System

Abstract

An object of a system according to an embodiment is to collectively and efficiently perform a procedure involved in moving.SOLUTION: A system according to an embodiment includes a reception unit, a proposal unit, a set proposal unit, and a proxy unit. The reception unit receives a user's request for moving. The proposal unit proposes the property on the basis of the information received by the reception unit. The set proposal unit proposes a set of mobile, fixed, electricity, water, and gas based on the property proposed by the proposal unit. The vicarious execution unit automatically vicariously executes the application procedure on the basis of the content proposed by the set proposal 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] Conventional techniques have had the problem that the various procedures involved in moving are complicated, making it difficult for users to complete all of the procedures at once.

[0005] The system according to the embodiment aims to efficiently carry out procedures associated with moving all at once. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a set proposal unit, and an agent unit. The reception unit accepts a user's request to move. The proposal unit proposes a property based on the information accepted by the reception unit. The set proposal unit proposes a set of mobile, fixed, electricity, water, and gas based on the property proposed by the proposal unit. The agent unit automatically carries out the application procedure based on the contents proposed by the set proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently carry out procedures associated with moving all at once. [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) A moving support system according to an embodiment of the present invention accepts a user's moving requests in one go, proposes optimal properties, proposes packages, and handles application procedures on behalf of the user. The moving support system allows the user to input their moving requests, and the system proposes optimal properties based on their desired conditions. Furthermore, the system proposes packages including mobile, landline, electricity, water, and gas services, and provides information on the surrounding area, including public safety and recommended spots. It also provides conversational answers to questions about the move. This allows the user to address all of their moving needs in one go. Next, the moving support system automatically handles all of the application procedures described above. The user can complete all contracts with just one input of information. For example, the moving support system allows the user to select the desired property and enter the necessary information, and the system automatically completes various procedures and completes the contract. This mechanism allows the user to complete all moving procedures hassle-free. As a result, the moving support system handles all of the user's moving procedures in one go. For example, when a user inputs their moving requests, they input the desired property conditions and the date and time of the move. The system searches a database based on the input conditions and lists the optimal properties. Furthermore, the system will propose a package including mobile, fixed, electricity, water, and gas, and will also introduce the surrounding area's security and recommended spots. This will help users choose a property. The system will also answer any questions about moving in a conversational format. Finally, the system will automatically handle all of the above application procedures, allowing users to complete all moving procedures hassle-free.

[0029] A moving support system according to an embodiment includes a reception unit, a proposal unit, a set proposal unit, and an agent unit. The reception unit accepts a user's request for moving. The user's request for moving may include, but is not limited to, the region of the destination, the budget, and desired property conditions. The reception unit, for example, stores the information entered by the user in a database and uses the information for subsequent processing. The proposal unit proposes properties based on the information accepted by the reception unit. The property proposal is made based on, for example, property selection criteria and a proposal format, for example, but is not limited to, the example. The proposal unit proposes multiple properties that meet the user's desired conditions and provides detailed information about each property. The set proposal unit proposes a set of mobile, fixed, electricity, water, and gas services based on the properties proposed by the proposal unit. The set proposal is made based on, for example, the type of service proposed and the proposal method, for example, but is not limited to, the example. The set proposal unit, for example, proposes a set of services including mobile, fixed, electricity, water, and gas services to be used at the destination, all at once, allowing the user to select one. The agent unit automatically performs the application procedures based on the content proposed by the set proposal unit. The application procedures are performed based on, for example, the type of procedure to be performed and the process of the procedure, but are not limited to these examples. For example, when the agent unit receives a request from a user and inputs the necessary information, the system automatically performs various procedures and completes the contract. As a result, the moving support system according to the embodiment can perform all procedures related to the user's move all at once.

[0030] The suggestion unit can introduce safety information or recommended spots around the property. The suggestion unit, for example, provides safety information around the property. Safety information includes, for example, crime rates and locations of police stations, but is not limited to these examples. The suggestion unit can also introduce recommended spots around the property. Recommended spots include, for example, tourist spots, restaurants, public facilities, etc., but are not limited to these examples. In this way, by providing safety information and recommended spots around the property, it is possible to support the user in selecting a property.

[0031] The suggestion unit can answer questions about moving in a conversational format. For example, the suggestion unit answers questions about moving in a conversational format. Conversational formats include, but are not limited to, chatbots and voice assistants. For example, the suggestion unit allows the system to automatically answer questions such as, "What procedures are necessary when moving?" This makes it possible to alleviate the user's anxiety by answering questions about moving in a conversational format.

[0032] The agency unit allows the user to complete the contract with just one information input. For example, when the user selects the desired property and inputs the necessary information, the system automatically performs various procedures and completes the contract. Information input by the user includes, but is not limited to, name, address, contact information, etc. The agency unit, for example, automatically generates various contract documents based on the input information and performs the necessary procedures. This allows the user to complete all contracts with just one information input.

[0033] The set proposal unit can propose contracts for the internet line, electricity, gas, and water to be used at the new address all at once. The set proposal unit, for example, proposes contracts for the internet line, electricity, gas, water, etc. to be used at the new address all at once. The proposed services include, for example, internet providers, electric power companies, gas companies, waterworks, etc., but are not limited to these examples. For example, when the set proposal unit selects the services the user wants and enters the necessary information, the system automatically performs various contract procedures. This can reduce the user's effort by proposing contracts for various services to be used at the new address all at once.

[0034] The reception unit can analyze the user's past moving history and select a reception method. The reception unit suggests the optimal reception method, for example, based on the moving companies and services the user has used in the past. The reception unit, for example, analyzes the user's tendency to move at specific times based on the user's past moving history and suggests the optimal reception timing. The reception unit also selects the optimal reception method based on specific conditions (for example, family composition or amount of luggage) based on the user's past moving history. In this way, by analyzing the user's past moving history, it is possible to provide the user with the optimal reception method.

[0035] The reception unit can perform filtering based on the user's current living situation and areas of interest when receiving a moving request. For example, if the user has a pet, the reception unit will preferentially suggest properties that allow pets. For example, if the user has children, the reception unit will suggest properties in a school district or an area with many facilities for children. Furthermore, if the user has a specific hobby, the reception unit will suggest properties that are close to facilities related to that hobby. In this way, by filtering based on the user's living situation and areas of interest, more appropriate properties can be suggested.

[0036] When accepting a moving request, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the moving request using voice recognition technology. For example, if the user selects text input, the acceptance unit accepts the moving request using text analysis technology. Furthermore, if the user selects image input, the acceptance unit accepts the moving request using image recognition technology. This allows for selecting the optimal acceptance means depending on the user's input method, thereby improving user convenience.

[0037] When accepting a request for moving, the acceptance unit can prioritize accepting highly relevant requests by taking into consideration the user's geographical location information. For example, if the user desires a property close to their current location, the acceptance unit prioritizes accepting the request. For example, if the user desires to move to a specific area, the acceptance unit prioritizes accepting requests related to that area. Furthermore, if the user desires a property far from their current location, the acceptance unit postpones the request. In this way, by taking into consideration the user's geographical location information, highly relevant requests can be prioritized.

[0038] When accepting a request for moving, the reception unit can analyze the user's social media activity and accept related requests. For example, the reception unit analyzes posts about moving shared by the user on social media and prioritizes accepting those requests. For example, the reception unit detects from the user's social media activity that the user has an interest in moving and prioritizes accepting those requests. The reception unit also refers to posts about moving made by the user's friends on social media and accepts related requests. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting related requests.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a moving request. The reception unit, for example, proposes the optimal reception method based on feedback provided by the user in the past. The reception unit, for example, analyzes the user's preference for a specific reception method from the user's past feedback and preferentially proposes that method. The reception unit also refers to the user's past feedback and customizes and provides the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0040] When proposing a property, the suggestion unit can adjust the level of detail of the proposal based on the importance of the property. For example, the suggestion unit provides detailed information for properties with high importance. For example, the suggestion unit provides concise information for properties with low importance. The suggestion unit also adjusts the level of detail of the proposal in stages according to the importance of the property. In this way, by adjusting the level of detail of the proposal based on the importance of the property, it is possible to provide optimal information for the user.

[0041] When proposing a property, the proposal unit can apply different proposal algorithms depending on the property category. For example, in the case of a rental property, the proposal unit applies a proposal algorithm that takes into account trends in the rental market. For example, in the case of a purchase property, the proposal unit applies a proposal algorithm that takes into account trends in the real estate market. Furthermore, in the case of a commercial property, the proposal unit applies a proposal algorithm that takes into account the characteristics of the commercial area. In this way, by applying different proposal algorithms depending on the property category, more appropriate property proposals can be made.

[0042] When proposing properties, the suggestion unit can improve the accuracy of the suggestions by referring to the user's past suggestion results. For example, the suggestion unit analyzes the characteristics of properties previously selected by the user and suggests similar properties. For example, the suggestion unit analyzes the characteristics of properties previously rejected by the user and suggests properties with different characteristics. The suggestion unit also adjusts the suggestion algorithm based on the user's past suggestion results to improve accuracy. In this way, the accuracy of the suggestions can be improved by referring to the user's past suggestion results.

[0043] When proposing a property, the proposal unit can determine the priority of the proposal based on the time of submission of the property. For example, the proposal unit gives priority to proposing newly arrived properties. For example, the proposal unit lowers the priority of a proposal for a property that has been submitted earlier. Furthermore, the proposal unit gradually adjusts the priority of the proposal depending on the time of submission. In this way, by determining the priority of the proposal based on the time of submission of the property, it is possible to provide the latest information preferentially.

[0044] When proposing properties, the suggestion unit can adjust the order of suggestions based on the relevance of the properties. For example, the suggestion unit preferentially suggests properties that are most relevant to the user's desired conditions. For example, the suggestion unit postpones the order of suggestions for properties that are less relevant. Furthermore, the suggestion unit gradually adjusts the order of suggestions according to the relevance of the properties. In this way, by adjusting the order of suggestions based on the relevance of the properties, it is possible to preferentially suggest properties that are most relevant to the user.

[0045] When proposing a property, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is knowledgeable about real estate, the suggestion unit makes a proposal that uses a lot of technical terminology. For example, if the user is not knowledgeable about real estate, the suggestion unit makes a concise proposal that avoids technical terminology. Furthermore, the suggestion unit gradually adjusts the use of technical terminology in the proposal according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to make a proposal that is easy for the user to understand.

[0046] When proposing a set, the set suggestion unit can analyze the user's past contract history and select the optimal set suggestion method. The set suggestion unit selects the optimal set suggestion method based on, for example, services the user has used in the past. The set suggestion unit analyzes, for example, the user's preferences for specific services from the user's past contract history and preferentially suggests those services. The set suggestion unit also selects the optimal set suggestion method based on the user's past contract history. In this way, the optimal set suggestion method can be provided by analyzing the user's past contract history.

[0047] When proposing a set, the set suggestion unit can customize the means for suggesting a set based on the user's current living situation. For example, if the user lives with his or her family, the set suggestion unit makes a set suggestion for families. For example, if the user lives alone, the set suggestion unit makes a set suggestion for single living. Furthermore, if the user has a pet, the set suggestion unit makes a set suggestion including services for pets. This enables more appropriate suggestions to be made by customizing the means for suggesting a set based on the user's current living situation.

[0048] The set suggestion unit can improve the set suggestion method by reflecting user feedback when suggesting a set. The set suggestion unit proposes an optimal set suggestion method, for example, based on feedback provided by the user in the past. The set suggestion unit analyzes, for example, the user's preference for a specific set suggestion method from the user's past feedback and preferentially suggests that method. The set suggestion unit also refers to the user's past feedback and customizes and provides the set suggestion method. In this way, the set suggestion method can be improved by reflecting user feedback, enabling more appropriate suggestions.

[0049] When proposing a set, the set suggestion unit can select the optimal set suggestion method by taking into consideration the user's geographical location information. For example, if the user desires a service close to their current location, the set suggestion unit will preferentially accept that set suggestion. For example, if the user desires to move to a specific area, the set suggestion unit will preferentially accept a set suggestion related to that area. Furthermore, if the user desires a service far from their current location, the set suggestion unit will postpone that set suggestion. In this way, by taking into consideration the user's geographical location information, the optimal set suggestion method can be provided.

[0050] When proposing a set, the set suggestion unit can analyze the user's social media activity and suggest a means for proposing a set. For example, the set suggestion unit analyzes posts about moving shared by the user on social media and preferentially accepts the set suggestion. For example, the set suggestion unit detects from the user's social media activity that the user has an interest in moving and preferentially accepts the set suggestion. The set suggestion unit also refers to posts about moving made by the user's friends on social media and accepts related set suggestions. In this way, by analyzing the user's social media activity, related set suggestions can be preferentially provided.

[0051] When proposing a set, the set suggestion unit can customize the set suggestion method by reflecting the user's past feedback. The set suggestion unit proposes an optimal set suggestion method, for example, based on feedback provided by the user in the past. The set suggestion unit analyzes the user's preference for a specific set suggestion method from the user's past feedback, and preferentially suggests that method. The set suggestion unit also refers to the user's past feedback and customizes and provides the set suggestion method. In this way, the optimal set suggestion method can be provided by reflecting the user's past feedback.

[0052] During proxy processing, the proxy unit can analyze the user's past contract history and select the optimal proxy processing method. The proxy unit selects the optimal proxy processing method, for example, based on services the user has used in the past. The proxy unit analyzes the user's preferences for specific services from the user's past contract history, for example, and prioritizes proxy processing for those services. The proxy unit also selects the optimal proxy processing method based on the user's past contract history. In this way, the optimal proxy processing method can be provided by analyzing the user's past contract history.

[0053] The proxy unit can customize the proxy procedure means based on the user's current living situation when performing the proxy procedure. For example, if the user lives with his / her family, the proxy unit performs proxy procedures for the family. For example, if the user lives alone, the proxy unit performs proxy procedures for the single person. Furthermore, if the user has a pet, the proxy unit performs proxy procedures including services for the pet. This allows for more appropriate response by customizing the proxy procedure means based on the user's current living situation.

[0054] The proxy unit can improve the proxy procedure method by reflecting user feedback during the proxy procedure. For example, the proxy unit proposes an optimal proxy procedure method based on feedback provided by the user in the past. For example, the proxy unit analyzes the user's preference for a specific proxy procedure method from the user's past feedback and preferentially proposes that method. The proxy unit also refers to the user's past feedback and customizes and provides the proxy procedure method. In this way, by reflecting user feedback, the proxy procedure method can be improved and more appropriate responses can be made.

[0055] When performing a proxy procedure, the proxy unit can select the optimal proxy procedure method by taking into consideration the user's geographical location information. For example, if the user desires a service close to their current location, the proxy unit will prioritize accepting that proxy procedure. For example, if the user desires to move to a specific area, the proxy unit will prioritize accepting proxy procedures related to that area. Furthermore, if the user desires a service far from their current location, the proxy unit will postpone that proxy procedure. In this way, by taking into consideration the user's geographical location information, the optimal proxy procedure method can be provided.

[0056] When performing a proxy procedure, the proxy unit can analyze the user's social media activity and suggest a proxy procedure method. For example, the proxy unit analyzes posts about moving shared by the user on social media and prioritizes accepting the proxy procedure. For example, the proxy unit detects from the user's social media activity that the user is interested in moving and prioritizes accepting the proxy procedure. The proxy unit also refers to posts about moving by the user's friends on social media and accepts related proxy procedures. In this way, by analyzing the user's social media activity, it is possible to prioritize providing related proxy procedures.

[0057] The proxy unit can customize the proxy procedure method by reflecting the user's past feedback during the proxy procedure. The proxy unit proposes an optimal proxy procedure method, for example, based on feedback provided by the user in the past. The proxy unit analyzes the user's preference for a specific proxy procedure method from the user's past feedback, and preferentially proposes that method. The proxy unit also refers to the user's past feedback and customizes and provides the proxy procedure method. In this way, the proxy unit can provide an optimal proxy procedure method by reflecting the user's past feedback.

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

[0059] When accepting a user's moving request, the acceptance unit can analyze the user's past moving history and suggest the optimal acceptance method. For example, the acceptance unit can suggest the optimal acceptance method based on the moving companies and services the user has used in the past. Furthermore, the acceptance unit can analyze the user's tendency to move at specific times based on the user's past moving history and suggest the optimal acceptance timing. It can also select the optimal acceptance method based on specific conditions (for example, family composition or amount of luggage) based on the user's past moving history. In this way, by analyzing the user's past moving history, the acceptance unit can offer the optimal acceptance method to the user.

[0060] The suggestion unit can introduce safety information or recommended spots around the property. For example, when providing safety information around the property, information such as the crime rate and the location of police stations can be included. Furthermore, when introducing recommended spots around the property, information on tourist spots, restaurants, public facilities, etc. can be provided. Furthermore, the suggestion unit can customize recommended spots based on the user's areas of interest. For example, if the user likes outdoor activities, nearby parks and hiking trails can be introduced. In this way, by providing safety information and recommended spots around the property, the suggestion unit can support the user in selecting a property.

[0061] The reception unit can analyze the user's past moving history and select a reception method. For example, it can suggest the optimal reception method based on the moving companies and services the user has used in the past. Furthermore, it can analyze the user's tendency to move at specific times based on the user's past moving history and suggest the optimal reception timing. It can also select the optimal reception method based on specific conditions (for example, family composition or amount of luggage) based on the user's past moving history. In this way, it is possible to provide the user with the optimal reception method by analyzing the user's past moving history.

[0062] When receiving a moving request, the reception unit can filter based on the user's current living situation and areas of interest. For example, if the user has a pet, properties that allow pets can be preferentially suggested. Also, if the user has children, properties in school districts and areas with many facilities for children can be suggested. Furthermore, if the user has a specific hobby, properties that are close to facilities related to that hobby can be suggested. In this way, by filtering based on the user's living situation and areas of interest, more appropriate properties can be suggested.

[0063] When accepting a moving request, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the moving request can be accepted using voice recognition technology. If the user selects text input, the moving request can be accepted using text analysis technology. Furthermore, if the user selects image input, the moving request can be accepted using image recognition technology. This allows the optimal acceptance means to be selected depending on the user's input method, thereby improving user convenience.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives the user's request for moving. The user's request for moving includes the area to move to, budget, desired property conditions, etc. The reception unit saves the information entered by the user in a database and uses it for subsequent processing. Step 2: The proposal unit proposes properties based on the information received by the reception unit. The proposal unit proposes multiple properties that meet the user's desired conditions and provides detailed information about each property. Step 3: The package proposal unit proposes a package of mobile, fixed, electricity, water, and gas based on the property proposed by the proposal unit. The package proposal unit proposes contracts for the internet line, electricity, gas, water, etc. to be used at the new address all at once, allowing the user to select. Step 4: The agent automatically carries out the application procedures based on the contents proposed by the package proposal unit. The agent selects the desired property and inputs the necessary information, and the system automatically carries out the various procedures and completes the contract.

[0066] (Example 2) A moving support system according to an embodiment of the present invention accepts a user's moving requests in one go, proposes optimal properties, proposes packages, and handles application procedures on behalf of the user. The moving support system allows the user to input their moving requests, and the system proposes optimal properties based on their desired conditions. Furthermore, the system proposes packages including mobile, landline, electricity, water, and gas services, and provides information on the surrounding area, including public safety and recommended spots. It also provides conversational answers to questions about the move. This allows the user to address all of their moving needs in one go. Next, the moving support system automatically handles all of the application procedures described above. The user can complete all contracts with just one input of information. For example, the moving support system allows the user to select the desired property and enter the necessary information, and the system automatically completes various procedures and completes the contract. This mechanism allows the user to complete all moving procedures hassle-free. As a result, the moving support system handles all of the user's moving procedures in one go. For example, when a user inputs their moving requests, they input the desired property conditions and the date and time of the move. The system searches a database based on the input conditions and lists the optimal properties. Furthermore, the system will propose a package including mobile, fixed, electricity, water, and gas, and will also introduce the surrounding area's security and recommended spots. This will help users choose a property. The system will also answer any questions about moving in a conversational format. Finally, the system will automatically handle all of the above application procedures, allowing users to complete all moving procedures hassle-free.

[0067] A moving support system according to an embodiment includes a reception unit, a proposal unit, a set proposal unit, and an agent unit. The reception unit accepts a user's request for moving. The user's request for moving may include, but is not limited to, the region of the destination, the budget, and desired property conditions. The reception unit, for example, stores the information entered by the user in a database and uses the information for subsequent processing. The proposal unit proposes properties based on the information accepted by the reception unit. The property proposal is made based on, for example, property selection criteria and a proposal format, for example, but is not limited to, the example. The proposal unit proposes multiple properties that meet the user's desired conditions and provides detailed information about each property. The set proposal unit proposes a set of mobile, fixed, electricity, water, and gas services based on the properties proposed by the proposal unit. The set proposal is made based on, for example, the type of service proposed and the proposal method, for example, but is not limited to, the example. The set proposal unit, for example, proposes a set of services including mobile, fixed, electricity, water, and gas services to be used at the destination, all at once, allowing the user to select one. The agent unit automatically performs the application procedures based on the content proposed by the set proposal unit. The application procedures are performed based on, for example, the type of procedure to be performed and the process of the procedure, but are not limited to these examples. For example, when the agent unit receives a request from a user and inputs the necessary information, the system automatically performs various procedures and completes the contract. As a result, the moving support system according to the embodiment can perform all procedures related to the user's move all at once.

[0068] The suggestion unit can introduce safety information or recommended spots around the property. The suggestion unit, for example, provides safety information around the property. Safety information includes, for example, crime rates and locations of police stations, but is not limited to these examples. The suggestion unit can also introduce recommended spots around the property. Recommended spots include, for example, tourist spots, restaurants, public facilities, etc., but are not limited to these examples. In this way, by providing safety information and recommended spots around the property, it is possible to support the user in selecting a property.

[0069] The suggestion unit can answer questions about moving in a conversational format. For example, the suggestion unit answers questions about moving in a conversational format. Conversational formats include, but are not limited to, chatbots and voice assistants. For example, the suggestion unit allows the system to automatically answer questions such as, "What procedures are necessary when moving?" This makes it possible to alleviate the user's anxiety by answering questions about moving in a conversational format.

[0070] The agency unit allows the user to complete the contract with just one information input. For example, when the user selects the desired property and inputs the necessary information, the system automatically performs various procedures and completes the contract. Information input by the user includes, but is not limited to, name, address, contact information, etc. The agency unit, for example, automatically generates various contract documents based on the input information and performs the necessary procedures. This allows the user to complete all contracts with just one information input.

[0071] The set proposal unit can propose contracts for the internet line, electricity, gas, and water to be used at the new address all at once. The set proposal unit, for example, proposes contracts for the internet line, electricity, gas, water, etc. to be used at the new address all at once. The proposed services include, for example, internet providers, electric power companies, gas companies, waterworks, etc., but are not limited to these examples. For example, when the set proposal unit selects the services the user wants and enters the necessary information, the system automatically performs various contract procedures. This can reduce the user's effort by proposing contracts for various services to be used at the new address all at once.

[0072] The reception unit can estimate the user's emotions and adjust the timing of accepting the moving request based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit delays the timing of accepting the request and waits until the user is relaxed. The emotion estimation is realized, for example, by using an emotion estimation function with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is relaxed, the reception unit immediately accepts the moving request and responds quickly. Furthermore, if the user is in a hurry, the reception unit prioritizes accepting the moving request and starts processing it quickly. In this way, by adjusting the acceptance timing according to the user's emotions, the request can be accepted at a more appropriate time.

[0073] The reception unit can analyze the user's past moving history and select a reception method. The reception unit suggests the optimal reception method, for example, based on the moving companies and services the user has used in the past. The reception unit, for example, analyzes the user's tendency to move at specific times based on the user's past moving history and suggests the optimal reception timing. The reception unit also selects the optimal reception method based on specific conditions (for example, family composition or amount of luggage) based on the user's past moving history. In this way, by analyzing the user's past moving history, it is possible to provide the user with the optimal reception method.

[0074] The reception unit can perform filtering based on the user's current living situation and areas of interest when receiving a moving request. For example, if the user has a pet, the reception unit will preferentially suggest properties that allow pets. For example, if the user has children, the reception unit will suggest properties in a school district or an area with many facilities for children. Furthermore, if the user has a specific hobby, the reception unit will suggest properties that are close to facilities related to that hobby. In this way, by filtering based on the user's living situation and areas of interest, more appropriate properties can be suggested.

[0075] When accepting a moving request, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the moving request using voice recognition technology. For example, if the user selects text input, the acceptance unit accepts the moving request using text analysis technology. Furthermore, if the user selects image input, the acceptance unit accepts the moving request using image recognition technology. This allows for selecting the optimal acceptance means depending on the user's input method, thereby improving user convenience.

[0076] The reception unit can estimate the user's emotions and determine the priority of received requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prioritizes the user's requests. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is relaxed, the reception unit prioritizes the requests of other users and postpones the relaxed user's request. Furthermore, if the user is in a hurry, the reception unit prioritizes the user's request. This allows for more appropriate responses by determining the priority of requests according to the user's emotions.

[0077] When accepting a request for moving, the acceptance unit can prioritize accepting highly relevant requests by taking into consideration the user's geographical location information. For example, if the user desires a property close to their current location, the acceptance unit prioritizes accepting the request. For example, if the user desires to move to a specific area, the acceptance unit prioritizes accepting requests related to that area. Furthermore, if the user desires a property far from their current location, the acceptance unit postpones the request. In this way, by taking into consideration the user's geographical location information, highly relevant requests can be prioritized.

[0078] When accepting a request for moving, the reception unit can analyze the user's social media activity and accept related requests. For example, the reception unit analyzes posts about moving shared by the user on social media and prioritizes accepting those requests. For example, the reception unit detects from the user's social media activity that the user has an interest in moving and prioritizes accepting those requests. The reception unit also refers to posts about moving made by the user's friends on social media and accepts related requests. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting related requests.

[0079] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a moving request. The reception unit, for example, proposes the optimal reception method based on feedback provided by the user in the past. The reception unit, for example, analyzes the user's preference for a specific reception method from the user's past feedback and preferentially proposes that method. The reception unit also refers to the user's past feedback and customizes and provides the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0080] The suggestion unit can estimate the user's emotions and adjust the presentation method of property suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit makes simple, highly visible property suggestions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is relaxed, the suggestion unit makes property suggestions that include detailed information. Furthermore, if the user is excited, the suggestion unit makes visually appealing property suggestions. This enables more appropriate suggestions to be made by adjusting the presentation method of property suggestions according to the user's emotions.

[0081] When proposing a property, the suggestion unit can adjust the level of detail of the proposal based on the importance of the property. For example, the suggestion unit provides detailed information for properties with high importance. For example, the suggestion unit provides concise information for properties with low importance. The suggestion unit also adjusts the level of detail of the proposal in stages according to the importance of the property. In this way, by adjusting the level of detail of the proposal based on the importance of the property, it is possible to provide optimal information for the user.

[0082] When proposing a property, the proposal unit can apply different proposal algorithms depending on the property category. For example, in the case of a rental property, the proposal unit applies a proposal algorithm that takes into account trends in the rental market. For example, in the case of a purchase property, the proposal unit applies a proposal algorithm that takes into account trends in the real estate market. Furthermore, in the case of a commercial property, the proposal unit applies a proposal algorithm that takes into account the characteristics of the commercial area. In this way, by applying different proposal algorithms depending on the property category, more appropriate property proposals can be made.

[0083] When proposing properties, the suggestion unit can improve the accuracy of the suggestions by referring to the user's past suggestion results. For example, the suggestion unit analyzes the characteristics of properties previously selected by the user and suggests similar properties. For example, the suggestion unit analyzes the characteristics of properties previously rejected by the user and suggests properties with different characteristics. The suggestion unit also adjusts the suggestion algorithm based on the user's past suggestion results to improve accuracy. In this way, the accuracy of the suggestions can be improved by referring to the user's past suggestion results.

[0084] The suggestion unit can estimate the user's emotions and adjust the length of the property proposal based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit makes short and to-the-point property proposals. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is relaxed, the suggestion unit makes longer property proposals with detailed descriptions. Furthermore, if the user is excited, the suggestion unit makes property proposals with visually stimulating effects. This allows for more appropriate proposals by adjusting the length of the property proposals according to the user's emotions.

[0085] When proposing a property, the proposal unit can determine the priority of the proposal based on the time of submission of the property. For example, the proposal unit gives priority to proposing newly arrived properties. For example, the proposal unit lowers the priority of a proposal for a property that has been submitted earlier. Furthermore, the proposal unit gradually adjusts the priority of the proposal depending on the time of submission. In this way, by determining the priority of the proposal based on the time of submission of the property, it is possible to provide the latest information preferentially.

[0086] When proposing properties, the suggestion unit can adjust the order of suggestions based on the relevance of the properties. For example, the suggestion unit preferentially suggests properties that are most relevant to the user's desired conditions. For example, the suggestion unit postpones the order of suggestions for properties that are less relevant. Furthermore, the suggestion unit gradually adjusts the order of suggestions according to the relevance of the properties. In this way, by adjusting the order of suggestions based on the relevance of the properties, it is possible to preferentially suggest properties that are most relevant to the user.

[0087] When proposing a property, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is knowledgeable about real estate, the suggestion unit makes a proposal that uses a lot of technical terminology. For example, if the user is not knowledgeable about real estate, the suggestion unit makes a concise proposal that avoids technical terminology. Furthermore, the suggestion unit gradually adjusts the use of technical terminology in the proposal according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to make a proposal that is easy for the user to understand.

[0088] The set suggestion unit can estimate the user's emotions and adjust the set suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, the set suggestion unit makes a simple and highly visible set suggestion. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is relaxed, the set suggestion unit makes a set suggestion that includes detailed information. Furthermore, if the user is excited, the set suggestion unit makes a visually appealing set suggestion. This allows for more appropriate suggestions by adjusting the set suggestion method according to the user's emotions.

[0089] When proposing a set, the set suggestion unit can analyze the user's past contract history and select the optimal set suggestion method. The set suggestion unit selects the optimal set suggestion method based on, for example, services the user has used in the past. The set suggestion unit analyzes, for example, the user's preferences for specific services from the user's past contract history and preferentially suggests those services. The set suggestion unit also selects the optimal set suggestion method based on the user's past contract history. In this way, the optimal set suggestion method can be provided by analyzing the user's past contract history.

[0090] When proposing a set, the set suggestion unit can customize the means for suggesting a set based on the user's current living situation. For example, if the user lives with his or her family, the set suggestion unit makes a set suggestion for families. For example, if the user lives alone, the set suggestion unit makes a set suggestion for single living. Furthermore, if the user has a pet, the set suggestion unit makes a set suggestion including services for pets. This enables more appropriate suggestions to be made by customizing the means for suggesting a set based on the user's current living situation.

[0091] The set suggestion unit can improve the set suggestion method by reflecting user feedback when suggesting a set. The set suggestion unit proposes an optimal set suggestion method, for example, based on feedback provided by the user in the past. The set suggestion unit analyzes, for example, the user's preference for a specific set suggestion method from the user's past feedback and preferentially suggests that method. The set suggestion unit also refers to the user's past feedback and customizes and provides the set suggestion method. In this way, the set suggestion method can be improved by reflecting user feedback, enabling more appropriate suggestions.

[0092] The set suggestion unit can estimate the user's emotions and prioritize set suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the system prioritizes the set suggestions of that user. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is relaxed, the set suggestion unit prioritizes set suggestions of other users and postpones the set suggestions of the relaxed user. Furthermore, if the user is in a hurry, the set suggestion unit processes the set suggestions of that user as the highest priority. This enables more appropriate responses by prioritizing set suggestions according to the user's emotions.

[0093] When proposing a set, the set suggestion unit can select the optimal set suggestion method by taking into consideration the user's geographical location information. For example, if the user desires a service close to their current location, the set suggestion unit will preferentially accept that set suggestion. For example, if the user desires to move to a specific area, the set suggestion unit will preferentially accept a set suggestion related to that area. Furthermore, if the user desires a service far from their current location, the set suggestion unit will postpone that set suggestion. In this way, by taking into consideration the user's geographical location information, the optimal set suggestion method can be provided.

[0094] When proposing a set, the set suggestion unit can analyze the user's social media activity and suggest a means for proposing a set. For example, the set suggestion unit analyzes posts about moving shared by the user on social media and preferentially accepts the set suggestion. For example, the set suggestion unit detects from the user's social media activity that the user has an interest in moving and preferentially accepts the set suggestion. The set suggestion unit also refers to posts about moving made by the user's friends on social media and accepts related set suggestions. In this way, by analyzing the user's social media activity, related set suggestions can be preferentially provided.

[0095] When proposing a set, the set suggestion unit can customize the set suggestion method by reflecting the user's past feedback. The set suggestion unit proposes an optimal set suggestion method, for example, based on feedback provided by the user in the past. The set suggestion unit analyzes the user's preference for a specific set suggestion method from the user's past feedback, and preferentially suggests that method. The set suggestion unit also refers to the user's past feedback and customizes and provides the set suggestion method. In this way, the optimal set suggestion method can be provided by reflecting the user's past feedback.

[0096] The proxy unit can estimate the user's emotions and adjust the proxy procedure method based on the estimated user emotions. For example, if the user is feeling stressed, the proxy unit performs a simple and highly visible proxy procedure. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is relaxed, the proxy unit performs a proxy procedure that includes detailed information. Furthermore, if the user is excited, the proxy unit performs a visually appealing proxy procedure. This allows for more appropriate responses by adjusting the proxy procedure method according to the user's emotions.

[0097] During proxy processing, the proxy unit can analyze the user's past contract history and select the optimal proxy processing method. The proxy unit selects the optimal proxy processing method, for example, based on services the user has used in the past. The proxy unit analyzes the user's preferences for specific services from the user's past contract history, for example, and prioritizes proxy processing for those services. The proxy unit also selects the optimal proxy processing method based on the user's past contract history. In this way, the optimal proxy processing method can be provided by analyzing the user's past contract history.

[0098] The proxy unit can customize the proxy procedure means based on the user's current living situation when performing the proxy procedure. For example, if the user lives with his / her family, the proxy unit performs proxy procedures for the family. For example, if the user lives alone, the proxy unit performs proxy procedures for the single person. Furthermore, if the user has a pet, the proxy unit performs proxy procedures including services for the pet. This allows for more appropriate response by customizing the proxy procedure means based on the user's current living situation.

[0099] The proxy unit can improve the proxy procedure method by reflecting user feedback during the proxy procedure. For example, the proxy unit proposes an optimal proxy procedure method based on feedback provided by the user in the past. For example, the proxy unit analyzes the user's preference for a specific proxy procedure method from the user's past feedback and preferentially proposes that method. The proxy unit also refers to the user's past feedback and customizes and provides the proxy procedure method. In this way, by reflecting user feedback, the proxy procedure method can be improved and more appropriate responses can be made.

[0100] The proxy unit can estimate the user's emotions and determine the priority of proxy procedures based on the estimated user emotions. For example, if a user is feeling stressed, the proxy unit prioritizes proxy procedures for that user. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if a user is relaxed, the proxy unit prioritizes proxy procedures for other users and postpones the proxy procedures for the relaxed user. Furthermore, if a user is in a hurry, the proxy unit prioritizes the proxy procedures for that user. This enables more appropriate responses by determining the priority of proxy procedures according to the user's emotions.

[0101] When performing a proxy procedure, the proxy unit can select the optimal proxy procedure method by taking into consideration the user's geographical location information. For example, if the user desires a service close to their current location, the proxy unit will prioritize accepting that proxy procedure. For example, if the user desires to move to a specific area, the proxy unit will prioritize accepting proxy procedures related to that area. Furthermore, if the user desires a service far from their current location, the proxy unit will postpone that proxy procedure. In this way, by taking into consideration the user's geographical location information, the optimal proxy procedure method can be provided.

[0102] When performing a proxy procedure, the proxy unit can analyze the user's social media activity and suggest a proxy procedure method. For example, the proxy unit analyzes posts about moving shared by the user on social media and prioritizes accepting the proxy procedure. For example, the proxy unit detects from the user's social media activity that the user is interested in moving and prioritizes accepting the proxy procedure. The proxy unit also refers to posts about moving by the user's friends on social media and accepts related proxy procedures. In this way, by analyzing the user's social media activity, it is possible to prioritize providing related proxy procedures.

[0103] The proxy unit can customize the proxy procedure method by reflecting the user's past feedback during the proxy procedure. The proxy unit proposes an optimal proxy procedure method, for example, based on feedback provided by the user in the past. The proxy unit analyzes the user's preference for a specific proxy procedure method from the user's past feedback, and preferentially proposes that method. The proxy unit also refers to the user's past feedback and customizes and provides the proxy procedure method. In this way, the proxy unit can provide an optimal proxy procedure method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, set proposal unit, and agent unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts a user's request for moving via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal property based on the user's desired conditions. The set proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a set of mobile, fixed, electricity, water, and gas. The agent unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically carries out the application procedure on behalf of the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, set proposal unit, and agent unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit accepts a user's request for moving via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal property based on the user's desired conditions. The set proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a set of mobile, fixed, electricity, water, and gas. The agent unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically performs application procedures on behalf of the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, set proposal unit, and agent unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit accepts a user's request for moving via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal property based on the user's desired conditions. The set proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a set of mobile, fixed, electricity, water, and gas. The agent unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically carries out the application procedure on behalf of the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, set proposal unit, and agent unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit accepts a user's request for moving via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal property based on the user's desired conditions. The set proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a set of mobile, fixed, electricity, water, and gas. The agent unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically carries out the application procedure on behalf of the user.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] When accepting a user's moving request, the acceptance unit can analyze the user's past moving history and suggest the optimal acceptance method. For example, the acceptance unit can suggest the optimal acceptance method based on the moving companies and services the user has used in the past. Furthermore, the acceptance unit can analyze the user's tendency to move at specific times based on the user's past moving history and suggest the optimal acceptance timing. It can also select the optimal acceptance method based on specific conditions (for example, family composition or amount of luggage) based on the user's past moving history. In this way, by analyzing the user's past moving history, the acceptance unit can offer the optimal acceptance method to the user.

[0106] The suggestion unit can introduce safety information or recommended spots around the property. For example, when providing safety information around the property, information such as the crime rate and the location of police stations can be included. Furthermore, when introducing recommended spots around the property, information on tourist spots, restaurants, public facilities, etc. can be provided. Furthermore, the suggestion unit can customize recommended spots based on the user's areas of interest. For example, if the user likes outdoor activities, nearby parks and hiking trails can be introduced. In this way, by providing safety information and recommended spots around the property, the suggestion unit can support the user in selecting a property.

[0107] The suggestion unit can provide conversational answers to questions about moving. For example, it can automatically answer the user's questions using a chatbot or a voice assistant. Furthermore, the suggestion unit can estimate the user's emotions and adjust the tone and content of the answer based on the estimated emotions. For example, if the user is feeling stressed, it can respond in a gentle tone to help the user relax. Also, if the user is in a hurry, it can provide a concise and quick answer. As a result, by providing conversational answers to questions about moving, it is possible to alleviate the user's anxiety and provide better support.

[0108] The proxy unit allows the user to complete the contract with just one input of information. For example, once the user selects the property they want and enters the necessary information, the system automatically performs various procedures and completes the contract. Furthermore, the proxy unit can estimate the user's emotions and adjust the proxy procedure method based on the estimated emotions. For example, if the user is feeling stressed, the proxy unit can perform a simple, highly visible proxy procedure. On the other hand, if the user is relaxed, the proxy procedure can be performed with detailed information. This allows for more appropriate responses by adjusting the proxy procedure method according to the user's emotions.

[0109] The set suggestion unit can propose contracts for the internet line, electricity, gas, and water to be used at the new address all at once. For example, when the user selects the desired services and enters the necessary information, the system can automatically carry out various contract procedures. Furthermore, the set suggestion unit can estimate the user's emotions and adjust the method of suggesting sets based on the estimated emotions. For example, if the user is feeling stressed, a simple and highly visible set suggestion can be made. On the other hand, if the user is relaxed, a set suggestion including detailed information can be made. In this way, by adjusting the method of suggesting sets according to the user's emotions, more appropriate suggestions can be made.

[0110] The reception unit can estimate the user's emotions and adjust the timing of accepting the moving request based on the estimated user emotions. For example, if the user is feeling stressed, the timing of accepting the request can be delayed until the user is relaxed. Also, if the user is in a hurry, the reception unit can prioritize the moving request and start processing it quickly. Furthermore, the reception unit can use an emotion engine or generation AI to estimate the user's emotions. This allows the reception timing to be adjusted according to the user's emotions, making it possible to accept the request at a more appropriate time.

[0111] The reception unit can analyze the user's past moving history and select a reception method. For example, it can suggest the optimal reception method based on the moving companies and services the user has used in the past. Furthermore, it can analyze the user's tendency to move at specific times based on the user's past moving history and suggest the optimal reception timing. It can also select the optimal reception method based on specific conditions (for example, family composition or amount of luggage) based on the user's past moving history. In this way, it is possible to provide the user with the optimal reception method by analyzing the user's past moving history.

[0112] When receiving a moving request, the reception unit can filter based on the user's current living situation and areas of interest. For example, if the user has a pet, properties that allow pets can be preferentially suggested. Also, if the user has children, properties in school districts and areas with many facilities for children can be suggested. Furthermore, if the user has a specific hobby, properties that are close to facilities related to that hobby can be suggested. In this way, by filtering based on the user's living situation and areas of interest, more appropriate properties can be suggested.

[0113] When accepting a moving request, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the moving request can be accepted using voice recognition technology. If the user selects text input, the moving request can be accepted using text analysis technology. Furthermore, if the user selects image input, the moving request can be accepted using image recognition technology. This allows the optimal acceptance means to be selected depending on the user's input method, thereby improving user convenience.

[0114] The reception unit can estimate the user's emotions and determine the priority of received requests based on the estimated user emotions. For example, if a user is feeling stressed, the system can prioritize processing the user's requests. Also, if a user is relaxed, the system can prioritize requests from other users and postpone the relaxed user's requests. Furthermore, if a user is in a hurry, the system can prioritize processing the user's requests. This allows for more appropriate responses by determining the priority of requests according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives the user's request for moving. The user's request for moving includes the area to move to, budget, desired property conditions, etc. The reception unit saves the information entered by the user in a database and uses it for subsequent processing. Step 2: The proposal unit proposes properties based on the information received by the reception unit. The proposal unit proposes multiple properties that meet the user's desired conditions and provides detailed information about each property. Step 3: The package proposal unit proposes a package of mobile, fixed, electricity, water, and gas based on the property proposed by the proposal unit. The package proposal unit proposes contracts for the internet line, electricity, gas, water, etc. to be used at the new address all at once, allowing the user to select. Step 4: The agent automatically carries out the application procedures based on the contents proposed by the package proposal unit. The agent selects the desired property and inputs the necessary information, and the system automatically carries out the various procedures and completes the contract.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a user's request for moving; a proposal unit that proposes properties based on the information received by the reception unit; a set proposal unit that proposes a set of mobile, fixed, electricity, water, and gas services based on the property proposed by the proposal unit; an agent unit that automatically performs application procedures on behalf of the customer based on the content proposed by the set proposal unit. A system characterized by:

2. The proposal unit Introducing safety information and recommended spots around the property The system of claim 1 .

3. The proposal unit Answering moving-related questions in a conversational format The system of claim 1 .

4. The agent unit: Users only need to enter information once to complete the contract The system of claim 1 .

5. The set suggestion unit Propose a one-stop contract for internet, electricity, gas, and water to be used at your new home The system of claim 1 .

6. The reception unit Estimate the user's emotions and adjust the timing of accepting moving requests based on the estimated user emotions. The system of claim 1 .

7. The reception unit Analyze the user's past moving history and select the reception method The system of claim 1 .

8. The reception unit When accepting moving requests, filter based on the user's current living situation and areas of interest. The system of claim 1 .

9. The reception unit When accepting a moving request, select the most appropriate acceptance method depending on the user's input method. The system of claim 1 .

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