system

Abstract

The system according to the embodiment aims to enable a user to easily understand and execute a data migration method. [Solution] A system according to an embodiment includes a questioning unit, a suggestion unit, a monitoring unit, and a change unit. The questioning unit asks questions to understand the user's situation. The suggestion unit suggests an appropriate data migration method based on information collected by the questioning unit. The monitoring unit monitors the user's level of understanding as the data migration proceeds according to the manual suggested by the suggestion unit. The change unit changes the manual based on the results obtained by the monitoring 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 technology has had the problem that users who purchase mobile phones online find it difficult to understand how to transfer data.

[0005] The system according to the embodiment aims to enable a user to easily understand and execute a data migration method. [Means for solving the problem]

[0006] The system according to the embodiment includes a questioning unit, a suggestion unit, a monitoring unit, and a change unit. The questioning unit asks questions to understand the user's situation. The suggestion unit suggests an appropriate data migration method based on information collected by the questioning unit. The monitoring unit monitors the user's level of understanding as the data migration proceeds according to the manual suggested by the suggestion unit. The change unit changes the manual based on the results obtained by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to easily understand and execute a data migration method. [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 data migration support system according to an embodiment of the present invention assists users who have purchased a mobile phone online in smoothly migrating the necessary data after purchase. This data migration support system asks the user several questions to ascertain the user's current status, such as the mobile phone's operating system and whether or not Wi-Fi is available. Based on the user's answers, the system then presents a manual proposing the optimal data migration method. Furthermore, as the user follows the manual to proceed with the data migration, the system adapts the manual to suit the user's level of understanding and provides appropriate support. For example, the system asks the user questions such as, "What is the operating system of your new mobile phone?" and "Do you have Wi-Fi?" to ascertain the user's current status. This information is input into the system. Based on the user's answers, the system then generates a manual proposing the optimal data migration method. For example, if a user is migrating data from iOS to Android, the system presents a manual detailing the procedure. If a user does not have Wi-Fi, the system suggests an offline data migration method. As the user follows the manual to proceed with the data migration, the system monitors the user's level of understanding. For example, if the user stumbles on a particular step, the system presents a manual that explains that step in more detail. Furthermore, if the user is progressing smoothly, the system provides concise instructions for the next step. This system allows users to easily find the data migration method that best suits their situation, enabling a smooth data migration. For example, even users who are using a smartphone for the first time can perform data migration with support from the system. In addition, the system changes the manual according to the user's level of understanding, allowing users to proceed with data migration at their own pace. This allows the data migration support system to propose the best data migration method according to the user's situation and provide support according to their level of understanding, thereby achieving a smooth data migration.

[0029] A data migration assistance system according to an embodiment includes a questioning unit, a suggestion unit, a monitoring unit, and a change unit. The questioning unit asks questions to understand the user's situation. For example, the questioning unit asks the user questions such as the operating system of the mobile phone and whether Wi-Fi is available. For example, the questioning unit asks questions such as, "What is the operating system of your new mobile phone?" and "Do you have Wi-Fi?" to understand the user's situation. The suggestion unit generates a manual that suggests an optimal data migration method based on the information collected by the questioning unit. For example, if a user wants to migrate data from iOS to Android, the suggestion unit presents a manual that explains the procedure in detail. Furthermore, if a Wi-Fi environment is unavailable, the suggestion unit can also suggest an offline data migration method. The monitoring unit monitors the user's level of understanding as the user proceeds with the data migration according to the manual. For example, the monitoring unit monitors the level of understanding based on the user's operation time and the frequency of error messages. For example, if the user stumbles on a particular step, the monitoring unit presents a manual that explains that step in more detail. Furthermore, if the user is progressing smoothly, the monitoring unit can provide concise instructions for moving on to the next step. The modification unit modifies the manual based on the results obtained by the monitoring unit. For example, the modification unit modifies the manual to provide more detailed explanations depending on the user's level of understanding. For example, if the user stumbles at a particular step, the modification unit presents a manual that explains that step in more detail. Furthermore, if the user is progressing smoothly, the modification unit can also provide a concise explanation for proceeding to the next step. As a result, the data migration assistance system according to the embodiment can achieve smooth data migration by proposing an optimal data migration method depending on the user's situation and providing support depending on the user's level of understanding.

[0030] The interrogation unit can ask the user questions regarding the mobile phone's OS or whether or not Wi-Fi is available. The interrogation unit grasps the user's situation by asking, for example, questions such as "What is the OS of your new mobile phone?" and "Do you have a Wi-Fi environment?" Examples of mobile phone OS include, but are not limited to, iOS, Android, Windows Phone, etc. Examples of whether or not Wi-Fi is available include, but are not limited to, whether or not a Wi-Fi connection is available and the strength of the Wi-Fi network. In this way, the interrogation unit can grasp the user's situation, such as the OS of the mobile phone and whether or not Wi-Fi is available, and collect information for proposing an optimal data migration method.

[0031] The suggestion unit can generate a manual proposing an optimal data migration method based on the user's answers. For example, when a user migrates data from iOS to Android, the suggestion unit presents a manual that explains the procedure in detail. The suggestion unit can also suggest an offline data migration method when a Wi-Fi environment is not available. Examples of optimal data migration methods include, but are not limited to, online migration, offline migration, and cloud-based migration. In this way, the suggestion unit can suggest an optimal data migration method based on the user's answers, allowing the user to smoothly perform data migration.

[0032] The monitoring unit can monitor the user's level of understanding based on the operation time and frequency of error messages when the user is performing data migration in accordance with the manual. The monitoring unit monitors the user's level of understanding based on, for example, the user's operation time and frequency of error messages. The operation time includes, for example, but is not limited to, the time from the start to the end of an operation and the time required for a specific operation. The frequency of error messages includes, for example, but is not limited to, the number of error messages within a certain period of time and the number of error messages during a specific operation. In this way, the monitoring unit can provide appropriate support and increase the success rate of data migration by monitoring the user's level of understanding.

[0033] The modification unit can modify the manual to provide more detailed explanations based on the results obtained by the monitoring unit. For example, if the user stumbles at a particular step, the modification unit presents a manual that provides more detailed explanations of that step. Furthermore, if the user is progressing smoothly, the modification unit can provide concise explanations for moving on to the next step. The detailed explanations can include, but are not limited to, detailed procedural information, precautions, troubleshooting, and the like. In this way, the modification unit can modify the manual according to the user's level of understanding, allowing the user to smoothly proceed with data migration.

[0034] The suggestion unit can suggest an offline data migration method when a Wi-Fi environment is not available. For example, when a Wi-Fi environment is not available, the suggestion unit suggests an offline data migration method such as migration using a USB cable or migration using local storage. Offline data migration methods include, but are not limited to, migration using a USB cable or migration using local storage. In this way, the suggestion unit enables the user to perform data migration even when a Wi-Fi environment is not available.

[0035] The questioning unit can analyze the user's past device usage history and select the most appropriate question. The questioning unit can ask relevant questions based on, for example, the OS and settings of the device the user used in the past. The questioning unit can also select the most appropriate question by referring to the data migration method the user used in the past. The questioning unit can also prioritize questions about frequently used functions based on the user's past device usage history. The past device usage history includes, for example, usage time, used apps, operation history, etc., but is not limited to these examples. This allows the questioning unit to efficiently collect information by asking the most appropriate question based on the user's past device usage history.

[0036] When asking a question, the questioning unit can customize the question based on the user's current device settings and app usage status. For example, the questioning unit can check the user's device settings and ask questions related to the settings. The questioning unit can also ask questions about apps based on the status of apps currently being used by the user. The questioning unit can also check the remaining battery level and connection status of the user's device and ask questions accordingly. Current device settings include, but are not limited to, the OS version, installed apps, and network settings. This allows the questioning unit to collect more appropriate information by customizing the question based on the user's current device settings and app usage status.

[0037] When asking a question, the questioning unit can prioritize relevant questions by taking into account the user's geographical location information. For example, the questioning unit can ask questions about region-specific settings and services based on the user's current location. If the user is traveling, the questioning unit can also ask questions about device usage at the travel destination. The questioning unit can also ask questions about the optimal data migration method based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, Wi-Fi location information, etc. This allows the questioning unit to collect more appropriate information by taking into account the user's geographical location information.

[0038] When asking a question, the questioning unit can analyze the user's social media activity and ask related questions. For example, the questioning unit can ask questions about social media apps that the user frequently uses. The questioning unit can also ask questions about related data migration based on the user's social media activity. The questioning unit can also ask questions about contact migration taking into account the user's social media friendships. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. This allows the questioning unit to collect more appropriate information by asking questions based on the user's social media activity.

[0039] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the data migration. For example, in the case of an important data migration, the suggestion unit can make a proposal including detailed procedures. In addition, in the case of a less important data migration, the suggestion unit can make a proposal including simple procedures. In addition, the suggestion unit can suggest necessary tools and apps depending on the importance of the data migration. Examples of the importance of the data migration include, but are not limited to, the type of data, the amount of data, and the value of the data. In this way, the suggestion unit can adjust the level of detail of the proposal depending on the importance of the data migration, allowing the user to appropriately receive the information they need.

[0040] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the device category. For example, in the case of a smartphone, the suggestion unit can apply a suggestion algorithm specialized for smartphones. Furthermore, in the case of a tablet, the suggestion unit can also apply a suggestion algorithm specialized for tablets. Furthermore, in the case of a smartwatch, the suggestion unit can also apply a suggestion algorithm specialized for smartwatches. Device categories include, for example, smartphones, tablets, and personal computers, but are not limited to these examples. In this way, the suggestion unit can apply a suggestion algorithm according to the device category, allowing the user to receive optimal suggestions.

[0041] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the date of purchase of the device. For example, for a recently purchased device, the suggestion unit can prioritize proposing the latest data migration method. Also, for an older device, the suggestion unit can prioritize proposing a compatible data migration method. Also, the suggestion unit can suggest the optimal data migration tool or app depending on the date of purchase of the device. The date of purchase of the device includes, but is not limited to, purchase history, warranty information, user declaration, etc. In this way, the suggestion unit can prioritize the suggestions based on the date of purchase of the device, allowing the user to receive the optimal suggestion.

[0042] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the devices. For example, if a user owns multiple devices, the suggestion unit can suggest devices in order of most relevance. The suggestion unit can also suggest devices in order of most frequently used devices based on the frequency of device use. The suggestion unit can also suggest devices in order of optimal devices based on device functions and performance. Device relevance includes, but is not limited to, for example, the same manufacturer, the same OS, and the same network environment. In this way, the suggestion unit can adjust the order of suggestions based on the relevance of the devices, allowing the user to receive the optimal suggestion.

[0043] The monitoring unit can perform monitoring while taking into account the geographical distribution of users. For example, the monitoring unit monitors region-specific settings and services based on the user's current location. If the user is traveling, the monitoring unit can also monitor device usage at the user's destination. The monitoring unit can also monitor the optimal data migration method based on the user's geographical distribution. Examples of geographical distribution include, but are not limited to, GPS data, IP addresses, and Wi-Fi location information. In this way, the monitoring unit can provide information about region-specific settings and services by performing monitoring while taking into account the user's geographical distribution.

[0044] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the user during monitoring. For example, the monitoring unit performs related monitoring based on literature previously referenced by the user. The monitoring unit can also improve the accuracy of monitoring by referring to literature related to the user's operations. The monitoring unit can also analyze the user's past literature reference history and perform monitoring based on the most relevant literature. Examples of related literature include, but are not limited to, technical literature, user manuals, and research papers. In this way, the monitoring unit can improve the accuracy of monitoring by referring to the user's related literature, thereby providing more appropriate support.

[0045] When making a change, the change unit can analyze the user's past operation history and select the optimal change method. For example, the change unit changes the manual by adding detailed explanations for operations that the user has previously performed that have caused errors. The change unit can also change the manual for the most important operations based on the user's past operation history. The change unit can also analyze the user's operation history and select the optimal change method. The past operation history includes, for example, operation time, operation content, error logs, etc., but is not limited to these examples. In this way, the change unit can select the optimal change method by analyzing the user's past operation history, allowing the user to smoothly proceed with data migration.

[0046] When making changes, the modification unit can customize the content of the manual based on the user's current device settings. For example, the modification unit can check the user's device settings and modify the manual by adding explanations related to the settings. The modification unit can also modify the manual by adding explanations about apps based on the status of apps currently being used by the user. The modification unit can also modify the manual by checking the remaining battery level and connection status of the user's device and adding explanations accordingly. Current device settings include, but are not limited to, the OS version, installed apps, and network settings. This allows the modification unit to customize the content of the manual based on the user's current device settings, allowing the user to smoothly proceed with data migration.

[0047] When making a change, the change unit can select the optimal change method taking into account the user's geographical location information. For example, the change unit can change the manual by adding explanations about region-specific settings and services based on the user's current location. If the user is traveling, the change unit can also change the manual by adding explanations about using the device at the travel destination. The change unit can also change the manual by adding explanations about the optimal data migration method based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, Wi-Fi location information, etc. This allows the change unit to select the optimal change method taking into account the user's geographical location information, allowing the user to smoothly proceed with data migration.

[0048] When making changes, the modification unit can analyze the user's social media activity and suggest a method for modifying the manual. For example, the modification unit modifies the manual by adding an explanation about social media apps frequently used by the user. The modification unit can also modify the manual by adding an explanation about related data migration based on the user's social media activity. The modification unit can also modify the manual by adding an explanation about contact migration, taking into account the user's social media friendships. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. In this way, the modification unit can analyze the user's social media activity to suggest an optimal method for modifying the manual, allowing the user to smoothly proceed with data migration.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The questioning unit can analyze the user's past device usage history and select the most appropriate question. For example, the questioning unit can ask relevant questions based on the OS and settings of the device the user used in the past. The questioning unit can also select the most appropriate question by referring to the data migration method the user used in the past. The questioning unit can also prioritize questions about frequently used functions based on the user's past device usage history. The past device usage history includes, but is not limited to, for example, usage time, used apps, and operation history. This allows the questioning unit to efficiently collect information by asking the most appropriate question based on the user's past device usage history.

[0051] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the device category. For example, in the case of a smartphone, the suggestion unit can apply a suggestion algorithm specialized for smartphones. In addition, in the case of a tablet, the suggestion unit can also apply a suggestion algorithm specialized for tablets. In addition, in the case of a smartwatch, the suggestion unit can also apply a suggestion algorithm specialized for smartwatches. Device categories include, but are not limited to, smartphones, tablets, and personal computers, for example. In this way, the suggestion unit can apply a suggestion algorithm according to the device category, allowing the user to receive optimal suggestions.

[0052] The monitoring unit may perform monitoring while taking into account the geographical distribution of users. For example, the monitoring unit may monitor region-specific settings and services based on the user's current location. If the user is traveling, the monitoring unit may also monitor device usage at the user's destination. The monitoring unit may also monitor the optimal data migration method based on the user's geographical distribution. Examples of geographical distribution include, but are not limited to, GPS data, IP addresses, and Wi-Fi location information. In this way, the monitoring unit may provide information about region-specific settings and services by performing monitoring while taking into account the user's geographical distribution.

[0053] When making a change, the change unit can analyze the user's past operation history and select the optimal change method. For example, the change unit changes the manual by adding detailed explanations for operations that the user has previously performed that have caused errors. The change unit can also change the manual for the most important operations based on the user's past operation history. The change unit can also analyze the user's operation history and select the optimal change method. The past operation history includes, for example, operation time, operation content, error logs, etc., but is not limited to these examples. In this way, the change unit can select the optimal change method by analyzing the user's past operation history, allowing the user to smoothly proceed with data migration.

[0054] When asking a question, the questioning unit can customize the question based on the user's current device settings and app usage status. For example, the questioning unit can check the user's device settings and ask questions related to the settings. The questioning unit can also ask questions about apps based on the status of apps currently being used by the user. The questioning unit can also check the remaining battery level and connection status of the user's device and ask questions accordingly. Current device settings include, but are not limited to, the OS version, installed apps, and network settings. This allows the questioning unit to collect more appropriate information by customizing the question based on the user's current device settings and app usage status.

[0055] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the date of purchase of the device. For example, for a recently purchased device, the suggestion unit can prioritize the latest data migration method. Also, for an older device, the suggestion unit can prioritize the compatible data migration method. The suggestion unit can also suggest the optimal data migration tool or app depending on the date of purchase of the device. The date of purchase of the device includes, but is not limited to, purchase history, warranty information, user declaration, etc. In this way, the suggestion unit can prioritize the suggestions based on the date of purchase of the device, allowing the user to receive the optimal suggestion.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The interrogation unit asks questions to understand the user's situation. For example, the interrogation unit asks the user questions such as the operating system of the mobile phone and whether or not Wi-Fi is available. Specifically, the interrogation unit asks questions such as "What is the operating system of your new mobile phone?" and "Do you have a Wi-Fi environment?" to understand the user's situation. Step 2: The suggestion unit generates a manual that suggests the optimal data transfer method based on the information collected by the questioning unit. For example, if a user wants to transfer data from iOS to Android, the suggestion unit presents a manual that explains the procedure in detail. It can also suggest offline data transfer methods if there is no Wi-Fi environment. Step 3: The monitoring unit monitors the user's level of understanding as they proceed with the data migration according to the manual. For example, it monitors the user's level of understanding based on the time it takes to perform the operation and the frequency of error messages. If the user stumbles at a particular step, it presents a manual that explains that step in more detail. If the user is progressing smoothly, it can also provide a concise explanation for moving on to the next step. Step 4: The modification unit modifies the manual based on the results obtained by the monitoring unit. For example, the modification unit modifies the manual to provide more detailed explanations depending on the user's level of understanding. If the user stumbles at a particular step, the modification unit presents a manual that explains that step in more detail. Also, if the user is progressing smoothly, the modification unit can provide a concise explanation for moving on to the next step.

[0058] (Example 2) A data migration support system according to an embodiment of the present invention assists users who have purchased a mobile phone online in smoothly migrating the necessary data after purchase. This data migration support system asks the user several questions to ascertain the user's current status, such as the mobile phone's operating system and whether or not Wi-Fi is available. Based on the user's answers, the system then presents a manual proposing the optimal data migration method. Furthermore, as the user follows the manual to proceed with the data migration, the system adapts the manual to suit the user's level of understanding and provides appropriate support. For example, the system asks the user questions such as, "What is the operating system of your new mobile phone?" and "Do you have Wi-Fi?" to ascertain the user's current status. This information is input into the system. Based on the user's answers, the system then generates a manual proposing the optimal data migration method. For example, if a user is migrating data from iOS to Android, the system presents a manual detailing the procedure. If a user does not have Wi-Fi, the system suggests an offline data migration method. As the user follows the manual to proceed with the data migration, the system monitors the user's level of understanding. For example, if the user stumbles on a particular step, the system presents a manual that explains that step in more detail. Furthermore, if the user is progressing smoothly, the system provides concise instructions for the next step. This system allows users to easily find the data migration method that best suits their situation, enabling a smooth data migration. For example, even users who are using a smartphone for the first time can perform data migration with support from the system. In addition, the system changes the manual according to the user's level of understanding, allowing users to proceed with data migration at their own pace. This allows the data migration support system to propose the best data migration method according to the user's situation and provide support according to their level of understanding, thereby achieving a smooth data migration.

[0059] A data migration assistance system according to an embodiment includes a questioning unit, a suggestion unit, a monitoring unit, and a change unit. The questioning unit asks questions to understand the user's situation. For example, the questioning unit asks the user questions such as the operating system of the mobile phone and whether Wi-Fi is available. For example, the questioning unit asks questions such as, "What is the operating system of your new mobile phone?" and "Do you have Wi-Fi?" to understand the user's situation. The suggestion unit generates a manual that suggests an optimal data migration method based on the information collected by the questioning unit. For example, if a user wants to migrate data from iOS to Android, the suggestion unit presents a manual that explains the procedure in detail. Furthermore, if a Wi-Fi environment is unavailable, the suggestion unit can also suggest an offline data migration method. The monitoring unit monitors the user's level of understanding as the user proceeds with the data migration according to the manual. For example, the monitoring unit monitors the level of understanding based on the user's operation time and the frequency of error messages. For example, if the user stumbles on a particular step, the monitoring unit presents a manual that explains that step in more detail. Furthermore, if the user is progressing smoothly, the monitoring unit can provide concise instructions for moving on to the next step. The modification unit modifies the manual based on the results obtained by the monitoring unit. For example, the modification unit modifies the manual to provide more detailed explanations depending on the user's level of understanding. For example, if the user stumbles at a particular step, the modification unit presents a manual that explains that step in more detail. Furthermore, if the user is progressing smoothly, the modification unit can also provide a concise explanation for proceeding to the next step. As a result, the data migration assistance system according to the embodiment can achieve smooth data migration by proposing an optimal data migration method depending on the user's situation and providing support depending on the user's level of understanding.

[0060] The interrogation unit can ask the user questions regarding the mobile phone's OS or whether or not Wi-Fi is available. The interrogation unit grasps the user's situation by asking, for example, questions such as "What is the OS of your new mobile phone?" and "Do you have a Wi-Fi environment?" Examples of mobile phone OS include, but are not limited to, iOS, Android, Windows Phone, etc. Examples of whether or not Wi-Fi is available include, but are not limited to, whether or not a Wi-Fi connection is available and the strength of the Wi-Fi network. In this way, the interrogation unit can grasp the user's situation, such as the OS of the mobile phone and whether or not Wi-Fi is available, and collect information for proposing an optimal data migration method.

[0061] The suggestion unit can generate a manual proposing an optimal data migration method based on the user's answers. For example, when a user migrates data from iOS to Android, the suggestion unit presents a manual that explains the procedure in detail. The suggestion unit can also suggest an offline data migration method when a Wi-Fi environment is not available. Examples of optimal data migration methods include, but are not limited to, online migration, offline migration, and cloud-based migration. In this way, the suggestion unit can suggest an optimal data migration method based on the user's answers, allowing the user to smoothly perform data migration.

[0062] The monitoring unit can monitor the user's level of understanding based on the operation time and frequency of error messages when the user is performing data migration in accordance with the manual. The monitoring unit monitors the user's level of understanding based on, for example, the user's operation time and frequency of error messages. The operation time includes, for example, but is not limited to, the time from the start to the end of an operation and the time required for a specific operation. The frequency of error messages includes, for example, but is not limited to, the number of error messages within a certain period of time and the number of error messages during a specific operation. In this way, the monitoring unit can provide appropriate support and increase the success rate of data migration by monitoring the user's level of understanding.

[0063] The modification unit can modify the manual to provide more detailed explanations based on the results obtained by the monitoring unit. For example, if the user stumbles at a particular step, the modification unit presents a manual that provides more detailed explanations of that step. Furthermore, if the user is progressing smoothly, the modification unit can provide concise explanations for moving on to the next step. The detailed explanations can include, but are not limited to, detailed procedural information, precautions, troubleshooting, and the like. In this way, the modification unit can modify the manual according to the user's level of understanding, allowing the user to smoothly proceed with data migration.

[0064] The suggestion unit can suggest an offline data migration method when a Wi-Fi environment is not available. For example, when a Wi-Fi environment is not available, the suggestion unit suggests an offline data migration method such as migration using a USB cable or migration using local storage. Offline data migration methods include, but are not limited to, migration using a USB cable or migration using local storage. In this way, the suggestion unit enables the user to perform data migration even when a Wi-Fi environment is not available.

[0065] The questioning unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user's emotions. For example, if the user is feeling anxious, the questioning unit can first ask simple, reassuring questions and gradually move on to more detailed questions. Also, if the user is relaxed, the questioning unit can ask detailed questions from the beginning to efficiently collect information. Also, if the user is in a hurry, the questioning unit can prioritize the most important questions and quickly collect information. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. As a result, the questioning unit can adjust the order and content of questions according to the user's emotions, allowing the user to provide information in a relaxed state.

[0066] The questioning unit can analyze the user's past device usage history and select the most appropriate question. The questioning unit can ask relevant questions based on, for example, the OS and settings of the device the user used in the past. The questioning unit can also select the most appropriate question by referring to the data migration method the user used in the past. The questioning unit can also prioritize questions about frequently used functions based on the user's past device usage history. The past device usage history includes, for example, usage time, used apps, operation history, etc., but is not limited to these examples. This allows the questioning unit to efficiently collect information by asking the most appropriate question based on the user's past device usage history.

[0067] When asking a question, the questioning unit can customize the question based on the user's current device settings and app usage status. For example, the questioning unit can check the user's device settings and ask questions related to the settings. The questioning unit can also ask questions about apps based on the status of apps currently being used by the user. The questioning unit can also check the remaining battery level and connection status of the user's device and ask questions accordingly. Current device settings include, but are not limited to, the OS version, installed apps, and network settings. This allows the questioning unit to collect more appropriate information by customizing the question based on the user's current device settings and app usage status.

[0068] The questioning unit can estimate the user's emotions and adjust the difficulty of the questions based on the estimated user's emotions. For example, if the user is feeling stressed, the questioning unit can start with easy questions and gradually increase the difficulty. Also, if the user is relaxed, the questioning unit can ask detailed and difficult questions from the beginning. Also, if the user is in a hurry, the questioning unit can prioritize important questions and quickly collect information. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. As a result, the questioning unit can adjust the difficulty of the questions according to the user's emotions, allowing the user to provide information in a relaxed state.

[0069] When asking a question, the questioning unit can prioritize relevant questions by taking into account the user's geographical location information. For example, the questioning unit can ask questions about region-specific settings and services based on the user's current location. If the user is traveling, the questioning unit can also ask questions about device usage at the travel destination. The questioning unit can also ask questions about the optimal data migration method based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, Wi-Fi location information, etc. This allows the questioning unit to collect more appropriate information by taking into account the user's geographical location information.

[0070] When asking a question, the questioning unit can analyze the user's social media activity and ask related questions. For example, the questioning unit can ask questions about social media apps that the user frequently uses. The questioning unit can also ask questions about related data migration based on the user's social media activity. The questioning unit can also ask questions about contact migration taking into account the user's social media friendships. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. This allows the questioning unit to collect more appropriate information by asking questions based on the user's social media activity.

[0071] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user's emotions. For example, if the user is feeling anxious, the suggestion unit can make the suggestion using gentle language. If the user is relaxed, the suggestion unit can also make the suggestion including detailed explanations. If the user is in a hurry, the suggestion unit can also make concise and quick suggestions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. As a result, the suggestion unit can adjust the way the suggestion is expressed based on the user's emotions, allowing the user to relax and accept the suggestion.

[0072] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the data migration. For example, in the case of an important data migration, the suggestion unit can make a proposal including detailed procedures. In addition, in the case of a less important data migration, the suggestion unit can make a proposal including simple procedures. In addition, the suggestion unit can suggest necessary tools and apps depending on the importance of the data migration. Examples of the importance of the data migration include, but are not limited to, the type of data, the amount of data, and the value of the data. In this way, the suggestion unit can adjust the level of detail of the proposal depending on the importance of the data migration, allowing the user to appropriately receive the information they need.

[0073] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the device category. For example, in the case of a smartphone, the suggestion unit can apply a suggestion algorithm specialized for smartphones. Furthermore, in the case of a tablet, the suggestion unit can also apply a suggestion algorithm specialized for tablets. Furthermore, in the case of a smartwatch, the suggestion unit can also apply a suggestion algorithm specialized for smartwatches. Device categories include, for example, smartphones, tablets, and personal computers, but are not limited to these examples. In this way, the suggestion unit can apply a suggestion algorithm according to the device category, allowing the user to receive optimal suggestions.

[0074] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, if the user is feeling anxious, the suggestion unit can make short, to-the-point suggestions. If the user is relaxed, the suggestion unit can also make longer suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can also make concise, quick suggestions. The estimation of emotions is performed using techniques such as facial expression recognition, voice analysis, and text analysis. As a result, the suggestion unit can adjust the length of the suggestions according to the user's emotions, allowing the user to relax and accept the suggestions.

[0075] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the date of purchase of the device. For example, for a recently purchased device, the suggestion unit can prioritize proposing the latest data migration method. Also, for an older device, the suggestion unit can prioritize proposing a compatible data migration method. Also, the suggestion unit can suggest the optimal data migration tool or app depending on the date of purchase of the device. The date of purchase of the device includes, but is not limited to, purchase history, warranty information, user declaration, etc. In this way, the suggestion unit can prioritize the suggestions based on the date of purchase of the device, allowing the user to receive the optimal suggestion.

[0076] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the devices. For example, if a user owns multiple devices, the suggestion unit can suggest devices in order of most relevance. The suggestion unit can also suggest devices in order of most frequently used devices based on the frequency of device use. The suggestion unit can also suggest devices in order of optimal devices based on device functions and performance. Device relevance includes, but is not limited to, for example, the same manufacturer, the same OS, and the same network environment. In this way, the suggestion unit can adjust the order of suggestions based on the relevance of the devices, allowing the user to receive the optimal suggestion.

[0077] The monitoring unit can estimate the user's emotions and adjust the order in which the monitoring results are displayed based on the estimated user's emotions. For example, if the user is feeling anxious, the monitoring unit can display important results first, providing a sense of security. The monitoring unit can also display detailed results in order if the user is relaxed. The monitoring unit can also display results that highlight the main points first if the user is in a hurry. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. As a result, the monitoring unit can adjust the order in which the monitoring results are displayed based on the user's emotions, allowing the user to check the results with peace of mind.

[0078] The monitoring unit can perform monitoring while taking into account the geographical distribution of users. For example, the monitoring unit monitors region-specific settings and services based on the user's current location. If the user is traveling, the monitoring unit can also monitor device usage at the user's destination. The monitoring unit can also monitor the optimal data migration method based on the user's geographical distribution. Examples of geographical distribution include, but are not limited to, GPS data, IP addresses, and Wi-Fi location information. In this way, the monitoring unit can provide information about region-specific settings and services by performing monitoring while taking into account the user's geographical distribution.

[0079] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the user during monitoring. For example, the monitoring unit performs related monitoring based on literature previously referenced by the user. The monitoring unit can also improve the accuracy of monitoring by referring to literature related to the user's operations. The monitoring unit can also analyze the user's past literature reference history and perform monitoring based on the most relevant literature. Examples of related literature include, but are not limited to, technical literature, user manuals, and research papers. In this way, the monitoring unit can improve the accuracy of monitoring by referring to the user's related literature, thereby providing more appropriate support.

[0080] The modification unit can estimate the user's emotion and adjust the method of modifying the manual based on the estimated user's emotion. For example, if the user feels anxious, the modification unit modifies the manual by adding a detailed explanation. Furthermore, if the user feels relaxed, the modification unit can modify the manual by adding a concise explanation. Furthermore, if the user is in a hurry, the modification unit can modify the manual by adding a brief explanation. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. In this way, the modification unit can adjust the method of modifying the manual according to the user's emotion, allowing the user to understand the manual in a relaxed state.

[0081] When making a change, the change unit can analyze the user's past operation history and select the optimal change method. For example, the change unit changes the manual by adding detailed explanations for operations that the user has previously performed that have caused errors. The change unit can also change the manual for the most important operations based on the user's past operation history. The change unit can also analyze the user's operation history and select the optimal change method. The past operation history includes, for example, operation time, operation content, error logs, etc., but is not limited to these examples. In this way, the change unit can select the optimal change method by analyzing the user's past operation history, allowing the user to smoothly proceed with data migration.

[0082] When making changes, the modification unit can customize the content of the manual based on the user's current device settings. For example, the modification unit can check the user's device settings and modify the manual by adding explanations related to the settings. The modification unit can also modify the manual by adding explanations about apps based on the status of apps currently being used by the user. The modification unit can also modify the manual by checking the remaining battery level and connection status of the user's device and adding explanations accordingly. Current device settings include, but are not limited to, the OS version, installed apps, and network settings. This allows the modification unit to customize the content of the manual based on the user's current device settings, allowing the user to smoothly proceed with data migration.

[0083] The modification unit can estimate the user's emotions and determine the priority of changes to the manual based on the estimated user's emotions. For example, if the user is feeling anxious, the modification unit can prioritize changes to important parts. Furthermore, if the user is relaxed, the modification unit can prioritize changes to detailed parts. Furthermore, if the user is in a hurry, the modification unit can prioritize changes to key points. Emotions are estimated using techniques such as facial expression recognition, voice analysis, and text analysis. In this way, the modification unit can determine the priority of changes to the manual based on the user's emotions, allowing the user to understand the manual in a relaxed state.

[0084] When making a change, the change unit can select the optimal change method taking into account the user's geographical location information. For example, the change unit can change the manual by adding explanations about region-specific settings and services based on the user's current location. If the user is traveling, the change unit can also change the manual by adding explanations about using the device at the travel destination. The change unit can also change the manual by adding explanations about the optimal data migration method based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, Wi-Fi location information, etc. This allows the change unit to select the optimal change method taking into account the user's geographical location information, allowing the user to smoothly proceed with data migration.

[0085] When making changes, the modification unit can analyze the user's social media activity and suggest a method for modifying the manual. For example, the modification unit modifies the manual by adding an explanation about social media apps frequently used by the user. The modification unit can also modify the manual by adding an explanation about related data migration based on the user's social media activity. The modification unit can also modify the manual by adding an explanation about contact migration, taking into account the user's social media friendships. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. In this way, the modification unit can analyze the user's social media activity to suggest an optimal method for modifying the manual, allowing the user to smoothly proceed with data migration. === Hard Collateral 1-1 === Each of the multiple elements, including the questioning unit, suggesting unit, monitoring unit, and changing unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the questioning unit is implemented by the control unit 46A of the smart device 14 and asks the user questions such as the mobile phone's operating system and whether Wi-Fi is available. The suggesting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates a manual proposing an optimal data migration method based on the user's answers. The monitoring unit is implemented, for example, by the control unit 46A of the smart device 14 and monitors the user's level of understanding based on the user's operation time and the frequency of error messages. The changing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and changes the manual according to the user's level of understanding. === Hard Collateral 1-2 === Each of the multiple elements, including the questioning unit, suggesting unit, monitoring unit, and changing 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 questioning unit is realized by the control unit 46A of the smart glasses 214 and asks the user questions such as the mobile phone's OS and whether Wi-Fi is available. The suggesting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a manual proposing an optimal data migration method based on the user's answers. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214 and monitors the user's level of understanding based on the user's operation time and the frequency of error messages. The changing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and changes the manual according to the user's level of understanding. === Hard Collateral 1-3 === Each of the multiple elements, including the questioning unit, suggesting unit, monitoring unit, and changing unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the questioning unit is realized by the control unit 46A of the headset-type terminal 314 and asks the user questions such as the mobile phone's OS and whether Wi-Fi is available. The suggesting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a manual proposing an optimal data migration method based on the user's answers. The monitoring unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and monitors the user's level of understanding based on the user's operation time and the frequency of error messages. The changing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and changes the manual according to the user's level of understanding. === Hard Collateral 1-4 === Each of the multiple elements, including the questioning unit, suggesting unit, monitoring unit, and changing unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the questioning unit is realized by the control unit 46A of the robot 414 and asks the user questions such as the mobile phone's OS and whether Wi-Fi is available. The suggesting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a manual proposing an optimal data migration method based on the user's answers. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and monitors the user's level of understanding based on the user's operation time and the frequency of error messages. The changing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and changes the manual according to the user's level of understanding.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The questioning unit can analyze the user's past device usage history and select the most appropriate question. For example, the questioning unit can ask relevant questions based on the OS and settings of the device the user used in the past. The questioning unit can also select the most appropriate question by referring to the data migration method the user used in the past. The questioning unit can also prioritize questions about frequently used functions based on the user's past device usage history. The past device usage history includes, but is not limited to, for example, usage time, used apps, and operation history. This allows the questioning unit to efficiently collect information by asking the most appropriate question based on the user's past device usage history.

[0088] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user's emotions. For example, if the user is feeling anxious, the suggestion unit can make the suggestion using gentle language. If the user is relaxed, the suggestion unit can also make the suggestion including detailed explanations. If the user is in a hurry, the suggestion unit can also make concise and quick suggestions. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. As a result, the suggestion unit can adjust the way the suggestion is expressed based on the user's emotions, allowing the user to relax and accept the suggestion.

[0089] The monitoring unit can estimate the user's emotions and adjust the order in which the monitoring results are displayed based on the estimated user's emotions. For example, if the user is feeling anxious, important results can be displayed first, providing a sense of security. The monitoring unit can also display detailed results in order if the user is relaxed. The monitoring unit can also display key results first if the user is in a hurry. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. As a result, the monitoring unit can adjust the order in which the monitoring results are displayed based on the user's emotions, allowing the user to check the results with peace of mind.

[0090] The modification unit can estimate the user's emotion and adjust the method of modifying the manual based on the estimated user's emotion. For example, if the user feels anxious, the modification unit modifies the manual by adding a detailed explanation. If the user feels relaxed, the modification unit can also modify the manual by adding a concise explanation. If the user is in a hurry, the modification unit can also modify the manual by adding a brief explanation. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. In this way, the modification unit can adjust the method of modifying the manual according to the user's emotion, allowing the user to understand the manual in a relaxed state.

[0091] The questioning unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user's emotions. For example, if the user is feeling anxious, the questioning unit can first ask simple, reassuring questions and gradually move on to more detailed questions. If the user is relaxed, the questioning unit can ask detailed questions from the beginning to efficiently collect information. If the user is in a hurry, the questioning unit can prioritize the most important questions and quickly collect information. Emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. As a result, the questioning unit can adjust the order and content of questions according to the user's emotions, allowing the user to provide information in a relaxed state.

[0092] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the device category. For example, in the case of a smartphone, the suggestion unit can apply a suggestion algorithm specialized for smartphones. In addition, in the case of a tablet, the suggestion unit can also apply a suggestion algorithm specialized for tablets. In addition, in the case of a smartwatch, the suggestion unit can also apply a suggestion algorithm specialized for smartwatches. Device categories include, but are not limited to, smartphones, tablets, and personal computers, for example. In this way, the suggestion unit can apply a suggestion algorithm according to the device category, allowing the user to receive optimal suggestions.

[0093] The monitoring unit may perform monitoring while taking into account the geographical distribution of users. For example, the monitoring unit may monitor region-specific settings and services based on the user's current location. If the user is traveling, the monitoring unit may also monitor device usage at the user's destination. The monitoring unit may also monitor the optimal data migration method based on the user's geographical distribution. Examples of geographical distribution include, but are not limited to, GPS data, IP addresses, and Wi-Fi location information. In this way, the monitoring unit may provide information about region-specific settings and services by performing monitoring while taking into account the user's geographical distribution.

[0094] When making a change, the change unit can analyze the user's past operation history and select the optimal change method. For example, the change unit changes the manual by adding detailed explanations for operations that the user has previously performed that have caused errors. The change unit can also change the manual for the most important operations based on the user's past operation history. The change unit can also analyze the user's operation history and select the optimal change method. The past operation history includes, for example, operation time, operation content, error logs, etc., but is not limited to these examples. In this way, the change unit can select the optimal change method by analyzing the user's past operation history, allowing the user to smoothly proceed with data migration.

[0095] When asking a question, the questioning unit can customize the question based on the user's current device settings and app usage status. For example, the questioning unit can check the user's device settings and ask questions related to the settings. The questioning unit can also ask questions about apps based on the status of apps currently being used by the user. The questioning unit can also check the remaining battery level and connection status of the user's device and ask questions accordingly. Current device settings include, but are not limited to, the OS version, installed apps, and network settings. This allows the questioning unit to collect more appropriate information by customizing the question based on the user's current device settings and app usage status.

[0096] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the date of purchase of the device. For example, for a recently purchased device, the suggestion unit can prioritize the latest data migration method. Also, for an older device, the suggestion unit can prioritize the compatible data migration method. The suggestion unit can also suggest the optimal data migration tool or app depending on the date of purchase of the device. The date of purchase of the device includes, but is not limited to, purchase history, warranty information, user declaration, etc. In this way, the suggestion unit can prioritize the suggestions based on the date of purchase of the device, allowing the user to receive the optimal suggestion.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The interrogation unit asks questions to understand the user's situation. For example, the interrogation unit asks the user questions such as the operating system of the mobile phone and whether or not Wi-Fi is available. Specifically, the interrogation unit asks questions such as "What is the operating system of your new mobile phone?" and "Do you have a Wi-Fi environment?" to understand the user's situation. Step 2: The suggestion unit generates a manual that suggests the optimal data transfer method based on the information collected by the questioning unit. For example, if a user wants to transfer data from iOS to Android, the suggestion unit presents a manual that explains the procedure in detail. It can also suggest offline data transfer methods if there is no Wi-Fi environment. Step 3: The monitoring unit monitors the user's level of understanding as they proceed with the data migration according to the manual. For example, it monitors the user's level of understanding based on the time it takes to perform the operation and the frequency of error messages. If the user stumbles at a particular step, it presents a manual that explains that step in more detail. If the user is progressing smoothly, it can also provide a concise explanation for moving on to the next step. Step 4: The modification unit modifies the manual based on the results obtained by the monitoring unit. For example, the modification unit modifies the manual to provide more detailed explanations depending on the user's level of understanding. If the user stumbles at a particular step, the modification unit presents a manual that explains that step in more detail. Also, if the user is progressing smoothly, the modification unit can provide a concise explanation for moving on to the next step.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] [Explanation of symbols]

[0171] 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 questioning unit that asks questions to understand the user's situation; a suggestion unit that suggests an appropriate data migration method based on the information collected by the questioning unit; a monitoring unit that monitors a user's level of understanding when the data migration is carried out in accordance with the manual proposed by the proposal unit; a modification unit that modifies the manual based on the results obtained by the monitoring unit; Equipped with A system characterized by:

2. The interrogation unit Ask the user questions about their mobile phone's operating system or whether they have Wi-Fi The system of claim 1 .

3. The proposal unit Generate a manual that suggests the best data migration method based on the user's answers The system of claim 1 .

4. The monitoring unit As users migrate data according to the manual, we monitor their level of understanding based on the time they spend operating the system and the frequency of error messages. The system of claim 1 .

5. The change unit Based on the results obtained by the monitoring unit, the manual is revised to provide more detailed explanations. The system of claim 1 .

6. The proposal unit If you don't have Wi-Fi, we'll suggest offline data transfer methods. The system of claim 1 .

7. The interrogation unit Infer user sentiment and adjust the order and content of questions based on the estimated sentiment The system of claim 1 .

8. The interrogation unit Analyze the user's past device usage history and select the most appropriate questions The system of claim 1 .

Images