

[0055] Step 7:

[0056] Ultimately, the server will notify the user via their terminal about the next steps and confirmations based on the negotiation results. This will allow the user to make an informed decision regarding the lease agreement.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Collecting and analyzing information to determine appropriate rents requires considerable time and effort, and there is a challenge in that it is difficult for users to negotiate rental terms based on sufficient information. A system is needed to solve this problem and facilitate rental agreements with fairer and more transparent terms for users.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for acquiring local information from users, means for collecting rental market data from external information sources, and means for using an artificial intelligence algorithm to derive an appropriate rent that takes local characteristics into account based on the acquired local information and the collected rental market data. As a result, users can quickly and accurately grasp an appropriate rent based on the latest information on the rental market and negotiate rental conditions rationally based on that information.

[0062] "User" refers to an individual or organization that uses this system to obtain information related to rental agreements and to conduct negotiations.

[0063] "Local information" refers to data about a specific geographical area necessary for rental market analysis, including addresses, postal codes, and area names.

[0064] "External information sources" refer to databases and platforms that provide data on the rental market, and are public and private data sources accessible via the internet.

[0065] "Rental market data" refers to a collection of information such as rental property prices, trends, vacancy rates, and historical transaction data for a specific area.

[0066] An "artificial intelligence algorithm" refers to a program or system that uses machine learning and data analysis techniques to analyze rental market data and calculate appropriate rent.

[0067] "Appropriate rent" refers to a fair and market-appropriate rental property fee calculated considering rental market data and regional characteristics.

[0068] This invention includes a system that provides users with fair rent and assists in negotiating rental terms. This system primarily operates as follows:

[0069] The user first enters information about their current or prospective location into the terminal. This information typically includes a specific address, area name, or postal code. Once the user has finished entering the information, the terminal sends it to the server.

[0070] The server collects rental market data for the relevant region from external sources based on the received regional information. These external sources include fixed databases and online information services. This data collection process may utilize technologies such as RESTful APIs and web scraping.

[0071] Subsequently, the server uses generative artificial intelligence (AI) algorithms to analyze the collected rental market data and local information. The AI ​​algorithms utilize models built using machine learning frameworks such as TENSORFLOW® and Scikit-learn. This algorithm processes the data to calculate appropriate rents that take local characteristics into account.

[0072] Once the calculation of the appropriate rent is complete, the server sends that information, along with supporting documents, to the terminal. The terminal uses a graphical user interface to present this information to the user in an easy-to-understand manner. This includes price trend graphs and summaries of market analysis reports.

[0073] Furthermore, if necessary, users can select an option within the system, and the server can use an AI assistant to automatically begin negotiating rental terms. This reduces the user's negotiation burden and helps them conclude a rental agreement on better terms.

[0074] Examples of specific prompts include phrases like, "Please tell me the appropriate rent for a 2LDK apartment in XX ward," or "Please analyze the rent trends in this area." By using these prompts, users can quickly obtain detailed rent information in areas of interest and make strategic decisions based on that information.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user enters information about the area they currently live in or are considering living in into the terminal. Specifically, they fill in their address, area name, or postal code in a form. The entered information is then transmitted to the system by the terminal. This becomes the basis for the next processing step.

[0078] Step 2:

[0079] The server receives regional information from the terminal as input. Using this information, it collects the latest rental market data for the relevant region from external sources. Data collection is performed by sending queries to the database using a RESTful API. Web scraping is also performed as needed. As an output of this step, a rental data set for the specified region is generated.

[0080] Step 3:

[0081] The server uses a collected rental market dataset as input to run an artificial intelligence (AI) algorithm. This algorithm analyzes the dataset and calculates the appropriate rent, taking into account local rent trends and market conditions. The AI ​​algorithm uses the machine learning framework TensorFlow. As a result, the appropriate rent value is output.

[0082] Step 4:

[0083] The server sends the calculated appropriate rent along with supporting documents to the terminal. These documents include graphs of past rent trends and market analysis reports. The terminal receives this information and presents it to the user in an easy-to-understand format. Visual elements on the user interface are used for this presentation.

[0084] Step 5:

[0085] The user directly negotiates the rental terms based on the appropriate rent and supporting documents presented by the device. If necessary, the server provides an automated negotiation function using an AI assistant. In this case, the AI ​​assistant receives the user's desired conditions as input and automatically proposes rent adjustments to the landlord. The final output is the new rental terms based on the negotiations.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] In today's real estate market, the inefficiency and lack of transparency in rental conditions are major problems for users. This problem stems from the difficulty in calculating appropriate rental fees, which in turn complicates negotiations and hinders smooth contract procedures and settlements. As a result, users may find it difficult to enter into contracts under fair conditions. Therefore, there is a need for a system that automatically calculates appropriate rental fees and supports smooth negotiations and settlements.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes means for acquiring location data from a user, means for collecting real estate market information from an external recording medium, means for calculating an appropriate usage fee that takes regional characteristics into account based on the acquired location data and collected real estate market information using generative artificial intelligence technology, and means for supporting contract procedures and settlements based on this. This enables the presentation of an appropriate usage fee and the efficient execution of negotiations and settlements based on it.

[0091] "Location data" refers to specific geographical information about a user's location, and is data used to identify individual locations.

[0092] "External storage media" refers to databases or information sources that are accessible to the server and contain information related to the rental market.

[0093] "Real estate market information" refers to information that includes all data related to the rental market, such as prices, trends, and transaction history of rental properties in a specific area.

[0094] "Generative artificial intelligence technology" is a technology that uses algorithms to analyze acquired data, detect patterns and trends, and automatically calculate appropriate usage fees.

[0095] "Appropriate rent" refers to rent that is calculated fairly and reasonably, taking into account the local market conditions and characteristics.

[0096] "Contract procedures" refer to the series of processes involved in determining the rental terms and concluding a formal contract based on mutual agreement.

[0097] "Method of payment" refers to the method used to make payments arising from a lease agreement, and this includes cash, electronic payments, and other similar methods.

[0098] To implement this invention, the following system is constructed. The user uses a smartphone to input location data of their desired place of residence into an application. The input location data is transmitted from the terminal to a server.

[0099] The server uses the Google® Maps API to obtain detailed geographical information from the entered location data. Furthermore, the server connects to an external storage medium to collect real estate market information for the relevant area. This real estate market information includes historical rental price data and trend information.

[0100] Next, the server runs a generated AI model using TensorFlow, which is built in Python. This AI model calculates an appropriate usage fee that takes regional characteristics into account, based on the acquired location data and real estate market information.

[0101] The calculated appropriate usage fee is notified to the terminal and presented to the user. Based on the presented amount, the server automatically negotiates the rental terms via an AI agent developed with Node.js. If the automated negotiation is successful, the server processes the user's electronic payment using the Stripe API.

[0102] As a concrete example, suppose a user enters an area around the Yamanote Line as their desired place to live. The server collects market data for that area in real time, calculates a fair rent, and presents it to the user. At that time, it automatically negotiates based on the presented rent and uses the Stripe API to settle the initial costs required for the contract.

[0103] An example of a prompt to a generating AI model is, "Please tell me a reasonable rent around the Yamanote Line. Please provide an estimate based on the latest information, taking into account past trends." Based on this prompt, the model calculates a reasonable usage fee.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The user opens a smartphone application and enters location data for their desired place of residence. This entered location data is sent from the device to the server. The input here is the name or address information of a specific place of residence and is sent to the server as a request to retrieve geographic information.

[0107] Step 2:

[0108] The server uses the Google Maps API to obtain detailed geographical information related to the received location data. By receiving location data as input and making API requests, it obtains detailed geographical data such as specific geographic coordinates and surrounding information as output.

[0109] Step 3:

[0110] The server accesses an external storage medium to collect real estate market information for the relevant area. The input for this step is very specific geographical information, and the output includes historical rental prices and trend data. The server then executes this data using database queries.

[0111] Step 4:

[0112] The server runs an AI model generated using TensorFlow, which is built in Python. Location data and market information are used as input. Based on this input, the server performs data processing and calculations, and the model calculates an appropriate usage fee that takes regional characteristics into account, and the appropriate usage fee is obtained as output.

[0113] Step 5:

[0114] The calculated appropriate usage fee is sent from the server to the terminal and presented to the user. Specifically, detailed information regarding the appropriate usage fee is output and displayed on the terminal screen.

[0115] Step 6:

[0116] The user requests that the system automatically negotiate a lease agreement based on the proposed fair usage fee. The input here is the user's request to execute the negotiation, and the AI ​​agent on the server automatically carries out the negotiation.

[0117] Step 7:

[0118] If the negotiation is successful, the server prepares to process the user's electronic payment using the Stripe API. Inputs include the negotiation result and payment details, while output is confirmation of the completed electronic payment. Specific actions include verifying the payment information and executing the transaction.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention aims to provide more accurate and personalized responses by incorporating a function that recognizes the user's emotions into a system that assists in negotiations in rental agreements. This system calculates appropriate rent and provides negotiation support based on geographical information, and by adding an emotion engine, it becomes possible to make suggestions that take into account the user's psychological reactions.

[0121] First, the user inputs geographical information about their residential area via their device. After input, this information is sent to a server, which then retrieves relevant rental market information from an external database. Using this information, the server employs a generative artificial intelligence model to calculate a fair rental price. This calculation takes into account regional characteristics and market trends.

[0122] Next, the server sends the calculated appropriate rent and related supporting documents to the terminal and presents them to the user. At this time, the emotion engine operates and recognizes the user's emotions through the terminal. The emotion engine analyzes the user's emotions from their voice and facial expressions, and the server uses the results to adjust the content and method of presentation. For example, if the server detects that the user is feeling anxious, it can provide more detailed information and additional explanations to reassure them.

[0123] Furthermore, if a user requests that an AI agent negotiate on their behalf, the server will select a negotiation communication strategy based on emotional data and support the user in securing more favorable terms. The entire negotiation process is managed by the server, and the results are sent to the terminal and notified to the user.

[0124] For example, if a user is dissatisfied with high rent, the emotion engine can recognize this dissatisfaction, and the server can propose a stronger negotiating position during the negotiation. This allows the user to achieve a more satisfying outcome with the support of AI that complements their own emotions.

[0125] This invention enables users to conduct reasonable and emotionally considerate negotiations in rental agreements, providing an effective means of achieving a better living environment.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user enters geographical information about their residential area via their device and presses the submit button. The entered information is then sent to the server.

[0129] Step 2:

[0130] Based on the geographical information it receives, the server quickly collects rental market information for the relevant area from an external database. The collected data includes information on average rental rates and types of properties in the area.

[0131] Step 3:

[0132] The server inputs the collected data into an artificial intelligence model to calculate appropriate rents that take into account regional characteristics and market trends. This reveals the optimal rent range that users should consider.

[0133] Step 4:

[0134] The server sends the calculated appropriate rent and its supporting evidence to the terminal. The terminal visually displays this information to the user, allowing the user to verify the content.

[0135] Step 5:

[0136] The device uses a built-in emotion engine to recognize the user's emotional state from their voice tone and facial expressions. The emotion engine analyzes the user's responses in real time.

[0137] Step 6:

[0138] The server receives the analysis results from the emotion engine and dynamically adjusts how information is presented according to the user's emotional state. If the server determines that the user is experiencing stress, it adds supplementary information or more detailed explanations to provide reassurance.

[0139] Step 7:

[0140] If the user chooses to have the AI ​​agent negotiate on their behalf, the server will formulate a negotiation strategy based on emotional data and flexibly negotiate the rental terms. The negotiation results will be analyzed by the server and reported to the user via the terminal.

[0141] This series of steps helps users obtain reasonable rental terms accompanied by a sense of emotional security.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] Traditional rental contract negotiations often rely solely on standard price calculations, lacking negotiation support that considers the user's emotions and psychological state. As a result, users frequently receive insufficient support to overcome their emotional and psychological burdens, leading to dissatisfaction with the outcome of the contract negotiations. Furthermore, automated negotiation support often fails to optimize negotiation results because strategic adjustments are not made to account for the user's psychological factors.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for acquiring location information from the user, means for collecting rental market data from external sources, means for calculating an appropriate price that takes regional characteristics into account based on the acquired location information and collected rental market data using generative artificial intelligence technology, means for recognizing the user's emotional state, and means for adjusting the information presented based on the emotional recognition. This makes it possible to support rational and individualized rental contract negotiations while addressing the user's emotions and psychological burden.

[0147] "User" refers to an individual or group that uses the system, inputs the necessary information, and ultimately enjoys the results of the lease agreement.

[0148] "Location information" refers to information provided by users to the system, such as addresses and regional names, that identifies a specific geographical area.

[0149] "External information sources" refer to external databases and information services used to collect rental market data, and are means of providing relevant information.

[0150] "Rental market data" refers to a series of data related to the rental market in a specific area, such as rent, property information, and supply and demand trends.

[0151] "Generative artificial intelligence technology" refers to AI models and algorithms used to perform calculations and analyses based on geographic information and market data.

[0152] "Fair price" refers to a rental property price that is considered fair and reasonable, calculated based on set conditions and regional characteristics.

[0153] "Emotional state" refers to the psychological and emotional state of a user, as analyzed from their voice and facial expressions, and is a factor that influences information provision and negotiation strategies.

[0154] "Information presented" refers to the type and format of information provided by the system to the user, and the method of presentation is adjusted based on the user's emotional state.

[0155] One embodiment of the present invention is a negotiation support system for rental agreements that provides a mechanism for making appropriate price offers and conducting negotiations while taking into account the user's feelings.

[0156] The user first uses a terminal to input location information about their residential area. This information includes specific geographical details such as address and postal code. The terminal then sends this information to the server.

[0157] The server collects rental market data from multiple external databases and information services. This data includes regional characteristics, historical rental rates, and current supply and demand balances. This data plays a crucial role in subsequent calculations.

[0158] Next, the server uses a generative AI model to calculate a fair price based on the collected information. This process utilizes machine learning algorithms and data analysis techniques to perform advanced analysis on the acquired data. The calculated fair price reflects regional characteristics and market trends.

[0159] The calculation results, along with supporting documents, are sent to the user's device and presented in an easy-to-understand format. At this point, the emotion engine operates, analyzing the user's emotional state by recognizing their voice and facial expressions.

[0160] Based on this emotional state, the server adjusts the information it presents and how it presents it. For example, if the emotion engine detects that the user is feeling anxious, it will provide additional explanations and information to reassure them.

[0161] Furthermore, if a user requests AI-powered negotiation assistance, the server will use emotional data to develop a negotiation strategy and provide support to help secure the best possible terms. The entire negotiation process is managed by the system, and the results are also communicated to the user's device.

[0162] For example, if a user is unhappy with high rent, the emotion engine recognizes this dissatisfaction, and the server proposes a stronger negotiation position during the negotiation. This support allows the user to obtain negotiation results that take into account their own emotions and psychological factors.

[0163] An example of a prompt message is, "Please tell me how to use an AI model to calculate a fair price based on the user's residential area location information, analyze their emotions, and provide negotiation support." Based on this prompt, the system performs appropriate data processing and analysis to provide information that meets the user's needs.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The user uses a terminal to enter location information for their residential area. Specifically, they enter their address and postal code, which is then sent to the server.

[0167] Step 2:

[0168] The server collects rental market data from external sources based on the received location information. It uses an API to access the database and retrieve data on historical and current rental rates and market trends for that area. The input here is location information, and the output is rental market data.

[0169] Step 3:

[0170] The server inputs location information and rental market data into a generating AI model to calculate a fair price. The AI ​​model considers regional characteristics and market trends, and calculates the optimal rent through data analysis and machine learning. The output is the calculated fair price.

[0171] Step 4:

[0172] The server sends the calculated fair price and related supporting documents to the terminal. These documents include the data and calculation criteria used and are presented to the user. The inputs here are the fair price and supporting documents, while the output is the information presented to the user.

[0173] Step 5:

[0174] The device activates an emotion engine to recognize the user's voice and facial expressions. Sensors capture the user's voice tone and facial expressions, and send this data to a server for analysis of their psychological tendencies. The input for this step is the user's voice and facial expressions, and the output is the result of the emotion analysis.

[0175] Step 6:

[0176] The server adjusts the information presented based on the sentiment analysis results received from the terminal. For example, if the user's emotions are anxious, the server will provide additional detailed explanations to help them feel more at ease. Here, the input is the result of the sentiment analysis, and the output is the adjusted information presentation.

[0177] Step 7:

[0178] If a user requests AI-driven negotiation, the server constructs a negotiation strategy incorporating emotional data. The generated AI model is then refined using prompts to optimize the negotiation strategy. The input for this step is the user's wishes and emotional data, while the output is the negotiation strategy.

[0179] Step 8:

[0180] The server sends the negotiation results to the terminal and notifies the user of the details. This includes the negotiated rental terms and instructions for the next steps. The input here is the negotiation result, and the output is the final notification to the user.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0183] In modern contract negotiations and decision-making, meticulous attention to the emotions and psychological state of users is required. However, this aspect is often neglected in current systems, which can place a psychological burden on users. Therefore, providing support that takes emotions into consideration and achieving smoother and more satisfying contract negotiations is a major challenge.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes means for acquiring location information from a user, means for collecting market information from external information resources, means for calculating a standard price that takes regional characteristics into account based on the acquired location information and collected market information using a generative artificial intelligence model, means for performing emotion recognition and adjusting the presented content based on the user's emotions, and means for presenting the calculated standard price to the user. This enables personalized information presentation and negotiation support that responds to the user's emotions.

[0186] "Location information" refers to data that indicates a specific place or point, representing the geographical coordinates of the user or object.

[0187] "External information resources" refer to external databases or information services that store or provide specific data.

[0188] "Market information" refers to data that shows the trading status and price trends of a specific product, and is information used to understand the current state of commercial activity in a particular region or time period.

[0189] A "generative artificial intelligence model" is a type of machine learning model that learns patterns from given data and uses them to generate or predict new data.

[0190] "Emotion recognition" is a technology that analyzes a user's voice and facial expressions to infer their psychological state and emotions.

[0191] "Standard price" refers to a reasonable and appropriate price standard calculated taking into account market information and regional characteristics.

[0192] "Adjusting the presentation content" is the process of modifying or changing the way information is presented and its content in accordance with the user's needs and emotions, based on the results of emotion recognition.

[0193] The system for realizing this invention is achieved through the coordinated operation of each component. A server acts as the central component, and this is realized using a location information acquisition device, an external information resource access device, an emotion recognition device, a generative artificial intelligence model, and a user interface.

[0194] The server uses location information obtained from the user's terminal to access external information resources via the internet and collect market information. Based on this collected data, the server applies a generative artificial intelligence model to calculate a standard price that takes regional characteristics into account.

[0195] Subsequently, the user's emotions are evaluated using an emotion recognition device. Emotion recognition utilizes the device's camera and microphone, employing facial recognition and speech analysis technologies (e.g., speech synthesis systems, image analysis algorithms). Following this, the information presentation method is adjusted according to the user's emotional state. The server generates the optimal information presentation method based on the emotion as the result of an artificial intelligence model. For example, if the user is feeling anxious, the information is made easier to understand, and detailed explanations are added to provide reassurance.

[0196] For example, if the server determines that a user is experiencing stress while reviewing their monthly expenses, it can offer advice on standard pricing and budget management strategies. An example of a prompt to the AI ​​in such a situation would be: "If the system analyzes that the user is experiencing financial stress, please generate budget management advice that is both helpful and specific."

[0197] The entire system utilizes a Python-based backend, emotion recognition using Microsoft Azure Cognitive Services, and OpenAI's GPT series as a generative AI. In this way, it is possible to provide rational and effective contract negotiation support that takes user emotions into consideration.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The user inputs location information using a device. The device receives this information and sends it to the server. The input location information is geographic coordinate data, and this data forms the basis for subsequent processing.

[0201] Step 2:

[0202] Based on the received location information, the server accesses external information resources and collects relevant market information. The output of this process is rental market trend data for the target area. The server retains this data for subsequent processing.

[0203] Step 3:

[0204] The server combines market information and location information and uses a generative AI model to calculate standard prices. The inputs are market information and geographical information, and after processing by the model, a standard price that takes regional characteristics into account is output.

[0205] Step 4:

[0206] The terminal captures the user's voice and facial expression data and sends it to the server. The input consists of voice data and image data. The server uses this data to activate an emotion recognition device and analyze the user's emotional state. The results of this analysis influence the next process.

[0207] Step 5:

[0208] The server adjusts the information presentation method based on the sentiment analysis results. The inputs are the sentiment analysis results and the calculated standard price. The processing here involves determining the content and tone of the information to be presented and instructing the generative AI model to produce an appropriate output format.

[0209] Step 6:

[0210] The server sends adjusted standard price information to the terminal and presents it to the user. The terminal displays the calculated price and supplementary information to the user. At this time, the information is presented in a way that is easiest for the user to understand.

[0211] Step 7:

[0212] If a user wishes to negotiate an additional contract, they send a request from their device to the server. The server receives this request, prompts an AI model to generate a negotiation strategy based on the standard price and sentiment analysis results, and then provides the user with feedback on the strategy. This feedback becomes the final output, providing effective negotiation support.

[0213] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0214] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0219] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0220] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0221] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0222] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0223] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0224] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0226] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0227] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0228] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0229] This invention provides a system that calculates appropriate rent in rental agreements and assists users in easily negotiating rent. An embodiment of the system according to the present invention is shown below.

[0230] First, the user inputs geographical information about their residential area via a terminal and sends it to the system. Based on this information, the server automatically collects the latest rental market information related to the specified area from an external database.

[0231] Next, the server uses a generative artificial intelligence model to analyze the collected data. This AI model calculates an appropriate rent based on the acquired rental market information and regional characteristics. Through this process, rents that reflect local rent market rates and trends in real time are calculated.

[0232] The calculated appropriate rent is transmitted to the terminal via the server and presented to the user. This presentation includes supporting documents as the basis for the rent, which the user can use to help negotiate rental terms.

[0233] For example, if a user inputs information about a specific area in Tokyo, the server calculates a fair rent based on the average rent and past trends in that area, and displays the result via the terminal. Furthermore, if necessary, the server can provide an automated negotiation function via an AI agent, negotiating rent with the landlord on behalf of the user.

[0234] This system allows users to easily obtain accurate rent information and facilitate smoother rental negotiations. By implementing this system, users can enjoy fairer and more transparent terms in their rental agreements.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The user uses a terminal to enter their address and geographical information about their residential area. Once the input is complete, this information is sent to the server.

[0238] Step 2:

[0239] Based on the geographical information it receives, the server selects an appropriate external database and collects the latest rental market information for the specified area. This includes rental property prices and trends for each region.

[0240] Step 3:

[0241] The server organizes the collected rental market information and analyzes the data using a generative artificial intelligence model. This analysis calculates an appropriate rent based on current market conditions, taking regional characteristics into account.

[0242] Step 4:

[0243] The calculated appropriate rent information and supporting documents are transmitted from the server to the terminal. The terminal presents this information to the user in a format that makes it easy to compare with the current rent.

[0244] Step 5:

[0245] Users can choose whether or not to negotiate rent based on the presented fair rent. Alternatively, users can request the server to handle automated negotiations via an AI agent.

[0246] Step 6:

[0247] Based on the user's selection, the server automatically initiates negotiations with the landlord via an AI agent as needed. The negotiation results are reported to the terminal via the server and presented to the user.

[0248] Step 7:

[0249] Ultimately, the server will notify the user via their terminal about the next steps and confirmations based on the negotiation results. This will allow the user to make an informed decision regarding the lease agreement.

[0250] (Example 1)

[0251] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0252] Collecting and analyzing information to determine appropriate rents requires considerable time and effort, and there is a challenge in that it is difficult for users to negotiate rental terms based on sufficient information. A system is needed to solve this problem and facilitate rental agreements with fairer and more transparent terms for users.

[0253] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0254] In this invention, the server includes means for acquiring local information from users, means for collecting rental market data from external information sources, and means for using an artificial intelligence algorithm to derive an appropriate rent that takes local characteristics into account based on the acquired local information and the collected rental market data. As a result, users can quickly and accurately grasp an appropriate rent based on the latest information on the rental market and negotiate rental conditions rationally based on that information.

[0255] "User" refers to an individual or organization that uses this system to obtain information related to rental agreements and to conduct negotiations.

[0256] "Local information" refers to data about a specific geographical area necessary for rental market analysis, including addresses, postal codes, and area names.

[0257] "External information sources" refer to databases and platforms that provide data on the rental market, and are public and private data sources accessible via the internet.

[0258] "Rental market data" refers to a collection of information such as rental property prices, trends, vacancy rates, and historical transaction data for a specific area.

[0259] An "artificial intelligence algorithm" refers to a program or system that uses machine learning and data analysis techniques to analyze rental market data and calculate appropriate rent.

[0260] "Appropriate rent" refers to a fair and market-appropriate rental property fee calculated considering rental market data and regional characteristics.

[0261] This invention includes a system that provides users with fair rent and assists in negotiating rental terms. This system primarily operates as follows:

[0262] The user first enters information about their current or prospective location into the terminal. This information typically includes a specific address, area name, or postal code. Once the user has finished entering the information, the terminal sends it to the server.

[0263] The server collects rental market data for the relevant region from external sources based on the received regional information. These external sources include fixed databases and online information services. This data collection process may utilize technologies such as RESTful APIs and web scraping.

[0264] Subsequently, the server uses generative artificial intelligence (AI) algorithms to analyze the collected rental market data and local information. The AI ​​algorithms utilize models built using machine learning frameworks such as TensorFlow and Scikit-learn. This algorithm processes the data to calculate appropriate rents that take local characteristics into account.

[0265] Once the calculation of the appropriate rent is complete, the server sends that information, along with supporting documents, to the terminal. The terminal uses a graphical user interface to present this information to the user in an easy-to-understand manner. This includes price trend graphs and summaries of market analysis reports.

[0266] Furthermore, if necessary, users can select an option within the system, and the server can use an AI assistant to automatically begin negotiating rental terms. This reduces the user's negotiation burden and helps them conclude a rental agreement on better terms.

[0267] Examples of specific prompts include phrases like, "Please tell me the appropriate rent for a 2LDK apartment in XX ward," or "Please analyze the rent trends in this area." By using these prompts, users can quickly obtain detailed rent information in areas of interest and make strategic decisions based on that information.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The user enters information about the area they currently live in or are considering living in into the terminal. Specifically, they fill in their address, area name, or postal code in a form. The entered information is then transmitted to the system by the terminal. This becomes the basis for the next processing step.

[0271] Step 2:

[0272] The server receives regional information from the terminal as input. Using this information, it collects the latest rental market data for the relevant region from external sources. Data collection is performed by sending queries to the database using a RESTful API. Web scraping is also performed as needed. As an output of this step, a rental data set for the specified region is generated.

[0273] Step 3:

[0274] The server uses a collected rental market dataset as input to run an artificial intelligence (AI) algorithm. This algorithm analyzes the dataset and calculates the appropriate rent, taking into account local rent trends and market conditions. The AI ​​algorithm uses the machine learning framework TensorFlow. As a result, the appropriate rent value is output.

[0275] Step 4:

[0276] The server sends the calculated appropriate rent along with supporting documents to the terminal. These documents include graphs of past rent trends and market analysis reports. The terminal receives this information and presents it to the user in an easy-to-understand format. Visual elements on the user interface are used for this presentation.

[0277] Step 5:

[0278] The user directly negotiates the rental terms based on the appropriate rent and supporting documents presented by the device. If necessary, the server provides an automated negotiation function using an AI assistant. In this case, the AI ​​assistant receives the user's desired conditions as input and automatically proposes rent adjustments to the landlord. The final output is the new rental terms based on the negotiations.

[0279] (Application Example 1)

[0280] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0281] In today's real estate market, the inefficiency and lack of transparency in rental conditions are major problems for users. This problem stems from the difficulty in calculating appropriate rental fees, which in turn complicates negotiations and hinders smooth contract procedures and settlements. As a result, users may find it difficult to enter into contracts under fair conditions. Therefore, there is a need for a system that automatically calculates appropriate rental fees and supports smooth negotiations and settlements.

[0282] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0283] In this invention, the server includes means for obtaining location data from a user, means for collecting real estate market information from an external recording medium, means for calculating an appropriate usage fee considering regional characteristics based on the obtained location data and the collected real estate market information using generative artificial intelligence technology, and means for assisting contract procedures and settlement based thereon. As a result, it becomes possible to present an appropriate usage fee and efficiently execute negotiation and settlement based thereon.

[0284] "Location data" refers to specific geographical information where the user is located and is data for identifying individual locations.

[0285] "External recording medium" refers to a database or information source accessible by the server where information related to the rental market is stored.

[0286] "Real estate market information" is information including all data related to the rental market, such as the price, trend, transaction history, etc. of rental properties in a specific region.

[0287] "Generative artificial intelligence technology" is a technology that analyzes the obtained data, has an algorithm for detecting patterns and trends, and is used to automatically calculate an appropriate usage fee.

[0288] "Appropriate usage fee" refers to a rent that is fairly and reasonably calculated considering the market conditions and characteristics of the region.

[0289] "Contract procedures" refer to a series of processes for determining rental conditions and concluding a formal contract upon mutual agreement.

[0290] "Settlement means" refers to a method for executing payments arising from a rental contract, which includes cash, electronic settlement, etc.

[0291] As a form for implementing this invention, the following system is constructed. The user uses a smartphone to input location data of the desired residence into the application. The input location data is transmitted from the terminal to the server.

[0292] The server uses the Google Maps API to obtain detailed geographical information from the entered location data. Furthermore, the server connects to an external storage medium to collect real estate market information for the relevant area. This real estate market information includes historical rental price data and trend information.

[0293] Next, the server runs a generated AI model using TensorFlow, which is built in Python. This AI model calculates an appropriate usage fee that takes regional characteristics into account, based on the acquired location data and real estate market information.

[0294] The calculated appropriate usage fee is notified to the terminal and presented to the user. Based on the presented amount, the server automatically negotiates the rental terms via an AI agent developed with Node.js. If the automated negotiation is successful, the server processes the user's electronic payment using the Stripe API.

[0295] As a concrete example, suppose a user enters an area around the Yamanote Line as their desired place to live. The server collects market data for that area in real time, calculates a fair rent, and presents it to the user. At that time, it automatically negotiates based on the presented rent and uses the Stripe API to settle the initial costs required for the contract.

[0296] An example of a prompt to a generating AI model is, "Please tell me a reasonable rent around the Yamanote Line. Please provide an estimate based on the latest information, taking into account past trends." Based on this prompt, the model calculates a reasonable usage fee.

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The user opens the smartphone application and enters the location data of the desired residence. This entered location data is sent from the terminal to the server. The input here is the name or address information of a specific residence, which is sent to the server as a request for obtaining geographical information.

[0300] Step 2:

[0301] The server uses the Google Maps API to obtain detailed geographical information related to the received location data. By receiving the location data as input and making an API request, detailed geographical data such as specific geographical coordinates and surrounding information are obtained as output.

[0302] Step 3:

[0303] The server accesses an external recording medium and collects real estate market information for the corresponding area. The input for this step is very specific geographical information, and past rental prices and trend data are collected as output. The server executes this using a database query.

[0304] Step 4:

[0305] The server executes a generative AI model built with TensorFlow in Python. Location data and market information are used as input. The server performs data processing and calculations based on this input, calculates an appropriate usage fee considering regional characteristics using the model, and obtains the appropriate usage fee as output.

[0306] Step 5:

[0307] The calculated appropriate usage fee is sent from the server to the terminal and presented to the user. Specifically, detailed information regarding the appropriate usage fee is output in a form displayed on the terminal screen.

[0308] Step 6:

[0309] The user requests that the system automatically negotiate a lease agreement based on the proposed fair usage fee. The input here is the user's request to execute the negotiation, and the AI ​​agent on the server automatically carries out the negotiation.

[0310] Step 7:

[0311] If the negotiation is successful, the server prepares to process the user's electronic payment using the Stripe API. Inputs include the negotiation result and payment details, while output is confirmation of the completed electronic payment. Specific actions include verifying the payment information and executing the transaction.

[0312] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0313] This invention aims to provide more accurate and personalized responses by incorporating a function that recognizes the user's emotions into a system that assists in negotiations in rental agreements. This system calculates appropriate rent and provides negotiation support based on geographical information, and by adding an emotion engine, it becomes possible to make suggestions that take into account the user's psychological reactions.

[0314] First, the user inputs geographical information about their residential area via their device. After input, this information is sent to a server, which then retrieves relevant rental market information from an external database. Using this information, the server employs a generative artificial intelligence model to calculate a fair rental price. This calculation takes into account regional characteristics and market trends.

[0315] Next, the server sends the calculated appropriate rent and related supporting documents to the terminal and presents them to the user. At this time, the emotion engine operates and recognizes the user's emotions through the terminal. The emotion engine analyzes the user's emotions from their voice and facial expressions, and the server uses the results to adjust the content and method of presentation. For example, if the server detects that the user is feeling anxious, it can provide more detailed information and additional explanations to reassure them.

[0316] Furthermore, if a user requests that an AI agent negotiate on their behalf, the server will select a negotiation communication strategy based on emotional data and support the user in securing more favorable terms. The entire negotiation process is managed by the server, and the results are sent to the terminal and notified to the user.

[0317] For example, if a user is dissatisfied with high rent, the emotion engine can recognize this dissatisfaction, and the server can propose a stronger negotiating position during the negotiation. This allows the user to achieve a more satisfying outcome with the support of AI that complements their own emotions.

[0318] This invention enables users to conduct reasonable and emotionally considerate negotiations in rental agreements, providing an effective means of achieving a better living environment.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The user enters geographical information about their residential area via their device and presses the submit button. The entered information is then sent to the server.

[0322] Step 2:

[0323] Based on the geographical information it receives, the server quickly collects rental market information for the relevant area from an external database. The collected data includes information on average rental rates and types of properties in the area.

[0324] Step 3:

[0325] The server inputs the collected data into an artificial intelligence model to calculate appropriate rents that take into account regional characteristics and market trends. This reveals the optimal rent range that users should consider.

[0326] Step 4:

[0327] The server sends the calculated appropriate rent and its supporting evidence to the terminal. The terminal visually displays this information to the user, allowing the user to verify the content.

[0328] Step 5:

[0329] The device uses a built-in emotion engine to recognize the user's emotional state from their voice tone and facial expressions. The emotion engine analyzes the user's responses in real time.

[0330] Step 6:

[0331] The server receives the analysis results from the emotion engine and dynamically adjusts how information is presented according to the user's emotional state. If the server determines that the user is experiencing stress, it adds supplementary information or more detailed explanations to provide reassurance.

[0332] Step 7:

[0333] If the user chooses to have the AI ​​agent negotiate on their behalf, the server will formulate a negotiation strategy based on emotional data and flexibly negotiate the rental terms. The negotiation results will be analyzed by the server and reported to the user via the terminal.

[0334] This series of steps helps users obtain reasonable rental terms accompanied by a sense of emotional security.

[0335] (Example 2)

[0336] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0337] Traditional rental contract negotiations often rely solely on standard price calculations, lacking negotiation support that considers the user's emotions and psychological state. As a result, users frequently receive insufficient support to overcome their emotional and psychological burdens, leading to dissatisfaction with the outcome of the contract negotiations. Furthermore, automated negotiation support often fails to optimize negotiation results because strategic adjustments are not made to account for the user's psychological factors.

[0338] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0339] In this invention, the server includes means for acquiring location information from the user, means for collecting rental market data from external sources, means for calculating an appropriate price that takes regional characteristics into account based on the acquired location information and collected rental market data using generative artificial intelligence technology, means for recognizing the user's emotional state, and means for adjusting the information presented based on the emotional recognition. This makes it possible to support rational and individualized rental contract negotiations while addressing the user's emotions and psychological burden.

[0340] "User" refers to an individual or group that uses the system, inputs the necessary information, and ultimately enjoys the results of the lease agreement.

[0341] "Location information" refers to information provided by users to the system, such as addresses and regional names, that identifies a specific geographical area.

[0342] "External information sources" refer to external databases and information services used to collect rental market data, and are means of providing relevant information.

[0343] "Rental market data" refers to a series of data related to the rental market in a specific area, such as rent, property information, and supply and demand trends.

[0344] "Generative artificial intelligence technology" refers to AI models and algorithms used to perform calculations and analyses based on geographic information and market data.

[0345] "Fair price" refers to a rental property price that is considered fair and reasonable, calculated based on set conditions and regional characteristics.

[0346] "Emotional state" refers to the psychological and emotional state of a user, as analyzed from their voice and facial expressions, and is a factor that influences information provision and negotiation strategies.

[0347] "Information presented" refers to the type and format of information provided by the system to the user, and the method of presentation is adjusted based on the user's emotional state.

[0348] One embodiment of the present invention is a negotiation support system for rental agreements that provides a mechanism for making appropriate price offers and conducting negotiations while taking into account the user's feelings.

[0349] The user first uses a terminal to input location information about their residential area. This information includes specific geographical details such as address and postal code. The terminal then sends this information to the server.

[0350] The server collects rental market data from multiple external databases and information services. This data includes regional characteristics, historical rental rates, and current supply and demand balances. This data plays a crucial role in subsequent calculations.

[0351] Next, the server uses a generative AI model to calculate a fair price based on the collected information. This process utilizes machine learning algorithms and data analysis techniques to perform advanced analysis on the acquired data. The calculated fair price reflects regional characteristics and market trends.

[0352] The calculation results, along with supporting documents, are sent to the user's device and presented in an easy-to-understand format. At this point, the emotion engine operates, analyzing the user's emotional state by recognizing their voice and facial expressions.

[0353] Based on this emotional state, the server adjusts the information it presents and how it presents it. For example, if the emotion engine detects that the user is feeling anxious, it will provide additional explanations and information to reassure them.

[0354] Furthermore, if a user requests AI-powered negotiation assistance, the server will use emotional data to develop a negotiation strategy and provide support to help secure the best possible terms. The entire negotiation process is managed by the system, and the results are also communicated to the user's device.

[0355] For example, if a user is unhappy with high rent, the emotion engine recognizes this dissatisfaction, and the server proposes a stronger negotiation position during the negotiation. This support allows the user to obtain negotiation results that take into account their own emotions and psychological factors.

[0356] An example of a prompt message is, "Please tell me how to use an AI model to calculate a fair price based on the user's residential area location information, analyze their emotions, and provide negotiation support." Based on this prompt, the system performs appropriate data processing and analysis to provide information that meets the user's needs.

[0357] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0358] Step 1:

[0359] The user uses a terminal to enter location information for their residential area. Specifically, they enter their address and postal code, which is then sent to the server.

[0360] Step 2:

[0361] The server collects rental market data from external sources based on the received location information. It uses an API to access the database and retrieve data on historical and current rental rates and market trends for that area. The input here is location information, and the output is rental market data.

[0362] Step 3:

[0363] The server inputs location information and rental market data into a generating AI model to calculate a fair price. The AI ​​model considers regional characteristics and market trends, and calculates the optimal rent through data analysis and machine learning. The output is the calculated fair price.

[0364] Step 4:

[0365] The server sends the calculated fair price and related supporting documents to the terminal. These documents include the data and calculation criteria used and are presented to the user. The inputs here are the fair price and supporting documents, while the output is the information presented to the user.

[0366] Step 5:

[0367] The device activates an emotion engine to recognize the user's voice and facial expressions. Sensors capture the user's voice tone and facial expressions, and send this data to a server for analysis of their psychological tendencies. The input for this step is the user's voice and facial expressions, and the output is the result of the emotion analysis.

[0368] Step 6:

[0369] The server adjusts the information presented based on the sentiment analysis results received from the terminal. For example, if the user's emotions are anxious, the server will provide additional detailed explanations to help them feel more at ease. Here, the input is the result of the sentiment analysis, and the output is the adjusted information presentation.

[0370] Step 7:

[0371] If a user requests AI-driven negotiation, the server constructs a negotiation strategy incorporating emotional data. The generated AI model is then refined using prompts to optimize the negotiation strategy. The input for this step is the user's wishes and emotional data, while the output is the negotiation strategy.

[0372] Step 8:

[0373] The server sends the negotiation results to the terminal and notifies the user of the details. This includes the negotiated rental terms and instructions for the next steps. The input here is the negotiation result, and the output is the final notification to the user.

[0374] (Application Example 2)

[0375] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0376] In modern contract negotiations and decision-making, meticulous attention to the emotions and psychological state of users is required. However, this aspect is often neglected in current systems, which can place a psychological burden on users. Therefore, providing support that takes emotions into consideration and achieving smoother and more satisfying contract negotiations is a major challenge.

[0377] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0378] In this invention, the server includes means for acquiring location information from a user, means for collecting market information from external information resources, means for calculating a standard price that takes regional characteristics into account based on the acquired location information and collected market information using a generative artificial intelligence model, means for performing emotion recognition and adjusting the presented content based on the user's emotions, and means for presenting the calculated standard price to the user. This enables personalized information presentation and negotiation support that responds to the user's emotions.

[0379] "Location information" refers to data that indicates a specific place or point, representing the geographical coordinates of the user or object.

[0380] "External information resources" refer to external databases or information services that store or provide specific data.

[0381] "Market information" refers to data that shows the trading status and price trends of a specific product, and is information used to understand the current state of commercial activity in a particular region or time period.

[0382] A "generative artificial intelligence model" is a type of machine learning model that learns patterns from given data and uses them to generate or predict new data.

[0383] "Emotion recognition" is a technology that analyzes a user's voice and facial expressions to infer their psychological state and emotions.

[0384] "Standard price" refers to a reasonable and appropriate price standard calculated taking into account market information and regional characteristics.

[0385] "Adjusting the presentation content" is the process of modifying or changing the way information is presented and its content in accordance with the user's needs and emotions, based on the results of emotion recognition.

[0386] The system for realizing this invention is achieved through the coordinated operation of each component. A server acts as the central component, and this is realized using a location information acquisition device, an external information resource access device, an emotion recognition device, a generative artificial intelligence model, and a user interface.

[0387] The server uses location information obtained from the user's terminal to access external information resources via the internet and collect market information. Based on this collected data, the server applies a generative artificial intelligence model to calculate a standard price that takes regional characteristics into account.

[0388] Subsequently, the user's emotions are evaluated using an emotion recognition device. Emotion recognition utilizes the device's camera and microphone, employing facial recognition and speech analysis technologies (e.g., speech synthesis systems, image analysis algorithms). Following this, the information presentation method is adjusted according to the user's emotional state. The server generates the optimal information presentation method based on the emotion as the result of an artificial intelligence model. For example, if the user is feeling anxious, the information is made easier to understand, and detailed explanations are added to provide reassurance.

[0389] For example, if the server determines that a user is experiencing stress while reviewing their monthly expenses, it can offer advice on standard pricing and budget management strategies. An example of a prompt to the AI ​​in such a situation would be: "If the system analyzes that the user is experiencing financial stress, please generate budget management advice that is both helpful and specific."

[0390] The entire system utilizes a Python-based backend, Microsoft Azure Cognitive Services for emotion recognition, and OpenAI's GPT series as the generative AI. In this way, it is possible to provide rational and effective contract negotiation support that takes user emotions into consideration.

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The user inputs location information using a device. The device receives this information and sends it to the server. The input location information is geographic coordinate data, and this data forms the basis for subsequent processing.

[0394] Step 2:

[0395] Based on the received location information, the server accesses external information resources and collects relevant market information. The output of this process is rental market trend data for the target area. The server retains this data for subsequent processing.

[0396] Step 3:

[0397] The server combines market information and location information and uses a generative AI model to calculate standard prices. The inputs are market information and geographical information, and after processing by the model, a standard price that takes regional characteristics into account is output.

[0398] Step 4:

[0399] The terminal captures the user's voice and facial expression data and sends it to the server. The input consists of voice data and image data. The server uses this data to activate an emotion recognition device and analyze the user's emotional state. The results of this analysis influence the next process.

[0400] Step 5:

[0401] The server adjusts the information presentation method based on the sentiment analysis results. The inputs are the sentiment analysis results and the calculated standard price. The processing here involves determining the content and tone of the information to be presented and instructing the generative AI model to produce an appropriate output format.

[0402] Step 6:

[0403] The server sends adjusted standard price information to the terminal and presents it to the user. The terminal displays the calculated price and supplementary information to the user. At this time, the information is presented in a way that is easiest for the user to understand.

[0404] Step 7:

[0405] If a user wishes to negotiate an additional contract, they send a request from their device to the server. The server receives this request, prompts an AI model to generate a negotiation strategy based on the standard price and sentiment analysis results, and then provides the user with feedback on the strategy. This feedback becomes the final output, providing effective negotiation support.

[0406] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0412] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0413] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0414] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0415] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0416] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0417] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0418] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0419] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0420] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0421] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0422] This invention provides a system that calculates appropriate rent in rental agreements and assists users in easily negotiating rent. An embodiment of the system according to the present invention is shown below.

[0423] First, the user inputs geographical information about their residential area via a terminal and sends it to the system. Based on this information, the server automatically collects the latest rental market information related to the specified area from an external database.

[0424] Next, the server uses a generative artificial intelligence model to analyze the collected data. This AI model calculates an appropriate rent based on the acquired rental market information and regional characteristics. Through this process, rents that reflect local rent market rates and trends in real time are calculated.

[0425] The calculated appropriate rent is transmitted to the terminal via the server and presented to the user. This presentation includes supporting documents as the basis for the rent, which the user can use to help negotiate rental terms.

[0426] For example, if a user inputs information about a specific area in Tokyo, the server calculates a fair rent based on the average rent and past trends in that area, and displays the result via the terminal. Furthermore, if necessary, the server can provide an automated negotiation function via an AI agent, negotiating rent with the landlord on behalf of the user.

[0427] This system allows users to easily obtain accurate rent information and facilitate smoother rental negotiations. By implementing this system, users can enjoy fairer and more transparent terms in their rental agreements.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The user uses a terminal to enter their address and geographical information about their residential area. Once the input is complete, this information is sent to the server.

[0431] Step 2:

[0432] Based on the geographical information it receives, the server selects an appropriate external database and collects the latest rental market information for the specified area. This includes rental property prices and trends for each region.

[0433] Step 3:

[0434] The server organizes the collected rental market information and analyzes the data using a generative artificial intelligence model. This analysis calculates an appropriate rent based on current market conditions, taking regional characteristics into account.

[0435] Step 4:

[0436] The calculated appropriate rent information and supporting documents are transmitted from the server to the terminal. The terminal presents this information to the user in a format that makes it easy to compare with the current rent.

[0437] Step 5:

[0438] Users can choose whether or not to negotiate rent based on the presented fair rent. Alternatively, users can request the server to handle automated negotiations via an AI agent.

[0439] Step 6:

[0440] Based on the user's selection, the server automatically initiates negotiations with the landlord via an AI agent as needed. The negotiation results are reported to the terminal via the server and presented to the user.

[0441] Step 7:

[0442] Ultimately, the server will notify the user via their terminal about the next steps and confirmations based on the negotiation results. This will allow the user to make an informed decision regarding the lease agreement.

[0443] (Example 1)

[0444] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0445] Collecting and analyzing information to determine appropriate rents requires considerable time and effort, and there is a challenge in that it is difficult for users to negotiate rental terms based on sufficient information. A system is needed to solve this problem and facilitate rental agreements with fairer and more transparent terms for users.

[0446] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0447] In this invention, the server includes means for acquiring local information from users, means for collecting rental market data from external information sources, and means for using an artificial intelligence algorithm to derive an appropriate rent that takes local characteristics into account based on the acquired local information and the collected rental market data. As a result, users can quickly and accurately grasp an appropriate rent based on the latest information on the rental market and negotiate rental conditions rationally based on that information.

[0448] "User" refers to an individual or organization that uses this system to obtain information related to rental agreements and to conduct negotiations.

[0449] "Local information" refers to data about a specific geographical area necessary for rental market analysis, including addresses, postal codes, and area names.

[0450] "External information sources" refer to databases and platforms that provide data on the rental market, and are public and private data sources accessible via the internet.

[0451] "Rental market data" refers to a collection of information such as rental property prices, trends, vacancy rates, and historical transaction data for a specific area.

[0452] An "artificial intelligence algorithm" refers to a program or system that uses machine learning and data analysis techniques to analyze rental market data and calculate appropriate rent.

[0453] "Appropriate rent" refers to a fair and market-appropriate rental property fee calculated considering rental market data and regional characteristics.

[0454] This invention includes a system that provides users with fair rent and assists in negotiating rental terms. This system primarily operates as follows:

[0455] The user first enters information about their current or prospective location into the terminal. This information typically includes a specific address, area name, or postal code. Once the user has finished entering the information, the terminal sends it to the server.

[0456] The server collects rental market data for the relevant region from external sources based on the received regional information. These external sources include fixed databases and online information services. This data collection process may utilize technologies such as RESTful APIs and web scraping.

[0457] Subsequently, the server uses generative artificial intelligence (AI) algorithms to analyze the collected rental market data and local information. The AI ​​algorithms utilize models built using machine learning frameworks such as TensorFlow and Scikit-learn. This algorithm processes the data to calculate appropriate rents that take local characteristics into account.

[0458] Once the calculation of the appropriate rent is complete, the server sends that information, along with supporting documents, to the terminal. The terminal uses a graphical user interface to present this information to the user in an easy-to-understand manner. This includes price trend graphs and summaries of market analysis reports.

[0459] Furthermore, if necessary, users can select an option within the system, and the server can use an AI assistant to automatically begin negotiating rental terms. This reduces the user's negotiation burden and helps them conclude a rental agreement on better terms.

[0460] Examples of specific prompts include phrases like, "Please tell me the appropriate rent for a 2LDK apartment in XX ward," or "Please analyze the rent trends in this area." By using these prompts, users can quickly obtain detailed rent information in areas of interest and make strategic decisions based on that information.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The user enters information about the area they currently live in or are considering living in into the terminal. Specifically, they fill in their address, area name, or postal code in a form. The entered information is then transmitted to the system by the terminal. This becomes the basis for the next processing step.

[0464] Step 2:

[0465] The server receives regional information from the terminal as input. Using this information, it collects the latest rental market data for the relevant region from external sources. Data collection is performed by sending queries to the database using a RESTful API. Web scraping is also performed as needed. As an output of this step, a rental data set for the specified region is generated.

[0466] Step 3:

[0467] The server uses a collected rental market dataset as input to run an artificial intelligence (AI) algorithm. This algorithm analyzes the dataset and calculates the appropriate rent, taking into account local rent trends and market conditions. The AI ​​algorithm uses the machine learning framework TensorFlow. As a result, the appropriate rent value is output.

[0468] Step 4:

[0469] The server sends the calculated appropriate rent along with supporting documents to the terminal. These documents include graphs of past rent trends and market analysis reports. The terminal receives this information and presents it to the user in an easy-to-understand format. Visual elements on the user interface are used for this presentation.

[0470] Step 5:

[0471] The user directly negotiates the rental terms based on the appropriate rent and supporting documents presented by the device. If necessary, the server provides an automated negotiation function using an AI assistant. In this case, the AI ​​assistant receives the user's desired conditions as input and automatically proposes rent adjustments to the landlord. The final output is the new rental terms based on the negotiations.

[0472] (Application Example 1)

[0473] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0474] In today's real estate market, the inefficiency and lack of transparency in rental conditions are major problems for users. This problem stems from the difficulty in calculating appropriate rental fees, which in turn complicates negotiations and hinders smooth contract procedures and settlements. As a result, users may find it difficult to enter into contracts under fair conditions. Therefore, there is a need for a system that automatically calculates appropriate rental fees and supports smooth negotiations and settlements.

[0475] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0476] In this invention, the server includes means for acquiring location data from a user, means for collecting real estate market information from an external recording medium, means for calculating an appropriate usage fee that takes regional characteristics into account based on the acquired location data and collected real estate market information using generative artificial intelligence technology, and means for supporting contract procedures and settlements based on this. This enables the presentation of an appropriate usage fee and the efficient execution of negotiations and settlements based on it.

[0477] "Location data" refers to specific geographical information about a user's location, and is data used to identify individual locations.

[0478] "External storage media" refers to databases or information sources that are accessible to the server and contain information related to the rental market.

[0479] "Real estate market information" refers to information that includes all data related to the rental market, such as prices, trends, and transaction history of rental properties in a specific area.

[0480] "Generative artificial intelligence technology" is a technology that uses algorithms to analyze acquired data, detect patterns and trends, and automatically calculate appropriate usage fees.

[0481] "Appropriate rent" refers to rent that is calculated fairly and reasonably, taking into account the local market conditions and characteristics.

[0482] "Contract procedures" refer to the series of processes involved in determining the rental terms and concluding a formal contract based on mutual agreement.

[0483] "Method of payment" refers to the method used to make payments arising from a lease agreement, and this includes cash, electronic payments, and other similar methods.

[0484] To implement this invention, the following system is constructed. The user uses a smartphone to input location data of their desired place of residence into an application. The input location data is transmitted from the terminal to a server.

[0485] The server uses the Google Maps API to obtain detailed geographical information from the entered location data. Furthermore, the server connects to an external storage medium to collect real estate market information for the relevant area. This real estate market information includes historical rental price data and trend information.

[0486] Next, the server runs a generated AI model using TensorFlow, which is built in Python. This AI model calculates an appropriate usage fee that takes regional characteristics into account, based on the acquired location data and real estate market information.

[0487] The calculated appropriate usage fee is notified to the terminal and presented to the user. Based on the presented amount, the server automatically negotiates the rental terms via an AI agent developed with Node.js. If the automated negotiation is successful, the server processes the user's electronic payment using the Stripe API.

[0488] As a concrete example, suppose a user enters an area around the Yamanote Line as their desired place to live. The server collects market data for that area in real time, calculates a fair rent, and presents it to the user. At that time, it automatically negotiates based on the presented rent and uses the Stripe API to settle the initial costs required for the contract.

[0489] An example of a prompt to a generating AI model is, "Please tell me a reasonable rent around the Yamanote Line. Please provide an estimate based on the latest information, taking into account past trends." Based on this prompt, the model calculates a reasonable usage fee.

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] The user opens a smartphone application and enters location data for their desired place of residence. This entered location data is sent from the device to the server. The input here is the name or address information of a specific place of residence and is sent to the server as a request to retrieve geographic information.

[0493] Step 2:

[0494] The server uses the Google Maps API to obtain detailed geographical information related to the received location data. By receiving location data as input and making API requests, it obtains detailed geographical data such as specific geographic coordinates and surrounding information as output.

[0495] Step 3:

[0496] The server accesses an external storage medium to collect real estate market information for the relevant area. The input for this step is very specific geographical information, and the output includes historical rental prices and trend data. The server then executes this data using database queries.

[0497] Step 4:

[0498] The server runs an AI model generated using TensorFlow, which is built in Python. Location data and market information are used as input. Based on this input, the server performs data processing and calculations, and the model calculates an appropriate usage fee that takes regional characteristics into account, and the appropriate usage fee is obtained as output.

[0499] Step 5:

[0500] The calculated appropriate usage fee is sent from the server to the terminal and presented to the user. Specifically, detailed information regarding the appropriate usage fee is output and displayed on the terminal screen.

[0501] Step 6:

[0502] The user requests that the system automatically negotiate a lease agreement based on the proposed fair usage fee. The input here is the user's request to execute the negotiation, and the AI ​​agent on the server automatically carries out the negotiation.

[0503] Step 7:

[0504] If the negotiation is successful, the server prepares to process the user's electronic payment using the Stripe API. Inputs include the negotiation result and payment details, while output is confirmation of the completed electronic payment. Specific actions include verifying the payment information and executing the transaction.

[0505] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0506] This invention aims to provide more accurate and personalized responses by incorporating a function that recognizes the user's emotions into a system that assists in negotiations in rental agreements. This system calculates appropriate rent and provides negotiation support based on geographical information, and by adding an emotion engine, it becomes possible to make suggestions that take into account the user's psychological reactions.

[0507] First, the user inputs geographical information about their residential area via their device. After input, this information is sent to a server, which then retrieves relevant rental market information from an external database. Using this information, the server employs a generative artificial intelligence model to calculate a fair rental price. This calculation takes into account regional characteristics and market trends.

[0508] Next, the server sends the calculated appropriate rent and related supporting documents to the terminal and presents them to the user. At this time, the emotion engine operates and recognizes the user's emotions through the terminal. The emotion engine analyzes the user's emotions from their voice and facial expressions, and the server uses the results to adjust the content and method of presentation. For example, if the server detects that the user is feeling anxious, it can provide more detailed information and additional explanations to reassure them.

[0509] Furthermore, if a user requests that an AI agent negotiate on their behalf, the server will select a negotiation communication strategy based on emotional data and support the user in securing more favorable terms. The entire negotiation process is managed by the server, and the results are sent to the terminal and notified to the user.

[0510] For example, if a user is dissatisfied with high rent, the emotion engine can recognize this dissatisfaction, and the server can propose a stronger negotiating position during the negotiation. This allows the user to achieve a more satisfying outcome with the support of AI that complements their own emotions.

[0511] This invention enables users to conduct reasonable and emotionally considerate negotiations in rental agreements, providing an effective means of achieving a better living environment.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The user enters geographical information about their residential area via their device and presses the submit button. The entered information is then sent to the server.

[0515] Step 2:

[0516] Based on the geographical information it receives, the server quickly collects rental market information for the relevant area from an external database. The collected data includes information on average rental rates and types of properties in the area.

[0517] Step 3:

[0518] The server inputs the collected data into an artificial intelligence model to calculate appropriate rents that take into account regional characteristics and market trends. This reveals the optimal rent range that users should consider.

[0519] Step 4:

[0520] The server sends the calculated appropriate rent and its supporting evidence to the terminal. The terminal visually displays this information to the user, allowing the user to verify the content.

[0521] Step 5:

[0522] The device uses a built-in emotion engine to recognize the user's emotional state from their voice tone and facial expressions. The emotion engine analyzes the user's responses in real time.

[0523] Step 6:

[0524] The server receives the analysis results from the emotion engine and dynamically adjusts how information is presented according to the user's emotional state. If the server determines that the user is experiencing stress, it adds supplementary information or more detailed explanations to provide reassurance.

[0525] Step 7:

[0526] If the user chooses to have the AI ​​agent negotiate on their behalf, the server will formulate a negotiation strategy based on emotional data and flexibly negotiate the rental terms. The negotiation results will be analyzed by the server and reported to the user via the terminal.

[0527] This series of steps helps users obtain reasonable rental terms accompanied by a sense of emotional security.

[0528] (Example 2)

[0529] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0530] Traditional rental contract negotiations often rely solely on standard price calculations, lacking negotiation support that considers the user's emotions and psychological state. As a result, users frequently receive insufficient support to overcome their emotional and psychological burdens, leading to dissatisfaction with the outcome of the contract negotiations. Furthermore, automated negotiation support often fails to optimize negotiation results because strategic adjustments are not made to account for the user's psychological factors.

[0531] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0532] In this invention, the server includes means for acquiring location information from the user, means for collecting rental market data from external sources, means for calculating an appropriate price that takes regional characteristics into account based on the acquired location information and collected rental market data using generative artificial intelligence technology, means for recognizing the user's emotional state, and means for adjusting the information presented based on the emotional recognition. This makes it possible to support rational and individualized rental contract negotiations while addressing the user's emotions and psychological burden.

[0533] "User" refers to an individual or group that uses the system, inputs the necessary information, and ultimately enjoys the results of the lease agreement.

[0534] "Location information" refers to information provided by users to the system, such as addresses and regional names, that identifies a specific geographical area.

[0535] "External information sources" refer to external databases and information services used to collect rental market data, and are means of providing relevant information.

[0536] "Rental market data" refers to a series of data related to the rental market in a specific area, such as rent, property information, and supply and demand trends.

[0537] "Generative artificial intelligence technology" refers to AI models and algorithms used to perform calculations and analyses based on geographic information and market data.

[0538] "Fair price" refers to a rental property price that is considered fair and reasonable, calculated based on set conditions and regional characteristics.

[0539] "Emotional state" refers to the psychological and emotional state of a user, as analyzed from their voice and facial expressions, and is a factor that influences information provision and negotiation strategies.

[0540] "Information presented" refers to the type and format of information provided by the system to the user, and the method of presentation is adjusted based on the user's emotional state.

[0541] One embodiment of the present invention is a negotiation support system for rental agreements that provides a mechanism for making appropriate price offers and conducting negotiations while taking into account the user's feelings.

[0542] The user first uses a terminal to input location information about their residential area. This information includes specific geographical details such as address and postal code. The terminal then sends this information to the server.

[0543] The server collects rental market data from multiple external databases and information services. This data includes regional characteristics, historical rental rates, and current supply and demand balances. This data plays a crucial role in subsequent calculations.

[0544] Next, the server uses a generative AI model to calculate a fair price based on the collected information. This process utilizes machine learning algorithms and data analysis techniques to perform advanced analysis on the acquired data. The calculated fair price reflects regional characteristics and market trends.

[0545] The calculation results, along with supporting documents, are sent to the user's device and presented in an easy-to-understand format. At this point, the emotion engine operates, analyzing the user's emotional state by recognizing their voice and facial expressions.

[0546] Based on this emotional state, the server adjusts the information it presents and how it presents it. For example, if the emotion engine detects that the user is feeling anxious, it will provide additional explanations and information to reassure them.

[0547] Furthermore, if a user requests AI-powered negotiation assistance, the server will use emotional data to develop a negotiation strategy and provide support to help secure the best possible terms. The entire negotiation process is managed by the system, and the results are also communicated to the user's device.

[0548] For example, if a user is unhappy with high rent, the emotion engine recognizes this dissatisfaction, and the server proposes a stronger negotiation position during the negotiation. This support allows the user to obtain negotiation results that take into account their own emotions and psychological factors.

[0549] An example of a prompt message is, "Please tell me how to use an AI model to calculate a fair price based on the user's residential area location information, analyze their emotions, and provide negotiation support." Based on this prompt, the system performs appropriate data processing and analysis to provide information that meets the user's needs.

[0550] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0551] Step 1:

[0552] The user uses a terminal to enter location information for their residential area. Specifically, they enter their address and postal code, which is then sent to the server.

[0553] Step 2:

[0554] The server collects rental market data from external sources based on the received location information. It uses an API to access the database and retrieve data on historical and current rental rates and market trends for that area. The input here is location information, and the output is rental market data.

[0555] Step 3:

[0556] The server inputs location information and rental market data into a generating AI model to calculate a fair price. The AI ​​model considers regional characteristics and market trends, and calculates the optimal rent through data analysis and machine learning. The output is the calculated fair price.

[0557] Step 4:

[0558] The server sends the calculated fair price and related supporting documents to the terminal. These documents include the data and calculation criteria used and are presented to the user. The inputs here are the fair price and supporting documents, while the output is the information presented to the user.

[0559] Step 5:

[0560] The device activates an emotion engine to recognize the user's voice and facial expressions. Sensors capture the user's voice tone and facial expressions, and send this data to a server for analysis of their psychological tendencies. The input for this step is the user's voice and facial expressions, and the output is the result of the emotion analysis.

[0561] Step 6:

[0562] The server adjusts the information presented based on the sentiment analysis results received from the terminal. For example, if the user's emotions are anxious, the server will provide additional detailed explanations to help them feel more at ease. Here, the input is the result of the sentiment analysis, and the output is the adjusted information presentation.

[0563] Step 7:

[0564] If a user requests AI-driven negotiation, the server constructs a negotiation strategy incorporating emotional data. The generated AI model is then refined using prompts to optimize the negotiation strategy. The input for this step is the user's wishes and emotional data, while the output is the negotiation strategy.

[0565] Step 8:

[0566] The server sends the negotiation results to the terminal and notifies the user of the details. This includes the negotiated rental terms and instructions for the next steps. The input here is the negotiation result, and the output is the final notification to the user.

[0567] (Application Example 2)

[0568] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0569] In modern contract negotiations and decision-making, meticulous attention to the emotions and psychological state of users is required. However, this aspect is often neglected in current systems, which can place a psychological burden on users. Therefore, providing support that takes emotions into consideration and achieving smoother and more satisfying contract negotiations is a major challenge.

[0570] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0571] In this invention, the server includes means for acquiring location information from a user, means for collecting market information from external information resources, means for calculating a standard price that takes regional characteristics into account based on the acquired location information and collected market information using a generative artificial intelligence model, means for performing emotion recognition and adjusting the presented content based on the user's emotions, and means for presenting the calculated standard price to the user. This enables personalized information presentation and negotiation support that responds to the user's emotions.

[0572] "Location information" refers to data that indicates a specific place or point, representing the geographical coordinates of the user or object.

[0573] "External information resources" refer to external databases or information services that store or provide specific data.

[0574] "Market information" refers to data that shows the trading status and price trends of a specific product, and is information used to understand the current state of commercial activity in a particular region or time period.

[0575] A "generative artificial intelligence model" is a type of machine learning model that learns patterns from given data and uses them to generate or predict new data.

[0576] "Emotion recognition" is a technology that analyzes a user's voice and facial expressions to infer their psychological state and emotions.

[0577] "Standard price" refers to a reasonable and appropriate price standard calculated taking into account market information and regional characteristics.

[0578] "Adjusting the presentation content" is the process of modifying or changing the way information is presented and its content in accordance with the user's needs and emotions, based on the results of emotion recognition.

[0579] The system for realizing this invention is achieved through the coordinated operation of each component. A server acts as the central component, and this is realized using a location information acquisition device, an external information resource access device, an emotion recognition device, a generative artificial intelligence model, and a user interface.

[0580] The server uses location information obtained from the user's terminal to access external information resources via the internet and collect market information. Based on this collected data, the server applies a generative artificial intelligence model to calculate a standard price that takes regional characteristics into account.

[0581] Subsequently, the user's emotions are evaluated using an emotion recognition device. Emotion recognition utilizes the device's camera and microphone, employing facial recognition and speech analysis technologies (e.g., speech synthesis systems, image analysis algorithms). Following this, the information presentation method is adjusted according to the user's emotional state. The server generates the optimal information presentation method based on the emotion as the result of an artificial intelligence model. For example, if the user is feeling anxious, the information is made easier to understand, and detailed explanations are added to provide reassurance.

[0582] For example, if the server determines that a user is experiencing stress while reviewing their monthly expenses, it can offer advice on standard pricing and budget management strategies. An example of a prompt to the AI ​​in such a situation would be: "If the system analyzes that the user is experiencing financial stress, please generate budget management advice that is both helpful and specific."

[0583] The entire system utilizes a Python-based backend, Microsoft Azure Cognitive Services for emotion recognition, and OpenAI's GPT series as the generative AI. In this way, it is possible to provide rational and effective contract negotiation support that takes user emotions into consideration.

[0584] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0585] Step 1:

[0586] The user inputs location information using a device. The device receives this information and sends it to the server. The input location information is geographic coordinate data, and this data forms the basis for subsequent processing.

[0587] Step 2:

[0588] Based on the received location information, the server accesses external information resources and collects relevant market information. The output of this process is rental market trend data for the target area. The server retains this data for subsequent processing.

[0589] Step 3:

[0590] The server combines market information and location information and uses a generative AI model to calculate standard prices. The inputs are market information and geographical information, and after processing by the model, a standard price that takes regional characteristics into account is output.

[0591] Step 4:

[0592] The terminal captures the user's voice and facial expression data and sends it to the server. The input consists of voice data and image data. The server uses this data to activate an emotion recognition device and analyze the user's emotional state. The results of this analysis influence the next process.

[0593] Step 5:

[0594] The server adjusts the information presentation method based on the sentiment analysis results. The inputs are the sentiment analysis results and the calculated standard price. The processing here involves determining the content and tone of the information to be presented and instructing the generative AI model to produce an appropriate output format.

[0595] Step 6:

[0596] The server sends adjusted standard price information to the terminal and presents it to the user. The terminal displays the calculated price and supplementary information to the user. At this time, the information is presented in a way that is easiest for the user to understand.

[0597] Step 7:

[0598] If a user wishes to negotiate an additional contract, they send a request from their device to the server. The server receives this request, prompts an AI model to generate a negotiation strategy based on the standard price and sentiment analysis results, and then provides the user with feedback on the strategy. This feedback becomes the final output, providing effective negotiation support.

[0599] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0600] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0601] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0602] [Fourth Embodiment]

[0603] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0604] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0605] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0606] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0607] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0608] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0609] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0610] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0611] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0612] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0613] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0614] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0615] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0616] This invention provides a system that calculates appropriate rent in rental agreements and assists users in easily negotiating rent. An embodiment of the system according to the present invention is shown below.

[0617] First, the user inputs geographical information about their residential area via a terminal and sends it to the system. Based on this information, the server automatically collects the latest rental market information related to the specified area from an external database.

[0618] Next, the server uses a generative artificial intelligence model to analyze the collected data. This AI model calculates an appropriate rent based on the acquired rental market information and regional characteristics. Through this process, rents that reflect local rent market rates and trends in real time are calculated.

[0619] The calculated appropriate rent is transmitted to the terminal via the server and presented to the user. This presentation includes supporting documents as the basis for the rent, which the user can use to help negotiate rental terms.

[0620] For example, if a user inputs information about a specific area in Tokyo, the server calculates a fair rent based on the average rent and past trends in that area, and displays the result via the terminal. Furthermore, if necessary, the server can provide an automated negotiation function via an AI agent, negotiating rent with the landlord on behalf of the user.

[0621] This system allows users to easily obtain accurate rent information and facilitate smoother rental negotiations. By implementing this system, users can enjoy fairer and more transparent terms in their rental agreements.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The user uses a terminal to enter their address and geographical information about their residential area. Once the input is complete, this information is sent to the server.

[0625] Step 2:

[0626] Based on the geographical information it receives, the server selects an appropriate external database and collects the latest rental market information for the specified area. This includes rental property prices and trends for each region.

[0627] Step 3:

[0628] The server organizes the collected rental market information and analyzes the data using a generative artificial intelligence model. This analysis calculates an appropriate rent based on current market conditions, taking regional characteristics into account.

[0629] Step 4:

[0630] The calculated appropriate rent information and supporting documents are transmitted from the server to the terminal. The terminal presents this information to the user in a format that makes it easy to compare with the current rent.

[0631] Step 5:

[0632] Users can choose whether or not to negotiate rent based on the presented fair rent. Alternatively, users can request the server to handle automated negotiations via an AI agent.

[0633] Step 6:

[0634] Based on the user's selection, the server automatically initiates negotiations with the landlord via an AI agent as needed. The negotiation results are reported to the terminal via the server and presented to the user.

[0635] Step 7:

[0636] Ultimately, the server will notify the user via their terminal about the next steps and confirmations based on the negotiation results. This will allow the user to make an informed decision regarding the lease agreement.

[0637] (Example 1)

[0638] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] Collecting and analyzing information to determine appropriate rents requires considerable time and effort, and there is a challenge in that it is difficult for users to negotiate rental terms based on sufficient information. A system is needed to solve this problem and facilitate rental agreements with fairer and more transparent terms for users.

[0640] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0641] In this invention, the server includes means for acquiring local information from users, means for collecting rental market data from external information sources, and means for using an artificial intelligence algorithm to derive an appropriate rent that takes local characteristics into account based on the acquired local information and the collected rental market data. As a result, users can quickly and accurately grasp an appropriate rent based on the latest information on the rental market and negotiate rental conditions rationally based on that information.

[0642] "User" refers to an individual or organization that uses this system to obtain information related to rental agreements and to conduct negotiations.

[0643] "Local information" refers to data about a specific geographical area necessary for rental market analysis, including addresses, postal codes, and area names.

[0644] "External information sources" refer to databases and platforms that provide data on the rental market, and are public and private data sources accessible via the internet.

[0645] "Rental market data" refers to a collection of information such as rental property prices, trends, vacancy rates, and historical transaction data for a specific area.

[0646] An "artificial intelligence algorithm" refers to a program or system that uses machine learning and data analysis techniques to analyze rental market data and calculate appropriate rent.

[0647] "Appropriate rent" refers to a fair and market-appropriate rental property fee calculated considering rental market data and regional characteristics.

[0648] This invention includes a system that provides users with fair rent and assists in negotiating rental terms. This system primarily operates as follows:

[0649] The user first enters information about their current or prospective location into the terminal. This information typically includes a specific address, area name, or postal code. Once the user has finished entering the information, the terminal sends it to the server.

[0650] The server collects rental market data for the relevant region from external sources based on the received regional information. These external sources include fixed databases and online information services. This data collection process may utilize technologies such as RESTful APIs and web scraping.

[0651] Subsequently, the server uses generative artificial intelligence (AI) algorithms to analyze the collected rental market data and local information. The AI ​​algorithms utilize models built using machine learning frameworks such as TensorFlow and Scikit-learn. This algorithm processes the data to calculate appropriate rents that take local characteristics into account.

[0652] Once the calculation of the appropriate rent is complete, the server sends that information, along with supporting documents, to the terminal. The terminal uses a graphical user interface to present this information to the user in an easy-to-understand manner. This includes price trend graphs and summaries of market analysis reports.

[0653] Furthermore, if necessary, users can select an option within the system, and the server can use an AI assistant to automatically begin negotiating rental terms. This reduces the user's negotiation burden and helps them conclude a rental agreement on better terms.

[0654] Examples of specific prompts include phrases like, "Please tell me the appropriate rent for a 2LDK apartment in XX ward," or "Please analyze the rent trends in this area." By using these prompts, users can quickly obtain detailed rent information in areas of interest and make strategic decisions based on that information.

[0655] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0656] Step 1:

[0657] The user enters information about the area they currently live in or are considering living in into the terminal. Specifically, they fill in their address, area name, or postal code in a form. The entered information is then transmitted to the system by the terminal. This becomes the basis for the next processing step.

[0658] Step 2:

[0659] The server receives regional information from the terminal as input. Using this information, it collects the latest rental market data for the relevant region from external sources. Data collection is performed by sending queries to the database using a RESTful API. Web scraping is also performed as needed. As an output of this step, a rental data set for the specified region is generated.

[0660] Step 3:

[0661] The server uses a collected rental market dataset as input to run an artificial intelligence (AI) algorithm. This algorithm analyzes the dataset and calculates the appropriate rent, taking into account local rent trends and market conditions. The AI ​​algorithm uses the machine learning framework TensorFlow. As a result, the appropriate rent value is output.

[0662] Step 4:

[0663] The server sends the calculated appropriate rent along with supporting documents to the terminal. These documents include graphs of past rent trends and market analysis reports. The terminal receives this information and presents it to the user in an easy-to-understand format. Visual elements on the user interface are used for this presentation.

[0664] Step 5:

[0665] The user directly negotiates the rental terms based on the appropriate rent and supporting documents presented by the device. If necessary, the server provides an automated negotiation function using an AI assistant. In this case, the AI ​​assistant receives the user's desired conditions as input and automatically proposes rent adjustments to the landlord. The final output is the new rental terms based on the negotiations.

[0666] (Application Example 1)

[0667] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0668] In today's real estate market, the inefficiency and lack of transparency in rental conditions are major problems for users. This problem stems from the difficulty in calculating appropriate rental fees, which in turn complicates negotiations and hinders smooth contract procedures and settlements. As a result, users may find it difficult to enter into contracts under fair conditions. Therefore, there is a need for a system that automatically calculates appropriate rental fees and supports smooth negotiations and settlements.

[0669] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0670] In this invention, the server includes means for acquiring location data from a user, means for collecting real estate market information from an external recording medium, means for calculating an appropriate usage fee that takes regional characteristics into account based on the acquired location data and collected real estate market information using generative artificial intelligence technology, and means for supporting contract procedures and settlements based on this. This enables the presentation of an appropriate usage fee and the efficient execution of negotiations and settlements based on it.

[0671] "Location data" refers to specific geographical information about a user's location, and is data used to identify individual locations.

[0672] "External storage media" refers to databases or information sources that are accessible to the server and contain information related to the rental market.

[0673] "Real estate market information" refers to information that includes all data related to the rental market, such as prices, trends, and transaction history of rental properties in a specific area.

[0674] "Generative artificial intelligence technology" is a technology that uses algorithms to analyze acquired data, detect patterns and trends, and automatically calculate appropriate usage fees.

[0675] "Appropriate rent" refers to rent that is calculated fairly and reasonably, taking into account the local market conditions and characteristics.

[0676] "Contract procedures" refer to the series of processes involved in determining the rental terms and concluding a formal contract based on mutual agreement.

[0677] "Method of payment" refers to the method used to make payments arising from a lease agreement, and this includes cash, electronic payments, and other similar methods.

[0678] To implement this invention, the following system is constructed. The user uses a smartphone to input location data of their desired place of residence into an application. The input location data is transmitted from the terminal to a server.

[0679] The server uses the Google Maps API to obtain detailed geographical information from the entered location data. Furthermore, the server connects to an external storage medium to collect real estate market information for the relevant area. This real estate market information includes historical rental price data and trend information.

[0680] Next, the server runs a generated AI model using TensorFlow, which is built in Python. This AI model calculates an appropriate usage fee that takes regional characteristics into account, based on the acquired location data and real estate market information.

[0681] The calculated appropriate usage fee is notified to the terminal and presented to the user. Based on the presented amount, the server automatically negotiates the rental terms via an AI agent developed with Node.js. If the automated negotiation is successful, the server processes the user's electronic payment using the Stripe API.

[0682] As a concrete example, suppose a user enters an area around the Yamanote Line as their desired place to live. The server collects market data for that area in real time, calculates a fair rent, and presents it to the user. At that time, it automatically negotiates based on the presented rent and uses the Stripe API to settle the initial costs required for the contract.

[0683] An example of a prompt to a generating AI model is, "Please tell me a reasonable rent around the Yamanote Line. Please provide an estimate based on the latest information, taking into account past trends." Based on this prompt, the model calculates a reasonable usage fee.

[0684] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0685] Step 1:

[0686] The user opens a smartphone application and enters location data for their desired place of residence. This entered location data is sent from the device to the server. The input here is the name or address information of a specific place of residence and is sent to the server as a request to retrieve geographic information.

[0687] Step 2:

[0688] The server uses the Google Maps API to obtain detailed geographical information related to the received location data. By receiving location data as input and making API requests, it obtains detailed geographical data such as specific geographic coordinates and surrounding information as output.

[0689] Step 3:

[0690] The server accesses an external storage medium to collect real estate market information for the relevant area. The input for this step is very specific geographical information, and the output includes historical rental prices and trend data. The server then executes this data using database queries.

[0691] Step 4:

[0692] The server runs an AI model generated using TensorFlow, which is built in Python. Location data and market information are used as input. Based on this input, the server performs data processing and calculations, and the model calculates an appropriate usage fee that takes regional characteristics into account, and the appropriate usage fee is obtained as output.

[0693] Step 5:

[0694] The calculated appropriate usage fee is sent from the server to the terminal and presented to the user. Specifically, detailed information regarding the appropriate usage fee is output and displayed on the terminal screen.

[0695] Step 6:

[0696] The user requests that the system automatically negotiate a lease agreement based on the proposed fair usage fee. The input here is the user's request to execute the negotiation, and the AI ​​agent on the server automatically carries out the negotiation.

[0697] Step 7:

[0698] If the negotiation is successful, the server prepares to process the user's electronic payment using the Stripe API. Inputs include the negotiation result and payment details, while output is confirmation of the completed electronic payment. Specific actions include verifying the payment information and executing the transaction.

[0699] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0700] This invention aims to provide more accurate and personalized responses by incorporating a function that recognizes the user's emotions into a system that assists in negotiations in rental agreements. This system calculates appropriate rent and provides negotiation support based on geographical information, and by adding an emotion engine, it becomes possible to make suggestions that take into account the user's psychological reactions.

[0701] First, the user inputs geographical information about their residential area via their device. After input, this information is sent to a server, which then retrieves relevant rental market information from an external database. Using this information, the server employs a generative artificial intelligence model to calculate a fair rental price. This calculation takes into account regional characteristics and market trends.

[0702] Next, the server sends the calculated appropriate rent and related supporting documents to the terminal and presents them to the user. At this time, the emotion engine operates and recognizes the user's emotions through the terminal. The emotion engine analyzes the user's emotions from their voice and facial expressions, and the server uses the results to adjust the content and method of presentation. For example, if the server detects that the user is feeling anxious, it can provide more detailed information and additional explanations to reassure them.

[0703] Furthermore, if a user requests that an AI agent negotiate on their behalf, the server will select a negotiation communication strategy based on emotional data and support the user in securing more favorable terms. The entire negotiation process is managed by the server, and the results are sent to the terminal and notified to the user.

[0704] For example, if a user is dissatisfied with high rent, the emotion engine can recognize this dissatisfaction, and the server can propose a stronger negotiating position during the negotiation. This allows the user to achieve a more satisfying outcome with the support of AI that complements their own emotions.

[0705] This invention enables users to conduct reasonable and emotionally considerate negotiations in rental agreements, providing an effective means of achieving a better living environment.

[0706] The following describes the processing flow.

[0707] Step 1:

[0708] The user enters geographical information about their residential area via their device and presses the submit button. The entered information is then sent to the server.

[0709] Step 2:

[0710] Based on the geographical information it receives, the server quickly collects rental market information for the relevant area from an external database. The collected data includes information on average rental rates and types of properties in the area.

[0711] Step 3:

[0712] The server inputs the collected data into an artificial intelligence model to calculate appropriate rents that take into account regional characteristics and market trends. This reveals the optimal rent range that users should consider.

[0713] Step 4:

[0714] The server sends the calculated appropriate rent and its supporting evidence to the terminal. The terminal visually displays this information to the user, allowing the user to verify the content.

[0715] Step 5:

[0716] The device uses a built-in emotion engine to recognize the user's emotional state from their voice tone and facial expressions. The emotion engine analyzes the user's responses in real time.

[0717] Step 6:

[0718] The server receives the analysis results from the emotion engine and dynamically adjusts how information is presented according to the user's emotional state. If the server determines that the user is experiencing stress, it adds supplementary information or more detailed explanations to provide reassurance.

[0719] Step 7:

[0720] If the user chooses to have the AI ​​agent negotiate on their behalf, the server will formulate a negotiation strategy based on emotional data and flexibly negotiate the rental terms. The negotiation results will be analyzed by the server and reported to the user via the terminal.

[0721] This series of steps helps users obtain reasonable rental terms accompanied by a sense of emotional security.

[0722] (Example 2)

[0723] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] Traditional rental contract negotiations often rely solely on standard price calculations, lacking negotiation support that considers the user's emotions and psychological state. As a result, users frequently receive insufficient support to overcome their emotional and psychological burdens, leading to dissatisfaction with the outcome of the contract negotiations. Furthermore, automated negotiation support often fails to optimize negotiation results because strategic adjustments are not made to account for the user's psychological factors.

[0725] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0726] In this invention, the server includes means for acquiring location information from the user, means for collecting rental market data from external sources, means for calculating an appropriate price that takes regional characteristics into account based on the acquired location information and collected rental market data using generative artificial intelligence technology, means for recognizing the user's emotional state, and means for adjusting the information presented based on the emotional recognition. This makes it possible to support rational and individualized rental contract negotiations while addressing the user's emotions and psychological burden.

[0727] "User" refers to an individual or group that uses the system, inputs the necessary information, and ultimately enjoys the results of the lease agreement.

[0728] "Location information" refers to information provided by users to the system, such as addresses and regional names, that identifies a specific geographical area.

[0729] "External information sources" refer to external databases and information services used to collect rental market data, and are means of providing relevant information.

[0730] "Rental market data" refers to a series of data related to the rental market in a specific area, such as rent, property information, and supply and demand trends.

[0731] "Generative artificial intelligence technology" refers to AI models and algorithms used to perform calculations and analyses based on geographic information and market data.

[0732] "Fair price" refers to a rental property price that is considered fair and reasonable, calculated based on set conditions and regional characteristics.

[0733] "Emotional state" refers to the psychological and emotional state of a user, as analyzed from their voice and facial expressions, and is a factor that influences information provision and negotiation strategies.

[0734] "Information presented" refers to the type and format of information provided by the system to the user, and the method of presentation is adjusted based on the user's emotional state.

[0735] One embodiment of the present invention is a negotiation support system for rental agreements that provides a mechanism for making appropriate price offers and conducting negotiations while taking into account the user's feelings.

[0736] The user first uses a terminal to input location information about their residential area. This information includes specific geographical details such as address and postal code. The terminal then sends this information to the server.

[0737] The server collects rental market data from multiple external databases and information services. This data includes regional characteristics, historical rental rates, and current supply and demand balances. This data plays a crucial role in subsequent calculations.

[0738] Next, the server uses a generative AI model to calculate a fair price based on the collected information. This process utilizes machine learning algorithms and data analysis techniques to perform advanced analysis on the acquired data. The calculated fair price reflects regional characteristics and market trends.

[0739] The calculation results, along with supporting documents, are sent to the user's device and presented in an easy-to-understand format. At this point, the emotion engine operates, analyzing the user's emotional state by recognizing their voice and facial expressions.

[0740] Based on this emotional state, the server adjusts the information it presents and how it presents it. For example, if the emotion engine detects that the user is feeling anxious, it will provide additional explanations and information to reassure them.

[0741] Furthermore, if a user requests AI-powered negotiation assistance, the server will use emotional data to develop a negotiation strategy and provide support to help secure the best possible terms. The entire negotiation process is managed by the system, and the results are also communicated to the user's device.

[0742] For example, if a user is unhappy with high rent, the emotion engine recognizes this dissatisfaction, and the server proposes a stronger negotiation position during the negotiation. This support allows the user to obtain negotiation results that take into account their own emotions and psychological factors.

[0743] An example of a prompt message is, "Please tell me how to use an AI model to calculate a fair price based on the user's residential area location information, analyze their emotions, and provide negotiation support." Based on this prompt, the system performs appropriate data processing and analysis to provide information that meets the user's needs.

[0744] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0745] Step 1:

[0746] The user uses a terminal to enter location information for their residential area. Specifically, they enter their address and postal code, which is then sent to the server.

[0747] Step 2:

[0748] The server collects rental market data from external sources based on the received location information. It uses an API to access the database and retrieve data on historical and current rental rates and market trends for that area. The input here is location information, and the output is rental market data.

[0749] Step 3:

[0750] The server inputs location information and rental market data into a generating AI model to calculate a fair price. The AI ​​model considers regional characteristics and market trends, and calculates the optimal rent through data analysis and machine learning. The output is the calculated fair price.

[0751] Step 4:

[0752] The server sends the calculated fair price and related supporting documents to the terminal. These documents include the data and calculation criteria used and are presented to the user. The inputs here are the fair price and supporting documents, while the output is the information presented to the user.

[0753] Step 5:

[0754] The device activates an emotion engine to recognize the user's voice and facial expressions. Sensors capture the user's voice tone and facial expressions, and send this data to a server for analysis of their psychological tendencies. The input for this step is the user's voice and facial expressions, and the output is the result of the emotion analysis.

[0755] Step 6:

[0756] The server adjusts the information presented based on the sentiment analysis results received from the terminal. For example, if the user's emotions are anxious, the server will provide additional detailed explanations to help them feel more at ease. Here, the input is the result of the sentiment analysis, and the output is the adjusted information presentation.

[0757] Step 7:

[0758] If a user requests AI-driven negotiation, the server constructs a negotiation strategy incorporating emotional data. The generated AI model is then refined using prompts to optimize the negotiation strategy. The input for this step is the user's wishes and emotional data, while the output is the negotiation strategy.

[0759] Step 8:

[0760] The server sends the negotiation results to the terminal and notifies the user of the details. This includes the negotiated rental terms and instructions for the next steps. The input here is the negotiation result, and the output is the final notification to the user.

[0761] (Application Example 2)

[0762] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0763] In modern contract negotiations and decision-making, meticulous attention to the emotions and psychological state of users is required. However, this aspect is often neglected in current systems, which can place a psychological burden on users. Therefore, providing support that takes emotions into consideration and achieving smoother and more satisfying contract negotiations is a major challenge.

[0764] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0765] In this invention, the server includes means for acquiring location information from a user, means for collecting market information from external information resources, means for calculating a standard price that takes regional characteristics into account based on the acquired location information and collected market information using a generative artificial intelligence model, means for performing emotion recognition and adjusting the presented content based on the user's emotions, and means for presenting the calculated standard price to the user. This enables personalized information presentation and negotiation support that responds to the user's emotions.

[0766] "Location information" refers to data that indicates a specific place or point, representing the geographical coordinates of the user or object.

[0767] "External information resources" refer to external databases or information services that store or provide specific data.

[0768] "Market information" refers to data that shows the trading status and price trends of a specific product, and is information used to understand the current state of commercial activity in a particular region or time period.

[0769] A "generative artificial intelligence model" is a type of machine learning model that learns patterns from given data and uses them to generate or predict new data.

[0770] "Emotion recognition" is a technology that analyzes a user's voice and facial expressions to infer their psychological state and emotions.

[0771] "Standard price" refers to a reasonable and appropriate price standard calculated taking into account market information and regional characteristics.

[0772] "Adjusting the presentation content" is the process of modifying or changing the way information is presented and its content in accordance with the user's needs and emotions, based on the results of emotion recognition.

[0773] The system for realizing this invention is achieved through the coordinated operation of each component. A server acts as the central component, and this is realized using a location information acquisition device, an external information resource access device, an emotion recognition device, a generative artificial intelligence model, and a user interface.

[0774] The server uses location information obtained from the user's terminal to access external information resources via the internet and collect market information. Based on this collected data, the server applies a generative artificial intelligence model to calculate a standard price that takes regional characteristics into account.

[0775] Subsequently, the user's emotions are evaluated using an emotion recognition device. Emotion recognition utilizes the device's camera and microphone, employing facial recognition and speech analysis technologies (e.g., speech synthesis systems, image analysis algorithms). Following this, the information presentation method is adjusted according to the user's emotional state. The server generates the optimal information presentation method based on the emotion as the result of an artificial intelligence model. For example, if the user is feeling anxious, the information is made easier to understand, and detailed explanations are added to provide reassurance.

[0776] For example, if the server determines that a user is experiencing stress while reviewing their monthly expenses, it can offer advice on standard pricing and budget management strategies. An example of a prompt to the AI ​​in such a situation would be: "If the system analyzes that the user is experiencing financial stress, please generate budget management advice that is both helpful and specific."

[0777] The entire system utilizes a Python-based backend, Microsoft Azure Cognitive Services for emotion recognition, and OpenAI's GPT series as the generative AI. In this way, it is possible to provide rational and effective contract negotiation support that takes user emotions into consideration.

[0778] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0779] Step 1:

[0780] The user inputs location information using a device. The device receives this information and sends it to the server. The input location information is geographic coordinate data, and this data forms the basis for subsequent processing.

[0781] Step 2:

[0782] Based on the received location information, the server accesses external information resources and collects relevant market information. The output of this process is rental market trend data for the target area. The server retains this data for subsequent processing.

[0783] Step 3:

[0784] The server combines market information and location information and uses a generative AI model to calculate standard prices. The inputs are market information and geographical information, and after processing by the model, a standard price that takes regional characteristics into account is output.

[0785] Step 4:

[0786] The terminal captures the user's voice and facial expression data and sends it to the server. The input consists of voice data and image data. The server uses this data to activate an emotion recognition device and analyze the user's emotional state. The results of this analysis influence the next process.

[0787] Step 5:

[0788] The server adjusts the information presentation method based on the sentiment analysis results. The inputs are the sentiment analysis results and the calculated standard price. The processing here involves determining the content and tone of the information to be presented and instructing the generative AI model to produce an appropriate output format.

[0789] Step 6:

[0790] The server sends adjusted standard price information to the terminal and presents it to the user. The terminal displays the calculated price and supplementary information to the user. At this time, the information is presented in a way that is easiest for the user to understand.

[0791] Step 7:

[0792] If a user wishes to negotiate an additional contract, they send a request from their device to the server. The server receives this request, prompts an AI model to generate a negotiation strategy based on the standard price and sentiment analysis results, and then provides the user with feedback on the strategy. This feedback becomes the final output, providing effective negotiation support.

[0793] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0794] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0795] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0796] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0797] Figure 9 shows an emotion map 400 in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0798] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0799] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0800] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0801] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0802] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0803] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0804] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0805] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0806] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0807] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0808] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0809] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0810] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0811] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0812] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0813] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0814] The following is further disclosed regarding the embodiments described above.

[0815] (Claim 1)

[0816] Means of obtaining geographic information from users,

[0817] Methods for collecting rental market information from external databases,

[0818] A method for calculating appropriate rents that take regional characteristics into account, based on acquired geographic information and collected rental market information, using a generative artificial intelligence model.

[0819] A means of presenting the calculated appropriate rent to the user,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, which includes means for automatically negotiating rental terms on behalf of the user based on the calculated appropriate rent.

[0823] (Claim 3)

[0824] The system according to claim 1, comprising means for generating evidence relating to the presented appropriate rent and providing such evidence to the user.

[0825] "Example 1"

[0826] (Claim 1)

[0827] Means of obtaining local information from users,

[0828] Means of collecting rental market data from external sources,

[0829] A means of deriving appropriate rents that take regional characteristics into account, based on acquired regional information and collected rental market data, using an artificial intelligence algorithm.

[0830] A means of visually presenting the derived appropriate rent to the user,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, comprising means for automatically adjusting rental conditions based on the derived appropriate rent on behalf of the user.

[0834] (Claim 3)

[0835] The system according to claim 1, comprising means for generating evidence data relating to the presented appropriate rent and providing said evidence data to the user.

[0836] "Application Example 1"

[0837] (Claim 1)

[0838] A means of obtaining location data from users,

[0839] A means of collecting real estate market information from external storage media,

[0840] A means for calculating appropriate usage fees that take regional characteristics into account, based on acquired location data and collected real estate market information, using generative artificial intelligence technology.

[0841] A means of presenting the calculated appropriate usage fee to the user,

[0842] A means to support contract procedures and settlements based on appropriate usage fees,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, which includes a means for automatically negotiating contract terms on behalf of the user based on the calculated appropriate usage fee, and for settling the payment when an agreement is reached.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising means for generating certifying documents relating to the presented appropriate usage fee, providing such certifying documents to the user, and managing settlement information based on the contractual agreement.

[0848] "Example 2 of combining an emotion engine"

[0849] (Claim 1)

[0850] A means of obtaining location information from users,

[0851] Means of collecting rental market data from external sources,

[0852] A means for calculating a fair price that takes regional characteristics into account, based on acquired location information and collected rental market data, using generative artificial intelligence technology.

[0853] A means of presenting the calculated fair price to the user,

[0854] Means for recognizing the emotional state of the user,

[0855] A means of adjusting the content of information presented based on emotion recognition,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, including means for automatically negotiating adjustments to rental conditions based on a calculated fair price on behalf of the user.

[0859] (Claim 3)

[0860] The system according to claim 1, comprising means for generating evidence relating to the presented fair price and providing such evidence to the user.

[0861] "Application example 2 when combining with an emotional engine"

[0862] (Claim 1)

[0863] A means of obtaining location information from the user,

[0864] Means of collecting market information from external information resources,

[0865] A means for calculating a standard price that takes regional characteristics into account, based on acquired location information and collected market information, using a generative artificial intelligence model.

[0866] A means of recognizing emotions and adjusting the content presented based on the user's emotions,

[0867] A means of presenting the calculated standard price to the user,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, comprising means for automatically negotiating terms on behalf of the user based on a calculated standard price and the user's sentiment data.

[0871] (Claim 3)

[0872] The system according to claim 1, comprising means for generating evidence information relating to a presented standard price and providing such evidence information to a user. [Explanation of Symbols]

[0873] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining geographic information from users, Methods for collecting rental market information from external databases, A method for calculating appropriate rents that take regional characteristics into account, based on acquired geographic information and collected rental market information, using a generative artificial intelligence model. A means of presenting the calculated appropriate rent to the user, A system that includes this.

2. The system according to claim 1, including means for automatically negotiating rental terms on behalf of the user based on the calculated appropriate rent.

3. The system according to claim 1, comprising means for generating evidence relating to the presented appropriate rent and providing such evidence to the user.

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