System

Abstract

An object of a system according to an embodiment is to calculate a rent market price and provide advice for effective rent negotiation.SOLUTION: A system includes an input analysis part, a rent market price calculation part, and a negotiation advice part. The input analysis unit analyzes an input from a user. The rent market price calculation unit calculates a rent market price based on the information analyzed by the input analysis unit. The negotiation advice unit provides advice for rent negotiation based on the rent market price calculated by the rent market price calculation unit.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] With conventional technology, it was difficult to accurately grasp market rents and conduct effective rent negotiations.

[0005] The system according to the embodiment aims to calculate the market rent and provide advice for effective rent negotiations. [Means for solving the problem]

[0006] The system according to the embodiment includes an input analysis unit, a market rent calculation unit, and a negotiation advice unit. The input analysis unit analyzes input from a user. The market rent calculation unit calculates a market rent based on the information analyzed by the input analysis unit. The negotiation advice unit provides advice for rent negotiation based on the market rent calculated by the market rent calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can calculate the market rent and provide advice for effective rent negotiations. [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 rent negotiation support system according to an embodiment of the present invention is equipped with a chat generation AI, analyzes input from users, calculates the market rent, and provides advice on rent negotiations based on that information. As a result, the rent negotiation support system supports users in rent negotiations and reduces the burden on household finances.

[0029] A rent negotiation support system according to an embodiment includes an input analysis unit, a market rent calculation unit, and a negotiation advice unit. The input analysis unit analyzes input from a user. For example, it analyzes text input to understand the user's intent. The input analysis unit can also analyze voice input and convert it into text using voice recognition technology. The input analysis unit can also analyze option input and perform analysis based on the items selected by the user. The market rent calculation unit calculates market rents based on the information analyzed by the input analysis unit. For example, the generation AI collects local rent data and calculates average market rents. The generation AI can also analyze local statistical data to calculate market rents. The generation AI can also analyze past rent data, grasp trends, and calculate market rents. The negotiation advice unit provides advice for rent negotiations based on the market rent calculated by the market rent calculation unit. For example, the generation AI compares the user's current rent with the calculated market rent to determine whether there is room for negotiation. The generation AI can also consider the user's contract terms and propose an optimal negotiation method. The generation AI can also propose effective negotiation methods based on past negotiation cases. As a result, the rent negotiation support system according to the embodiment can support the user in rent negotiations and reduce the burden on the household budget. For example, the user can negotiate rent based on the advice of the generation AI and successfully lower the rent. The user can also review the terms of the contract based on the advice of the generation AI and renew the contract on more favorable terms. The user can also search for a new property based on the advice of the generation AI and move under better terms.

[0030] The rent market price calculation unit can calculate more detailed rent market prices by analyzing not only local rent data but also information on the surrounding living environment or transportation access. For example, the generation AI analyzes the local rent data as well as the surrounding living environment (e.g., proximity to schools and hospitals, availability of commercial facilities) and reflects this in the rent market price. This allows the calculation of a market price that takes into account quality of life, rather than just rent data. The generation AI also analyzes transportation access information (e.g., distance to the nearest station and location of bus stops) and incorporates it as a factor that affects the rent market price. This provides a rent market price that takes into account convenience for commuting to work or school. The generation AI also collects environmental data such as local security information and noise levels and reflects this in the rent market price. This allows the system to present more appropriate rent market prices to users who prioritize safety and quietness. By providing detailed rent market prices, users can obtain more accurate information.

[0031] The rent market price calculation unit can analyze trends in past rent data and predict future rent prices. In the rent market price calculation unit, for example, the generation AI analyzes rent data from the past few years to understand trends. For example, it predicts future rent prices taking into account seasonal fluctuations and the impact of economic conditions. The generation AI also analyzes fluctuation patterns in rent data by region and predicts future rent prices. For example, it takes into account differences between urban and suburban areas and the impact of new development projects. The generation AI also combines past rent data with economic indicators (e.g., unemployment rate and inflation rate) to predict future rent prices. This provides a prediction that responds to changes in the economic situation. This makes it easier for users to plan for the future by predicting future rent prices.

[0032] The average rent calculation unit can collect not only average rent prices but also property ratings and reviews to provide a comprehensive property evaluation. For example, the generation AI in the average rent calculation unit collects property ratings and reviews from real estate and review sites and provides an overall property evaluation along with the average rent price. For example, it analyzes user ratings and comments to clearly indicate the advantages and disadvantages of the property. The generation AI also analyzes property evaluation data and incorporates it as a factor that influences the average rent price. For example, it takes into account that highly rated properties tend to have higher rents. The generation AI also provides detailed information about the property to the user based on the property reviews. For example, it presents specific information such as livability and management status. This allows the user to select a better property by providing a comprehensive property evaluation.

[0033] The average rent calculation unit can also analyze average rents in other cities or countries and make comparisons from a global perspective. For example, the generation AI in the average rent calculation unit collects rent data from other cities or countries and compares average rents from a global perspective. For example, it compares average rents in major cities and provides relative information to the user. The generation AI also analyzes international rent data and compares average rents in different regions. For example, it shows how much rent a property with the same conditions would cost in different regions. The generation AI also presents average rents in different regions to the user based on global rent data. For example, it provides average rents in the destination area to a user considering a transfer or study abroad. This allows users to understand average rents in different regions by making comparisons from a global perspective.

[0034] The input analysis unit can analyze the user's input and automatically generate a prompt for calculating the optimal average rent based on the input. For example, the input analysis unit uses a generation AI to analyze the user's input and automatically generate a prompt for calculating the optimal average rent. For example, it creates a detailed prompt based on the area and conditions entered by the user. The generation AI also automatically collects additional information for calculating the average rent based on the user's input. For example, it incorporates local rent data and information about the surrounding environment into the prompt. The generation AI also analyzes the user's input and adjusts the prompt for calculating the optimal average rent in real time. For example, it updates the prompt each time the user enters additional information. This automatically generates the optimal prompt based on the user's input, improving the accuracy of calculating the average rent.

[0035] The input analysis unit analyzes a user's past search history and behavioral patterns to present a more personalized average rent price. For example, the generation AI analyzes a user's past search history to present a personalized average rent price. For example, it calculates the optimal average rent price taking into account the areas and conditions previously searched. The generation AI also analyzes the user's behavioral patterns to present a personalized average rent price. For example, it provides the optimal average rent price based on the conditions and time periods the user frequently searches for. The generation AI also updates the personalized average rent price in real time based on the user's past search history and behavioral patterns. For example, it adjusts the average rent price each time a new search history is added. In this way, by analyzing a user's past search history and behavioral patterns, a more personalized average rent price can be provided.

[0036] The input analysis unit can compare the user's input with other users' data and present average rent prices with similar conditions. For example, the generation AI compares the user's input with other users' data and presents average rent prices with similar conditions. For example, it calculates average rent prices based on the data of other users who searched for the same area or conditions. The generation AI also analyzes the user's input and compares it with the data of other users to present the optimal average rent price. For example, it provides average rent prices with similar conditions based on past data. The generation AI also compares the user's input with the data of other users and updates the average rent price in real time. For example, it adjusts the average rent price each time new data is added. In this way, by comparing the user's input with other users' data, it is possible to provide average rent prices with similar conditions.

[0037] The input analysis unit can analyze the user's input and simultaneously provide related real estate information and market trends. For example, the generation AI analyzes the user's input and provides related real estate information. For example, it presents local real estate market trends and new property information. The generation AI also provides related market trends based on the user's input. For example, it provides data showing rising rent trends and changes in demand. The generation AI also analyzes the user's input and updates related real estate information and market trends in real time. For example, it updates the data every time new information is added. This allows the user to obtain more information by providing related real estate information and market trends based on the user's input.

[0038] The negotiation advice unit can analyze past successful cases of rent negotiations and suggest the most effective negotiation method. For example, the generation AI collects and analyzes past successful cases of rent negotiations. For example, it analyzes the conditions and methods of successful negotiations and suggests the most effective negotiation method. The generation AI also identifies negotiation methods with a high success rate based on past negotiation data. For example, it shows that negotiations under specific conditions and timing are more likely to be successful. The generation AI also analyzes past successful cases of rent negotiations and suggests specific negotiation methods to the user. For example, it provides phrases and materials to use during negotiations. In this way, by analyzing past successful cases, the most effective negotiation method can be provided to the user.

[0039] The negotiation advice unit can perform a detailed analysis of the user's current rent and contract terms and present specific negotiation points. For example, the generation AI can perform a detailed analysis of the user's current rent and contract terms and present specific negotiation points. For example, it can indicate room for negotiation by taking into account the contract period and renewal conditions. The generation AI can also identify points that should be emphasized during negotiations based on the user's rent payment history and contract terms. For example, it can present the benefits of being a long-term contract holder. The generation AI can also analyze the user's current rent and contract terms and provide specific negotiation points in real time. For example, it can present data and materials to be used during negotiations. This allows the unit to provide specific negotiation points by performing a detailed analysis of the user's current rent and contract terms.

[0040] The negotiation advice unit can provide not only advice on rent negotiations, but also advice on moving and remodeling. For example, the generation AI provides advice on moving in addition to advice on rent negotiations. For example, it suggests how to select a moving destination and how to choose a moving company. The generation AI also provides advice on remodeling in addition to advice on rent negotiations. For example, it shows the costs and effects of remodeling and suggests it as part of rent negotiations. The generation AI also provides specific plans for moving and remodeling in addition to advice on rent negotiations. For example, it presents a moving schedule and a detailed plan for remodeling. This broadens the user's options by providing advice on moving and remodeling in addition to advice on rent negotiations.

[0041] The negotiation advice unit can analyze the negotiation results of other users and present negotiation methods with a high success rate in a ranked format. In the negotiation advice unit, for example, the generation AI collects the negotiation results of other users and presents negotiation methods with a high success rate in a ranked format. For example, it shows the conditions and methods of successful negotiations in a ranked format. The generation AI also identifies negotiation methods with a high success rate based on the negotiation data of other users and presents them in a ranked format. For example, it shows that negotiations under specific conditions and timing are more likely to be successful. The generation AI also analyzes the negotiation results of other users and provides negotiation methods with a high success rate in a ranked format. For example, it shows the phrases and materials used during negotiations in a ranked format. In this way, by analyzing the negotiation results of other users and presenting negotiation methods with a high success rate in a ranked format, users can select an effective negotiation method.

[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 rent negotiation support system can also customize the average rent based on the user's lifestyle. For example, if the user has a pet, it can prioritize average rents for pet-friendly properties. If the user works remotely, it can prioritize average rents for properties with good internet connections. Furthermore, if the user owns a car, it can also consider average rents for properties with parking spaces. This can help users choose a more appropriate property by providing average rents that match their lifestyle.

[0044] The rent negotiation support system can also customize the average rent based on the user's health condition. For example, if the user has allergies, it can prioritize average rents for properties with air purifiers or properties that do not allow pets. If the user is elderly, it can prioritize average rents for barrier-free properties. Furthermore, if the user enjoys exercise, it can also consider average rents for properties near gyms or parks. This can support a healthier lifestyle by providing average rents that are tailored to the user's health condition.

[0045] The rent negotiation support system can also customize the average rent based on the user's hobbies and interests. For example, if the user's hobby is music, the system can prioritize the average rent for properties with soundproofing. If the user's hobby is cooking, the system can prioritize the average rent for properties with well-equipped kitchens. Furthermore, if the user's hobby is gardening, the system can also take into account the average rent for properties with gardens. This allows the system to support a more fulfilling life by providing average rent that matches the user's hobbies and interests.

[0046] The rent negotiation support system can also customize the average rent based on the user's family structure. For example, if the user has children, it can prioritize the average rent for properties near schools and parks. If the user lives with elderly parents, it can prioritize the average rent for properties near medical facilities. Furthermore, if the user lives alone, it can also take into account the average rent for properties with excellent security. This allows it to provide average rent prices tailored to the user's family structure, helping them live a more secure and comfortable life.

[0047] The rent negotiation support system can also customize the average rent based on the user's occupation. For example, if the user is a medical professional, it can prioritize the average rent for properties near hospitals. If the user is a teacher, it can prioritize the average rent for properties near schools. Furthermore, if the user is an engineer, it can also consider the average rent for properties in areas where IT companies are concentrated. This can support more efficient commuting and work environments by providing average rents tailored to the user's occupation.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The input analysis unit analyzes the input from the user. For example, it analyzes text input to understand the user's intent. It can also analyze voice input and convert it into text using voice recognition technology. It can also analyze option input and perform analysis based on the item selected by the user. Step 2: The market rent calculation unit calculates market rents based on the information analyzed by the input analysis unit. For example, the generation AI collects rent data for the area and calculates the average market rent. It can also analyze local statistical data and past rent data to understand trends and calculate market rents. Step 3: The negotiation advice unit provides advice for rent negotiations based on the market rent calculated by the market rent calculation unit. For example, the generation AI compares the user's current rent with the calculated market rent to determine whether there is room for negotiation. It can also propose optimal negotiation methods taking into account the user's contract terms. It can also propose effective negotiation methods based on past negotiation cases.

[0050] (Example 2) The rent negotiation support system according to an embodiment of the present invention is equipped with a chat generation AI, analyzes input from users, calculates the market rent, and provides advice on rent negotiations based on that information. As a result, the rent negotiation support system supports users in rent negotiations and reduces the burden on household finances.

[0051] A rent negotiation support system according to an embodiment includes an input analysis unit, a market rent calculation unit, and a negotiation advice unit. The input analysis unit analyzes input from a user. For example, it analyzes text input to understand the user's intent. The input analysis unit can also analyze voice input and convert it into text using voice recognition technology. The input analysis unit can also analyze option input and perform analysis based on the items selected by the user. The market rent calculation unit calculates market rents based on the information analyzed by the input analysis unit. For example, the generation AI collects local rent data and calculates average market rents. The generation AI can also analyze local statistical data to calculate market rents. The generation AI can also analyze past rent data, grasp trends, and calculate market rents. The negotiation advice unit provides advice for rent negotiations based on the market rent calculated by the market rent calculation unit. For example, the generation AI compares the user's current rent with the calculated market rent to determine whether there is room for negotiation. The generation AI can also consider the user's contract terms and propose an optimal negotiation method. The generation AI can also propose effective negotiation methods based on past negotiation cases. As a result, the rent negotiation support system according to the embodiment can support the user in rent negotiations and reduce the burden on the household budget. For example, the user can negotiate rent based on the advice of the generation AI and successfully lower the rent. The user can also review the terms of the contract based on the advice of the generation AI and renew the contract on more favorable terms. The user can also search for a new property based on the advice of the generation AI and move under better terms.

[0052] The rent market price calculation unit can calculate more detailed rent market prices by analyzing not only local rent data but also information on the surrounding living environment or transportation access. For example, the generation AI analyzes the local rent data as well as the surrounding living environment (e.g., proximity to schools and hospitals, availability of commercial facilities) and reflects this in the rent market price. This allows the calculation of a market price that takes into account quality of life, rather than just rent data. The generation AI also analyzes transportation access information (e.g., distance to the nearest station and location of bus stops) and incorporates it as a factor that affects the rent market price. This provides a rent market price that takes into account convenience for commuting to work or school. The generation AI also collects environmental data such as local security information and noise levels and reflects this in the rent market price. This allows the system to present more appropriate rent market prices to users who prioritize safety and quietness. By providing detailed rent market prices, users can obtain more accurate information.

[0053] The rent market price calculation unit can analyze trends in past rent data and predict future rent prices. In the rent market price calculation unit, for example, the generation AI analyzes rent data from the past few years to understand trends. For example, it predicts future rent prices taking into account seasonal fluctuations and the impact of economic conditions. The generation AI also analyzes fluctuation patterns in rent data by region and predicts future rent prices. For example, it takes into account differences between urban and suburban areas and the impact of new development projects. The generation AI also combines past rent data with economic indicators (e.g., unemployment rate and inflation rate) to predict future rent prices. This provides a prediction that responds to changes in the economic situation. This makes it easier for users to plan for the future by predicting future rent prices.

[0054] The rent market price calculation unit uses the emotion estimation function to analyze the user's emotions regarding rent market prices and presents rent market prices that are easy for the user to accept. For example, the generation AI in the rent market price calculation unit analyzes the user's emotions when inputting and calculates an emotion score for the rent market price. For example, if the user is feeling anxious or dissatisfied, the rent market price calculation unit presents rent market prices that take those emotions into consideration. The emotion estimation function is also used to adjust the price so that the user has positive emotions regarding the rent market price. For example, it presents reasons and data that are easy for the user to accept. The generation AI also customizes the way the rent market price is presented based on the user's emotion data. For example, if the emotion score is high, detailed data is provided, and if it is low, concise information is presented. In this way, rent market prices that take the user's emotions into consideration are presented, thereby improving user satisfaction.

[0055] The average rent calculation unit can collect not only average rent prices but also property ratings and reviews to provide a comprehensive property evaluation. For example, the generation AI in the average rent calculation unit collects property ratings and reviews from real estate and review sites and provides an overall property evaluation along with the average rent price. For example, it analyzes user ratings and comments to clearly indicate the advantages and disadvantages of the property. The generation AI also analyzes property evaluation data and incorporates it as a factor that influences the average rent price. For example, it takes into account that highly rated properties tend to have higher rents. The generation AI also provides detailed information about the property to the user based on the property reviews. For example, it presents specific information such as livability and management status. This allows the user to select a better property by providing a comprehensive property evaluation.

[0056] The average rent calculation unit can also analyze average rents in other cities or countries and make comparisons from a global perspective. For example, the generation AI in the average rent calculation unit collects rent data from other cities or countries and compares average rents from a global perspective. For example, it compares average rents in major cities and provides relative information to the user. The generation AI also analyzes international rent data and compares average rents in different regions. For example, it shows how much rent a property with the same conditions would cost in different regions. The generation AI also presents average rents in different regions to the user based on global rent data. For example, it provides average rents in the destination area to a user considering a transfer or study abroad. This allows users to understand average rents in different regions by making comparisons from a global perspective.

[0057] The market rent calculation unit uses the emotion estimation function to analyze the emotions of the user when entering the market rent in real time, and can present market rents that elicit positive emotions. The market rent calculation unit, for example, uses the emotion estimation function to analyze the emotions of the user when entering the market rent in real time. For example, if the user is feeling anxious, it provides information that gives a sense of security. Furthermore, the generation AI presents market rents that elicit positive emotions based on the user's emotion data. For example, it presents reasons and data that are easy for the user to understand. Furthermore, it uses the emotion estimation function to analyze the emotions of the user when entering the market rent, and provides an interface that elicits positive emotions. For example, it presents encouraging messages and success stories. In this way, the user's emotions are analyzed in real time, eliciting positive emotions, and improving user satisfaction.

[0058] The input analysis unit can analyze the user's input and automatically generate a prompt for calculating the optimal average rent based on the input. For example, the input analysis unit uses a generation AI to analyze the user's input and automatically generate a prompt for calculating the optimal average rent. For example, it creates a detailed prompt based on the area and conditions entered by the user. The generation AI also automatically collects additional information for calculating the average rent based on the user's input. For example, it incorporates local rent data and information about the surrounding environment into the prompt. The generation AI also analyzes the user's input and adjusts the prompt for calculating the optimal average rent in real time. For example, it updates the prompt each time the user enters additional information. This automatically generates the optimal prompt based on the user's input, improving the accuracy of calculating the average rent.

[0059] The input analysis unit analyzes a user's past search history and behavioral patterns to present a more personalized average rent price. For example, the generation AI analyzes a user's past search history to present a personalized average rent price. For example, it calculates the optimal average rent price taking into account the areas and conditions previously searched. The generation AI also analyzes the user's behavioral patterns to present a personalized average rent price. For example, it provides the optimal average rent price based on the conditions and time periods the user frequently searches for. The generation AI also updates the personalized average rent price in real time based on the user's past search history and behavioral patterns. For example, it adjusts the average rent price each time a new search history is added. In this way, by analyzing a user's past search history and behavioral patterns, a more personalized average rent price can be provided.

[0060] The input analysis unit can use the emotion estimation function to analyze the emotion the user expressed when entering information and present average rent prices according to that emotion. The input analysis unit, for example, uses the emotion estimation function to analyze the emotion the user expressed when entering information and present average rent prices according to that emotion. For example, if the user is feeling anxious, it provides information that gives a sense of security. The generation AI also presents average rent prices according to the user's emotion based on the user's emotion data. For example, it presents reasons and data that are easy for the user to understand. The emotion estimation function also analyzes the emotion the user expressed when entering information in real time and presents average rent prices according to that emotion. For example, it adjusts the emotion so that the user has a positive emotion. This improves user satisfaction by presenting average rent prices according to the user's emotion.

[0061] The input analysis unit can compare the user's input with other users' data and present average rent prices with similar conditions. For example, the generation AI compares the user's input with other users' data and presents average rent prices with similar conditions. For example, it calculates average rent prices based on the data of other users who searched for the same area or conditions. The generation AI also analyzes the user's input and compares it with the data of other users to present the optimal average rent price. For example, it provides average rent prices with similar conditions based on past data. The generation AI also compares the user's input with the data of other users and updates the average rent price in real time. For example, it adjusts the average rent price each time new data is added. In this way, by comparing the user's input with other users' data, it is possible to provide average rent prices with similar conditions.

[0062] The input analysis unit can analyze the user's input and simultaneously provide related real estate information and market trends. For example, the generation AI analyzes the user's input and provides related real estate information. For example, it presents local real estate market trends and new property information. The generation AI also provides related market trends based on the user's input. For example, it provides data showing rising rent trends and changes in demand. The generation AI also analyzes the user's input and updates related real estate information and market trends in real time. For example, it updates the data every time new information is added. This allows the user to obtain more information by providing related real estate information and market trends based on the user's input.

[0063] The input analysis unit uses the emotion estimation function to analyze the user's emotions when entering input, and can provide advice or suggestions according to those emotions. The input analysis unit, for example, uses the emotion estimation function to analyze the user's emotions when entering input, and provides advice according to those emotions. For example, if the user is feeling anxious, it provides advice that gives a sense of security. The generation AI also makes suggestions according to the user's emotions based on the user's emotional data. For example, it presents reasons and data that are easy for the user to accept. The emotion estimation function also analyzes the user's emotions when entering input in real time, and provides advice or suggestions according to those emotions. For example, it adjusts the user's emotions so that they have positive emotions. In this way, by providing advice or suggestions according to the user's emotions, user satisfaction is improved.

[0064] The negotiation advice unit can analyze past successful cases of rent negotiations and suggest the most effective negotiation method. For example, the generation AI collects and analyzes past successful cases of rent negotiations. For example, it analyzes the conditions and methods of successful negotiations and suggests the most effective negotiation method. The generation AI also identifies negotiation methods with a high success rate based on past negotiation data. For example, it shows that negotiations under specific conditions and timing are more likely to be successful. The generation AI also analyzes past successful cases of rent negotiations and suggests specific negotiation methods to the user. For example, it provides phrases and materials to use during negotiations. In this way, by analyzing past successful cases, the most effective negotiation method can be provided to the user.

[0065] The negotiation advice unit can perform a detailed analysis of the user's current rent and contract terms and present specific negotiation points. For example, the generation AI can perform a detailed analysis of the user's current rent and contract terms and present specific negotiation points. For example, it can indicate room for negotiation by taking into account the contract period and renewal conditions. The generation AI can also identify points that should be emphasized during negotiations based on the user's rent payment history and contract terms. For example, it can present the benefits of being a long-term contract holder. The generation AI can also analyze the user's current rent and contract terms and provide specific negotiation points in real time. For example, it can present data and materials to be used during negotiations. This allows the unit to provide specific negotiation points by performing a detailed analysis of the user's current rent and contract terms.

[0066] The negotiation advice unit can use the emotion estimation function to analyze the emotions felt by the user during negotiations and provide negotiation advice according to the emotions. The negotiation advice unit, for example, uses the emotion estimation function to analyze the emotions felt by the user during negotiations and provide negotiation advice according to the emotions. For example, if the user is feeling anxious, advice that gives a sense of security is provided. In addition, the generation AI provides negotiation advice according to the emotions based on the user's emotion data. For example, it presents reasons and data that are easy for the user to accept. In addition, the emotion estimation function is used to analyze the emotions felt by the user during negotiations in real time and provide negotiation advice according to the emotions. For example, it adjusts the user's emotions so that the user has positive emotions. In this way, by providing negotiation advice according to the user's emotions, the success rate of negotiations is improved.

[0067] The negotiation advice unit can provide not only advice on rent negotiations, but also advice on moving and remodeling. For example, the generation AI provides advice on moving in addition to advice on rent negotiations. For example, it suggests how to select a moving destination and how to choose a moving company. The generation AI also provides advice on remodeling in addition to advice on rent negotiations. For example, it shows the costs and effects of remodeling and suggests it as part of rent negotiations. The generation AI also provides specific plans for moving and remodeling in addition to advice on rent negotiations. For example, it presents a moving schedule and a detailed plan for remodeling. This broadens the user's options by providing advice on moving and remodeling in addition to advice on rent negotiations.

[0068] The negotiation advice unit can analyze the negotiation results of other users and present negotiation methods with a high success rate in a ranked format. In the negotiation advice unit, for example, the generation AI collects the negotiation results of other users and presents negotiation methods with a high success rate in a ranked format. For example, it shows the conditions and methods of successful negotiations in a ranked format. The generation AI also identifies negotiation methods with a high success rate based on the negotiation data of other users and presents them in a ranked format. For example, it shows that negotiations under specific conditions and timing are more likely to be successful. The generation AI also analyzes the negotiation results of other users and provides negotiation methods with a high success rate in a ranked format. For example, it shows the phrases and materials used during negotiations in a ranked format. In this way, by analyzing the negotiation results of other users and presenting negotiation methods with a high success rate in a ranked format, users can select an effective negotiation method.

[0069] The negotiation advice unit uses the emotion estimation function to analyze the emotions felt by the user during negotiations in real time and provide advice that elicits positive emotions. The negotiation advice unit, for example, uses the emotion estimation function to analyze the emotions felt by the user during negotiations in real time and provide advice that elicits positive emotions. For example, if the user is feeling anxious, it provides advice that gives a sense of security. The generation AI also provides advice that elicits positive emotions based on the user's emotion data. For example, it presents reasons and data that are easy for the user to understand. The emotion estimation function also analyzes the emotions felt by the user during negotiations in real time and provides an interface for eliciting positive emotions. For example, it presents encouraging messages and success stories. In this way, the success rate of negotiations is improved by analyzing the user's emotions in real time and providing advice that elicits positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The rent negotiation support system can also customize the average rent based on the user's lifestyle. For example, if the user has a pet, it can prioritize average rents for pet-friendly properties. If the user works remotely, it can prioritize average rents for properties with good internet connections. Furthermore, if the user owns a car, it can also consider average rents for properties with parking spaces. This can help users choose a more appropriate property by providing average rents that match their lifestyle.

[0072] The rent negotiation support system can also customize the average rent based on the user's health condition. For example, if the user has allergies, it can prioritize average rents for properties with air purifiers or properties that do not allow pets. If the user is elderly, it can prioritize average rents for barrier-free properties. Furthermore, if the user enjoys exercise, it can also consider average rents for properties near gyms or parks. This can support a healthier lifestyle by providing average rents that are tailored to the user's health condition.

[0073] The rent negotiation support system can also customize the average rent based on the user's hobbies and interests. For example, if the user's hobby is music, the system can prioritize the average rent for properties with soundproofing. If the user's hobby is cooking, the system can prioritize the average rent for properties with well-equipped kitchens. Furthermore, if the user's hobby is gardening, the system can also take into account the average rent for properties with gardens. This allows the system to support a more fulfilling life by providing average rent that matches the user's hobbies and interests.

[0074] The rent negotiation support system can also customize the average rent based on the user's family structure. For example, if the user has children, it can prioritize the average rent for properties near schools and parks. If the user lives with elderly parents, it can prioritize the average rent for properties near medical facilities. Furthermore, if the user lives alone, it can also take into account the average rent for properties with excellent security. This allows it to provide average rent prices tailored to the user's family structure, helping them live a more secure and comfortable life.

[0075] The rent negotiation support system can also customize the average rent based on the user's occupation. For example, if the user is a medical professional, it can prioritize the average rent for properties near hospitals. If the user is a teacher, it can prioritize the average rent for properties near schools. Furthermore, if the user is an engineer, it can also consider the average rent for properties in areas where IT companies are concentrated. This can support more efficient commuting and work environments by providing average rents tailored to the user's occupation.

[0076] The rent negotiation support system uses emotion estimation to analyze the user's emotions regarding the outcome of rent negotiations and can provide follow-up after the negotiation. For example, if the user is dissatisfied with the negotiation result, the system can provide additional negotiation advice. If the user is satisfied with the negotiation result, the system can support preparations for the next negotiation. Furthermore, if the user has neutral feelings toward the negotiation result, the system can suggest other options. This allows for follow-up based on the user's emotions to help achieve better negotiation results.

[0077] The rent negotiation support system uses emotion estimation to analyze the emotions a user feels during the preparation stage of rent negotiations and can provide advice at the appropriate time. For example, if the user is feeling anxious, the system can provide advice on how to relax before negotiating. If the user is confident, the system can suggest points to emphasize during negotiations. Furthermore, if the user is unsure, the system can simulate a negotiation and provide specific advice. This can improve the success rate of negotiations by supporting preparations based on the user's emotions.

[0078] The rent negotiation support system uses emotion estimation to analyze the emotions felt by the user during the rent negotiation process and provide real-time advice. For example, if the user feels nervous during the negotiation, it can provide advice on how to relax. If the user loses confidence during the negotiation, it can send an encouraging message. Furthermore, if the user becomes confused during the negotiation, it can organize the negotiation progress and suggest the next action to take. This can improve the success rate of negotiations by providing real-time support based on the user's emotions.

[0079] The rent negotiation support system can use its emotion estimation function to analyze the user's emotions regarding the results of rent negotiations and provide feedback for the next negotiation. For example, if the user is satisfied with the negotiation results, the system can highlight the success points and provide advice to apply to the next negotiation. If the user is dissatisfied with the negotiation results, the system can point out areas for improvement and provide specific advice for the next negotiation. Furthermore, if the user has neutral feelings toward the negotiation results, the system can suggest other options and support preparation for the next negotiation. In this way, by providing feedback based on the user's emotions, the success rate of the next negotiation can be improved.

[0080] The rent negotiation support system uses emotion estimation to analyze the emotions felt by the user during the rent negotiation process and provide advice according to the progress of the negotiation. For example, if the user feels anxious in the early stages of the negotiation, the system can explain the basic points of negotiation. If the user loses confidence in the middle of the negotiation, the system can introduce past success stories to motivate the user. Furthermore, if the user is unsure in the final stages of the negotiation, the system can provide specific advice to support the final decision. This can improve the success rate of negotiations by providing support according to the progress of the negotiation based on the user's emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The input analysis unit analyzes the input from the user. For example, it analyzes text input to understand the user's intent. It can also analyze voice input and convert it into text using voice recognition technology. It can also analyze option input and perform analysis based on the item selected by the user. Step 2: The market rent calculation unit calculates market rents based on the information analyzed by the input analysis unit. For example, the generation AI collects rent data for the area and calculates the average market rent. It can also analyze local statistical data and past rent data to understand trends and calculate market rents. Step 3: The negotiation advice unit provides advice for rent negotiations based on the market rent calculated by the market rent calculation unit. For example, the generation AI compares the user's current rent with the calculated market rent to determine whether there is room for negotiation. It can also propose optimal negotiation methods taking into account the user's contract terms. It can also propose effective negotiation methods based on past negotiation cases.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. an input analysis unit that analyzes input from a user; a rent market price calculation unit that calculates a rent market price based on the information analyzed by the input analysis unit; a negotiation advice unit that provides advice for rent negotiations based on the market rent calculated by the market rent calculation unit. A system characterized by:

2. The rent market price calculation unit Analyzes not only local rent data but also information on the surrounding living environment and transportation access to calculate more detailed average rent prices 2. The system of claim 1.

3. The rent market price calculation unit Analyze trends in past rent data and predict future rent prices 2. The system of claim 1.

4. The rent market price calculation unit Analyzing the user's feelings about the market rent and presenting a market rent that is easy for the user to accept 2. The system of claim 1.

5. The rent market price calculation unit In addition to the rent market price, we also collect property ratings and reviews to provide a comprehensive property evaluation.

2. The system of claim 1.

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