system

Abstract

The system according to the embodiment aims to efficiently manage the many tasks involved in moving and ensure smooth progress. [Solution] The system according to the embodiment comprises a reception unit, a plan proposal unit, an arrangement unit, an instruction unit, and a support unit. The reception unit inputs the user's moving information. The plan proposal unit proposes a moving plan based on the information input by the reception unit. The arrangement unit arranges for a moving company based on the plan proposed by the plan proposal unit and handles government procedures. The instruction unit gives instructions based on packing or the progress of the day, based on the company or procedures arranged by the arrangement unit. The support unit supports the cleanup instructed by the instruction unit.

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 of making it difficult to efficiently manage the many tasks involved in moving and ensure smooth progress.

[0005] The system according to the embodiment aims to efficiently manage the many tasks involved in moving and ensure smooth progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a plan proposal unit, an arrangement unit, an instruction unit, and a support unit. The reception unit inputs the user's moving information. The plan proposal unit proposes a moving plan based on the information input by the reception unit. The arrangement unit arranges for a moving company based on the plan proposed by the plan proposal unit and handles government procedures. The instruction unit gives instructions based on packing or the progress of the day, based on the company or procedures arranged by the arrangement unit. The support unit supports the cleanup instructed by the instruction unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage the many tasks involved in moving and ensure smooth progress. [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 uses AI to provide one-stop support for tedious moving-related tasks for various scenarios, such as families, those moving away from home, students, and new employees, in a moving market where approximately 2 million households move annually. When a user plans a move, the AI ​​proposes an optimal plan, arranges for moving companies, handles government and other related procedures, streamlines packing, provides instructions based on the progress of the day, and even provides support for cleanup. This significantly reduces the time and effort required for moving, minimizing the inconvenience to the user. For example, when a user plans a move, the AI ​​proposes an optimal plan. The AI ​​proposes an optimal moving plan simply by inputting information such as the user's destination, moving date, and amount of luggage. For example, by inputting the destination address, moving date, and amount of luggage, the AI ​​proposes the optimal moving company and moving date. Next, the AI ​​handles the arrangements for moving companies and government and other related procedures. For example, the AI ​​handles the arrangements for moving companies and the procedures for filing moving-out and moving-in notifications with government offices. This allows users to prepare for their move without any hassle. Furthermore, AI can streamline packing, provide instructions based on progress on the day, and even support cleanup after the move. For example, when packing, AI can suggest efficient packing methods and provide instructions based on progress on the day. AI can also support cleanup after the move. This significantly reduces the time and effort required for moving, reducing inconvenience for users. This system allows AI to single-handedly handle all the tedious tasks associated with moving, allowing for a planned, efficient, and smooth move. For example, it can accommodate a variety of moves, such as those for families, those working away from home, students, and new employees. This can improve user satisfaction in the moving market. This allows the moving support system to handle everything from inputting information about the user's move to planning, arranging, providing instructions, and providing support.

[0029] A moving support system according to an embodiment includes a reception unit, a plan proposal unit, an arrangement unit, an instruction unit, and a support unit. The reception unit inputs a user's moving information. The user's moving information includes, but is not limited to, the address of the moving destination, the moving date, and the amount of luggage. The reception unit provides, for example, an interface through which the user inputs the address of the moving destination, the moving date, and the amount of luggage. The reception unit also stores the user's input information in a database for use in subsequent processing. The plan proposal unit proposes an optimal moving plan based on the information input by the reception unit. The plan proposal unit proposes an optimal moving company and moving date based on information such as the user's moving destination, the moving date, and the amount of luggage. The plan proposal unit can use AI to generate a plan optimal for the user's moving requirements. For example, the plan proposal unit can use an AI model that receives the user's address of the moving destination, the moving date, and the amount of luggage as input and outputs the optimal moving company and moving date. The arrangement unit arranges for a moving company and handles government procedures based on the plan proposed by the plan proposal unit. The arrangement unit, for example, arranges for a moving company and handles procedures for filing moving-out and moving-in notifications with local government offices on behalf of the user. The arrangement unit can use AI to automate the procedures necessary for the user's move. For example, the arrangement unit can receive the user's moving address and moving date as input, and use an AI model that arranges for the most suitable moving company and outputs procedures at local government offices. The instruction unit gives instructions for packing and the progress of the day based on the company and procedures arranged by the arrangement unit. For example, the instruction unit gives instructions for packing efficiency and the progress of the day. The instruction unit can use AI to make the user's moving work more efficient. For example, the instruction unit can use an AI model that receives the amount of luggage the user has and the moving date as input, and outputs an efficient packing method and work instructions for the day. The support unit supports post-moving cleanup as instructed by the instruction unit. For example, the support unit supports post-moving cleanup. The support unit can use AI to make the user's post-moving work more efficient.For example, the support unit can use an AI model that receives the address of the user's new home and the amount of luggage as input and outputs an efficient cleaning method. This allows the moving support system according to the embodiment to consistently handle everything from inputting information about the user's move to planning, arranging, giving instructions, and providing support.

[0030] The plan proposal unit can propose a moving plan based on information about the user's moving destination, moving date, and amount of luggage. The plan proposal unit can propose an optimal moving plan based on, for example, information about the user's moving destination, moving date, and amount of luggage. The plan proposal unit can use AI to generate a plan that is optimal for the user's moving conditions. For example, the plan proposal unit can use an AI model that receives the user's moving destination address, moving date, and amount of luggage as input and outputs an optimal moving company and moving date. The plan proposal unit implements an algorithm for proposing an optimal moving plan based on the user's moving conditions. For example, the plan proposal unit can receive the user's moving destination address, moving date, and amount of luggage as input and output an optimal moving company and moving date. This makes it possible to propose an optimal plan based on the user's specific moving conditions. Some or all of the above-described processing in the plan proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan proposal unit can propose a moving plan using an AI model that receives the user's moving destination address, moving date, and amount of luggage as input and outputs an optimal moving company and moving date.

[0031] The arrangement unit can arrange for a moving company and process moving-out and moving-in notifications with local government offices. For example, the arrangement unit handles the procedures for arranging for a moving company and filing moving-out and moving-in notifications with local government offices on behalf of the user. The arrangement unit can use AI to automate the procedures necessary for a user's move. For example, the arrangement unit can use an AI model that receives the user's moving address and moving date as input and outputs the optimal arrangement for a moving company and the procedures at local government offices. The arrangement unit implements an algorithm for efficiently performing the procedures necessary for a user's move. For example, the arrangement unit can receive the user's moving address and moving date as input and output the optimal arrangement for a moving company and the procedures at local government offices. This allows the user to prepare for the move without hassle. Some or all of the above-described processing in the arrangement unit may be performed using AI, for example, or may be performed without AI. For example, the arrangement unit can use an AI model that receives the user's moving address and moving date as input and outputs the optimal arrangement for a moving company and the procedures at local government offices.

[0032] The instruction unit can provide instructions based on the efficiency of packing and the progress of the day. For example, the instruction unit provides instructions based on the efficiency of packing and the progress of the day. The instruction unit can use AI to improve the efficiency of the user's moving work. For example, the instruction unit can use an AI model that receives the amount of luggage of the user and the moving schedule as input and outputs an efficient packing method and work instructions for the day. The instruction unit implements an algorithm for efficiently progressing the user's moving work. For example, the instruction unit receives the amount of luggage of the user and the moving schedule as input and outputs an efficient packing method and work instructions for the day. This allows the work on the day of the move to proceed efficiently. Some or all of the above-described processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can provide instructions using an AI model that receives the amount of luggage of the user and the moving schedule as input and outputs an efficient packing method and work instructions for the day.

[0033] The support unit can support cleaning up after a move. The support unit supports cleaning up after a move, for example. The support unit can use AI to streamline the user's post-move work. For example, the support unit can use an AI model that receives the user's new address and the amount of luggage as input and outputs an efficient cleaning method. The support unit implements an algorithm for efficiently progressing the user's post-move work. For example, the support unit receives the user's new address and the amount of luggage as input and outputs an efficient cleaning method. This allows for efficient cleaning up after a move. Some or all of the above-described processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can support cleaning up using an AI model that receives the user's new address and the amount of luggage as input and outputs an efficient cleaning method.

[0034] The reception unit can analyze the user's past movement history and select the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also automatically input frequently used information from the user's past movement history. The reception unit can also analyze the user's past movement history and provide the optimal input interface. This makes it possible to provide the optimal input method based on the user's past movement history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past movement history data into the generation AI and have the generation AI select the optimal input method.

[0035] When inputting moving information, the reception unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user has a pet, the reception unit can prioritize inputting information about the pet. If the user has children, the reception unit can also prioritize inputting information about the children. If the user is elderly, the reception unit can also prioritize inputting information for elderly people. This allows information according to the user's living situation and areas of interest to be prioritized. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data about the user's living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0036] When inputting moving information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, when the user inputs a moving destination close to the current location, the reception unit can prioritize inputting relevant information. When the user inputs a moving destination far away, the reception unit can also prioritize inputting information related to long-distance moves. When the user is moving to a specific area, the reception unit can also prioritize inputting information related to that area. This allows highly relevant information to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize highly relevant information.

[0037] The reception unit can analyze the user's social media activity and input related information when entering moving information. For example, the reception unit automatically inputs information about the moving destination that the user shared on social media. The reception unit can also preferentially input information about places the user follows on social media. The reception unit can also input information about places the user has shown interest in on social media. This makes it possible to input related information based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input related information.

[0038] When proposing a plan, the plan proposal unit can adjust the level of detail of the plan based on the importance of the move. For example, in the case of an important move, the plan proposal unit proposes a detailed plan. For an easy move, the plan proposal unit can also propose a simple plan. For an urgent move, the plan proposal unit can also propose a plan that can be executed quickly. This makes it possible to propose a plan with an optimal level of detail depending on the importance of the move. Some or all of the above-mentioned processing in the plan proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the plan proposal unit can input data on the importance of the move into the generation AI and cause the generation AI to adjust the level of detail of the plan.

[0039] When proposing a plan, the plan proposal unit can apply different planning algorithms depending on the category of the move. For example, in the case of a family move, the plan proposal unit proposes a plan that takes into account the schedules of all family members. In the case of a single-person transfer, the plan proposal unit can also propose a plan that emphasizes the individual's schedule. In the case of a student move, the plan proposal unit can also propose a plan that takes into account the start of the semester. This makes it possible to apply the optimal planning algorithm depending on the category of the move. Some or all of the above-mentioned processing in the plan proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan proposal unit can input moving category data into the generation AI and cause the generation AI to apply the planning algorithm.

[0040] When proposing a plan, the plan proposal unit can determine the priority of the plan based on the submission date of the move. For example, in the case of an urgent move, the plan proposal unit proposes a plan with the highest priority. The plan proposal unit can also propose a detailed plan if the move is scheduled for a later date. The plan proposal unit can also propose a plan that can be implemented quickly if the move is due to be submitted soon. This makes it possible to propose a plan with optimal priority depending on the submission date of the move. Some or all of the above-mentioned processing in the plan proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the plan proposal unit can input data on the submission date of the move into the generation AI and have the generation AI determine the priority of the plans.

[0041] When proposing a plan, the plan proposal unit can adjust the order of the plan based on the relevance of the move. For example, the plan proposal unit prioritizes incorporating important tasks into the plan. The plan proposal unit can also incorporate highly relevant tasks into the plan together. The plan proposal unit can also postpone incorporating less relevant tasks into the plan. This makes it possible to propose a plan in the optimal order based on the relevance of the move. Some or all of the above-mentioned processing in the plan proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the plan proposal unit can input relevance data of the move into a generation AI and have the generation AI adjust the order of the plan.

[0042] When making arrangements, the arrangement unit can select the most suitable company by referring to the moving company's past evaluations. For example, the arrangement unit prioritizes the selection of highly rated companies. The arrangement unit can also select companies with stable past evaluations. The arrangement unit can also select a company that meets the user's needs based on the evaluations. This allows the selection of the most suitable company based on the moving company's past evaluations. Some or all of the above-mentioned processing in the arrangement unit may be performed using AI, for example, or may be performed without using AI. For example, the arrangement unit can input past evaluation data of moving companies into the generation AI and have the generation AI select the most suitable company.

[0043] When making arrangements, the arrangement department can adjust the order of arrangements based on the progress of government procedures. For example, if a government procedure has been completed, the arrangement department will proceed with the next arrangement. If a government procedure is delayed, the arrangement department can also prioritize other arrangements. The arrangement department can also adjust the order of arrangements based on the progress of government procedures. This allows arrangements to be made in the optimal order based on the progress of government procedures. Some or all of the above-mentioned processing in the arrangement department may be performed using AI, for example, or may be performed without using AI. For example, the arrangement department can input progress data of government procedures into the generation AI and have the generation AI adjust the order of arrangements.

[0044] When making arrangements, the arrangement unit can select the most suitable company by taking into consideration the geographic distribution of moving companies. For example, the arrangement unit prioritizes selecting a company close to the user's moving destination. The arrangement unit can also select a company that is geographically convenient. The arrangement unit can also select a company that is familiar with the area of ​​the moving destination. This allows the most suitable company to be selected based on the geographic distribution of moving companies. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can input geographic distribution data of moving companies into the generation AI and have the generation AI select the most suitable company.

[0045] When making arrangements, the arrangement department can improve the accuracy of the arrangements by referring to literature related to government procedures. The arrangement department, for example, makes arrangements by referring to the latest information on government procedures. The arrangement department can also make arrangements by referring to past cases related to government procedures. The arrangement department can also improve the accuracy of the arrangements by referring to literature related to government procedures. In this way, the accuracy of the arrangements can be improved by referring to literature related to government procedures. Some or all of the above-mentioned processing in the arrangement department may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement department can input literature data related to government procedures into the generation AI and have the generation AI improve the accuracy of the arrangements.

[0046] When giving instructions, the instruction unit can monitor the progress of packing in real time and provide optimal instructions. For example, the instruction unit can monitor the progress of packing in real time and instruct the next task to be performed. If packing is behind schedule, the instruction unit can also instruct an efficient work method. If packing is progressing smoothly, the instruction unit can also instruct the next step. This makes it possible to provide optimal instructions in real time according to the progress of packing. Some or all of the above-mentioned processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can input packing progress data to the generation AI and cause the generation AI to provide optimal instructions.

[0047] When giving instructions, the instruction unit can adjust the instructions taking into account weather information on the day of the move. For example, the instruction unit instructs how to protect luggage when it is raining. The instruction unit can also instruct an efficient way to transport luggage when it is sunny. The instruction unit can also instruct a route that is less slippery when it is snowy. This makes it possible to provide optimal instructions based on weather information on the day of the move. Some or all of the above-mentioned processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can input weather information data into the generation AI and have the generation AI adjust the instructions.

[0048] When giving instructions, the instruction unit can adjust the instructions taking into account traffic information on the day of the move. For example, if traffic congestion occurs, the instruction unit can instruct a detour route. If traffic is smooth, the instruction unit can also instruct the shortest route. The instruction unit can also instruct the optimal transportation time based on the traffic information. This makes it possible to provide optimal instructions based on traffic information on the day of the move. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input traffic information data to the generation AI and have the generation AI adjust the instructions.

[0049] When giving instructions, the instruction unit can monitor the progress of the moving company in real time and provide optimal instructions. For example, the instruction unit can monitor the progress of the moving company in real time and instruct the moving company on the next task to be performed. If the moving company is running late, the instruction unit can also instruct the moving company on an efficient method of working. If the moving company is progressing smoothly, the instruction unit can also instruct the moving company on the next step. This makes it possible to provide optimal instructions in real time depending on the moving company's progress. Some or all of the above-mentioned processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can input data on the moving company's progress into the generation AI and cause the generation AI to provide optimal instructions.

[0050] When cleaning up, the support unit can select the optimal support method by referring to the user's past moving history. For example, the support unit prioritizes suggesting cleaning methods that the user has used in the past. The support unit can also suggest efficient cleaning methods based on the user's past moving history. The support unit can also analyze the user's past moving history and select the optimal support method. This makes it possible to provide the optimal support method based on the user's past moving history. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's past moving history data into the generation AI and have the generation AI select the optimal support method.

[0051] The support unit can provide support during tidying up, taking into consideration the layout information of the destination. For example, the support unit proposes an efficient tidying method based on the layout information of the destination. The support unit can also propose an optimal arrangement method taking into consideration the layout information of the destination. The support unit can also propose a tidying order based on the layout information of the destination. This makes it possible to provide an optimal tidying method based on the layout information of the destination. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input layout information data of the destination to the generation AI and have the generation AI execute support.

[0052] When tidying up, the support unit can select the optimal support method taking into account the geographical location information of the destination. For example, the support unit proposes an efficient tidying method based on the geographical location information of the destination. The support unit can also propose an optimal placement method taking into account the geographical location information of the destination. The support unit can also propose a tidying order based on the geographical location information of the destination. This makes it possible to provide an optimal tidying method based on the geographical location information of the destination. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input geographical location information data of the destination to the generation AI and have the generation AI execute support.

[0053] The support unit can improve the accuracy of support when tidying up by referring to related literature of the new destination. For example, the support unit can suggest an efficient tidying method by referring to related literature of the new destination. The support unit can also suggest an optimal arrangement method based on related literature of the new destination. The support unit can also suggest a tidying order by referring to related literature of the new destination. This makes it possible to provide an optimal tidying method based on related literature of the new destination. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input related literature data of the new destination into the generation AI and have the generation AI execute support.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When inputting the user's moving information, the reception unit can suggest the optimal input method by referring to the user's past moving history. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. It can also automatically input information that the user has frequently used in the past. Furthermore, it can analyze the user's past moving history and provide the optimal input interface. This makes it possible to provide the optimal input method based on the user's past moving history.

[0056] When arranging for a moving company or filing a moving-out or moving-in notification at the government office, the arrangements department can adjust the order of arrangements based on the progress of the procedures at the government office. For example, if a procedure at the government office has been completed, the next arrangement can be made. If a procedure at the government office is behind schedule, other arrangements can be given priority. The order of arrangements can also be adjusted depending on the progress of the procedures at the government office. This allows arrangements to be made in the optimal order depending on the progress of the procedures at the government office.

[0057] When supporting post-move tidying up, the support unit can select the optimal support method by referring to the user's past moving history. For example, the support unit can prioritize suggesting tidying up methods that the user has used in the past. It can also suggest efficient tidying up methods based on the user's past moving history. It can also analyze the user's past moving history and select the optimal support method. This makes it possible to provide the optimal support method based on the user's past moving history.

[0058] When proposing a plan, the plan suggestion unit can apply different planning algorithms depending on the category of the move. For example, in the case of a family move, it can suggest a plan that takes into account the schedules of all family members. In the case of a single-person move, it can also suggest a plan that emphasizes the individual's schedule. In the case of a student move, it can also suggest a plan that takes into account the start of the semester. This makes it possible to apply the optimal planning algorithm depending on the category of the move.

[0059] When giving instructions, the instruction unit can adjust the instructions taking into account weather information on the day of the move. For example, if it is raining, the instruction unit can give instructions on how to protect luggage. If it is sunny, the instruction unit can also give instructions on how to transport luggage efficiently. If it is snowing, the instruction unit can also give instructions on a route that is less slippery. This allows the unit to provide optimal instructions based on weather information on the day of the move.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit inputs the user's moving information. The user's moving information includes the address of the new location, the moving date, the amount of luggage, etc. The reception unit provides an interface for the user to input this information and saves the input information in a database. Step 2: The plan proposal unit proposes the optimal moving plan based on the information entered by the reception unit. The plan proposal unit proposes the optimal moving company and moving date based on information such as the user's moving destination, moving date, and amount of luggage. Using AI, it generates a plan that is optimal for the user's moving conditions. Step 3: The Arrangements Department arranges for a moving company based on the plan proposed by the Plan Proposal Department and handles the procedures at the local government office. The Arrangements Department handles the arrangements for the moving company and the procedures for filing moving-out and moving-in notifications at the local government office on behalf of the user. AI is used to automate the procedures required for the user's move. Step 4: The instruction department gives instructions based on the packing process and progress on the day, based on the companies and procedures arranged by the arrangement department. The instruction department gives instructions to improve the efficiency of packing and progress on the day, using AI to make the user's moving process more efficient. Step 5: The support unit supports the cleanup instructed by the instruction unit. The support unit supports the cleanup after the move. AI is used to make the user's post-move work more efficient.

[0062] (Example 2) A moving support system according to an embodiment of the present invention uses AI to provide one-stop support for tedious moving-related tasks for various scenarios, such as families, those moving away from home, students, and new employees, in a moving market where approximately 2 million households move annually. When a user plans a move, the AI ​​proposes an optimal plan, arranges for moving companies, handles government and other related procedures, streamlines packing, provides instructions based on the progress of the day, and even provides support for cleanup. This significantly reduces the time and effort required for moving, minimizing the inconvenience to the user. For example, when a user plans a move, the AI ​​proposes an optimal plan. The AI ​​proposes an optimal moving plan simply by inputting information such as the user's destination, moving date, and amount of luggage. For example, by inputting the destination address, moving date, and amount of luggage, the AI ​​proposes the optimal moving company and moving date. Next, the AI ​​handles the arrangements for moving companies and government and other related procedures. For example, the AI ​​handles the arrangements for moving companies and the procedures for filing moving-out and moving-in notifications with government offices. This allows users to prepare for their move without any hassle. Furthermore, AI can streamline packing, provide instructions based on progress on the day, and even support cleanup after the move. For example, when packing, AI can suggest efficient packing methods and provide instructions based on progress on the day. AI can also support cleanup after the move. This significantly reduces the time and effort required for moving, reducing inconvenience for users. This system allows AI to single-handedly handle all the tedious tasks associated with moving, allowing for a planned, efficient, and smooth move. For example, it can accommodate a variety of moves, such as those for families, those working away from home, students, and new employees. This can improve user satisfaction in the moving market. This allows the moving support system to handle everything from inputting information about the user's move to planning, arranging, providing instructions, and providing support.

[0063] A moving support system according to an embodiment includes a reception unit, a plan proposal unit, an arrangement unit, an instruction unit, and a support unit. The reception unit inputs a user's moving information. The user's moving information includes, but is not limited to, the address of the moving destination, the moving date, and the amount of luggage. The reception unit provides, for example, an interface through which the user inputs the address of the moving destination, the moving date, and the amount of luggage. The reception unit also stores the user's input information in a database for use in subsequent processing. The plan proposal unit proposes an optimal moving plan based on the information input by the reception unit. The plan proposal unit proposes an optimal moving company and moving date based on information such as the user's moving destination, the moving date, and the amount of luggage. The plan proposal unit can use AI to generate a plan optimal for the user's moving requirements. For example, the plan proposal unit can use an AI model that receives the user's address of the moving destination, the moving date, and the amount of luggage as input and outputs the optimal moving company and moving date. The arrangement unit arranges for a moving company and handles government procedures based on the plan proposed by the plan proposal unit. The arrangement unit, for example, arranges for a moving company and handles procedures for filing moving-out and moving-in notifications with local government offices on behalf of the user. The arrangement unit can use AI to automate the procedures necessary for the user's move. For example, the arrangement unit can receive the user's moving address and moving date as input, and use an AI model that arranges for the most suitable moving company and outputs procedures at local government offices. The instruction unit gives instructions for packing and the progress of the day based on the company and procedures arranged by the arrangement unit. For example, the instruction unit gives instructions for packing efficiency and the progress of the day. The instruction unit can use AI to make the user's moving work more efficient. For example, the instruction unit can use an AI model that receives the amount of luggage the user has and the moving date as input, and outputs an efficient packing method and work instructions for the day. The support unit supports post-moving cleanup as instructed by the instruction unit. For example, the support unit supports post-moving cleanup. The support unit can use AI to make the user's post-moving work more efficient.For example, the support unit can use an AI model that receives the address of the user's new home and the amount of luggage as input and outputs an efficient cleaning method. This allows the moving support system according to the embodiment to consistently handle everything from inputting information about the user's move to planning, arranging, giving instructions, and providing support.

[0064] The plan proposal unit can propose a moving plan based on information about the user's moving destination, moving date, and amount of luggage. The plan proposal unit can propose an optimal moving plan based on, for example, information about the user's moving destination, moving date, and amount of luggage. The plan proposal unit can use AI to generate a plan that is optimal for the user's moving conditions. For example, the plan proposal unit can use an AI model that receives the user's moving destination address, moving date, and amount of luggage as input and outputs an optimal moving company and moving date. The plan proposal unit implements an algorithm for proposing an optimal moving plan based on the user's moving conditions. For example, the plan proposal unit can receive the user's moving destination address, moving date, and amount of luggage as input and output an optimal moving company and moving date. This makes it possible to propose an optimal plan based on the user's specific moving conditions. Some or all of the above-described processing in the plan proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan proposal unit can propose a moving plan using an AI model that receives the user's moving destination address, moving date, and amount of luggage as input and outputs an optimal moving company and moving date.

[0065] The arrangement unit can arrange for a moving company and process moving-out and moving-in notifications with local government offices. For example, the arrangement unit handles the procedures for arranging for a moving company and filing moving-out and moving-in notifications with local government offices on behalf of the user. The arrangement unit can use AI to automate the procedures necessary for a user's move. For example, the arrangement unit can use an AI model that receives the user's moving address and moving date as input and outputs the optimal arrangement for a moving company and the procedures at local government offices. The arrangement unit implements an algorithm for efficiently performing the procedures necessary for a user's move. For example, the arrangement unit can receive the user's moving address and moving date as input and output the optimal arrangement for a moving company and the procedures at local government offices. This allows the user to prepare for the move without hassle. Some or all of the above-described processing in the arrangement unit may be performed using AI, for example, or may be performed without AI. For example, the arrangement unit can use an AI model that receives the user's moving address and moving date as input and outputs the optimal arrangement for a moving company and the procedures at local government offices.

[0066] The instruction unit can provide instructions based on the efficiency of packing and the progress of the day. For example, the instruction unit provides instructions based on the efficiency of packing and the progress of the day. The instruction unit can use AI to improve the efficiency of the user's moving work. For example, the instruction unit can use an AI model that receives the amount of luggage of the user and the moving schedule as input and outputs an efficient packing method and work instructions for the day. The instruction unit implements an algorithm for efficiently progressing the user's moving work. For example, the instruction unit receives the amount of luggage of the user and the moving schedule as input and outputs an efficient packing method and work instructions for the day. This allows the work on the day of the move to proceed efficiently. Some or all of the above-described processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can provide instructions using an AI model that receives the amount of luggage of the user and the moving schedule as input and outputs an efficient packing method and work instructions for the day.

[0067] The support unit can support cleaning up after a move. The support unit supports cleaning up after a move, for example. The support unit can use AI to streamline the user's post-move work. For example, the support unit can use an AI model that receives the user's new address and the amount of luggage as input and outputs an efficient cleaning method. The support unit implements an algorithm for efficiently progressing the user's post-move work. For example, the support unit receives the user's new address and the amount of luggage as input and outputs an efficient cleaning method. This allows for efficient cleaning up after a move. Some or all of the above-described processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can support cleaning up using an AI model that receives the user's new address and the amount of luggage as input and outputs an efficient cleaning method.

[0068] The reception unit can estimate the user's emotions and adjust the timing of inputting moving information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input moving information at a time when the AI ​​can relax. If the user is busy, the reception unit can also have the AI ​​analyze the user's schedule and suggest the optimal input timing. If the user is relaxed, the reception unit can also prompt the user to input detailed information at that time. This allows the moving information to be input at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0069] The reception unit can analyze the user's past movement history and select the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also automatically input frequently used information from the user's past movement history. The reception unit can also analyze the user's past movement history and provide the optimal input interface. This makes it possible to provide the optimal input method based on the user's past movement history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past movement history data into the generation AI and have the generation AI select the optimal input method.

[0070] When inputting moving information, the reception unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user has a pet, the reception unit can prioritize inputting information about the pet. If the user has children, the reception unit can also prioritize inputting information about the children. If the user is elderly, the reception unit can also prioritize inputting information for elderly people. This allows information according to the user's living situation and areas of interest to be prioritized. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data about the user's living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0071] The reception unit can estimate the user's emotions and determine the priority of the moving information to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit prioritizes input of important information. When the user is relaxed, the reception unit can also prioritize input of detailed information. When the user is in a hurry, the reception unit can also prioritize input of minimal information. This allows important information to be input preferentially according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0072] When inputting moving information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, when the user inputs a moving destination close to the current location, the reception unit can prioritize inputting relevant information. When the user inputs a moving destination far away, the reception unit can also prioritize inputting information related to long-distance moves. When the user is moving to a specific area, the reception unit can also prioritize inputting information related to that area. This allows highly relevant information to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize highly relevant information.

[0073] The reception unit can analyze the user's social media activity and input related information when entering moving information. For example, the reception unit automatically inputs information about the moving destination that the user shared on social media. The reception unit can also preferentially input information about places the user follows on social media. The reception unit can also input information about places the user has shown interest in on social media. This makes it possible to input related information based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input related information.

[0074] The plan proposal unit can estimate the user's emotions and adjust the way the plan is presented based on the estimated user emotions. For example, if the user is feeling stressed, the plan proposal unit can propose a simple and easy-to-understand plan. If the user is relaxed, the plan proposal unit can also propose a detailed plan. If the user is in a hurry, the plan proposal unit can also propose a plan that focuses on the main points. This makes it possible to propose a plan in an optimal presentation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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. Some or all of the above-described processing in the plan proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plan proposal unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the plan is presented.

[0075] When proposing a plan, the plan proposal unit can adjust the level of detail of the plan based on the importance of the move. For example, in the case of an important move, the plan proposal unit proposes a detailed plan. For an easy move, the plan proposal unit can also propose a simple plan. For an urgent move, the plan proposal unit can also propose a plan that can be executed quickly. This makes it possible to propose a plan with an optimal level of detail depending on the importance of the move. Some or all of the above-mentioned processing in the plan proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the plan proposal unit can input data on the importance of the move into the generation AI and cause the generation AI to adjust the level of detail of the plan.

[0076] When proposing a plan, the plan proposal unit can apply different planning algorithms depending on the category of the move. For example, in the case of a family move, the plan proposal unit proposes a plan that takes into account the schedules of all family members. In the case of a single-person transfer, the plan proposal unit can also propose a plan that emphasizes the individual's schedule. In the case of a student move, the plan proposal unit can also propose a plan that takes into account the start of the semester. This makes it possible to apply the optimal planning algorithm depending on the category of the move. Some or all of the above-mentioned processing in the plan proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan proposal unit can input moving category data into the generation AI and cause the generation AI to apply the planning algorithm.

[0077] The plan proposal unit can estimate the user's emotions and adjust the length of the plan based on the estimated user emotions. For example, if the user is feeling stressed, the plan proposal unit can propose a short and concise plan. If the user is relaxed, the plan proposal unit can also propose a detailed plan. If the user is in a hurry, the plan proposal unit can also propose a plan that can be quickly executed. This makes it possible to propose a plan with an optimal length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the plan proposal unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the plan proposal unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the plan.

[0078] When proposing a plan, the plan proposal unit can determine the priority of the plan based on the submission date of the move. For example, in the case of an urgent move, the plan proposal unit proposes a plan with the highest priority. The plan proposal unit can also propose a detailed plan if the move is scheduled for a later date. The plan proposal unit can also propose a plan that can be implemented quickly if the move is due to be submitted soon. This makes it possible to propose a plan with optimal priority depending on the submission date of the move. Some or all of the above-mentioned processing in the plan proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the plan proposal unit can input data on the submission date of the move into the generation AI and have the generation AI determine the priority of the plans.

[0079] When proposing a plan, the plan proposal unit can adjust the order of the plan based on the relevance of the move. For example, the plan proposal unit prioritizes incorporating important tasks into the plan. The plan proposal unit can also incorporate highly relevant tasks into the plan together. The plan proposal unit can also postpone incorporating less relevant tasks into the plan. This makes it possible to propose a plan in the optimal order based on the relevance of the move. Some or all of the above-mentioned processing in the plan proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the plan proposal unit can input relevance data of the move into a generation AI and have the generation AI adjust the order of the plan.

[0080] The dispatch unit can estimate the user's emotions and adjust the dispatch method based on the estimated user emotions. For example, if the user is feeling stressed, the dispatch unit can suggest a simple and quick dispatch method. If the user is relaxed, the dispatch unit can also suggest a detailed dispatch method. If the user is in a hurry, the dispatch unit can also suggest a quick dispatch method. This makes it possible to provide the optimal dispatch method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dispatch unit may be performed using an AI, for example, or without an AI. For example, the dispatch unit can input the user's emotion data into the generation AI and have the generation AI adjust the dispatch method.

[0081] When making arrangements, the arrangement unit can select the most suitable company by referring to the moving company's past evaluations. For example, the arrangement unit prioritizes the selection of highly rated companies. The arrangement unit can also select companies with stable past evaluations. The arrangement unit can also select a company that meets the user's needs based on the evaluations. This allows the selection of the most suitable company based on the moving company's past evaluations. Some or all of the above-mentioned processing in the arrangement unit may be performed using AI, for example, or may be performed without using AI. For example, the arrangement unit can input past evaluation data of moving companies into the generation AI and have the generation AI select the most suitable company.

[0082] When making arrangements, the arrangement department can adjust the order of arrangements based on the progress of government procedures. For example, if a government procedure has been completed, the arrangement department will proceed with the next arrangement. If a government procedure is delayed, the arrangement department can also prioritize other arrangements. The arrangement department can also adjust the order of arrangements based on the progress of government procedures. This allows arrangements to be made in the optimal order based on the progress of government procedures. Some or all of the above-mentioned processing in the arrangement department may be performed using AI, for example, or may be performed without using AI. For example, the arrangement department can input progress data of government procedures into the generation AI and have the generation AI adjust the order of arrangements.

[0083] The dispatch unit can estimate the user's emotions and determine the priority of arrangements based on the estimated user emotions. For example, if the user is feeling stressed, the dispatch unit can prioritize important arrangements. If the user is relaxed, the dispatch unit can also prioritize detailed arrangements. If the user is in a hurry, the dispatch unit can also prioritize items that can be arranged quickly. This allows arrangements to be made with optimal priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dispatch unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the dispatch unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of arrangements.

[0084] When making arrangements, the arrangement unit can select the most suitable company by taking into consideration the geographic distribution of moving companies. For example, the arrangement unit prioritizes selecting a company close to the user's moving destination. The arrangement unit can also select a company that is geographically convenient. The arrangement unit can also select a company that is familiar with the area of ​​the moving destination. This allows the most suitable company to be selected based on the geographic distribution of moving companies. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can input geographic distribution data of moving companies into the generation AI and have the generation AI select the most suitable company.

[0085] When making arrangements, the arrangement department can improve the accuracy of the arrangements by referring to literature related to government procedures. The arrangement department, for example, makes arrangements by referring to the latest information on government procedures. The arrangement department can also make arrangements by referring to past cases related to government procedures. The arrangement department can also improve the accuracy of the arrangements by referring to literature related to government procedures. In this way, the accuracy of the arrangements can be improved by referring to literature related to government procedures. Some or all of the above-mentioned processing in the arrangement department may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement department can input literature data related to government procedures into the generation AI and have the generation AI improve the accuracy of the arrangements.

[0086] The instruction unit can estimate the user's emotions and adjust the display method of instructions based on the estimated user emotions. For example, if the user is feeling stressed, the instruction unit can provide a simple, highly visible display method. If the user is relaxed, the instruction unit can also provide a display method including detailed information. If the user is in a hurry, the instruction unit can also provide a display method that focuses on the main points. This makes it possible to provide instructions in an optimal display method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the instruction unit can be performed using an AI, for example, or without an AI. For example, the instruction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of instructions.

[0087] When giving instructions, the instruction unit can monitor the progress of packing in real time and provide optimal instructions. For example, the instruction unit can monitor the progress of packing in real time and instruct the next task to be performed. If packing is behind schedule, the instruction unit can also instruct an efficient work method. If packing is progressing smoothly, the instruction unit can also instruct the next step. This makes it possible to provide optimal instructions in real time according to the progress of packing. Some or all of the above-mentioned processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can input packing progress data to the generation AI and cause the generation AI to provide optimal instructions.

[0088] When giving instructions, the instruction unit can adjust the instructions taking into account weather information on the day of the move. For example, the instruction unit instructs how to protect luggage when it is raining. The instruction unit can also instruct an efficient way to transport luggage when it is sunny. The instruction unit can also instruct a route that is less slippery when it is snowy. This makes it possible to provide optimal instructions based on weather information on the day of the move. Some or all of the above-mentioned processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can input weather information data into the generation AI and have the generation AI adjust the instructions.

[0089] The instruction unit can estimate the user's emotions and determine the priority of instructions based on the estimated user emotions. For example, if the user is feeling stressed, the instruction unit can prioritize important instructions. If the user is relaxed, the instruction unit can also prioritize detailed instructions. If the user is in a hurry, the instruction unit can also prioritize instructions that can be executed quickly. This allows instructions to be provided with optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the instruction unit can be performed using an AI, for example, or without an AI. For example, the instruction unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of instructions.

[0090] When giving instructions, the instruction unit can adjust the instructions taking into account traffic information on the day of the move. For example, if traffic congestion occurs, the instruction unit can instruct a detour route. If traffic is smooth, the instruction unit can also instruct the shortest route. The instruction unit can also instruct the optimal transportation time based on the traffic information. This makes it possible to provide optimal instructions based on traffic information on the day of the move. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input traffic information data to the generation AI and have the generation AI adjust the instructions.

[0091] When giving instructions, the instruction unit can monitor the progress of the moving company in real time and provide optimal instructions. For example, the instruction unit can monitor the progress of the moving company in real time and instruct the moving company on the next task to be performed. If the moving company is running late, the instruction unit can also instruct the moving company on an efficient method of working. If the moving company is progressing smoothly, the instruction unit can also instruct the moving company on the next step. This makes it possible to provide optimal instructions in real time depending on the moving company's progress. Some or all of the above-mentioned processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can input data on the moving company's progress into the generation AI and cause the generation AI to provide optimal instructions.

[0092] The support unit can estimate the user's emotions and adjust the tidying up method based on the estimated user emotions. For example, if the user is feeling stressed, the support unit can suggest a simple and quick tidying up method. If the user is relaxed, the support unit can also suggest a detailed tidying up method. If the user is in a hurry, the support unit can also suggest a quick tidying up method. This makes it possible to provide the optimal tidying up method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit can be performed using AI, for example, or without AI. For example, the support unit can input the user's emotion data into the generation AI and have the generation AI adjust the tidying up method.

[0093] When cleaning up, the support unit can select the optimal support method by referring to the user's past moving history. For example, the support unit prioritizes suggesting cleaning methods that the user has used in the past. The support unit can also suggest efficient cleaning methods based on the user's past moving history. The support unit can also analyze the user's past moving history and select the optimal support method. This makes it possible to provide the optimal support method based on the user's past moving history. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's past moving history data into the generation AI and have the generation AI select the optimal support method.

[0094] The support unit can provide support during tidying up, taking into consideration the layout information of the destination. For example, the support unit proposes an efficient tidying method based on the layout information of the destination. The support unit can also propose an optimal arrangement method taking into consideration the layout information of the destination. The support unit can also propose a tidying order based on the layout information of the destination. This makes it possible to provide an optimal tidying method based on the layout information of the destination. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input layout information data of the destination to the generation AI and have the generation AI execute support.

[0095] The support unit can estimate the user's emotions and determine the priority of cleaning up based on the estimated user emotions. For example, if the user is feeling stressed, the support unit can prioritize important cleaning up. If the user is relaxed, the support unit can also prioritize detailed cleaning up. If the user is in a hurry, the support unit can also prioritize items that can be cleaned up quickly. This allows cleaning up to be performed with optimal priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit can be performed using, for example, an AI, or without an AI. For example, the support unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of cleaning up.

[0096] When tidying up, the support unit can select the optimal support method taking into account the geographical location information of the destination. For example, the support unit proposes an efficient tidying method based on the geographical location information of the destination. The support unit can also propose an optimal placement method taking into account the geographical location information of the destination. The support unit can also propose a tidying order based on the geographical location information of the destination. This makes it possible to provide an optimal tidying method based on the geographical location information of the destination. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input geographical location information data of the destination to the generation AI and have the generation AI execute support.

[0097] The support unit can improve the accuracy of support when tidying up by referring to related literature of the new destination. For example, the support unit can suggest an efficient tidying method by referring to related literature of the new destination. The support unit can also suggest an optimal arrangement method based on related literature of the new destination. The support unit can also suggest a tidying order by referring to related literature of the new destination. This makes it possible to provide an optimal tidying method based on related literature of the new destination. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input related literature data of the new destination into the generation AI and have the generation AI execute support. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, plan proposal unit, arrangement unit, instruction unit, and support 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 is realized by the control unit 46A of the smart device 14 and provides an interface for inputting the user's moving information. The plan proposal unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal plan based on the user's moving conditions. The arrangement unit is realized by the specific processing unit 290 of the data processing device 12 and arranges for a moving company and handles government procedures. The instruction unit is realized by the control unit 46A of the smart device 14 and issues instructions based on packing and the progress of the day. The support unit is realized by the control unit 46A of the smart device 14 and supports cleaning up after the move. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, plan proposal unit, arrangement unit, instruction unit, and support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for inputting the user's moving information. The plan proposal unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal plan based on the user's moving conditions. The arrangement unit is realized by the specific processing unit 290 of the data processing device 12 and arranges for a moving company and handles government procedures. The instruction unit is realized by the control unit 46A of the smart glasses 214 and gives instructions based on packing and the progress of the day. The support unit is realized by the control unit 46A of the smart glasses 214 and supports cleaning up after the move. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, plan proposal unit, arrangement unit, instruction unit, and support unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314 and provides an interface for inputting the user's moving information. The plan proposal unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal plan based on the user's moving conditions. The arrangement unit is realized by the specific processing unit 290 of the data processing device 12 and arranges for a moving company and handles government procedures. The instruction unit is realized by the control unit 46A of the headset terminal 314 and issues instructions based on packing and the progress of the day. The support unit is realized by the control unit 46A of the headset terminal 314 and supports cleaning up after the move. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, plan proposal unit, arrangement unit, instruction unit, and support unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for inputting the user's moving information. The plan proposal unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal plan based on the user's moving conditions. The arrangement unit is realized by the specific processing unit 290 of the data processing device 12 and arranges for a moving company and handles government procedures. The instruction unit is realized by the control unit 46A of the robot 414 and issues instructions based on packing and the progress of the day. The support unit is realized by the control unit 46A of the robot 414 and supports cleaning up after the move.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] When inputting the user's moving information, the reception unit can suggest the optimal input method by referring to the user's past moving history. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. It can also automatically input information that the user has frequently used in the past. Furthermore, it can analyze the user's past moving history and provide the optimal input interface. This makes it possible to provide the optimal input method based on the user's past moving history.

[0100] When proposing a moving plan based on information about the user's moving destination, moving date, and amount of luggage, the plan proposal unit can estimate the user's emotions and adjust the way the plan is presented based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand plan can be proposed. If the user is relaxed, a detailed plan can be proposed. If the user is in a hurry, a plan that focuses on the main points can be proposed. This makes it possible to propose a plan in the most appropriate way according to the user's emotions.

[0101] When arranging for a moving company or filing a moving-out or moving-in notification at the government office, the arrangements department can adjust the order of arrangements based on the progress of the procedures at the government office. For example, if a procedure at the government office has been completed, the next arrangement can be made. If a procedure at the government office is behind schedule, other arrangements can be given priority. The order of arrangements can also be adjusted depending on the progress of the procedures at the government office. This allows arrangements to be made in the optimal order depending on the progress of the procedures at the government office.

[0102] When giving instructions to improve packing efficiency or according to the progress of the day, the instruction unit can estimate the user's emotions and adjust the display method of the instructions based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide instructions in the optimal display method according to the user's emotions.

[0103] When supporting post-move tidying up, the support unit can select the optimal support method by referring to the user's past moving history. For example, the support unit can prioritize suggesting tidying up methods that the user has used in the past. It can also suggest efficient tidying up methods based on the user's past moving history. It can also analyze the user's past moving history and select the optimal support method. This makes it possible to provide the optimal support method based on the user's past moving history.

[0104] The reception unit can estimate the user's emotions and adjust the timing of inputting moving information based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​will prompt them to input information at a time when they are able to relax. If the user is busy, the AI ​​can analyze the user's schedule and suggest the optimal time to input information. If the user is relaxed, the AI ​​can also prompt them to input detailed information at that time. This allows moving information to be input at the optimal time according to the user's emotions.

[0105] When proposing a plan, the plan suggestion unit can apply different planning algorithms depending on the category of the move. For example, in the case of a family move, it can suggest a plan that takes into account the schedules of all family members. In the case of a single-person move, it can also suggest a plan that emphasizes the individual's schedule. In the case of a student move, it can also suggest a plan that takes into account the start of the semester. This makes it possible to apply the optimal planning algorithm depending on the category of the move.

[0106] The dispatch unit can estimate the user's emotions and adjust the dispatch method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and quick dispatch method can be suggested. If the user is relaxed, a detailed dispatch method can be suggested. If the user is in a hurry, a quick dispatch method can be suggested. In this way, the optimal dispatch method can be provided according to the user's emotions.

[0107] When giving instructions, the instruction unit can adjust the instructions taking into account weather information on the day of the move. For example, if it is raining, the instruction unit can give instructions on how to protect luggage. If it is sunny, the instruction unit can also give instructions on how to transport luggage efficiently. If it is snowing, the instruction unit can also give instructions on a route that is less slippery. This allows the unit to provide optimal instructions based on weather information on the day of the move.

[0108] The support unit can estimate the user's emotions and adjust the tidying up method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and quick tidying up method can be suggested. If the user is relaxed, a detailed tidying up method can be suggested. If the user is in a hurry, a quick tidying up method can be suggested. In this way, the optimal tidying up method can be provided according to the user's emotions.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit inputs the user's moving information. The user's moving information includes the address of the new location, the moving date, the amount of luggage, etc. The reception unit provides an interface for the user to input this information and saves the input information in a database. Step 2: The plan proposal unit proposes the optimal moving plan based on the information entered by the reception unit. The plan proposal unit proposes the optimal moving company and moving date based on information such as the user's moving destination, moving date, and amount of luggage. Using AI, it generates a plan that is optimal for the user's moving conditions. Step 3: The Arrangements Department arranges for a moving company based on the plan proposed by the Plan Proposal Department and handles the procedures at the local government office. The Arrangements Department handles the arrangements for the moving company and the procedures for filing moving-out and moving-in notifications at the local government office on behalf of the user. AI is used to automate the procedures required for the user's move. Step 4: The instruction department gives instructions based on the packing process and progress on the day, based on the companies and procedures arranged by the arrangement department. The instruction department gives instructions to improve the efficiency of packing and progress on the day, using AI to make the user's moving process more efficient. Step 5: The support unit supports the cleanup instructed by the instruction unit. The support unit supports the cleanup after the move. AI is used to make the user's post-move work more efficient.

[0111] 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.

[0112] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 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 for inputting user's moving information; a plan proposal unit that proposes a moving plan based on the information input by the reception unit; an arrangement department that arranges for a moving company based on the plan proposed by the plan proposal department and carries out government procedures; an instruction unit that gives instructions according to packing or progress on the day based on the vendor or procedure arranged by the arrangement unit; a support unit that supports the cleaning up instructed by the instruction unit. A system characterized by:

2. The plan proposal unit Proposes moving plans based on the user's destination, moving date, and amount of luggage 2. The system of claim 1.

3. The arrangement department Arrange for a moving company and file moving-out and moving-in notifications with the local government office.

2. The system of claim 1.

4. The instruction unit Providing instructions to improve packing efficiency and progress on the day 2. The system of claim 1.

5. The support portion is Support for cleaning up after moving 2. The system of claim 1.

6. The reception unit Estimates the user's emotions and adjusts the timing of inputting moving information based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past moving history and select the optimal input method 2. The system of claim 1.

8. The reception unit Filtering moving information based on the user's current living situation and interests 2. The system of claim 1.

9. The reception unit Estimate the user's emotions and determine the priority of the moving information to be entered based on the estimated user emotions.

2. The system of claim 1.

10. The reception unit When entering moving information, the system takes into account the user's geographic location and prioritizes entering the most relevant information.

2. The system of claim 1.

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