system

Abstract

The system according to this embodiment aims to enable elderly people to easily operate smartphones. [Solution] The system according to the embodiment comprises an operation assist unit, an error prevention unit, a recovery unit, a spam call handling unit, and a schedule management unit. The operation assist unit provides voice operation assistance. The error prevention unit prevents errors caused by the operation assist unit. The recovery unit performs recovery when an error occurs due to the error prevention unit. The spam call handling unit provides alternative responses to spam calls. The schedule management unit manages daily schedules and reminders.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that elderly people often feel misoperations or difficulties in operating smartphones, which hinders the popularization of smartphones.

[0005] The system according to the embodiment aims to enable elderly people to easily operate smartphones.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an operation assistance unit, an error prevention unit, a recovery unit, a spam call handling unit, and a schedule management unit. The operation assistance unit provides voice-based operation assistance. The error prevention unit prevents errors caused by the operation assistance unit. The recovery unit performs recovery when an error occurs due to the error prevention unit. The spam call handling unit provides alternative responses to spam calls. The schedule management unit manages daily schedules and reminders. [Effects of the Invention]

[0007] The system according to this embodiment can enable elderly people to easily operate smartphones. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG.  1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent service according to an embodiment of the present invention is a system for promoting the adoption of smartphones among senior citizens and solving difficulties in their operation. This system aims to improve the quality of life (QOL) of senior citizens by solving difficulties in operating smartphones and tablets. For example, to solve difficulties in operating smartphones and tablets for senior citizens, it provides a voice operation assistance function. For example, if a senior citizen has difficulty touching the screen, they can prevent accidental operation by giving voice instructions. Furthermore, if an accidental operation occurs, the AI ​​agent can automatically recover and return to the original state. Next, it provides a function for the AI ​​agent to provide alternative responses to spam calls. For example, if a senior citizen answers a spam call, the AI ​​agent can automatically answer and set up call rejection, protecting the senior citizen. In addition, it provides daily schedule management and reminder functions. For example, by proactively reminding seniors of medication times, hospital appointments, and daycare schedules, they can avoid forgetting their appointments. It also provides multi-functional support such as assistance with digital use for hobbies and daily exercise support for maintaining health. This AI agent aims not only to assist seniors in their daily lives but also to prevent social isolation and enrich their digital lives. For example, by conversing with seniors, the AI ​​agent can perform frailty checks (physical, mental, psychological, and social), supporting early detection and prevention. This improves the quality of life (QOL) of seniors, enabling them to lead more fulfilling lives. In this way, the AI ​​agent service can solve the difficulties seniors face when using smartphones and tablets, thereby improving their QOL.

[0029] The AI ​​agent service according to the embodiment comprises an operation assistance unit, an error prevention unit, a recovery unit, a spam call handling unit, and a schedule management unit. The operation assistance unit provides voice-based operation assistance. For example, the operation assistance unit gives voice instructions when it is difficult for a senior citizen to touch the screen. For example, the operation assistance unit uses voice recognition technology to recognize voice commands from a senior citizen and perform appropriate operations. For example, the operation assistance unit increases the types of voice commands so that senior citizens can perform more operations by voice. The error prevention unit prevents errors caused by the operation assistance unit. For example, the error prevention unit issues a warning if a senior citizen makes an error. For example, the error prevention unit reduces the occurrence of errors by improving the error detection method. For example, the error prevention unit diversifies the means of preventing errors so that senior citizens can operate with peace of mind. The recovery unit performs recovery when an error occurs due to the error prevention unit. For example, the recovery unit automatically performs recovery when an error occurs. The recovery unit makes it easier for seniors to perform recovery by, for example, simplifying the recovery procedure. The recovery unit makes it easier for seniors to operate by, for example, expanding the scope of recovery. The spam call handling unit provides alternative answers to spam calls. The spam call handling unit automatically answers spam calls and sets call rejection settings. The spam call handling unit reduces the occurrence of spam calls by, for example, improving the spam call detection method. The spam call handling unit makes it easier for seniors to use the phone by, for example, diversifying the means of handling spam calls. The schedule management unit manages daily schedules and reminders. The schedule management unit proactively reminds seniors of things like medication times, hospital appointments, and daycare schedules. The schedule management unit makes it easier for seniors to remember appointments by, for example, adjusting the timing of reminders. The schedule management unit makes it easier for seniors to use reminders by, for example, customizing the content of reminders.As a result, the AI ​​agent service according to this embodiment can solve the difficulties senior citizens face when operating smartphones and tablets, thereby improving their quality of life (QOL).

[0030] The operation assistance unit provides voice-based operation assistance. Specifically, it gives voice instructions when senior users have difficulty touching the screen. The operation assistance unit uses voice recognition technology to recognize voice commands from senior users and perform appropriate operations. For example, if a senior user says, "Make a phone call," the operation assistance unit recognizes the voice command, opens the phone app, and calls the specified contact. If they say, "Send a message," it opens the messaging app, converts the voice input into text, and sends it. The operation assistance unit will increase the types of voice commands so that senior users can perform more operations by voice. For example, it will enable voice control of functions used daily, such as launching apps, changing settings, searching the internet, and playing music. Furthermore, the operation assistance unit learns the characteristics and speaking habits of senior users to improve the accuracy of voice recognition. This allows senior users to perform voice operations without stress. The operation assistance unit also has a voice feedback function that notifies users by voice when an operation is completed. For example, it provides feedback such as "You have made a phone call" or "You have sent a message" to give senior users a sense of security. This allows the operation assistance unit to make smartphones and tablets easier for seniors to use and reduce the burden of operation.

[0031] The error prevention unit prevents errors through the operation assistance unit. Specifically, it issues warnings when senior users make mistakes. For example, if a user attempts to change an important setting or accidentally delete an app, it displays a confirmation message such as "Are you sure you want to perform this operation?" to prevent errors. The error prevention unit also reduces the occurrence of errors by improving the error detection method. For example, it analyzes the operation history and detects unusual operation patterns to detect the possibility of errors early. Furthermore, the error prevention unit diversifies the means of preventing errors to ensure that senior users can operate the device with confidence. For example, in addition to voice confirmation messages, it displays large buttons on the screen to visually prompt confirmation. It also combines vibration and voice feedback to allow for multi-sensory confirmation of operations. In this way, the error prevention unit minimizes the risk of senior users making mistakes and provides an environment where they can use the device with peace of mind.

[0032] The recovery unit performs recovery when an error occurs due to the error prevention unit. Specifically, it automatically performs recovery when an error occurs. For example, if an app is accidentally deleted, the recovery unit will automatically reinstall the app and restore it to its original state. Also, if settings are accidentally changed, the recovery unit provides a function to restore the original settings. The recovery unit simplifies the recovery procedure to make it easy for seniors to perform recovery. For example, it makes the recovery operation a one-touch operation, eliminating complex procedures. Furthermore, the recovery unit expands the scope of recovery to allow seniors to operate the device with peace of mind. For example, it regularly backs up app settings and data so that they can be quickly restored in the event of an error. In this way, the recovery unit can quickly and reliably restore the device to its original state even if a senior makes an error, providing an environment in which seniors can use the device with peace of mind.

[0033] The spam call handling unit provides alternative responses to spam calls. Specifically, it automatically answers spam calls and sets them to be blocked. For example, when a spam call comes in, the spam call handling unit automatically answers and plays a recorded message to inform the caller of the intention to block the call. The spam call handling unit also reduces the occurrence of spam calls by improving its spam call detection methods. For example, it learns spam call patterns and automatically blocks calls from specific numbers or callers. Furthermore, the spam call handling unit diversifies its spam call handling methods to enable seniors to use the phone with peace of mind. For example, if a call is suspected to be a spam call, it issues a warning message so that seniors can check before answering. It also records the history of spam calls and makes it possible to review it later, making it easier for seniors to take measures against spam calls. In this way, the spam call handling unit provides an environment where seniors can use the phone with peace of mind without being troubled by spam calls.

[0034] The Schedule Management Department manages daily schedules and reminders. Specifically, it proactively reminds seniors of medication times, hospital appointments, and daycare schedules. For example, as medication time approaches, the Schedule Management Department sends voice and notification reminders to ensure seniors don't forget to take their medication. Similarly, for hospital appointments, it sends a reminder the day before to prompt seniors to confirm their schedule for the day. By adjusting the timing of reminders, the Schedule Management Department helps seniors remember their appointments. For example, it sends multiple reminders before important appointments to ensure they fully understand their schedule. Furthermore, by customizing the content of reminders, it provides seniors with more user-friendly reminders. For instance, it allows seniors to change the content of voice messages to their preferences and choose their notification method. This enables seniors to remember their daily schedules and improve their quality of life.

[0035] The operation assistance unit can provide voice instructions when senior citizens have difficulty touching the screen. The operation assistance unit recognizes voice commands from senior citizens using, for example, voice recognition technology and performs appropriate operations. The operation assistance unit can enable senior citizens to perform more operations by voice by, for example, increasing the types of voice commands. The operation assistance unit can enable senior citizens to perform operations more accurately by, for example, improving the accuracy of voice commands. This makes operation easier for senior citizens even when they have difficulty touching the screen, as they can receive voice instructions. Some or all of the above processing in the operation assistance unit may be performed using, for example, AI, or without AI. For example, the operation assistance unit can input voice commands from senior citizens into a generating AI and have the generating AI perform voice command recognition.

[0036] The recovery unit can automatically perform recovery in the event of an error. For example, the recovery unit automatically performs recovery when an error occurs. For example, the recovery unit simplifies the recovery procedure to make it easier for senior users to perform recovery. For example, the recovery unit expands the scope of recovery to allow senior users to operate with confidence. As a result, even if an error occurs, the system can be restored to its original state by automatically performing recovery. Some or all of the above-described processes in the recovery unit may be performed using AI, for example, or without AI. For example, the recovery unit can input data on the occurrence of an error into a generating AI and have the generating AI execute the recovery procedure.

[0037] The spam call handling unit can automatically answer spam calls and set call rejection settings. For example, the spam call handling unit can automatically answer spam calls and set call rejection settings. For example, the spam call handling unit can reduce the occurrence of spam calls by improving the spam call detection method. For example, the spam call handling unit can enable seniors to use the phone with peace of mind by diversifying the means of handling spam calls. This can protect seniors by automatically answering spam calls and setting call rejection settings. Some or all of the above processing in the spam call handling unit may be performed using AI, for example, or without AI. For example, the spam call handling unit can input spam call data into a generating AI and have the generating AI perform spam call detection and response.

[0038] The schedule management unit can proactively remind users of things like medication times, hospital appointments, and daycare schedules. For example, the schedule management unit proactively reminds users of things like medication times, hospital appointments, and daycare schedules. The schedule management unit can help seniors remember their appointments by adjusting the timing of reminders. For example, the schedule management unit can provide seniors with more user-friendly reminders by customizing the content of the reminders. This helps seniors remember their appointments. Some or all of the above processes in the schedule management unit may be performed using AI, or not. For example, the schedule management unit can input senior users' schedule data into a generating AI and have the generating AI adjust the timing and content of the reminders.

[0039] The Schedule Management Department can provide support for digital use in hobbies and support for daily exercise to maintain health. For example, the Schedule Management Department can provide support for digital use in hobbies. For example, the Schedule Management Department can provide support for daily exercise to maintain health. For example, the Schedule Management Department can customize the content of digital use support to make it more enjoyable for seniors. For example, the Schedule Management Department can customize the content of daily exercise support to make it easier for seniors to maintain their health. This allows the department to support seniors in digital use of hobbies and maintaining their health. Some or all of the above processing in the Schedule Management Department may be performed using AI, for example, or without AI. For example, the Schedule Management Department can input senior citizens' hobby and health data into a generating AI and have the generating AI execute the content of digital use support and daily exercise support.

[0040] The operation assistance unit can analyze the user's past operation history and select the optimal operation assistance method. For example, the operation assistance unit may prioritize suggesting operation methods that the user has frequently used in the past. For example, the operation assistance unit may focus assistance on areas where the user has made many errors based on the user's past operation history. For example, the operation assistance unit may analyze the user's operation patterns and suggest the most efficient operation assistance method. In this way, by analyzing the user's past operation history, the optimal operation assistance method can be provided. Some or all of the above processing in the operation assistance unit may be performed using AI, for example, or without AI. For example, the operation assistance unit can input the user's operation history data into a generating AI and have the generating AI select the optimal operation assistance method.

[0041] The operation assistance unit can customize the assistance content based on the user's current situation and environment during operation assistance. For example, if the user is out, the operation assistance unit provides concise and easily visible assistance content. For example, if the user is at home, the operation assistance unit provides assistance content that includes detailed explanations. For example, if the user is in a public place, the operation assistance unit provides text-based assistance instead of voice assistance. This allows for more appropriate operation assistance to be provided by customizing the assistance content based on the user's current situation and environment. Some or all of the above processing in the operation assistance unit may be performed using AI, for example, or without AI. For example, the operation assistance unit can input user situation and environment data into a generating AI and have the generating AI perform the customization of the assistance content.

[0042] The operation assistance unit can provide highly relevant assistance by considering the user's geographical location information during operation assistance. For example, if the user is in a specific location, the operation assistance unit can provide operation assistance related to that location. For example, if the user is traveling, the operation assistance unit can provide operation assistance related to travel. For example, if the user is at home, the operation assistance unit can provide assistance related to operations at home. In this way, by considering the user's geographical location information, highly relevant operation assistance can be provided. Some or all of the above processing in the operation assistance unit may be performed using AI, for example, or without using AI. For example, the operation assistance unit can input the user's geographical location information into a generating AI and cause the generating AI to perform the provision of highly relevant assistance content.

[0043] The operation assistance unit can analyze the user's social media activity and provide relevant assistance during operation assistance. For example, the operation assistance unit can provide operation assistance related to functions that the user frequently uses on social media. For example, the operation assistance unit can provide operation assistance related to topics of interest from the user's social media activity. For example, the operation assistance unit can analyze the user's social media activity and provide the most needed operation assistance. In this way, relevant operation assistance can be provided by analyzing the user's social media activity. Some or all of the above processing in the operation assistance unit may be performed using AI, for example, or without AI. For example, the operation assistance unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant assistance content.

[0044] The error prevention unit can analyze the user's past error history and select the optimal prevention method when preventing errors. For example, the error prevention unit may focus on preventing errors in areas where the user has frequently made errors in the past. For example, the error prevention unit may select a prevention method for a specific operation based on the user's past error history. For example, the error prevention unit may analyze the user's error patterns and propose the most effective prevention method. In this way, by analyzing the user's past error history, the optimal error prevention method can be provided. Some or all of the above processing in the error prevention unit may be performed using AI, for example, or without AI. For example, the error prevention unit may input the user's error history data into a generating AI and have the generating AI select the optimal prevention method.

[0045] The error prevention unit can customize the prevention measures based on the user's current situation when preventing an error. For example, if the user is out, the error prevention unit provides a concise and highly visible prevention measure. For example, if the user is at home, the error prevention unit provides a prevention measure that includes a detailed explanation. For example, if the user is in a public place, the error prevention unit provides a text-based prevention measure with minimal voice prevention. This allows for more appropriate error prevention by customizing the prevention measures based on the user's current situation. Some or all of the above processing in the error prevention unit may be performed using AI, for example, or without AI. For example, the error prevention unit can input user situation data into a generating AI and have the generating AI perform the customization of the prevention measures.

[0046] The error prevention unit can select the optimal prevention method by considering the user's geographical location information when preventing errors. For example, if the user is in a specific location, the error prevention unit provides error prevention related to that location. For example, if the user is traveling, the error prevention unit provides error prevention related to travel. For example, if the user is at home, the error prevention unit provides error prevention at home. In this way, by considering the user's geographical location information, the optimal error prevention method can be provided. Some or all of the above processing in the error prevention unit may be performed using AI, for example, or without AI. For example, the error prevention unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal prevention method.

[0047] The error prevention unit can analyze the user's social media activity and propose prevention measures when an error occurs. For example, the error prevention unit can provide error prevention related to functions that the user frequently uses on social media. For example, the error prevention unit can provide error prevention related to topics of interest from the user's social media activity. For example, the error prevention unit can analyze the user's social media activity and provide the most needed error prevention. In this way, by analyzing the user's social media activity, the optimal error prevention measures can be provided. Some or all of the above processing in the error prevention unit may be performed using AI, for example, or without AI. For example, the error prevention unit can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for prevention measures.

[0048] The recovery unit can analyze the user's past error history during recovery and select the optimal recovery method. For example, the recovery unit may focus on recovering areas where the user has frequently made errors in the past. For example, the recovery unit may select a recovery method for a specific operation based on the user's past error history. For example, the recovery unit may analyze the user's error patterns and propose the most effective recovery method. In this way, the recovery unit can provide the optimal recovery method by analyzing the user's past error history. Some or all of the above processes in the recovery unit may be performed using AI, for example, or without AI. For example, the recovery unit may input the user's error history data into a generating AI and have the generating AI select the optimal recovery method.

[0049] The recovery unit can customize the recovery means based on the user's current situation during recovery. For example, if the user is out, the recovery unit provides a concise and easily visible recovery means. For example, if the user is at home, the recovery unit provides a recovery means that includes detailed explanations. For example, if the user is in a public place, the recovery unit provides a text-based recovery means instead of a voice recovery. This allows for more appropriate recovery by customizing the recovery means based on the user's current situation. Some or all of the above processing in the recovery unit may be performed using AI, for example, or without AI. For example, the recovery unit can input user situation data into a generating AI and have the generating AI perform the customization of the recovery means.

[0050] The recovery unit can select the optimal recovery method during recovery, taking into account the user's geographical location information. For example, if the user is in a specific location, the recovery unit provides a recovery method related to that location. For example, if the user is traveling, the recovery unit provides a recovery method related to travel. For example, if the user is at home, the recovery unit provides a recovery method for home use. In this way, the optimal recovery method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the recovery unit may be performed using AI, for example, or without using AI. For example, the recovery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal recovery method.

[0051] The recovery unit can analyze the user's social media activity during recovery and propose recovery measures. For example, the recovery unit can provide recovery related to features the user frequently uses on social media. For example, the recovery unit can provide recovery related to topics of interest from the user's social media activity. For example, the recovery unit can analyze the user's social media activity and provide the most needed recovery. In this way, by analyzing the user's social media activity, the optimal recovery measures can be provided. Some or all of the above processing in the recovery unit may be performed using AI, for example, or without AI. For example, the recovery unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of recovery measures.

[0052] The spam call handling unit can analyze the user's past spam call history to select the optimal handling method when handling spam calls. For example, the spam call handling unit can automatically block similar numbers based on numbers the user has previously blocked. For example, the spam call handling unit can detect specific patterns from the user's past spam call history and select a handling method. For example, the spam call handling unit can analyze the user's spam call history and propose the most effective handling method. In this way, by analyzing the user's past spam call history, the optimal spam call handling method can be provided. Some or all of the above processing in the spam call handling unit may be performed using AI, for example, or without AI. For example, the spam call handling unit can input the user's spam call history data into a generating AI and have the generating AI select the optimal handling method.

[0053] The spam call handling unit can customize its response methods based on the user's current situation when handling spam calls. For example, if the user is out, the spam call handling unit can automatically block the spam call and notify the user later. For example, if the user is at home, the spam call handling unit can briefly notify the user of the spam call's content and suggest a response method. For example, if the user is in a public place, the spam call handling unit can automatically answer the spam call and notify the user later. This allows for more appropriate spam call handling by customizing the response methods based on the user's current situation. Some or all of the above processing in the spam call handling unit may be performed using AI, for example, or without AI. For example, the spam call handling unit can input user situation data into a generating AI and have the generating AI perform the customization of the response methods.

[0054] The spam call handling unit can select the optimal handling method when handling spam calls, taking into account the user's geographical location information. For example, if the user is in a specific location, the spam call handling unit will provide spam call handling related to that location. For example, if the user is traveling, the spam call handling unit will provide spam call handling related to the trip. For example, if the user is at home, the spam call handling unit will provide spam call handling at home. In this way, the optimal spam call handling method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the spam call handling unit may be performed using AI, for example, or without using AI. For example, the spam call handling unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal handling method.

[0055] The spam call handling unit can analyze the user's social media activity and propose handling measures when handling spam calls. For example, the spam call handling unit can provide spam call handling related to features the user frequently uses on social media. For example, the spam call handling unit can provide spam call handling related to topics of interest based on the user's social media activity. For example, the spam call handling unit can analyze the user's social media activity and provide the most necessary spam call handling measures. In this way, by analyzing the user's social media activity, the optimal spam call handling measures can be provided. Some or all of the above processing in the spam call handling unit may be performed using AI, for example, or without AI. For example, the spam call handling unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of handling measures.

[0056] The schedule management unit can analyze the user's past schedule history and select the optimal management method during schedule management. For example, the schedule management unit can focus notifications on appointments that the user has frequently forgotten in the past. For example, the schedule management unit can notify the user of important appointments in specific time slots based on the user's past schedule history. For example, the schedule management unit can analyze the user's schedule patterns and propose the most effective management method. In this way, by analyzing the user's past schedule history, the optimal schedule management method can be provided. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's schedule history data into a generating AI and have the generating AI select the optimal management method.

[0057] The schedule management unit can customize the management methods based on the user's current situation when managing schedules. For example, if the user is out, the schedule management unit provides a concise and highly visible schedule notification. For example, if the user is at home, the schedule management unit provides a schedule notification with a detailed explanation. For example, if the user is in a public place, the schedule management unit provides a text-based schedule notification instead of a voice notification. This allows for more appropriate schedule management by customizing the management methods based on the user's current situation. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input user situation data into a generating AI and have the generating AI perform the customization of the management methods.

[0058] The schedule management unit can select the optimal management method when managing schedules, taking into account the user's geographical location information. For example, if the user is in a specific location, the schedule management unit provides schedule notifications related to that location. For example, if the user is traveling, the schedule management unit provides schedule notifications related to the trip. For example, if the user is at home, the schedule management unit provides schedule notifications for home. In this way, the optimal schedule management method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without using AI. For example, the schedule management unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0059] The schedule management unit can analyze the user's social media activity and propose management methods when managing schedules. For example, the schedule management unit can provide schedule notifications related to features that the user frequently uses on social media. For example, the schedule management unit can provide schedule notifications related to topics of interest based on the user's social media activity. For example, the schedule management unit can analyze the user's social media activity and provide the most needed schedule notifications. In this way, by analyzing the user's social media activity, the optimal schedule management method can be provided. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of management methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The operation assistance unit can analyze the user's operation history and provide the optimal operation assistance method. For example, it can prioritize suggesting operation methods that the user has frequently used in the past. It can also analyze the user's operation patterns and suggest the most efficient operation assistance method. Furthermore, it can focus assistance on areas where the user has made many errors based on their operation history. In this way, by analyzing the user's past operation history, the optimal operation assistance method can be provided.

[0062] The spam call handling unit can analyze a user's past spam call history and select the most appropriate response method. For example, it can automatically block similar numbers based on numbers the user has previously blocked. It can also detect specific patterns from a user's past spam call history and select a response method. Furthermore, it can analyze a user's spam call history and propose the most effective response method. In this way, by analyzing a user's past spam call history, it can provide the most optimal spam call handling method.

[0063] The operation assistance unit can customize the assistance content based on the user's current situation and environment. For example, if the user is out, it can provide concise and highly visible assistance. If the user is at home, it can provide assistance that includes detailed explanations. Furthermore, if the user is in a public place, it can reduce the voice assistance and provide text-based assistance. In this way, by customizing the assistance content based on the user's current situation and environment, it can provide more appropriate operation assistance.

[0064] The recovery unit can analyze the user's past error history during recovery and select the optimal recovery method. For example, it can focus recovery on areas where the user frequently made errors in the past. It can also select a recovery method for specific operations based on the user's past error history. Furthermore, it can analyze the user's error patterns and propose the most effective recovery method. In this way, by analyzing the user's past error history, the optimal recovery method can be provided.

[0065] The schedule management unit can analyze a user's past schedule history to select the optimal management method. For example, it can prioritize notifications for appointments the user frequently forgot in the past. It can also notify users of important appointments at specific time slots based on their past schedule history. Furthermore, it can analyze the user's schedule patterns and propose the most effective management method. In this way, by analyzing the user's past schedule history, it can provide the optimal schedule management method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The operation assistance unit provides voice-based operation assistance. For example, if a senior citizen has difficulty touching the screen, it will give voice instructions. The operation assistance unit uses voice recognition technology to recognize the senior citizen's voice commands and perform the appropriate operation. By increasing the types of voice commands, senior citizens will be able to perform more operations by voice. Step 2: The error prevention unit prevents errors through the operation assistance unit. For example, it issues a warning if a senior citizen makes an error. By improving the error detection method, the occurrence of errors will be reduced. By diversifying the means of preventing errors, senior citizens will be able to operate the device with peace of mind. Step 3: The recovery unit performs recovery when an error occurs due to the error prevention unit. For example, it automatically performs recovery when an error occurs. By simplifying the recovery procedure, senior users can easily perform recovery. By expanding the scope of recovery, senior users can operate the device with peace of mind. Step 4: The spam call handling unit provides alternative responses to spam calls. For example, it automatically answers spam calls and sets them to be blocked. By improving the spam call detection method, the occurrence of spam calls will be reduced. By diversifying the means of handling spam calls, senior citizens will be able to use their phones with peace of mind. Step 5: The schedule management department manages daily schedules and reminders. For example, it proactively reminds users of medication times, hospital appointments, and daycare schedules. By adjusting the timing of reminders, seniors can ensure they don't forget their appointments. By customizing the content of reminders, the system provides reminders that are easier for seniors to use.

[0068] (Example of form 2) The AI ​​agent service according to an embodiment of the present invention is a system for promoting the adoption of smartphones among senior citizens and solving difficulties in their operation. This system aims to improve the quality of life (QOL) of senior citizens by solving difficulties in operating smartphones and tablets. For example, to solve difficulties in operating smartphones and tablets for senior citizens, it provides a voice operation assistance function. For example, if a senior citizen has difficulty touching the screen, they can prevent accidental operation by giving voice instructions. Furthermore, if an accidental operation occurs, the AI ​​agent can automatically recover and return to the original state. Next, it provides a function for the AI ​​agent to provide alternative responses to spam calls. For example, if a senior citizen answers a spam call, the AI ​​agent can automatically answer and set up call rejection, protecting the senior citizen. In addition, it provides daily schedule management and reminder functions. For example, by proactively reminding seniors of medication times, hospital appointments, and daycare schedules, they can avoid forgetting their appointments. It also provides multi-functional support such as assistance with digital use for hobbies and daily exercise support for maintaining health. This AI agent aims not only to assist seniors in their daily lives but also to prevent social isolation and enrich their digital lives. For example, by conversing with seniors, the AI ​​agent can perform frailty checks (physical, mental, psychological, and social), supporting early detection and prevention. This improves the quality of life (QOL) of seniors, enabling them to lead more fulfilling lives. In this way, the AI ​​agent service can solve the difficulties seniors face when using smartphones and tablets, thereby improving their QOL.

[0069] The AI ​​agent service according to the embodiment comprises an operation assistance unit, an error prevention unit, a recovery unit, a spam call handling unit, and a schedule management unit. The operation assistance unit provides voice-based operation assistance. For example, the operation assistance unit gives voice instructions when it is difficult for a senior citizen to touch the screen. For example, the operation assistance unit uses voice recognition technology to recognize voice commands from a senior citizen and perform appropriate operations. For example, the operation assistance unit increases the types of voice commands so that senior citizens can perform more operations by voice. The error prevention unit prevents errors caused by the operation assistance unit. For example, the error prevention unit issues a warning if a senior citizen makes an error. For example, the error prevention unit reduces the occurrence of errors by improving the error detection method. For example, the error prevention unit diversifies the means of preventing errors so that senior citizens can operate with peace of mind. The recovery unit performs recovery when an error occurs due to the error prevention unit. For example, the recovery unit automatically performs recovery when an error occurs. The recovery unit makes it easier for seniors to perform recovery by, for example, simplifying the recovery procedure. The recovery unit makes it easier for seniors to operate by, for example, expanding the scope of recovery. The spam call handling unit provides alternative answers to spam calls. The spam call handling unit automatically answers spam calls and sets call rejection settings. The spam call handling unit reduces the occurrence of spam calls by, for example, improving the spam call detection method. The spam call handling unit makes it easier for seniors to use the phone by, for example, diversifying the means of handling spam calls. The schedule management unit manages daily schedules and reminders. The schedule management unit proactively reminds seniors of things like medication times, hospital appointments, and daycare schedules. The schedule management unit makes it easier for seniors to remember appointments by, for example, adjusting the timing of reminders. The schedule management unit makes it easier for seniors to use reminders by, for example, customizing the content of reminders.As a result, the AI ​​agent service according to this embodiment can solve the difficulties senior citizens face when operating smartphones and tablets, thereby improving their quality of life (QOL).

[0070] The operation assistance unit provides voice-based operation assistance. Specifically, it gives voice instructions when senior users have difficulty touching the screen. The operation assistance unit uses voice recognition technology to recognize voice commands from senior users and perform appropriate operations. For example, if a senior user says, "Make a phone call," the operation assistance unit recognizes the voice command, opens the phone app, and calls the specified contact. If they say, "Send a message," it opens the messaging app, converts the voice input into text, and sends it. The operation assistance unit will increase the types of voice commands so that senior users can perform more operations by voice. For example, it will enable voice control of functions used daily, such as launching apps, changing settings, searching the internet, and playing music. Furthermore, the operation assistance unit learns the characteristics and speaking habits of senior users to improve the accuracy of voice recognition. This allows senior users to perform voice operations without stress. The operation assistance unit also has a voice feedback function that notifies users by voice when an operation is completed. For example, it provides feedback such as "You have made a phone call" or "You have sent a message" to give senior users a sense of security. This allows the operation assistance unit to make smartphones and tablets easier for seniors to use and reduce the burden of operation.

[0071] The error prevention unit prevents errors through the operation assistance unit. Specifically, it issues warnings when senior users make mistakes. For example, if a user attempts to change an important setting or accidentally delete an app, it displays a confirmation message such as "Are you sure you want to perform this operation?" to prevent errors. The error prevention unit also reduces the occurrence of errors by improving the error detection method. For example, it analyzes the operation history and detects unusual operation patterns to detect the possibility of errors early. Furthermore, the error prevention unit diversifies the means of preventing errors to ensure that senior users can operate the device with confidence. For example, in addition to voice confirmation messages, it displays large buttons on the screen to visually prompt confirmation. It also combines vibration and voice feedback to allow for multi-sensory confirmation of operations. In this way, the error prevention unit minimizes the risk of senior users making mistakes and provides an environment where they can use the device with peace of mind.

[0072] The recovery unit performs recovery when an error occurs due to the error prevention unit. Specifically, it automatically performs recovery when an error occurs. For example, if an app is accidentally deleted, the recovery unit will automatically reinstall the app and restore it to its original state. Also, if settings are accidentally changed, the recovery unit provides a function to restore the original settings. The recovery unit simplifies the recovery procedure to make it easy for seniors to perform recovery. For example, it makes the recovery operation a one-touch operation, eliminating complex procedures. Furthermore, the recovery unit expands the scope of recovery to allow seniors to operate the device with peace of mind. For example, it regularly backs up app settings and data so that they can be quickly restored in the event of an error. In this way, the recovery unit can quickly and reliably restore the device to its original state even if a senior makes an error, providing an environment in which seniors can use the device with peace of mind.

[0073] The spam call handling unit provides alternative responses to spam calls. Specifically, it automatically answers spam calls and sets them to be blocked. For example, when a spam call comes in, the spam call handling unit automatically answers and plays a recorded message to inform the caller of the intention to block the call. The spam call handling unit also reduces the occurrence of spam calls by improving its spam call detection methods. For example, it learns spam call patterns and automatically blocks calls from specific numbers or callers. Furthermore, the spam call handling unit diversifies its spam call handling methods to enable seniors to use the phone with peace of mind. For example, if a call is suspected to be a spam call, it issues a warning message so that seniors can check before answering. It also records the history of spam calls and makes it possible to review it later, making it easier for seniors to take measures against spam calls. In this way, the spam call handling unit provides an environment where seniors can use the phone with peace of mind without being troubled by spam calls.

[0074] The Schedule Management Department manages daily schedules and reminders. Specifically, it proactively reminds seniors of medication times, hospital appointments, and daycare schedules. For example, as medication time approaches, the Schedule Management Department sends voice and notification reminders to ensure seniors don't forget to take their medication. Similarly, for hospital appointments, it sends a reminder the day before to prompt seniors to confirm their schedule for the day. By adjusting the timing of reminders, the Schedule Management Department helps seniors remember their appointments. For example, it sends multiple reminders before important appointments to ensure they fully understand their schedule. Furthermore, by customizing the content of reminders, it provides seniors with more user-friendly reminders. For instance, it allows seniors to change the content of voice messages to their preferences and choose their notification method. This enables seniors to remember their daily schedules and improve their quality of life.

[0075] The operation assistance unit can provide voice instructions when senior citizens have difficulty touching the screen. The operation assistance unit recognizes voice commands from senior citizens using, for example, voice recognition technology and performs appropriate operations. The operation assistance unit can enable senior citizens to perform more operations by voice by, for example, increasing the types of voice commands. The operation assistance unit can enable senior citizens to perform operations more accurately by, for example, improving the accuracy of voice commands. This makes operation easier for senior citizens even when they have difficulty touching the screen, as they can receive voice instructions. Some or all of the above processing in the operation assistance unit may be performed using, for example, AI, or without AI. For example, the operation assistance unit can input voice commands from senior citizens into a generating AI and have the generating AI perform voice command recognition.

[0076] The recovery unit can automatically perform recovery in the event of an error. For example, the recovery unit automatically performs recovery when an error occurs. For example, the recovery unit simplifies the recovery procedure to make it easier for senior users to perform recovery. For example, the recovery unit expands the scope of recovery to allow senior users to operate with confidence. As a result, even if an error occurs, the system can be restored to its original state by automatically performing recovery. Some or all of the above-described processes in the recovery unit may be performed using AI, for example, or without AI. For example, the recovery unit can input data on the occurrence of an error into a generating AI and have the generating AI execute the recovery procedure.

[0077] The spam call handling unit can automatically answer spam calls and set call rejection settings. For example, the spam call handling unit can automatically answer spam calls and set call rejection settings. For example, the spam call handling unit can reduce the occurrence of spam calls by improving the spam call detection method. For example, the spam call handling unit can enable seniors to use the phone with peace of mind by diversifying the means of handling spam calls. This can protect seniors by automatically answering spam calls and setting call rejection settings. Some or all of the above processing in the spam call handling unit may be performed using AI, for example, or without AI. For example, the spam call handling unit can input spam call data into a generating AI and have the generating AI perform spam call detection and response.

[0078] The schedule management unit can proactively remind users of things like medication times, hospital appointments, and daycare schedules. For example, the schedule management unit proactively reminds users of things like medication times, hospital appointments, and daycare schedules. The schedule management unit can help seniors remember their appointments by adjusting the timing of reminders. For example, the schedule management unit can provide seniors with more user-friendly reminders by customizing the content of the reminders. This helps seniors remember their appointments. Some or all of the above processes in the schedule management unit may be performed using AI, or not. For example, the schedule management unit can input senior users' schedule data into a generating AI and have the generating AI adjust the timing and content of the reminders.

[0079] The Schedule Management Department can provide support for digital use in hobbies and support for daily exercise to maintain health. For example, the Schedule Management Department can provide support for digital use in hobbies. For example, the Schedule Management Department can provide support for daily exercise to maintain health. For example, the Schedule Management Department can customize the content of digital use support to make it more enjoyable for seniors. For example, the Schedule Management Department can customize the content of daily exercise support to make it easier for seniors to maintain their health. This allows the department to support seniors in digital use of hobbies and maintaining their health. Some or all of the above processing in the Schedule Management Department may be performed using AI, for example, or without AI. For example, the Schedule Management Department can input senior citizens' hobby and health data into a generating AI and have the generating AI execute the content of digital use support and daily exercise support.

[0080] The Schedule Management Department can support early detection and prevention of frailty by having an AI agent converse with senior citizens. For example, the Schedule Management Department can perform physical frailty checks by having an AI agent converse with senior citizens. For example, the Schedule Management Department can perform mental and psychological frailty checks by having an AI agent converse with senior citizens. For example, the Schedule Management Department can perform social frailty checks by having an AI agent converse with senior citizens. This enables frailty checks for senior citizens and supports early detection and prevention. The frailty check is implemented using emotion estimation functions, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Schedule Management Department may be performed using AI, for example, or without AI. For example, the Schedule Management Department can input conversation data with senior citizens into the generative AI and have the generative AI perform the frailty check.

[0081] The operation assistance unit can estimate the user's emotions and adjust the timing of operation assistance based on the estimated emotions. For example, if the user is stressed, the operation assistance unit reduces the frequency of operation assistance and provides assistance only when needed. For example, if the user is relaxed, the operation assistance unit increases the frequency of operation assistance and provides detailed explanations. For example, if the user is in a hurry, the operation assistance unit provides operation assistance quickly to complete the operation in a short time. In this way, by adjusting the timing of operation assistance according to the user's emotions, more appropriate operation assistance can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the operation assistance unit may be performed using AI, for example, or without AI. For example, the operation assistance unit can input user emotion data into a generative AI and have the generative AI adjust the timing of operation assistance.

[0082] The operation assistance unit can analyze the user's past operation history and select the optimal operation assistance method. For example, the operation assistance unit may prioritize suggesting operation methods that the user has frequently used in the past. For example, the operation assistance unit may focus assistance on areas where the user has made many errors based on the user's past operation history. For example, the operation assistance unit may analyze the user's operation patterns and suggest the most efficient operation assistance method. In this way, by analyzing the user's past operation history, the optimal operation assistance method can be provided. Some or all of the above processing in the operation assistance unit may be performed using AI, for example, or without AI. For example, the operation assistance unit can input the user's operation history data into a generating AI and have the generating AI select the optimal operation assistance method.

[0083] The operation assistance unit can customize the assistance content based on the user's current situation and environment during operation assistance. For example, if the user is out, the operation assistance unit provides concise and easily visible assistance content. For example, if the user is at home, the operation assistance unit provides assistance content that includes detailed explanations. For example, if the user is in a public place, the operation assistance unit provides text-based assistance instead of voice assistance. This allows for more appropriate operation assistance to be provided by customizing the assistance content based on the user's current situation and environment. Some or all of the above processing in the operation assistance unit may be performed using AI, for example, or without AI. For example, the operation assistance unit can input user situation and environment data into a generating AI and have the generating AI perform the customization of the assistance content.

[0084] The operation assistance unit can estimate the user's emotions and determine the priority of operation assistance based on the estimated user emotions. For example, if the user is stressed, the operation assistance unit will prioritize providing important operation assistance. For example, if the user is relaxed, the operation assistance unit will provide all operation assistance equally. For example, if the user is in a hurry, the operation assistance unit will quickly provide the most important operation assistance. In this way, by determining the priority of operation assistance according to the user's emotions, more appropriate operation assistance can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the operation assistance unit may be performed using AI, for example, or without AI. For example, the operation assistance unit can input user emotion data into a generative AI and have the generative AI perform the determination of operation assistance priorities.

[0085] The operation assistance unit can provide highly relevant assistance by considering the user's geographical location information during operation assistance. For example, if the user is in a specific location, the operation assistance unit can provide operation assistance related to that location. For example, if the user is traveling, the operation assistance unit can provide operation assistance related to travel. For example, if the user is at home, the operation assistance unit can provide assistance related to operations at home. In this way, by considering the user's geographical location information, highly relevant operation assistance can be provided. Some or all of the above processing in the operation assistance unit may be performed using AI, for example, or without using AI. For example, the operation assistance unit can input the user's geographical location information into a generating AI and cause the generating AI to perform the provision of highly relevant assistance content.

[0086] The operation assistance unit can analyze the user's social media activity and provide relevant assistance during operation assistance. For example, the operation assistance unit can provide operation assistance related to functions that the user frequently uses on social media. For example, the operation assistance unit can provide operation assistance related to topics of interest from the user's social media activity. For example, the operation assistance unit can analyze the user's social media activity and provide the most needed operation assistance. In this way, relevant operation assistance can be provided by analyzing the user's social media activity. Some or all of the above processing in the operation assistance unit may be performed using AI, for example, or without AI. For example, the operation assistance unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant assistance content.

[0087] The error prevention unit can estimate the user's emotions and adjust the error prevention method based on the estimated user emotions. For example, if the user is stressed, the error prevention unit increases the frequency of error prevention to draw attention. For example, if the user is relaxed, the error prevention unit decreases the frequency of error prevention to encourage natural operation. For example, if the user is in a hurry, the error prevention unit quickly prevents errors to make the operation smoother. In this way, by adjusting the error prevention method according to the user's emotions, more appropriate error prevention can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the error prevention unit may be performed using AI, for example, or without AI. For example, the error prevention unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the error prevention method.

[0088] The error prevention unit can analyze the user's past error history and select the optimal prevention method when preventing errors. For example, the error prevention unit may focus on preventing errors in areas where the user has frequently made errors in the past. For example, the error prevention unit may select a prevention method for a specific operation based on the user's past error history. For example, the error prevention unit may analyze the user's error patterns and propose the most effective prevention method. In this way, by analyzing the user's past error history, the optimal error prevention method can be provided. Some or all of the above processing in the error prevention unit may be performed using AI, for example, or without AI. For example, the error prevention unit may input the user's error history data into a generating AI and have the generating AI select the optimal prevention method.

[0089] The error prevention unit can customize the prevention measures based on the user's current situation when preventing an error. For example, if the user is out, the error prevention unit provides a concise and highly visible prevention measure. For example, if the user is at home, the error prevention unit provides a prevention measure that includes a detailed explanation. For example, if the user is in a public place, the error prevention unit provides a text-based prevention measure with minimal voice prevention. This allows for more appropriate error prevention by customizing the prevention measures based on the user's current situation. Some or all of the above processing in the error prevention unit may be performed using AI, for example, or without AI. For example, the error prevention unit can input user situation data into a generating AI and have the generating AI perform the customization of the prevention measures.

[0090] The error prevention unit can estimate the user's emotions and determine the priority of error prevention based on the estimated user emotions. For example, if the user is stressed, the error prevention unit will prioritize providing important error prevention. For example, if the user is relaxed, the error prevention unit will provide all error prevention equally. For example, if the user is in a hurry, the error prevention unit will quickly provide the most important error prevention. In this way, by determining the priority of error prevention according to the user's emotions, more appropriate error prevention can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the error prevention unit may be performed using AI, for example, or without AI. For example, the error prevention unit can input user emotion data into a generative AI and have the generative AI perform the determination of error prevention priorities.

[0091] The error prevention unit can select the optimal prevention method by considering the user's geographical location information when preventing errors. For example, if the user is in a specific location, the error prevention unit provides error prevention related to that location. For example, if the user is traveling, the error prevention unit provides error prevention related to travel. For example, if the user is at home, the error prevention unit provides error prevention at home. In this way, by considering the user's geographical location information, the optimal error prevention method can be provided. Some or all of the above processing in the error prevention unit may be performed using AI, for example, or without AI. For example, the error prevention unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal prevention method.

[0092] The error prevention unit can analyze the user's social media activity and propose prevention measures when an error occurs. For example, the error prevention unit can provide error prevention related to functions that the user frequently uses on social media. For example, the error prevention unit can provide error prevention related to topics of interest from the user's social media activity. For example, the error prevention unit can analyze the user's social media activity and provide the most needed error prevention. In this way, by analyzing the user's social media activity, the optimal error prevention measures can be provided. Some or all of the above processing in the error prevention unit may be performed using AI, for example, or without AI. For example, the error prevention unit can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for prevention measures.

[0093] The recovery unit can estimate the user's emotions and adjust the recovery method based on the estimated emotions. For example, if the user is stressed, the recovery unit can quickly perform a recovery and revert the operation. For example, if the user is relaxed, the recovery unit can provide a recovery method that includes a detailed explanation. For example, if the user is in a hurry, the recovery unit can quickly provide the most important recovery. This allows for more appropriate recovery by adjusting the recovery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recovery unit may be performed using AI or not using AI. For example, the recovery unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the recovery method.

[0094] The recovery unit can analyze the user's past error history during recovery and select the optimal recovery method. For example, the recovery unit may focus on recovering areas where the user has frequently made errors in the past. For example, the recovery unit may select a recovery method for a specific operation based on the user's past error history. For example, the recovery unit may analyze the user's error patterns and propose the most effective recovery method. In this way, the recovery unit can provide the optimal recovery method by analyzing the user's past error history. Some or all of the above processes in the recovery unit may be performed using AI, for example, or without AI. For example, the recovery unit may input the user's error history data into a generating AI and have the generating AI select the optimal recovery method.

[0095] The recovery unit can customize the recovery means based on the user's current situation during recovery. For example, if the user is out, the recovery unit provides a concise and easily visible recovery means. For example, if the user is at home, the recovery unit provides a recovery means that includes detailed explanations. For example, if the user is in a public place, the recovery unit provides a text-based recovery means instead of a voice recovery. This allows for more appropriate recovery by customizing the recovery means based on the user's current situation. Some or all of the above processing in the recovery unit may be performed using AI, for example, or without AI. For example, the recovery unit can input user situation data into a generating AI and have the generating AI perform the customization of the recovery means.

[0096] The recovery unit can estimate the user's emotions and determine the priority of recovery based on the estimated emotions. For example, if the user is stressed, the recovery unit will prioritize providing important recovery. If the user is relaxed, the recovery unit will provide all recovery equally. If the user is in a hurry, the recovery unit will quickly provide the most important recovery. This allows for more appropriate recovery by determining the priority of recovery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recovery unit may be performed using AI or not. For example, the recovery unit can input user emotion data into a generative AI and have the generative AI determine the priority of recovery.

[0097] The recovery unit can select the optimal recovery method during recovery, taking into account the user's geographical location information. For example, if the user is in a specific location, the recovery unit provides a recovery method related to that location. For example, if the user is traveling, the recovery unit provides a recovery method related to travel. For example, if the user is at home, the recovery unit provides a recovery method for home use. In this way, the optimal recovery method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the recovery unit may be performed using AI, for example, or without using AI. For example, the recovery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal recovery method.

[0098] The recovery unit can analyze the user's social media activity during recovery and propose recovery measures. For example, the recovery unit can provide recovery related to features the user frequently uses on social media. For example, the recovery unit can provide recovery related to topics of interest from the user's social media activity. For example, the recovery unit can analyze the user's social media activity and provide the most needed recovery. In this way, by analyzing the user's social media activity, the optimal recovery measures can be provided. Some or all of the above processing in the recovery unit may be performed using AI, for example, or without AI. For example, the recovery unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of recovery measures.

[0099] The spam call handling unit can estimate the user's emotions and adjust its spam call handling method based on the estimated emotions. For example, if the user is stressed, the spam call handling unit can immediately block the spam call and refrain from notifying the user. For example, if the user is relaxed, the spam call handling unit can briefly notify the user of the spam call's content and suggest a course of action. For example, if the user is in a hurry, the spam call handling unit can automatically answer the spam call and notify the user later. This allows for more appropriate spam call handling by adjusting the method of handling spam calls according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the spam call handling unit may be performed using AI or not. For example, the spam call handling unit can input user emotion data into a generative AI and have the generative AI adjust the spam call handling method.

[0100] The spam call handling unit can analyze the user's past spam call history to select the optimal handling method when handling spam calls. For example, the spam call handling unit can automatically block similar numbers based on numbers the user has previously blocked. For example, the spam call handling unit can detect specific patterns from the user's past spam call history and select a handling method. For example, the spam call handling unit can analyze the user's spam call history and propose the most effective handling method. In this way, by analyzing the user's past spam call history, the optimal spam call handling method can be provided. Some or all of the above processing in the spam call handling unit may be performed using AI, for example, or without AI. For example, the spam call handling unit can input the user's spam call history data into a generating AI and have the generating AI select the optimal handling method.

[0101] The spam call handling unit can customize its response methods based on the user's current situation when handling spam calls. For example, if the user is out, the spam call handling unit can automatically block the spam call and notify the user later. For example, if the user is at home, the spam call handling unit can briefly notify the user of the spam call's content and suggest a response method. For example, if the user is in a public place, the spam call handling unit can automatically answer the spam call and notify the user later. This allows for more appropriate spam call handling by customizing the response methods based on the user's current situation. Some or all of the above processing in the spam call handling unit may be performed using AI, for example, or without AI. For example, the spam call handling unit can input user situation data into a generating AI and have the generating AI perform the customization of the response methods.

[0102] The spam call handling unit can estimate the user's emotions and determine the priority of spam call handling based on the estimated emotions. For example, if the user is stressed, the spam call handling unit will prioritize important spam calls. For example, if the user is relaxed, the spam call handling unit will distribute all spam calls equally. For example, if the user is in a hurry, the spam call handling unit will quickly provide the most important spam calls. This allows for more appropriate spam call handling by determining the priority of spam call handling according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the spam call handling unit may be performed using AI or not using AI. For example, the spam call handling unit can input user emotion data into a generative AI and have the generative AI determine the priority of spam call handling.

[0103] The spam call handling unit can select the optimal handling method when handling spam calls, taking into account the user's geographical location information. For example, if the user is in a specific location, the spam call handling unit will provide spam call handling related to that location. For example, if the user is traveling, the spam call handling unit will provide spam call handling related to the trip. For example, if the user is at home, the spam call handling unit will provide spam call handling at home. In this way, the optimal spam call handling method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the spam call handling unit may be performed using AI, for example, or without using AI. For example, the spam call handling unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal handling method.

[0104] The spam call handling unit can analyze the user's social media activity and propose handling measures when handling spam calls. For example, the spam call handling unit can provide spam call handling related to features the user frequently uses on social media. For example, the spam call handling unit can provide spam call handling related to topics of interest based on the user's social media activity. For example, the spam call handling unit can analyze the user's social media activity and provide the most necessary spam call handling measures. In this way, by analyzing the user's social media activity, the optimal spam call handling measures can be provided. Some or all of the above processing in the spam call handling unit may be performed using AI, for example, or without AI. For example, the spam call handling unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of handling measures.

[0105] The schedule management unit can estimate the user's emotions and adjust the schedule management method based on the estimated emotions. For example, if the user is stressed, the schedule management unit will only notify the user of important appointments and refrain from notifying them of others. For example, if the user is relaxed, the schedule management unit will notify the user of all appointments in detail. For example, if the user is in a hurry, the schedule management unit will quickly notify the user of the most important appointments. In this way, by adjusting the schedule management method according to the user's emotions, more appropriate schedule management can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or not using AI. For example, the schedule management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the schedule management method.

[0106] The schedule management unit can analyze the user's past schedule history and select the optimal management method during schedule management. For example, the schedule management unit can focus notifications on appointments that the user has frequently forgotten in the past. For example, the schedule management unit can notify the user of important appointments in specific time slots based on the user's past schedule history. For example, the schedule management unit can analyze the user's schedule patterns and propose the most effective management method. In this way, by analyzing the user's past schedule history, the optimal schedule management method can be provided. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's schedule history data into a generating AI and have the generating AI select the optimal management method.

[0107] The schedule management unit can customize the management methods based on the user's current situation when managing schedules. For example, if the user is out, the schedule management unit provides a concise and highly visible schedule notification. For example, if the user is at home, the schedule management unit provides a schedule notification with a detailed explanation. For example, if the user is in a public place, the schedule management unit provides a text-based schedule notification instead of a voice notification. This allows for more appropriate schedule management by customizing the management methods based on the user's current situation. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input user situation data into a generating AI and have the generating AI perform the customization of the management methods.

[0108] The schedule management unit can estimate the user's emotions and determine schedule management priorities based on the estimated emotions. For example, if the user is stressed, the schedule management unit will prioritize notifying the user of important schedules. For example, if the user is relaxed, the schedule management unit will notify the user of all schedules equally. For example, if the user is in a hurry, the schedule management unit will quickly notify the user of the most important schedules. This allows for more appropriate schedule management by determining schedule management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input user emotion data into a generative AI and have the generative AI determine the schedule management priorities.

[0109] The schedule management unit can select the optimal management method when managing schedules, taking into account the user's geographical location information. For example, if the user is in a specific location, the schedule management unit provides schedule notifications related to that location. For example, if the user is traveling, the schedule management unit provides schedule notifications related to the trip. For example, if the user is at home, the schedule management unit provides schedule notifications for home. In this way, the optimal schedule management method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without using AI. For example, the schedule management unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0110] The schedule management unit can analyze the user's social media activity and propose management methods when managing schedules. For example, the schedule management unit can provide schedule notifications related to features that the user frequently uses on social media. For example, the schedule management unit can provide schedule notifications related to topics of interest based on the user's social media activity. For example, the schedule management unit can analyze the user's social media activity and provide the most needed schedule notifications. In this way, by analyzing the user's social media activity, the optimal schedule management method can be provided. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of management methods.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The operation assistance unit can analyze the user's operation history and provide the optimal operation assistance method. For example, it can prioritize suggesting operation methods that the user has frequently used in the past. It can also analyze the user's operation patterns and suggest the most efficient operation assistance method. Furthermore, it can focus assistance on areas where the user has made many errors based on their operation history. In this way, by analyzing the user's past operation history, the optimal operation assistance method can be provided.

[0113] The recovery unit can estimate the user's emotions and adjust the recovery method based on those emotions. For example, if the user is stressed, it can quickly perform a recovery and revert the operation. If the user is relaxed, it can provide a recovery method that includes detailed explanations. Furthermore, if the user is in a hurry, it can quickly provide the most important recovery steps. In this way, by adjusting the recovery method according to the user's emotions, a more appropriate recovery can be provided.

[0114] The spam call handling unit can analyze a user's past spam call history and select the most appropriate response method. For example, it can automatically block similar numbers based on numbers the user has previously blocked. It can also detect specific patterns from a user's past spam call history and select a response method. Furthermore, it can analyze a user's spam call history and propose the most effective response method. In this way, by analyzing a user's past spam call history, it can provide the most optimal spam call handling method.

[0115] The scheduling unit can estimate the user's emotions and adjust the scheduling method based on those emotions. For example, if the user is stressed, it can notify them only of important appointments and refrain from notifying them of others. If the user is relaxed, it can notify them of all appointments in detail. Furthermore, if the user is in a hurry, it can quickly notify them of the most important appointments. In this way, by adjusting the scheduling method according to the user's emotions, it can provide more appropriate scheduling management.

[0116] The operation assistance unit can customize the assistance content based on the user's current situation and environment. For example, if the user is out, it can provide concise and highly visible assistance. If the user is at home, it can provide assistance that includes detailed explanations. Furthermore, if the user is in a public place, it can reduce the voice assistance and provide text-based assistance. In this way, by customizing the assistance content based on the user's current situation and environment, it can provide more appropriate operation assistance.

[0117] The error prevention unit can estimate the user's emotions and adjust the error prevention method based on those emotions. For example, if the user is stressed, the frequency of error prevention can be increased to draw attention. Conversely, if the user is relaxed, the frequency of error prevention can be decreased to encourage natural operation. Furthermore, if the user is in a hurry, error prevention can be performed quickly to ensure smooth operation. In this way, by adjusting the error prevention method according to the user's emotions, more appropriate error prevention can be provided.

[0118] The recovery unit can analyze the user's past error history during recovery and select the optimal recovery method. For example, it can focus recovery on areas where the user frequently made errors in the past. It can also select a recovery method for specific operations based on the user's past error history. Furthermore, it can analyze the user's error patterns and propose the most effective recovery method. In this way, by analyzing the user's past error history, the optimal recovery method can be provided.

[0119] The spam call handling unit can estimate the user's emotions and adjust its spam call handling method based on those emotions. For example, if the user is stressed, it can immediately block the spam call and refrain from sending notifications. If the user is relaxed, it can briefly inform them of the spam call's content and suggest how to handle it. Furthermore, if the user is in a hurry, it can automatically answer the spam call and notify them later. In this way, by adjusting the spam call handling method according to the user's emotions, it can provide more appropriate spam call handling.

[0120] The schedule management unit can analyze a user's past schedule history to select the optimal management method. For example, it can prioritize notifications for appointments the user frequently forgot in the past. It can also notify users of important appointments at specific time slots based on their past schedule history. Furthermore, it can analyze the user's schedule patterns and propose the most effective management method. In this way, by analyzing the user's past schedule history, it can provide the optimal schedule management method.

[0121] The operation assistance unit can estimate the user's emotions and determine the priority of operation assistance based on those emotions. For example, if the user is stressed, important operation assistance can be provided preferentially. If the user is relaxed, all operation assistance can be provided equally. Furthermore, if the user is in a hurry, the most important operation assistance can be provided quickly. In this way, by determining the priority of operation assistance according to the user's emotions, more appropriate operation assistance can be provided.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The operation assistance unit provides voice-based operation assistance. For example, if a senior citizen has difficulty touching the screen, it will give voice instructions. The operation assistance unit uses voice recognition technology to recognize the senior citizen's voice commands and perform the appropriate operation. By increasing the types of voice commands, senior citizens will be able to perform more operations by voice. Step 2: The error prevention unit prevents errors through the operation assistance unit. For example, it issues a warning if a senior citizen makes an error. By improving the error detection method, the occurrence of errors will be reduced. By diversifying the means of preventing errors, senior citizens will be able to operate the device with peace of mind. Step 3: The recovery unit performs recovery when an error occurs due to the error prevention unit. For example, it automatically performs recovery when an error occurs. By simplifying the recovery procedure, senior users can easily perform recovery. By expanding the scope of recovery, senior users can operate the device with peace of mind. Step 4: The spam call handling unit provides alternative responses to spam calls. For example, it automatically answers spam calls and sets them to be blocked. By improving the spam call detection method, the occurrence of spam calls will be reduced. By diversifying the means of handling spam calls, senior citizens will be able to use their phones with peace of mind. Step 5: The schedule management department manages daily schedules and reminders. For example, it proactively reminds users of medication times, hospital appointments, and daycare schedules. By adjusting the timing of reminders, seniors can ensure they don't forget their appointments. By customizing the content of reminders, the system provides reminders that are easier for seniors to use.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0127] Each of the multiple elements described above, including the operation assistance unit, error prevention unit, recovery unit, spam call handling unit, and schedule management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the operation assistance unit is implemented by the control unit 46A of the smart device 14, which recognizes voice commands from senior citizens using voice recognition technology and performs appropriate operations. The error prevention unit is implemented by the specific processing unit 290 of the data processing unit 12, which detects errors and issues warnings. The recovery unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically performs recovery when an error occurs. The spam call handling unit is implemented by the control unit 46A of the smart device 14, which automatically answers spam calls and sets call rejection settings. The schedule management unit is implemented by the specific processing unit 290 of the data processing unit 12, which manages daily schedules and reminders. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the operation assistance unit, error prevention unit, recovery unit, spam call handling unit, and schedule management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the operation assistance unit is implemented by the control unit 46A of the smart glasses 214, which recognizes voice commands from seniors using voice recognition technology and performs appropriate operations. The error prevention unit is implemented by the specific processing unit 290 of the data processing unit 12, which detects errors and issues warnings. The recovery unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically performs recovery when an error occurs. The spam call handling unit is implemented by the control unit 46A of the smart glasses 214, which automatically answers spam calls and sets call rejection settings. The schedule management unit is implemented by the specific processing unit 290 of the data processing unit 12, which manages daily schedules and reminders. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the operation assistance unit, error prevention unit, recovery unit, spam call handling unit, and schedule management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the operation assistance unit is implemented by the control unit 46A of the headset terminal 314, which recognizes voice commands from senior citizens using voice recognition technology and performs appropriate operations. The error prevention unit is implemented by the specific processing unit 290 of the data processing unit 12, which detects errors and issues warnings. The recovery unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically performs recovery when an error occurs. The spam call handling unit is implemented by the control unit 46A of the headset terminal 314, which automatically answers spam calls and sets call rejection settings. The schedule management unit is implemented by the specific processing unit 290 of the data processing unit 12, which manages daily schedules and reminders. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the operation assistance unit, error prevention unit, recovery unit, spam call handling unit, and schedule management unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the operation assistance unit is implemented by the control unit 46A of the robot 414, which recognizes voice commands from senior citizens using voice recognition technology and performs appropriate operations. The error prevention unit is implemented by the specific processing unit 290 of the data processing unit 12, which detects errors and issues warnings. The recovery unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically performs recovery when an error occurs. The spam call handling unit is implemented by the control unit 46A of the robot 414, which automatically answers spam calls and sets call rejection settings. The schedule management unit is implemented by the specific processing unit 290 of the data processing unit 12, which manages daily schedules and reminders. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) An operation assistance unit that provides voice-activated operation assistance, An error prevention unit that prevents incorrect operation by the aforementioned operation assist unit, The aforementioned malfunction prevention unit performs recovery when a malfunction occurs, The spam call handling department provides alternative answers to spam calls, It includes a schedule management section for managing daily schedules and reminders. A system characterized by the following features. (Note 2) The aforementioned operation assist unit is Voice guidance is provided for users who have difficulty touching the screen. The system described in Appendix 1, characterized by the features described herein. (Note 3) The recovery unit is Automatically recovers in case of user error. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned nuisance call handling department, Automatically answer and block unwanted calls. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned schedule management unit, Proactively reminds you of medication times, hospital appointments, and daycare schedules. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned schedule management unit, We provide support for using digital tools for hobbies and daily exercise for maintaining health. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned schedule management unit, AI agents converse with seniors to perform frailty checks and support early detection and prevention. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned operation assist unit is It estimates the user's emotions and adjusts the timing of operation assistance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned operation assist unit is Analyze the user's past operation history and select the optimal operation assistance method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned operation assist unit is During operation assistance, the assistance content is customized based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned operation assist unit is It estimates the user's emotions and determines the priority of operation assistance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned operation assist unit is When providing operational assistance, the system takes the user's geographical location into consideration to provide more relevant assistance. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned operation assist unit is During operation assistance, the system analyzes the user's social media activity and provides relevant assistance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned malfunction prevention unit is The system estimates the user's emotions and adjusts methods to prevent user errors based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned malfunction prevention unit is When preventing user errors, the system analyzes the user's past error history to select the most effective prevention method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned malfunction prevention unit is When preventing user errors, the prevention measures are customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned malfunction prevention unit is The system estimates the user's emotions and determines the priority of error prevention based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned malfunction prevention unit is When preventing user errors, the optimal prevention method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned malfunction prevention unit is To prevent user errors, we analyze the user's social media activity and propose preventative measures. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recovery unit is It estimates the user's emotions and adjusts the recovery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recovery unit is During recovery, the system analyzes the user's past error history to select the optimal recovery method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recovery unit is During recovery, the recovery method is customized based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recovery unit is The system estimates the user's emotions and determines recovery priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recovery unit is During recovery, the optimal recovery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recovery unit is During recovery, we analyze the user's social media activity and propose recovery methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned nuisance call handling department, The system estimates the user's emotions and adjusts how it handles spam calls based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned nuisance call handling department, When dealing with spam calls, the system analyzes the user's past spam call history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned nuisance call handling department, When dealing with spam calls, the response method is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned nuisance call handling department, The system estimates the user's emotions and prioritizes handling spam calls based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned nuisance call handling department, When dealing with spam calls, the system selects the most appropriate response method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned nuisance call handling department, When dealing with spam calls, we analyze the user's social media activity and suggest appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned schedule management unit, It estimates the user's emotions and adjusts the schedule management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned schedule management unit, When managing schedules, the system analyzes the user's past schedule history to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned schedule management unit, When managing schedules, customize the management method based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned schedule management unit, It estimates the user's emotions and determines schedule management priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned schedule management unit, When managing schedules, the optimal management method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned schedule management unit, When managing schedules, we analyze users' social media activity and suggest management methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An operation assistance unit that provides voice-activated operation assistance, An error prevention unit that prevents erroneous operation using the aforementioned operation assist unit, The aforementioned malfunction prevention unit performs recovery when a malfunction occurs, and The spam call handling department provides alternative answers to spam calls, It includes a schedule management section for managing daily schedules and reminders. A system characterized by the following features.

2. The aforementioned operation assist unit is Voice guidance is provided for users who have difficulty touching the screen. The system according to feature 1.

3. The recovery unit is Automatically recovers in case of user error. The system according to feature 1.

4. The aforementioned nuisance call handling department, Automatically answer and block unwanted calls. The system according to feature 1.

5. The aforementioned schedule management unit, Proactively reminds you of medication times, hospital appointments, and daycare schedules. The system according to feature 1.

6. The aforementioned schedule management unit, We provide support for using digital tools for hobbies and daily exercise for maintaining health. The system according to feature 1.

7. The aforementioned schedule management unit, AI agents engage in conversations with seniors to perform frailty checks and support early detection and prevention. The system according to feature 1.

8. The aforementioned operation assist unit is It estimates the user's emotions and adjusts the timing of operation assistance based on the estimated user emotions. The system according to feature 1.

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