System

Abstract

An object of a system according to an embodiment is to automatically match people living together in a share house and set fair rent conditions.SOLUTION: A system according to an embodiment includes a roommate matching unit, a rent setting unit, and a share house providing unit. The housemate matching unit automatically matches a housemate based on the desires and preferences of the homeowner and the resident using the generated AI. The rent setting part sets a fair rent condition on the basis of desires of the landlord and the resident and a market situation. The share house providing part provides an optimum share house on the basis of the desires and tastes of the house owner and the resident.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 faced the challenge of making it difficult to properly match housemates and set fair rent terms.

[0005] The system according to the embodiment aims to automatically match housemates in a shared house and set fair rent terms. [Means for solving the problem]

[0006] The system according to the embodiment includes a roommate matching unit, a rent setting unit, and a shared house provision unit. The roommate matching unit uses generation AI to automatically match roommates based on the wishes and preferences of the landlord and residents. The rent setting unit sets fair rent conditions based on the wishes and preferences of the landlord and residents and market conditions. The shared house provision unit provides the optimal shared house based on the wishes and preferences of the landlord and residents. [Effects of the Invention]

[0007] The system according to the embodiment can automatically match housemates in a shared house and set fair rent terms. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The real estate sharing platform according to an embodiment of the present invention is a system that uses generative AI to automatically match housemates and set fair rent terms, thereby improving the living environment and maximizing the profits of homeowners.

[0029] A real estate sharing platform according to an embodiment includes a roommate matching unit, a rent setting unit, and a shared house provision unit. The roommate matching unit uses a generation AI to automatically match roommates based on the preferences and tastes of landlords and residents. For example, the generation AI finds residents who meet the landlord's criteria, such as "prefers a quiet environment," "pets allowed," and "monthly rent of less than 50,000 yen." The generation AI can also suggest the optimal shared house based on the residents' desired criteria. The rent setting unit uses the generation AI to set fair rent terms based on the landlord and residents' preferences and market conditions. For example, the generation AI analyzes the market rent market and the residents' ability to pay and suggests an appropriate rent. This allows rent terms to be set that are acceptable to both the landlord and the residents. The shared house provision unit uses the generation AI to suggest the optimal shared house based on the landlord and residents' preferences and tastes. For example, the generation AI suggests a shared house that meets the conditions, such as "prefers a quiet environment" and "pets allowed." This allows residents to find a living environment that suits their preferences. As a result, the real estate sharing platform according to the embodiment can improve the living environment and maximize the profits of homeowners.

[0030] The housemate matching unit can analyze reviews and feedback from past housemates and select housemates who will cause less trouble. For example, the generation AI collects reviews and feedback from past housemates and selects housemates who will cause less trouble. For example, it prioritizes matching with users who have many positive reviews. It also analyzes housemate feedback data and identifies the causes of trouble to select housemates who will cause less trouble. For example, it prioritizes matching with users who have caused less trouble in the past. It also develops an algorithm to select housemates who will cause less trouble based on reviews and feedback from past housemates. For example, it prioritizes matching with users who have caused less negative feedback. This allows for the selection of housemates who will cause less trouble, thereby contributing to a stable living environment.

[0031] The housemate matching unit can analyze a user's lifestyle rhythm and daily behavior patterns to match housemates with a compatible lifestyle. For example, the housemate matching unit collects data from smart devices so that the generation AI can analyze the user's lifestyle rhythm and select housemates with a compatible lifestyle. For example, it can match users who go to bed early and get up early. It can also analyze users' daily behavior patterns and develop an algorithm to select housemates with a compatible lifestyle. For example, it can match users with the same hobbies and activities. The generation AI can also select housemates with a compatible lifestyle based on the user's lifestyle rhythm and behavior patterns. For example, it can match users who are active at the same time. This allows for a harmonious living environment by matching housemates with compatible lifestyles.

[0032] The housemate matching unit can analyze a user's hobbies and interests and match housemates with common hobbies. For example, the housemate matching unit collects social media and hobby-related data so that the generation AI can analyze the user's hobbies and interests and select housemates with common hobbies. For example, it can match users who enjoy the same sports. In addition, an algorithm can be developed to select housemates with common hobbies based on the user's hobbies and interests. For example, it can match users who like the same music genre. In addition, the generation AI can analyze the user's hobbies and interests and select housemates with common hobbies. For example, it can match users who enjoy the same movies and dramas. This can improve the living environment by matching housemates with common hobbies.

[0033] The housemate matching unit can analyze the user's health condition and dietary habits and match them with housemates who will help them manage their health. For example, the housemate matching unit collects data from health apps and wearable devices so that the generation AI can analyze the user's health condition and select housemates who will help them manage their health. For example, it can match users with the same health goals. It can also analyze the user's dietary habits and develop an algorithm to select housemates who will help them manage their health. For example, it can match users with the same dietary restrictions. The generation AI can also select housemates who will help them manage their health based on the user's health condition and dietary habits. For example, it can match users with the same exercise habits. This can improve the living environment by matching housemates who will help them manage their health.

[0034] The rent setting unit can analyze past rent payment history and credit information, and propose rent terms according to repayment ability. In the rent setting unit, for example, the generation AI analyzes the user's rent payment history and proposes rent terms according to repayment ability. For example, it proposes appropriate rent terms for users with a good past payment history. In addition, an algorithm is developed to propose rent terms according to repayment ability based on the user's credit information. For example, it proposes appropriate rent terms for users with a high credit score. In addition, the generation AI analyzes past rent payment history and credit information, and proposes rent terms according to repayment ability. For example, it proposes appropriate rent terms for users with high repayment ability. In this way, it is possible to reduce the burden on users by proposing rent terms according to their repayment ability.

[0035] The rent setting unit can analyze the local economic situation and future market trends to propose stable rent terms over the long term. For example, the generation AI in the rent setting unit analyzes the local economic situation and proposes stable rent terms over the long term. For example, rent terms are set based on the local economic growth rate and unemployment rate. In addition, an algorithm is developed to predict future market trends and propose stable rent terms over the long term. For example, rent terms are set based on future market rents and demand forecasts. In addition, the generation AI analyzes the local economic situation and future market trends to propose stable rent terms over the long term. For example, rent terms are set taking into account local infrastructure development and development plans. This can increase the user's sense of security by proposing stable rent terms over the long term.

[0036] The rent setting unit can analyze the user's living expenses and expenditure patterns and propose rent terms that take living expenses into consideration. For example, the generation AI in the rent setting unit analyzes the user's living expenses and expenditure patterns and proposes rent terms that take living expenses into consideration. For example, rent terms are set based on food and transportation costs. In addition, an algorithm is developed to propose rent terms that take living expenses into consideration based on the user's expenditure patterns. For example, rent terms are set taking into consideration items that are the largest expenses. In addition, the generation AI analyzes the user's living expenses and expenditure patterns and proposes rent terms that take living expenses into consideration. For example, rent terms are set based on the proportion of living expenses. In this way, by proposing rent terms that take living expenses into consideration, the user's quality of life can be improved.

[0037] The rent setting unit can analyze the stability of the user's occupation and income and propose rent terms according to income. For example, the generation AI in the rent setting unit analyzes the stability of the user's occupation and income and proposes rent terms according to income. For example, it proposes appropriate rent terms for users with stable incomes. In addition, an algorithm is developed to propose rent terms according to income based on the stability of the user's occupation and income. For example, it proposes appropriate rent terms for users with little fluctuation in income. In addition, the generation AI analyzes the stability of the user's occupation and income and proposes rent terms according to income. For example, it sets rent terms taking income stability into consideration. In this way, it is possible to reduce the financial burden on users by proposing rent terms according to income.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The roommate matching unit can analyze reviews and feedback from past roommates and select roommates who cause less trouble. For example, the generation AI can collect reviews and feedback from past roommates and select roommates who cause less trouble. Users with many positive reviews can be prioritized for matching. In addition, by analyzing roommate feedback data and identifying the causes of trouble, it is possible to select roommates who cause less trouble. For example, users who have caused less trouble in the past can be prioritized for matching. Furthermore, the generation AI can develop an algorithm to select roommates who cause less trouble based on reviews and feedback from past roommates. For example, it can prioritize matching users with less negative feedback. This allows for the selection of roommates who cause less trouble, thereby contributing to a stable living environment.

[0040] The roommate matching unit can analyze a user's lifestyle rhythm and daily behavior patterns to match them with roommates who share a similar lifestyle. For example, the generation AI can collect data from smart devices to analyze the user's lifestyle rhythm and select roommates with a similar lifestyle. Users who go to bed early and get up early can be matched. It is also possible to develop an algorithm that analyzes a user's daily behavior patterns and selects roommates with a similar lifestyle. For example, it can match users who share the same hobbies and activities. Furthermore, the generation AI can select roommates with a similar lifestyle based on the user's lifestyle rhythm and behavior patterns. For example, it can match users who are active at the same time. This allows for a harmonious living environment by matching roommates with similar lifestyles.

[0041] The housemate matching unit can analyze a user's hobbies and interests and match housemates with common hobbies. For example, the generation AI can collect social media and hobby-related data to analyze the user's hobbies and interests and select housemates with common hobbies. Users who enjoy the same sport can be matched. An algorithm can also be developed to select housemates with common hobbies based on the user's hobbies and interests. For example, users who like the same music genre can be matched. Furthermore, the generation AI can analyze a user's hobbies and interests and select housemates with common hobbies. For example, users who enjoy the same movies and dramas can be matched. This can improve the living environment by matching housemates with common hobbies.

[0042] The housemate matching unit can analyze a user's health condition and dietary habits and match them with housemates who are easy to manage their health. For example, the generation AI collects data from health apps and wearable devices to analyze the user's health condition and selects housemates who are easy to manage their health. Users with the same health goals can be matched. It is also possible to develop an algorithm that analyzes a user's dietary habits and selects housemates who are easy to manage their health. For example, users with the same dietary restrictions can be matched. Furthermore, the generation AI can select housemates who are easy to manage their health based on the user's health condition and dietary habits. For example, it can match users with the same exercise habits. This can improve the living environment by matching housemates who are easy to manage their health.

[0043] The rent setting unit can analyze past rent payment history and credit information to propose rent terms according to repayment ability. For example, the generation AI can analyze a user's rent payment history and propose rent terms according to repayment ability. Appropriate rent terms can be proposed for users with a good past payment history. It is also possible to develop an algorithm that proposes rent terms according to repayment ability based on a user's credit information. For example, it can propose appropriate rent terms for users with a high credit score. Furthermore, the generation AI can analyze past rent payment history and credit information to propose rent terms according to repayment ability. For example, it can propose appropriate rent terms for users with a high repayment ability. This reduces the burden on users by proposing rent terms according to their repayment ability.

[0044] The rent setting unit can analyze the local economic situation and future market trends to propose stable rent terms over the long term. For example, the generation AI can analyze the local economic situation and propose stable rent terms over the long term. Rent terms can be set based on the local economic growth rate and unemployment rate. It is also possible to develop an algorithm that predicts future market trends and proposes stable rent terms over the long term. For example, rent terms can be set based on future market rents and demand forecasts. Furthermore, the generation AI can analyze the local economic situation and future market trends to propose stable rent terms over the long term. For example, rent terms can be set taking into account local infrastructure development and development plans. This can increase users' sense of security by proposing stable rent terms over the long term.

[0045] The rent setting unit can analyze the user's living expenses and expenditure patterns and propose rent terms that take living expenses into consideration. For example, the generation AI can analyze the user's living expenses and expenditure patterns and propose rent terms that take living expenses into consideration. Rent terms can be set based on food and transportation costs. An algorithm can also be developed to propose rent terms that take living expenses into consideration based on the user's expenditure patterns. For example, rent terms can be set taking into consideration items with large expenditures. Furthermore, the generation AI can analyze the user's living expenses and expenditure patterns and propose rent terms that take living expenses into consideration. For example, rent terms can be set based on the proportion of living expenses. This can improve the user's quality of life by proposing rent terms that take living expenses into consideration.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The roommate matching unit uses generation AI to automatically match roommates based on the wishes and preferences of the landlord and resident. For example, the generation AI can find residents who meet the conditions set by the landlord, such as "prefers a quiet environment," "pets allowed," and "monthly rent of less than 50,000 yen." The generation AI can also suggest the most suitable shared house based on the desired conditions set by the resident. Step 2: The rent setting unit uses the generation AI to set fair rent terms based on the wishes of the landlord and resident and market conditions. For example, the generation AI analyzes the market rent and the resident's ability to pay, and proposes an appropriate rent. This allows rent terms to be set that are acceptable to both the landlord and resident. Step 3: The share house provider uses the generation AI to provide the optimal share house based on the wishes and preferences of the landlord and residents. For example, the generation AI will suggest share houses that meet conditions such as "preferring a quiet environment" and "pets allowed." This allows residents to find a living environment that suits their preferences.

[0048] (Example 2) The real estate sharing platform according to an embodiment of the present invention is a system that uses generative AI to automatically match housemates and set fair rent terms, thereby improving the living environment and maximizing the profits of homeowners.

[0049] A real estate sharing platform according to an embodiment includes a roommate matching unit, a rent setting unit, and a shared house provision unit. The roommate matching unit uses a generation AI to automatically match roommates based on the preferences and tastes of landlords and residents. For example, the generation AI finds residents who meet the landlord's criteria, such as "prefers a quiet environment," "pets allowed," and "monthly rent of less than 50,000 yen." The generation AI can also suggest the optimal shared house based on the residents' desired criteria. The rent setting unit uses the generation AI to set fair rent terms based on the landlord and residents' preferences and market conditions. For example, the generation AI analyzes the market rent market and the residents' ability to pay and suggests an appropriate rent. This allows rent terms to be set that are acceptable to both the landlord and the residents. The shared house provision unit uses the generation AI to suggest the optimal shared house based on the landlord and residents' preferences and tastes. For example, the generation AI suggests a shared house that meets the conditions, such as "prefers a quiet environment" and "pets allowed." This allows residents to find a living environment that suits their preferences. As a result, the real estate sharing platform according to the embodiment can improve the living environment and maximize the profits of homeowners.

[0050] The roommate matching unit can use the emotion estimation function to analyze the user's emotional state and prioritize matching with roommates who are emotionally compatible. For example, the roommate matching unit analyzes the user's facial expressions and voice input in real time and calculates an emotion score so that the generation AI can analyze the user's emotional state. For example, it prioritizes matching users with strong positive emotions. It also collects users' past emotional data and analyzes emotional fluctuation patterns to select emotionally stable roommates. For example, it matches users with low stress. It also uses the emotion estimation function to quantify the user's emotional state and prioritize matching with roommates who are emotionally compatible. For example, it prioritizes matching users with high emotion scores. This can improve the living environment by prioritizing matching with roommates who are emotionally compatible.

[0051] The housemate matching unit can analyze reviews and feedback from past housemates and select housemates who will cause less trouble. For example, the generation AI collects reviews and feedback from past housemates and selects housemates who will cause less trouble. For example, it prioritizes matching with users who have many positive reviews. It also analyzes housemate feedback data and identifies the causes of trouble to select housemates who will cause less trouble. For example, it prioritizes matching with users who have caused less trouble in the past. It also develops an algorithm to select housemates who will cause less trouble based on reviews and feedback from past housemates. For example, it prioritizes matching with users who have caused less negative feedback. This allows for the selection of housemates who will cause less trouble, thereby contributing to a stable living environment.

[0052] The housemate matching unit can analyze a user's lifestyle rhythm and daily behavior patterns to match housemates with a compatible lifestyle. For example, the housemate matching unit collects data from smart devices so that the generation AI can analyze the user's lifestyle rhythm and select housemates with a compatible lifestyle. For example, it can match users who go to bed early and get up early. It can also analyze users' daily behavior patterns and develop an algorithm to select housemates with a compatible lifestyle. For example, it can match users with the same hobbies and activities. The generation AI can also select housemates with a compatible lifestyle based on the user's lifestyle rhythm and behavior patterns. For example, it can match users who are active at the same time. This allows for a harmonious living environment by matching housemates with compatible lifestyles.

[0053] The housemate matching unit can analyze a user's hobbies and interests and match housemates with common hobbies. For example, the housemate matching unit collects social media and hobby-related data so that the generation AI can analyze the user's hobbies and interests and select housemates with common hobbies. For example, it can match users who enjoy the same sports. In addition, an algorithm can be developed to select housemates with common hobbies based on the user's hobbies and interests. For example, it can match users who like the same music genre. In addition, the generation AI can analyze the user's hobbies and interests and select housemates with common hobbies. For example, it can match users who enjoy the same movies and dramas. This can improve the living environment by matching housemates with common hobbies.

[0054] The housemate matching unit can analyze the user's health condition and dietary habits and match them with housemates who will help them manage their health. For example, the housemate matching unit collects data from health apps and wearable devices so that the generation AI can analyze the user's health condition and select housemates who will help them manage their health. For example, it can match users with the same health goals. It can also analyze the user's dietary habits and develop an algorithm to select housemates who will help them manage their health. For example, it can match users with the same dietary restrictions. The generation AI can also select housemates who will help them manage their health based on the user's health condition and dietary habits. For example, it can match users with the same exercise habits. This can improve the living environment by matching housemates who will help them manage their health.

[0055] The housemate matching unit can analyze the user's stress level using an emotion estimation function and match the user with a housemate who can reduce stress. For example, the housemate matching unit collects data from a stress measurement device so that the generation AI can analyze the user's stress level and select a housemate who can reduce stress. For example, it can match users who have hobbies that have a relaxing effect. It can also develop an algorithm to select a housemate who can reduce stress based on the user's stress level. For example, it can match users who are good at stress management. The generation AI can also analyze the user's stress level and select a housemate who can reduce stress. For example, it can match users who have similar methods of relieving stress. This can improve the living environment by matching housemates who can reduce stress.

[0056] The rent setting unit can use the emotion estimation function to analyze the user's satisfaction and suggest rent terms that provide high satisfaction. For example, the rent setting unit analyzes the user's facial expressions and voice in real time and calculates a satisfaction score so that the generation AI can analyze the user's satisfaction. For example, it prioritizes suggesting rent terms that provide high satisfaction. It also collects the user's past satisfaction data and analyzes patterns of fluctuation in satisfaction to suggest rent terms that provide high satisfaction. For example, it makes suggestions based on rent terms that have provided high satisfaction in the past. It also uses the emotion estimation function to quantify the user's satisfaction and suggest rent terms that provide high satisfaction. For example, it prioritizes suggesting rent terms that provide high satisfaction. This makes it possible to improve user satisfaction by suggesting rent terms that provide high satisfaction.

[0057] The rent setting unit can analyze past rent payment history and credit information, and propose rent terms according to repayment ability. In the rent setting unit, for example, the generation AI analyzes the user's rent payment history and proposes rent terms according to repayment ability. For example, it proposes appropriate rent terms for users with a good past payment history. In addition, an algorithm is developed to propose rent terms according to repayment ability based on the user's credit information. For example, it proposes appropriate rent terms for users with a high credit score. In addition, the generation AI analyzes past rent payment history and credit information, and proposes rent terms according to repayment ability. For example, it proposes appropriate rent terms for users with high repayment ability. In this way, it is possible to reduce the burden on users by proposing rent terms according to their repayment ability.

[0058] The rent setting unit can analyze the local economic situation and future market trends to propose stable rent terms over the long term. For example, the generation AI in the rent setting unit analyzes the local economic situation and proposes stable rent terms over the long term. For example, rent terms are set based on the local economic growth rate and unemployment rate. In addition, an algorithm is developed to predict future market trends and propose stable rent terms over the long term. For example, rent terms are set based on future market rents and demand forecasts. In addition, the generation AI analyzes the local economic situation and future market trends to propose stable rent terms over the long term. For example, rent terms are set taking into account local infrastructure development and development plans. This can increase the user's sense of security by proposing stable rent terms over the long term.

[0059] The rent setting unit can analyze the user's living expenses and expenditure patterns and propose rent terms that take living expenses into consideration. For example, the generation AI in the rent setting unit analyzes the user's living expenses and expenditure patterns and proposes rent terms that take living expenses into consideration. For example, rent terms are set based on food and transportation costs. In addition, an algorithm is developed to propose rent terms that take living expenses into consideration based on the user's expenditure patterns. For example, rent terms are set taking into consideration items that are the largest expenses. In addition, the generation AI analyzes the user's living expenses and expenditure patterns and proposes rent terms that take living expenses into consideration. For example, rent terms are set based on the proportion of living expenses. In this way, by proposing rent terms that take living expenses into consideration, the user's quality of life can be improved.

[0060] The rent setting unit can analyze the stability of the user's occupation and income and propose rent terms according to income. For example, the generation AI in the rent setting unit analyzes the stability of the user's occupation and income and proposes rent terms according to income. For example, it proposes appropriate rent terms for users with stable incomes. In addition, an algorithm is developed to propose rent terms according to income based on the stability of the user's occupation and income. For example, it proposes appropriate rent terms for users with little fluctuation in income. In addition, the generation AI analyzes the stability of the user's occupation and income and proposes rent terms according to income. For example, it sets rent terms taking income stability into consideration. In this way, it is possible to reduce the financial burden on users by proposing rent terms according to income.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The roommate matching unit can analyze the user's emotional state and prioritize matching with emotionally compatible roommates. For example, the generation AI can analyze the user's facial expressions and voice in real time when inputting and calculate an emotional score. Users with strong positive emotions can be prioritized when matching. In addition, by collecting the user's past emotional data and analyzing emotional fluctuation patterns, it is possible to select emotionally stable roommates. For example, it is possible to match users who are less stressed. Furthermore, the emotion estimation function can be used to quantify the user's emotional state and prioritize matching with emotionally compatible roommates. For example, it is possible to prioritize matching users with high emotional scores. This can improve the living environment by prioritizing matching with emotionally compatible roommates.

[0063] The roommate matching unit can analyze reviews and feedback from past roommates and select roommates who cause less trouble. For example, the generation AI can collect reviews and feedback from past roommates and select roommates who cause less trouble. Users with many positive reviews can be prioritized for matching. In addition, by analyzing roommate feedback data and identifying the causes of trouble, it is possible to select roommates who cause less trouble. For example, users who have caused less trouble in the past can be prioritized for matching. Furthermore, the generation AI can develop an algorithm to select roommates who cause less trouble based on reviews and feedback from past roommates. For example, it can prioritize matching users with less negative feedback. This allows for the selection of roommates who cause less trouble, thereby contributing to a stable living environment.

[0064] The roommate matching unit can analyze a user's lifestyle rhythm and daily behavior patterns to match them with roommates who share a similar lifestyle. For example, the generation AI can collect data from smart devices to analyze the user's lifestyle rhythm and select roommates with a similar lifestyle. Users who go to bed early and get up early can be matched. It is also possible to develop an algorithm that analyzes a user's daily behavior patterns and selects roommates with a similar lifestyle. For example, it can match users who share the same hobbies and activities. Furthermore, the generation AI can select roommates with a similar lifestyle based on the user's lifestyle rhythm and behavior patterns. For example, it can match users who are active at the same time. This allows for a harmonious living environment by matching roommates with similar lifestyles.

[0065] The housemate matching unit can analyze a user's hobbies and interests and match housemates with common hobbies. For example, the generation AI can collect social media and hobby-related data to analyze the user's hobbies and interests and select housemates with common hobbies. Users who enjoy the same sport can be matched. An algorithm can also be developed to select housemates with common hobbies based on the user's hobbies and interests. For example, users who like the same music genre can be matched. Furthermore, the generation AI can analyze a user's hobbies and interests and select housemates with common hobbies. For example, users who enjoy the same movies and dramas can be matched. This can improve the living environment by matching housemates with common hobbies.

[0066] The housemate matching unit can analyze a user's health condition and dietary habits and match them with housemates who are easy to manage their health. For example, the generation AI collects data from health apps and wearable devices to analyze the user's health condition and selects housemates who are easy to manage their health. Users with the same health goals can be matched. It is also possible to develop an algorithm that analyzes a user's dietary habits and selects housemates who are easy to manage their health. For example, users with the same dietary restrictions can be matched. Furthermore, the generation AI can select housemates who are easy to manage their health based on the user's health condition and dietary habits. For example, it can match users with the same exercise habits. This can improve the living environment by matching housemates who are easy to manage their health.

[0067] The roommate matching unit can analyze a user's stress level using the emotion estimation function and match them with a roommate who can help reduce stress. For example, the generation AI can collect data from a stress measurement device to analyze the user's stress level and select a roommate who can help reduce stress. Users with hobbies that have a relaxing effect can be matched. An algorithm can also be developed to select a roommate who can help reduce stress based on the user's stress level. For example, users who are good at stress management can be matched. Furthermore, the generation AI can analyze a user's stress level and select a roommate who can help reduce stress. For example, it can match users who have similar stress relief methods. This can improve the living environment by matching roommates who can help reduce stress.

[0068] The rent setting unit can use the emotion estimation function to analyze the user's satisfaction and suggest rent terms that provide high satisfaction. For example, to analyze the user's satisfaction, the generation AI analyzes the user's facial expressions and voice in real time and calculates a satisfaction score. It can prioritize suggestions of rent terms that provide high satisfaction. It can also collect the user's past satisfaction data and analyze fluctuation patterns of satisfaction to suggest rent terms that provide high satisfaction. For example, suggestions can be made based on rent terms that have provided high satisfaction in the past. Furthermore, it can use the emotion estimation function to quantify the user's satisfaction and suggest rent terms that provide high satisfaction. For example, it can prioritize suggestions of rent terms with high satisfaction scores. This makes it possible to improve user satisfaction by suggesting rent terms that provide high satisfaction.

[0069] The rent setting unit can analyze past rent payment history and credit information to propose rent terms according to repayment ability. For example, the generation AI can analyze a user's rent payment history and propose rent terms according to repayment ability. Appropriate rent terms can be proposed for users with a good past payment history. It is also possible to develop an algorithm that proposes rent terms according to repayment ability based on a user's credit information. For example, it can propose appropriate rent terms for users with a high credit score. Furthermore, the generation AI can analyze past rent payment history and credit information to propose rent terms according to repayment ability. For example, it can propose appropriate rent terms for users with a high repayment ability. This reduces the burden on users by proposing rent terms according to their repayment ability.

[0070] The rent setting unit can analyze the local economic situation and future market trends to propose stable rent terms over the long term. For example, the generation AI can analyze the local economic situation and propose stable rent terms over the long term. Rent terms can be set based on the local economic growth rate and unemployment rate. It is also possible to develop an algorithm that predicts future market trends and proposes stable rent terms over the long term. For example, rent terms can be set based on future market rents and demand forecasts. Furthermore, the generation AI can analyze the local economic situation and future market trends to propose stable rent terms over the long term. For example, rent terms can be set taking into account local infrastructure development and development plans. This can increase users' sense of security by proposing stable rent terms over the long term.

[0071] The rent setting unit can analyze the user's living expenses and expenditure patterns and propose rent terms that take living expenses into consideration. For example, the generation AI can analyze the user's living expenses and expenditure patterns and propose rent terms that take living expenses into consideration. Rent terms can be set based on food and transportation costs. An algorithm can also be developed to propose rent terms that take living expenses into consideration based on the user's expenditure patterns. For example, rent terms can be set taking into consideration items with large expenditures. Furthermore, the generation AI can analyze the user's living expenses and expenditure patterns and propose rent terms that take living expenses into consideration. For example, rent terms can be set based on the proportion of living expenses. This can improve the user's quality of life by proposing rent terms that take living expenses into consideration.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The roommate matching unit uses generation AI to automatically match roommates based on the wishes and preferences of the landlord and resident. For example, the generation AI can find residents who meet the conditions set by the landlord, such as "prefers a quiet environment," "pets allowed," and "monthly rent of less than 50,000 yen." The generation AI can also suggest the most suitable shared house based on the desired conditions set by the resident. Step 2: The rent setting unit uses the generation AI to set fair rent terms based on the wishes of the landlord and resident and market conditions. For example, the generation AI analyzes the market rent and the resident's ability to pay, and proposes an appropriate rent. This allows rent terms to be set that are acceptable to both the landlord and resident. Step 3: The share house provider uses the generation AI to provide the optimal share house based on the wishes and preferences of the landlord and residents. For example, the generation AI will suggest share houses that meet conditions such as "preferring a quiet environment" and "pets allowed." This allows residents to find a living environment that suits their preferences.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0088] 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.

[0089] 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.

[0090] 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 AI 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] 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 AI 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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. Using generative AI, A roommate matching section that automatically matches roommates based on the wishes and preferences of the landlord and resident; a rent setting unit that sets fair rent conditions based on the wishes of the landlord and the resident and market conditions; A shared house providing unit that provides an optimal shared house based on the wishes and preferences of the landlord and the resident. A system characterized by:

2. The roommate matching unit Analyzing the user's emotional state and prioritizing matching with emotionally compatible housemates 2. The system of claim 1.

3. The roommate matching unit Analyze reviews and feedback from past housemates to select housemates with the least amount of trouble 2. The system of claim 1.

4. The roommate matching unit Analyze the user's lifestyle and daily behavior patterns to match them with housemates who share a similar lifestyle.

2. The system of claim 1.

5. The roommate matching unit Analyzing the user's hobbies and interests and matching them with housemates who share the same hobbies 2. The system of claim 1.

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