System

Abstract

An object of a system according to an embodiment is to propose optimal matching based on needs and conditions of a user and to support a contract procedure.SOLUTION: A system according to an embodiment includes a need analysis unit, a matching proposal unit, and a contract support unit. The needs analysis unit analyzes needs and conditions of a user. The matching proposal unit proposes optimal matching based on the user's needs and conditions analyzed by the need analysis unit. The contract support unit supports a contract procedure after the matching proposed by the matching proposal unit is established.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to efficiently perform optimal matching based on the user's needs and conditions, and the contract procedures are complicated.

[0005] The system according to the embodiment aims to propose optimal matches based on the user's needs and conditions and to support contract procedures. [Means for solving the problem]

[0006] The system according to the embodiment includes a needs analysis unit, a matching proposal unit, and a contract support unit. The needs analysis unit analyzes the needs and conditions of a user. The matching proposal unit proposes an optimal match based on the user's needs and conditions analyzed by the needs analysis unit. The contract support unit supports contract procedures after the match proposed by the matching proposal unit is established. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal matches based on the user's needs and conditions and assist with contract procedures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The matching and contract support system according to an embodiment of the present invention is a system that uses AI to match people and businesses with different needs, such as "sellers," "renters," "buyers," "renters," and "businesses," and also provides contract support. As a result, the matching and contract support system can propose optimal matches based on the user's needs and support contract procedures.

[0029] A matching and contract support system according to an embodiment includes a needs analysis unit, a match proposal unit, and a contract support unit. The needs analysis unit analyzes a user's needs and requirements. For example, the needs analysis unit analyzes detailed information, prices, desired conditions, and budgets of properties offered by the user. The needs analysis unit can also predict future needs by analyzing the user's past behavioral history and transaction history. The needs analysis unit can also collect information from social media and public data to perform a more detailed needs analysis. For example, the needs analysis unit can analyze the user's social media accounts to understand their interests. The match proposal unit proposes optimal matches based on the user's needs and requirements analyzed by the needs analysis unit. For example, the match proposal unit notifies users who meet the same requirements. The match proposal unit can also improve accuracy by incorporating the user's past matching results and feedback. The match proposal unit can also make more realistic proposals by taking into account the user's geographical location and time zone constraints. For example, the match proposal unit can propose properties within walking distance of the user's current location. The contract support unit supports the contract procedures after the match proposed by the match proposal unit is established. For example, the contract support unit creates contracts, prepares necessary documents, and confirms contract contents. The contract support unit can also add a function that reflects the latest legal information in real time. Furthermore, the contract support unit can visualize the progress of contract procedures and provide a dashboard that users can intuitively understand. For example, each step of the contract can be visually displayed, allowing the progress to be grasped at a glance. This allows the matching and contract support system according to the embodiment to propose optimal matching based on the user's needs and support the contract procedures. For example, the user can efficiently find a trading partner and smoothly proceed with the contract procedures.

[0030] The needs analysis unit can incorporate an algorithm that analyzes a user's past behavioral history and transaction history and predicts future needs. The needs analysis unit, for example, analyzes a user's past property viewing history and inquiry history to predict future needs. For example, for a user who frequently views properties in a specific area or price range, the unit preferentially suggests newly arrived properties in that area and price range. The needs analysis unit can also analyze a user's past purchase history to predict future purchasing intentions. For example, for a user who has previously purchased a luxury property, the unit suggests similar luxury properties. Furthermore, the needs analysis unit can analyze a user's transaction history to predict future transaction needs. For example, for a user who has previously rented a property, the unit suggests future rental properties. In this way, the user's past behavioral history and transaction history can be analyzed to predict future needs.

[0031] The needs analysis unit can perform a more detailed needs analysis by collecting information from social media and public data in addition to the information provided by the user. The needs analysis unit, for example, analyzes the user's social media accounts to understand their interests. For example, it can suggest properties and services that the user might be interested in based on the content the user frequently posts and the accounts the user follows. The needs analysis unit can also collect public data and analyze the user's needs. For example, it can identify the user's needs based on government statistical data and open data. Furthermore, the needs analysis unit can combine the information provided by the user with social media and public data to perform a more detailed needs analysis. For example, it can combine the user's profile information with the content of social media posts to suggest properties and services that the user might be interested in. This allows the unit to collect information from social media and public data to perform a more detailed needs analysis.

[0032] The needs analysis unit can apply the user's needs analysis not only to properties and services, but also to different fields such as events and travel plans. The needs analysis unit, for example, applies the user's needs analysis to a travel plan to suggest optimal travel destinations and accommodations. For example, suggestions are made based on past travel history and tourist spots that the user is likely to be interested in. The needs analysis unit can also apply the user's needs analysis to events to suggest optimal events. For example, it can suggest concerts or seminars that the user is likely to be interested in. Furthermore, the needs analysis unit can apply the user's needs analysis to different fields to make optimal suggestions. For example, it can suggest resort stays or business trips that the user is likely to be interested in. This allows the user's needs analysis to be applied to different fields.

[0033] When analyzing a user's needs, the needs analysis unit can utilize information other than text using voice input or image analysis. The needs analysis unit, for example, analyzes the user's voice input to identify needs. For example, if a user inputs desired conditions by voice, the content is analyzed and the most suitable property or service is proposed. The needs analysis unit can also analyze the user's image data to identify needs. For example, if a user inputs a photo of a property using a camera, the photo is analyzed to make the most suitable proposal. Furthermore, the needs analysis unit can utilize information other than text using voice input or image analysis. For example, if a user inputs desired conditions by voice and provides a photo of a property as an image, both pieces of information are analyzed to make the most suitable proposal. This makes it possible to utilize information other than text using voice input or image analysis.

[0034] The matching suggestion unit can improve accuracy by reflecting the user's past matching results and feedback. The matching suggestion unit, for example, analyzes the user's past matching results and identifies patterns of successful matching. For example, based on the conditions of past successful matching, it prioritizes matching with users who have similar conditions. The matching suggestion unit can also collect user feedback and improve matching accuracy. For example, it adjusts the matching algorithm based on feedback provided by the user. Furthermore, the matching suggestion unit can improve accuracy by reflecting the user's past matching results and feedback. For example, it makes optimal suggestions based on the conditions of past successful matching. This makes it possible to reflect the past matching results and feedback and improve matching accuracy.

[0035] The matching suggestion unit can make more realistic suggestions by taking into account the user's geographical location information and time zone constraints. The matching suggestion unit, for example, analyzes the user's geographical location information and prioritizes suggesting nearby properties and services. For example, if the user is looking for a property within walking distance of their current location, the matching suggestion unit can suggest properties that meet those conditions. The matching suggestion unit can also make optimal suggestions by taking into account the user's time zone constraints. For example, if the user can only use the service during a specific time zone, the matching suggestion unit can suggest services that match that time zone. Furthermore, the matching suggestion unit can make more realistic suggestions by taking into account the user's geographical location information and time zone constraints. For example, the matching suggestion unit can suggest properties and services that are close to the user's current location and that are tailored to the time zones in which the service is available. This allows more realistic suggestions to be made by taking into account the geographical location information and time zone constraints.

[0036] The matching proposal unit can expand the target of matching beyond just properties and services to include different fields such as business partners and joint researchers. For example, the matching proposal unit can expand the target of matching to business partners and propose optimal partners. For example, it can match investors and co-founders that a startup company is looking for. The matching proposal unit can also expand the target of matching to joint researchers and propose optimal research partners. For example, it can match joint researchers that an academic researcher is looking for. Furthermore, the matching proposal unit can expand the target of matching to different fields and make optimal proposals. For example, it can propose business partners and joint researchers that the user is looking for. This allows the target of matching to be expanded to different fields.

[0037] The matching suggestion unit can customize the matching suggestion based on the lifestyle and hobbies and preferences of the user. The matching suggestion unit customizes the matching suggestion based on, for example, the lifestyle of the user. For example, to a user who likes the outdoors, the matching suggestion unit suggests properties with rich natural environments. The matching suggestion unit can also customize the matching suggestion based on the hobbies and preferences of the user. For example, to a user who likes music, the matching suggestion unit suggests properties in areas where music events are held. Furthermore, the matching suggestion unit can customize the matching suggestion based on the lifestyle and hobbies and preferences of the user. For example, to a health-conscious user, the matching suggestion unit suggests properties with excellent fitness facilities. In this way, the matching suggestion can be customized based on the lifestyle and hobbies and preferences.

[0038] The contract support department can add a function that reflects the latest legal information in real time when automatically generating contracts. For example, the contract support department adds a function that reflects the latest legal information in real time to the automatic contract generation system. For example, when new laws or regulations come into effect, that information is immediately reflected in the contract. The contract support department can also collect the latest legal information and update the content of the contract. For example, it checks whether the clauses in the contract comply with the latest laws and regulations and makes corrections as necessary. Furthermore, by reflecting the latest legal information in real time, the contract support department can improve the reliability of the contract. For example, the user can proceed with the contract procedure with peace of mind. This allows the latest legal information to be reflected in real time when automatically generating contracts.

[0039] The contract support unit can visualize the progress of the contract procedures and provide a dashboard that the user can intuitively understand. The contract support unit, for example, provides a dashboard that visualizes the progress of the contract procedures, allowing the user to intuitively understand. For example, each step of the contract can be visually displayed, allowing the progress to be grasped at a glance. The contract support unit can also notify the user of the progress of the contract procedures in real time through the dashboard. For example, when the creation of a contract is completed, that information is displayed on the dashboard. Furthermore, the contract support unit can make it easier for the user to grasp the progress of the contract procedures by providing a dashboard that the user can intuitively understand. For example, the user can check the progress of the contract procedures at a glance. This makes it possible to visualize the progress of the contract procedures and provide a dashboard that the user can intuitively understand.

[0040] The contract support unit can apply support for contract procedures not only to real estate transactions, but also to different fields such as car sales and freelance contracts. For example, the contract support unit applies support for contract procedures to car sales and supports the creation of contracts and the preparation of necessary documents. For example, it automatically generates a car sales contract and lists the necessary documents. The contract support unit can also apply support for contract procedures to freelance contracts and supports the creation of contracts and the confirmation of contract contents. For example, it automatically generates a freelance outsourcing contract and checks whether the contract contents are complete. Furthermore, the contract support unit can apply support for contract procedures to different fields and provide optimal support. For example, it provides support to smoothly proceed with the contract procedures desired by the user. This allows support for contract procedures to be applied to different fields.

[0041] The contract support unit can add a function for communicating with the user in real time using video calls or a chatbot during the contract procedures. The contract support unit, for example, adds a video call function during the contract procedures to communicate with the user in real time. For example, the contract details are confirmed and questions are answered via video call. The contract support unit can also communicate with the user in real time using a chatbot. For example, the chatbot immediately answers any questions or uncertainties that may arise during the contract procedures. Furthermore, the contract support unit can add a function for communicating with the user in real time using video calls or a chatbot. For example, the user can receive support in real time during the contract procedures. This allows real-time communication using video calls or a chatbot during the contract procedures.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The needs analysis unit can analyze the user's health condition and suggest health-conscious properties and services. For example, it can collect the user's health data and suggest properties that cater to allergies or specific health conditions. The needs analysis unit can also analyze the user's fitness data and suggest properties with extensive exercise facilities. Furthermore, the needs analysis unit can analyze the user's health condition and suggest health-conscious services. For example, it can suggest the provision of health foods or the use of health management apps. This allows for optimal suggestions to be made based on the user's health condition.

[0044] The matching suggestion unit can match hobby friends or people who can share a special skill with the user based on the user's hobbies and special skills. For example, if the user's hobby is music, it will suggest users who have the same hobby. The matching suggestion unit can also suggest people who can share a special skill with the user based on the user's special skill. For example, it will suggest cooking classes or cooking events to a user who is good at cooking. Furthermore, the matching suggestion unit can suggest hobby friends or people who can share a special skill with the user based on the user's hobbies and special skills. For example, it will suggest sporting events and teams to a user who likes sports. This makes it possible to make optimal suggestions based on the user's hobbies and special skills.

[0045] The contract support unit can provide support that takes into consideration the user's language and culture during the contract procedures. For example, the contents of the contract can be displayed in multiple languages ​​to make it easier for the user to understand. The contract support unit can also provide support that takes into consideration the user's cultural background. For example, it can display advice and messages that take cultural differences into consideration. Furthermore, by providing support that takes into consideration the user's language and culture, the contract support unit can smoothly proceed with the contract procedures. For example, it can enable the user to proceed with the contract procedures with peace of mind. This makes it possible to provide support that takes into consideration the user's language and culture.

[0046] The needs analysis unit can suggest optimal properties and services based on the user's life events. For example, if the user is planning to get married, the unit suggests properties that are suitable for life after marriage. The needs analysis unit can also suggest optimal services based on the user's life events. For example, if the user is planning to have children, the unit suggests properties that have a wide range of services and facilities for children. Furthermore, the needs analysis unit can make optimal suggestions based on the user's life events. For example, if the user is planning to retire, the unit suggests properties and services that are suitable for life after retirement. This makes it possible to make optimal suggestions based on the user's life events.

[0047] The contract support unit can add a function for communicating with the user in real time using video calls or a chatbot during the contract procedures. For example, a video call function can be added during the contract procedures to communicate with the user in real time. For example, the contract details can be confirmed and questions can be answered via video call. The contract support unit can also use a chatbot to communicate with the user in real time. For example, the chatbot can instantly answer any questions or uncertainties that may arise during the contract procedures. Furthermore, the contract support unit can add a function for communicating with the user in real time using video calls or a chatbot. For example, the user can receive support in real time during the contract procedures. This makes it possible to communicate in real time using video calls or a chatbot during the contract procedures.

[0048] The needs analysis unit can apply the user's needs analysis not only to properties and services, but also to different fields such as events and travel plans. For example, the user's needs analysis can be applied to a travel plan to suggest optimal travel destinations and accommodations. For example, suggestions can be made based on past travel history and tourist spots that the user is likely to be interested in. The needs analysis unit can also apply the user's needs analysis to events to suggest optimal events. For example, it can suggest concerts or seminars that the user is likely to be interested in. Furthermore, the needs analysis unit can apply the user's needs analysis to different fields to make optimal suggestions. For example, it can suggest resort stays or business trips that the user is likely to be interested in. This makes it possible to apply the user's needs analysis to different fields.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The needs analysis unit analyzes the user's needs and conditions. For example, it analyzes the detailed information, price, desired conditions, and budget of the property provided by the user. It can also analyze the user's past behavioral history and transaction history to predict future needs. It also collects information from social media and public data to understand the user's interests. Step 2: The matching suggestion unit proposes optimal matches based on the user's needs and conditions analyzed by the needs analysis unit. For example, it notifies users with matching conditions and improves accuracy by incorporating past matching results and feedback. Furthermore, it makes realistic proposals by taking into account geographical location information and time zone constraints. Step 3: The Contract Support Department supports the contract procedures after the matching proposed by the Matching Proposal Department is established. For example, it creates contracts, prepares necessary documents, and confirms contract contents. It also provides functions that update the latest legal information in real time and a dashboard that visualizes the progress of contract procedures.

[0051] (Example 2) The matching and contract support system according to an embodiment of the present invention is a system that uses AI to match people and businesses with different needs, such as "sellers," "renters," "buyers," "renters," and "businesses," and also provides contract support. As a result, the matching and contract support system can propose optimal matches based on the user's needs and support contract procedures.

[0052] A matching and contract support system according to an embodiment includes a needs analysis unit, a match proposal unit, and a contract support unit. The needs analysis unit analyzes a user's needs and requirements. For example, the needs analysis unit analyzes detailed information, prices, desired conditions, and budgets of properties offered by the user. The needs analysis unit can also predict future needs by analyzing the user's past behavioral history and transaction history. The needs analysis unit can also collect information from social media and public data to perform a more detailed needs analysis. For example, the needs analysis unit can analyze the user's social media accounts to understand their interests. The match proposal unit proposes optimal matches based on the user's needs and requirements analyzed by the needs analysis unit. For example, the match proposal unit notifies users who meet the same requirements. The match proposal unit can also improve accuracy by incorporating the user's past matching results and feedback. The match proposal unit can also make more realistic proposals by taking into account the user's geographical location and time zone constraints. For example, the match proposal unit can propose properties within walking distance of the user's current location. The contract support unit supports the contract procedures after the match proposed by the match proposal unit is established. For example, the contract support unit creates contracts, prepares necessary documents, and confirms contract contents. The contract support unit can also add a function that reflects the latest legal information in real time. Furthermore, the contract support unit can visualize the progress of contract procedures and provide a dashboard that users can intuitively understand. For example, each step of the contract can be visually displayed, allowing the progress to be grasped at a glance. This allows the matching and contract support system according to the embodiment to propose optimal matching based on the user's needs and support the contract procedures. For example, the user can efficiently find a trading partner and smoothly proceed with the contract procedures.

[0053] The needs analysis unit can incorporate an algorithm that analyzes a user's past behavioral history and transaction history and predicts future needs. The needs analysis unit, for example, analyzes a user's past property viewing history and inquiry history to predict future needs. For example, for a user who frequently views properties in a specific area or price range, the unit preferentially suggests newly arrived properties in that area and price range. The needs analysis unit can also analyze a user's past purchase history to predict future purchasing intentions. For example, for a user who has previously purchased a luxury property, the unit suggests similar luxury properties. Furthermore, the needs analysis unit can analyze a user's transaction history to predict future transaction needs. For example, for a user who has previously rented a property, the unit suggests future rental properties. In this way, the user's past behavioral history and transaction history can be analyzed to predict future needs.

[0054] The needs analysis unit can perform a more detailed needs analysis by collecting information from social media and public data in addition to the information provided by the user. The needs analysis unit, for example, analyzes the user's social media accounts to understand their interests. For example, it can suggest properties and services that the user might be interested in based on the content the user frequently posts and the accounts the user follows. The needs analysis unit can also collect public data and analyze the user's needs. For example, it can identify the user's needs based on government statistical data and open data. Furthermore, the needs analysis unit can combine the information provided by the user with social media and public data to perform a more detailed needs analysis. For example, it can combine the user's profile information with the content of social media posts to suggest properties and services that the user might be interested in. This allows the unit to collect information from social media and public data to perform a more detailed needs analysis.

[0055] The needs analysis unit can use the emotion estimation function to analyze the emotion behind the information entered by the user and identify needs based on the emotion. The needs analysis unit, for example, analyzes text data entered by the user and estimates the emotion. For example, if the emotion is strong, the unit suggests properties or services based on that emotion. The needs analysis unit can also analyze the user's voice data and estimate the emotion. For example, if the user enters their desired conditions by voice, the content of the input is analyzed to identify the emotion. Furthermore, the needs analysis unit can analyze the user's facial expression data and estimate the emotion. For example, if the user inputs their facial expression through a camera, the facial expression is analyzed to identify the emotion. In this way, the emotion estimation function can be used to analyze the emotion behind the information entered by the user and identify needs based on the emotion.

[0056] The needs analysis unit can apply the user's needs analysis not only to properties and services, but also to different fields such as events and travel plans. The needs analysis unit, for example, applies the user's needs analysis to a travel plan to suggest optimal travel destinations and accommodations. For example, suggestions are made based on past travel history and tourist spots that the user is likely to be interested in. The needs analysis unit can also apply the user's needs analysis to events to suggest optimal events. For example, it can suggest concerts or seminars that the user is likely to be interested in. Furthermore, the needs analysis unit can apply the user's needs analysis to different fields to make optimal suggestions. For example, it can suggest resort stays or business trips that the user is likely to be interested in. This allows the user's needs analysis to be applied to different fields.

[0057] When analyzing a user's needs, the needs analysis unit can utilize information other than text using voice input or image analysis. The needs analysis unit, for example, analyzes the user's voice input to identify needs. For example, if a user inputs desired conditions by voice, the content is analyzed and the most suitable property or service is proposed. The needs analysis unit can also analyze the user's image data to identify needs. For example, if a user inputs a photo of a property using a camera, the photo is analyzed to make the most suitable proposal. Furthermore, the needs analysis unit can utilize information other than text using voice input or image analysis. For example, if a user inputs desired conditions by voice and provides a photo of a property as an image, both pieces of information are analyzed to make the most suitable proposal. This makes it possible to utilize information other than text using voice input or image analysis.

[0058] The needs analysis unit can use the emotion estimation function to analyze the emotions of the user when entering data in real time and provide an interface for eliciting positive emotions. The needs analysis unit, for example, analyzes the emotions of the user when entering data in real time and displays a message for eliciting positive emotions. For example, it presents words of encouragement or success stories. The needs analysis unit can also analyze the emotions of the user in real time and provide an interface for eliciting positive emotions. For example, if the user feels stressed when entering data, it displays advice or a message to help the user relax. Furthermore, the needs analysis unit can use the emotion estimation function to analyze the emotions of the user when entering data in real time and provide an interface for eliciting positive emotions. For example, it provides an interface for eliciting positive emotions when the user enters data. This makes it possible to analyze the emotions of the user in real time and provide an interface for eliciting positive emotions.

[0059] The matching suggestion unit can improve accuracy by reflecting the user's past matching results and feedback. The matching suggestion unit, for example, analyzes the user's past matching results and identifies patterns of successful matching. For example, based on the conditions of past successful matching, it prioritizes matching with users who have similar conditions. The matching suggestion unit can also collect user feedback and improve matching accuracy. For example, it adjusts the matching algorithm based on feedback provided by the user. Furthermore, the matching suggestion unit can improve accuracy by reflecting the user's past matching results and feedback. For example, it makes optimal suggestions based on the conditions of past successful matching. This makes it possible to reflect the past matching results and feedback and improve matching accuracy.

[0060] The matching suggestion unit can make more realistic suggestions by taking into account the user's geographical location information and time zone constraints. The matching suggestion unit, for example, analyzes the user's geographical location information and prioritizes suggesting nearby properties and services. For example, if the user is looking for a property within walking distance of their current location, the matching suggestion unit can suggest properties that meet those conditions. The matching suggestion unit can also make optimal suggestions by taking into account the user's time zone constraints. For example, if the user can only use the service during a specific time zone, the matching suggestion unit can suggest services that match that time zone. Furthermore, the matching suggestion unit can make more realistic suggestions by taking into account the user's geographical location information and time zone constraints. For example, the matching suggestion unit can suggest properties and services that are close to the user's current location and that are tailored to the time zones in which the service is available. This allows more realistic suggestions to be made by taking into account the geographical location information and time zone constraints.

[0061] The matching suggestion unit can use the emotion estimation function to perform matching based on the user's emotional state and make suggestions that provide high emotional satisfaction. The matching suggestion unit, for example, analyzes the user's emotional state and suggests matching that provides high emotional satisfaction. For example, if the user is feeling stressed, the matching suggestion unit suggests properties with a relaxing environment. The matching suggestion unit can also analyze the user's emotional state in real time and make optimal suggestions. For example, if the user is expressing positive emotions, the matching suggestion unit suggests properties or services based on those emotions. Furthermore, the matching suggestion unit can use the emotion estimation function to perform matching based on the user's emotional state and make suggestions that provide high emotional satisfaction. For example, if a user receives suggestions that provide high emotional satisfaction, the success rate of transactions increases. In this way, the emotion estimation function can be used to make suggestions that provide high emotional satisfaction.

[0062] The matching proposal unit can expand the target of matching beyond just properties and services to include different fields such as business partners and joint researchers. For example, the matching proposal unit can expand the target of matching to business partners and propose optimal partners. For example, it can match investors and co-founders that a startup company is looking for. The matching proposal unit can also expand the target of matching to joint researchers and propose optimal research partners. For example, it can match joint researchers that an academic researcher is looking for. Furthermore, the matching proposal unit can expand the target of matching to different fields and make optimal proposals. For example, it can propose business partners and joint researchers that the user is looking for. This allows the target of matching to be expanded to different fields.

[0063] The matching suggestion unit can customize the matching suggestion based on the lifestyle and hobbies and preferences of the user. The matching suggestion unit customizes the matching suggestion based on, for example, the lifestyle of the user. For example, to a user who likes the outdoors, the matching suggestion unit suggests properties with rich natural environments. The matching suggestion unit can also customize the matching suggestion based on the hobbies and preferences of the user. For example, to a user who likes music, the matching suggestion unit suggests properties in areas where music events are held. Furthermore, the matching suggestion unit can customize the matching suggestion based on the lifestyle and hobbies and preferences of the user. For example, to a health-conscious user, the matching suggestion unit suggests properties with excellent fitness facilities. In this way, the matching suggestion can be customized based on the lifestyle and hobbies and preferences.

[0064] The matching suggestion unit can use the emotion estimation function to monitor the user's emotional response in real time when making a matching suggestion and continuously adjust the optimal suggestion. The matching suggestion unit, for example, uses the emotion estimation function to monitor the user's emotional response in real time when making a matching suggestion. For example, if the user has a positive reaction to a suggestion, the suggestion is preferentially displayed. The matching suggestion unit can also analyze the user's emotional response in real time and continuously adjust the optimal suggestion. For example, if the user has a negative reaction, the suggestion content is changed based on that reaction. Furthermore, the matching suggestion unit can use the emotion estimation function to monitor the user's emotional response in real time and continuously adjust the optimal suggestion. For example, if the user receives a suggestion that is emotionally satisfying, the success rate of the transaction increases. This allows the emotion estimation function to monitor the emotional response in real time and continuously adjust the optimal suggestion.

[0065] The contract support department can add a function that reflects the latest legal information in real time when automatically generating contracts. For example, the contract support department adds a function that reflects the latest legal information in real time to the automatic contract generation system. For example, when new laws or regulations come into effect, that information is immediately reflected in the contract. The contract support department can also collect the latest legal information and update the content of the contract. For example, it checks whether the clauses in the contract comply with the latest laws and regulations and makes corrections as necessary. Furthermore, by reflecting the latest legal information in real time, the contract support department can improve the reliability of the contract. For example, the user can proceed with the contract procedure with peace of mind. This allows the latest legal information to be reflected in real time when automatically generating contracts.

[0066] The contract support unit can visualize the progress of the contract procedures and provide a dashboard that the user can intuitively understand. The contract support unit, for example, provides a dashboard that visualizes the progress of the contract procedures, allowing the user to intuitively understand. For example, each step of the contract can be visually displayed, allowing the progress to be grasped at a glance. The contract support unit can also notify the user of the progress of the contract procedures in real time through the dashboard. For example, when the creation of a contract is completed, that information is displayed on the dashboard. Furthermore, the contract support unit can make it easier for the user to grasp the progress of the contract procedures by providing a dashboard that the user can intuitively understand. For example, the user can check the progress of the contract procedures at a glance. This makes it possible to visualize the progress of the contract procedures and provide a dashboard that the user can intuitively understand.

[0067] The contract support unit can use the emotion estimation function to provide support to reduce stress and anxiety of the user during the contract procedure. For example, the contract support unit can use the emotion estimation function to analyze the stress and anxiety of the user during the contract procedure in real time and provide appropriate support. For example, if the user is feeling stressed, the contract support unit can display advice or a message to help the user relax. The contract support unit can also monitor the user's emotional state in real time and provide support to reduce stress and anxiety. For example, if the user is feeling anxious, the contract support unit can provide information to reduce the anxiety. Furthermore, the contract support unit can use the emotion estimation function to provide support to reduce stress and anxiety of the user during the contract procedure. For example, the contract support unit can provide an environment where the user can relax. This makes it possible to provide support to reduce stress and anxiety of the user during the contract procedure.

[0068] The contract support unit can apply support for contract procedures not only to real estate transactions, but also to different fields such as car sales and freelance contracts. For example, the contract support unit applies support for contract procedures to car sales and supports the creation of contracts and the preparation of necessary documents. For example, it automatically generates a car sales contract and lists the necessary documents. The contract support unit can also apply support for contract procedures to freelance contracts and supports the creation of contracts and the confirmation of contract contents. For example, it automatically generates a freelance outsourcing contract and checks whether the contract contents are complete. Furthermore, the contract support unit can apply support for contract procedures to different fields and provide optimal support. For example, it provides support to smoothly proceed with the contract procedures desired by the user. This allows support for contract procedures to be applied to different fields.

[0069] The contract support unit can add a function for communicating with the user in real time using video calls or a chatbot during the contract procedures. The contract support unit, for example, adds a video call function during the contract procedures to communicate with the user in real time. For example, the contract details are confirmed and questions are answered via video call. The contract support unit can also communicate with the user in real time using a chatbot. For example, the chatbot immediately answers any questions or uncertainties that may arise during the contract procedures. Furthermore, the contract support unit can add a function for communicating with the user in real time using video calls or a chatbot. For example, the user can receive support in real time during the contract procedures. This allows real-time communication using video calls or a chatbot during the contract procedures.

[0070] The contract support unit can use the emotion estimation function to monitor the user's emotions during the contract procedure in real time and provide an interface for eliciting positive emotions. The contract support unit, for example, uses the emotion estimation function to monitor the user's emotions during the contract procedure in real time. For example, if the user is feeling anxious, it displays a message or advice to help the user relax. The contract support unit can also analyze the user's emotions in real time and provide an interface for eliciting positive emotions. For example, if the user is showing positive emotions, it displays a message to help the user maintain those emotions. Furthermore, the contract support unit can use the emotion estimation function to monitor the user's emotions during the contract procedure in real time and provide an interface for eliciting positive emotions. For example, it provides an environment where the user can relax. This makes it possible to monitor the user's emotions during the contract procedure in real time and provide an interface for eliciting positive emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The needs analysis unit can analyze the user's health condition and suggest health-conscious properties and services. For example, it can collect the user's health data and suggest properties that cater to allergies or specific health conditions. The needs analysis unit can also analyze the user's fitness data and suggest properties with extensive exercise facilities. Furthermore, the needs analysis unit can analyze the user's health condition and suggest health-conscious services. For example, it can suggest the provision of health foods or the use of health management apps. This allows for optimal suggestions to be made based on the user's health condition.

[0073] The matching suggestion unit can match hobby friends or people who can share a special skill with the user based on the user's hobbies and special skills. For example, if the user's hobby is music, it will suggest users who have the same hobby. The matching suggestion unit can also suggest people who can share a special skill with the user based on the user's special skill. For example, it will suggest cooking classes or cooking events to a user who is good at cooking. Furthermore, the matching suggestion unit can suggest hobby friends or people who can share a special skill with the user based on the user's hobbies and special skills. For example, it will suggest sporting events and teams to a user who likes sports. This makes it possible to make optimal suggestions based on the user's hobbies and special skills.

[0074] The contract support unit can provide support that takes into consideration the user's language and culture during the contract procedures. For example, the contents of the contract can be displayed in multiple languages ​​to make it easier for the user to understand. The contract support unit can also provide support that takes into consideration the user's cultural background. For example, it can display advice and messages that take cultural differences into consideration. Furthermore, by providing support that takes into consideration the user's language and culture, the contract support unit can smoothly proceed with the contract procedures. For example, it can enable the user to proceed with the contract procedures with peace of mind. This makes it possible to provide support that takes into consideration the user's language and culture.

[0075] The matching suggestion unit can use the emotion estimation function to perform matching based on the user's emotional state and make suggestions that provide high emotional satisfaction. For example, the matching suggestion unit can analyze the user's emotional state and suggest matching that provides high emotional satisfaction. For example, if the user is feeling stressed, the matching suggestion unit can suggest properties with a relaxing environment. The matching suggestion unit can also analyze the user's emotional state in real time and make optimal suggestions. For example, if the user is expressing positive emotions, the matching suggestion unit can suggest properties or services based on those emotions. Furthermore, the matching suggestion unit can use the emotion estimation function to perform matching based on the user's emotional state and make suggestions that provide high emotional satisfaction. For example, if a user receives suggestions that provide high emotional satisfaction, the success rate of transactions increases. In this way, the emotion estimation function can be used to make suggestions that provide high emotional satisfaction.

[0076] The contract support unit can use the emotion estimation function to monitor the user's emotions during the contract procedure in real time and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to monitor the user's emotions during the contract procedure in real time. For example, if the user is feeling anxious, a message or advice to help them relax can be displayed. The contract support unit can also analyze the user's emotions in real time and provide an interface for eliciting positive emotions. For example, if the user is expressing positive emotions, a message to help them maintain those emotions can be displayed. Furthermore, the contract support unit can use the emotion estimation function to monitor the user's emotions during the contract procedure in real time and provide an interface for eliciting positive emotions. For example, an environment in which the user can relax can be provided. This makes it possible to monitor the user's emotions during the contract procedure in real time and provide an interface for eliciting positive emotions.

[0077] The needs analysis unit can suggest optimal properties and services based on the user's life events. For example, if the user is planning to get married, the unit suggests properties that are suitable for life after marriage. The needs analysis unit can also suggest optimal services based on the user's life events. For example, if the user is planning to have children, the unit suggests properties that have a wide range of services and facilities for children. Furthermore, the needs analysis unit can make optimal suggestions based on the user's life events. For example, if the user is planning to retire, the unit suggests properties and services that are suitable for life after retirement. This makes it possible to make optimal suggestions based on the user's life events.

[0078] The matching suggestion unit can use the emotion estimation function to monitor the user's emotional reactions in real time and continuously adjust the optimal proposal. For example, the emotion estimation function is used to monitor the user's emotional reactions in real time when making a matching proposal. For example, if the user has a positive reaction to a proposal, the proposal is preferentially displayed. The matching suggestion unit can also analyze the user's emotional reactions in real time and continuously adjust the optimal proposal. For example, if the user has a negative reaction, the proposal content is changed based on that reaction. Furthermore, the matching suggestion unit can use the emotion estimation function to monitor the user's emotional reactions in real time and continuously adjust the optimal proposal. For example, if the user receives a proposal that is emotionally satisfying, the success rate of the transaction increases. As a result, the emotion estimation function can be used to monitor the emotional reactions in real time and continuously adjust the optimal proposal.

[0079] The contract support unit can add a function for communicating with the user in real time using video calls or a chatbot during the contract procedures. For example, a video call function can be added during the contract procedures to communicate with the user in real time. For example, the contract details can be confirmed and questions can be answered via video call. The contract support unit can also use a chatbot to communicate with the user in real time. For example, the chatbot can instantly answer any questions or uncertainties that may arise during the contract procedures. Furthermore, the contract support unit can add a function for communicating with the user in real time using video calls or a chatbot. For example, the user can receive support in real time during the contract procedures. This makes it possible to communicate in real time using video calls or a chatbot during the contract procedures.

[0080] The needs analysis unit can apply the user's needs analysis not only to properties and services, but also to different fields such as events and travel plans. For example, the user's needs analysis can be applied to a travel plan to suggest optimal travel destinations and accommodations. For example, suggestions can be made based on past travel history and tourist spots that the user is likely to be interested in. The needs analysis unit can also apply the user's needs analysis to events to suggest optimal events. For example, it can suggest concerts or seminars that the user is likely to be interested in. Furthermore, the needs analysis unit can apply the user's needs analysis to different fields to make optimal suggestions. For example, it can suggest resort stays or business trips that the user is likely to be interested in. This makes it possible to apply the user's needs analysis to different fields.

[0081] The contract support unit can use the emotion estimation function to provide support to reduce the stress and anxiety of the user during the contract procedure. For example, the emotion estimation function can be used to analyze the stress and anxiety of the user during the contract procedure in real time and provide appropriate support. For example, if the user is feeling stressed, the contract support unit can display advice or a message to help the user relax. The contract support unit can also monitor the user's emotional state in real time and provide support to reduce stress and anxiety. For example, if the user is feeling anxious, the contract support unit can provide information to reduce the anxiety. Furthermore, the contract support unit can use the emotion estimation function to provide support to reduce the stress and anxiety of the user during the contract procedure. For example, the contract support unit can provide an environment where the user can relax. This can provide support to reduce the stress and anxiety of the user during the contract procedure.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The needs analysis unit analyzes the user's needs and conditions. For example, it analyzes the detailed information, price, desired conditions, and budget of the property provided by the user. It can also analyze the user's past behavioral history and transaction history to predict future needs. It also collects information from social media and public data to understand the user's interests. Step 2: The matching suggestion unit proposes optimal matches based on the user's needs and conditions analyzed by the needs analysis unit. For example, it notifies users with matching conditions and improves accuracy by incorporating past matching results and feedback. Furthermore, it makes realistic proposals by taking into account geographical location information and time zone constraints. Step 3: The Contract Support Department supports the contract procedures after the matching proposed by the Matching Proposal Department is established. For example, it creates contracts, prepares necessary documents, and confirms contract contents. It also provides functions that update the latest legal information in real time and a dashboard that visualizes the progress of contract procedures.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a needs analysis unit for analyzing user needs and conditions; a matching suggestion unit that suggests optimal matching based on the needs and conditions of the user analyzed by the needs analysis unit; a contract support unit that supports contract procedures after the matching proposed by the matching proposal unit is established. A system characterized by:

2. The needs analysis unit Using an emotion estimation function, the emotion behind the information entered by the user is analyzed and needs are identified based on the emotion.

2. The system of claim 1.

3. The needs analysis unit Applying the user needs analysis to at least one different category of property or service, event or travel plan.

2. The system of claim 1.

4. The matching proposing unit Improve accuracy by reflecting the user's past matching results and feedback 2. The system of claim 1.

5. The contract support unit Add a function to automatically generate contracts that reflects the latest legal information in real time.

2. The system of claim 1.

6. The needs analysis unit Using emotion estimation functionality, the emotions entered by the user are analyzed in real time, providing an interface that draws out positive emotions.

2. The system of claim 1.

7. The matching proposing unit Using an emotion estimation function, matching is performed based on the emotional state of the user, and the proposal with high emotional satisfaction is made.

2. The system of claim 1.

8. The contract support unit Using an emotion estimation function, support is provided to reduce the stress and anxiety of the user during the contract procedure.

2. The system of claim 1.

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