System

Abstract

The system according to the embodiment aims to protect elderly people from fraudulent calls.SOLUTION: A system according to an embodiment includes an opening unit, an analysis unit, and a notification unit. The open portion opens various functions of the smartphone. The analysis unit analyzes the received telephone call using the function opened by the opening unit. The notification unit notifies the family of a problem based on the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that elderly people are vulnerable to fraudulent phone calls and do not provide sufficient means to prevent themselves from becoming victims of fraud.

[0005] The system according to the embodiment aims to protect the elderly from fraudulent phone calls. [Means for solving the problem]

[0006] The system according to the embodiment includes an opening unit, an analysis unit, and a notification unit. The opening unit opens various functions of the smartphone. The analysis unit analyzes received calls using the functions opened by the opening unit. The notification unit notifies family members if there is a problem based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can protect the elderly from fraudulent calls. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention is a smartphone system designed to protect elderly people from telephone fraud. With the elderly's prior consent, the smartphone system unlocks various smartphone functions, analyzes received calls, and notifies family members and other relevant parties if a problem is suspected. This reduces the risk of elderly people becoming victims of fraud. For example, the smartphone system obtains the elderly's prior consent and obtains access permissions to the smartphone's microphone, speaker, call history, and other devices. The smartphone system then analyzes the received calls. This analysis uses AI to analyze the voice characteristics and content of the conversation. For example, the AI ​​analyzes the tone of voice, speaking style, and specific keywords to determine whether there is a possibility of fraud. Furthermore, if there is a possibility of fraud, the smartphone system notifies family members and other relevant parties. For example, if a possible fraud is detected, a notification is sent to the family via SMS or email. This notification includes information such as the content of the call and the analysis results, allowing family members to respond promptly. This allows the smartphone system to provide an environment in which elderly people can use their smartphones with peace of mind.

[0029] A smartphone system according to an embodiment includes an unlocking unit, an analysis unit, and a notification unit. The unlocking unit unlocks various smartphone functions. For example, the unlocking unit obtains access permissions for the smartphone's microphone, speaker, call history, and the like. The unlocking unit can configure settings on the smartphone's settings screen to allow voice analysis and access to the call history. The analysis unit uses AI to analyze received calls. For example, the analysis unit analyzes the tone of voice, speaking style, and specific keywords to determine whether there is a possibility of fraud. The analysis unit analyzes the voice characteristics and the content of the conversation. For example, the analysis unit analyzes the tone of voice, speaking style, and specific keywords to determine whether there is a possibility of fraud. The notification unit notifies family members if there is a possible problem based on the analysis results by the analysis unit. For example, if the notification unit determines there is a possibility of fraud, it sends a notification to family members via SMS or email. The notification unit sends a notification including information such as the content of the call and the analysis results. This enables the smartphone system according to an embodiment to provide smartphones that protect elderly people from "it's my son" scams.

[0030] The opening unit can obtain access rights to the smartphone's microphone or speaker, call history, etc. The access rights include, but are not limited to, microphone access, speaker access, and call history access, for example. The opening unit can, for example, configure settings on the smartphone's settings screen to allow access to voice analysis and call history. The opening unit can also open various functions of the smartphone with the user's consent. This allows the authority to open various functions of the smartphone to be obtained.

[0031] The analysis unit can use AI to analyze voice tone, speaking style, and specific keywords. The analysis unit, for example, analyzes voice tone. For example, the analysis unit analyzes voice pitch, intensity, emotional tone, etc. The analysis unit can also analyze speaking style. For example, the analysis unit analyzes speaking speed, intonation, rhythm, etc. The analysis unit can also analyze specific keywords. For example, the analysis unit analyzes keywords related to fraud and warning words, etc. In this way, using AI improves the accuracy of analyzing fraudulent calls. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input voice data into a generation AI and have the generation AI analyze voice tone, speaking style, and specific keywords.

[0032] The notification unit can notify the family via SMS, email, or a dedicated app. For example, if it determines that there is a possibility of fraud, the notification unit sends a notification to the family via SMS. The notification unit can also send a notification by email. The notification unit can also send a notification via a dedicated app. This allows the family to be promptly notified if there is a possibility of fraud. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the results of the analysis by the analysis unit into a generation AI and have the generation AI generate the notification content.

[0033] The unlocking unit can analyze the user's past smartphone usage history and select an appropriate function unlocking method. For example, the unlocking unit prioritizes unlocking functions that the user has frequently used in the past. The unlocking unit can also predict and unlock functions that will be used during specific time periods based on the user's past usage history. The unlocking unit can also suggest an optimal unlocking method based on the user's past feedback. This makes it possible to provide an optimal function unlocking method based on the user's past usage history. Some or all of the above-described processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input the user's usage history data into a generation AI and have the generation AI select an optimal unlocking method.

[0034] When releasing functions, the release unit can perform filtering based on the user's current living situation and areas of interest. For example, when the user is out, the release unit releases only the minimum necessary functions. Furthermore, when the user is at home, the release unit can also release all functions. Furthermore, the release unit can preferentially release related functions based on the user's areas of interest. This makes it possible to release functions according to the user's living situation and areas of interest. Some or all of the above-mentioned processing in the release unit may be performed using AI, for example, or may be performed without using AI. For example, the release unit can input the user's living situation data into the generation AI and have the generation AI perform filtering.

[0035] When releasing a function, the release unit can select an appropriate release means according to the user's input method. For example, if the user prefers voice input, the release unit can release the function with a voice command. Furthermore, if the user prefers text input, the release unit can also release the function with a simple text input. Furthermore, if the user prefers gesture input, the release unit can also release the function with a specific gesture. This makes it possible to provide an optimal release means according to the user's input method. Some or all of the above-described processing in the release unit may be performed using, for example, AI, or may be performed without using AI. For example, the release unit can input the user's input data into a generation AI and have the generation AI select the optimal release means.

[0036] When releasing functions, the release unit can prioritize releasing highly relevant functions based on the user's geographical location information. For example, when the user is at home, the release unit can prioritize releasing a home automation function. Furthermore, when the user is out, the release unit can prioritize releasing a navigation function. Furthermore, when the user is in a specific facility, the release unit can prioritize releasing functions related to the facility. This makes it possible to release optimal functions based on the user's geographical location information. The geographical location information is obtained, for example, using GPS data or a location information service. Some or all of the above-mentioned processing in the release unit may be performed using, for example, AI, or may be performed without using AI. For example, the release unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant functions.

[0037] When releasing a function, the release unit can analyze the user's social media activity and release the related function. For example, the release unit can release a function related to a location where the user checked in on social media. The release unit can also analyze the content of the user's social media posts and release the related function. The release unit can also release the related function based on the activity of the user's friends on social media. This makes it possible to release the optimal function based on the user's social media activity. Social media activity can be analyzed, for example, by analyzing the content of posts, the number of likes, the number of followers, etc. Some or all of the above-mentioned processing in the release unit can be performed using, or without, AI. For example, the release unit can input the user's social media data into a generation AI and have the generation AI select the related function.

[0038] When unlocking a function, the unlocking unit can customize the unlocking method by reflecting the user's past feedback. For example, the unlocking unit may prioritize the unlocking method that the user previously preferred. The unlocking unit can also improve the unlocking procedure based on the user's past feedback. The unlocking unit can also suggest a new unlocking method by referring to the user's past feedback. This makes it possible to provide an optimal unlocking method based on the user's past feedback. Feedback is analyzed based on, for example, user reviews and survey results. Some or all of the above-described processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input user feedback data into a generation AI and have the generation AI select a customized unlocking method.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the call. For example, in the case of an important call, the analysis unit performs a detailed analysis. In addition, the analysis unit can also perform a simplified analysis in the case of a general call. In addition, the analysis unit can also perform a quick analysis in the case of an emergency call. This allows optimal analysis to be performed depending on the importance of the call. The importance of the call is evaluated based on, for example, the content of the call and the importance of the caller. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input call data to a generation AI and have the generation AI adjust the level of detail based on the importance.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the call category. For example, in the case of a business call, the analysis unit applies a business analysis algorithm. Furthermore, in the case of a private call, the analysis unit can also apply a private analysis algorithm. Furthermore, in the case of an emergency call, the analysis unit can also apply an emergency analysis algorithm. This allows optimal analysis to be performed depending on the call category. Call categories are classified into, for example, business calls, personal calls, emergency calls, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input call data into a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also introduce new analysis methods by referring to the user's past analysis results. This allows the analysis accuracy to be improved based on the user's past analysis results. Past analysis results are analyzed based on, for example, past call analysis data and analysis history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time when the call was received. For example, in the case of an emergency call, the analysis unit performs analysis with the highest priority. In addition, in the case of a normal call, the analysis unit can also perform analysis with normal priority. In addition, in the case of a past call, the analysis unit can postpone analysis. This makes it possible to provide optimal analysis priority based on the time when the call was received. The time when the call was received is evaluated based on, for example, the date and time of the call and the frequency of the calls. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input call reception time data into the generation AI and have the generation AI determine the priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the calls. For example, in the case of an important call, the analysis unit performs analysis with the highest priority. In addition, in the case of a general call, the analysis unit can also perform analysis in the normal order. In addition, in the case of a call with low relevance, the analysis unit can postpone analysis. This makes it possible to provide an optimal analysis order based on the relevance of the calls. The relevance of the calls is evaluated based on, for example, the similarity of the call content and the relationship between the call partners. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input call relevance data to a generation AI and have the generation AI adjust the order.

[0044] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user has general knowledge, the analysis unit can also provide analysis results that use simple terminology. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results that avoid technical terminology. This makes it possible to provide optimal analysis results according to the user's level of expertise. The level of expertise is evaluated based on, for example, the user's occupation, educational background, past experience, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI to adjust the use of technical terminology.

[0045] At the time of notification, the notification unit can select the optimal notification method by analyzing the user's past notification history. For example, the notification unit prioritizes the use of a notification method that the user previously preferred. The notification unit can also suggest the optimal notification method for a specific time period based on the user's past notification history. The notification unit can also improve the notification method based on the user's past feedback. This makes it possible to provide the optimal notification method based on the user's past notification history. The notification history is analyzed based on, for example, the content of past notifications and the frequency of notifications. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input notification history data into a generation AI and have the generation AI select the optimal notification method.

[0046] The notification unit can customize the notification method based on the user's current living situation when notifying. For example, if the user is out, the notification unit can notify by SMS. If the user is at home, the notification unit can also notify by voice. If the user is in a meeting, the notification unit can also notify by vibration. This makes it possible to provide the optimal notification method according to the user's living situation. The living situation is evaluated based on, for example, the user's current activity status and daily routine. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input living situation data into a generation AI and have the generation AI customize the notification method.

[0047] The notification unit can improve the notification method by reflecting user feedback at the time of notification. For example, the notification unit prioritizes the adoption of a notification method that the user has previously preferred. The notification unit can also improve the notification procedure based on the user's past feedback. The notification unit can also suggest a new notification method by referring to the user's past feedback. This makes it possible to provide an optimal notification method based on user feedback. The feedback is analyzed based on, for example, user reviews and survey results. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input feedback data into a generation AI and have the generation AI improve the notification method.

[0048] The notification unit can select the optimal notification method based on the user's geographical location information when notifying the user. For example, if the user is at home, the notification unit can provide a voice notification. If the user is out, the notification unit can also provide an SMS notification. If the user is in a specific facility, the notification unit can also select a notification method appropriate for that facility. This makes it possible to provide the optimal notification method based on the user's geographical location information. The geographical location information is obtained, for example, using GPS data or a location information service. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input geographical location information data to a generation AI and have the generation AI select the optimal notification method.

[0049] The notification unit can analyze the user's social media activity and suggest a notification method when providing a notification. For example, if the user is active on social media, the notification unit can provide an in-app notification. The notification unit can also analyze the user's social media posts and provide related notifications. The notification unit can also provide related notifications based on the activity of the user's friends on social media. This makes it possible to provide the optimal notification method based on the user's social media activity. Social media activity is analyzed based on, for example, the post content, the number of likes, the number of followers, etc. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input social media data into a generation AI and have the generation AI suggest a notification method.

[0050] The notification unit can customize the notification method by reflecting the user's past feedback when providing a notification. For example, the notification unit prioritizes the use of a notification method that the user previously preferred. The notification unit can also improve the notification procedure based on the user's past feedback. The notification unit can also suggest a new notification method by referring to the user's past feedback. This makes it possible to provide an optimal notification method based on the user's past feedback. The feedback is analyzed based on, for example, user reviews and survey results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input feedback data into a generation AI and have the generation AI customize the notification method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The analysis unit can evaluate the importance of a call based on the content of the call and determine the priority of analysis according to the importance. For example, for a highly urgent call, analysis is given top priority. For a business call, analysis can be performed with normal priority. For a private call, analysis can be postponed. This makes it possible to provide optimal analysis priority according to the importance of the call. The importance of a call is evaluated based on, for example, the content of the call and the importance of the caller. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input call data into a generation AI and have the generation AI determine the priority based on the importance.

[0053] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, it can prioritize the notification method that the user previously preferred. It can also suggest the optimal notification method for a specific time period based on the user's past notification history. It can also improve the notification method based on the user's past feedback. This makes it possible to provide the optimal notification method based on the user's past notification history. The notification history is analyzed based on, for example, the content of past notifications and the frequency of notifications. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input notification history data into a generation AI and have the generation AI select the optimal notification method.

[0054] The opening unit can perform filtering based on the user's current living situation and areas of interest. For example, when the user is out, only the minimum necessary functions can be opened. Also, when the user is at home, all functions can be opened. Also, based on the user's areas of interest, related functions can be opened preferentially. This makes it possible to open functions according to the user's living situation and areas of interest. Some or all of the above-mentioned processing in the opening unit may be performed using AI, for example, or may be performed without using AI. For example, the opening unit can input the user's living situation data into the generation AI and have the generation AI perform filtering.

[0055] The analysis unit can apply different analysis algorithms depending on the call category. For example, in the case of a business call, a business analysis algorithm can be applied. In addition, in the case of a private call, a private analysis algorithm can be applied. In addition, in the case of an emergency call, an emergency analysis algorithm can be applied. This allows optimal analysis to be performed depending on the call category. Call categories are classified into, for example, business calls, personal calls, emergency calls, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input call data into a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0056] The notification unit can select the optimal notification method based on the user's geographical location information. For example, if the user is at home, a voice notification can be sent. If the user is out, an SMS notification can be sent. If the user is in a specific facility, a notification method appropriate for that facility can be selected. This makes it possible to provide the optimal notification method based on the user's geographical location information. The geographical location information is obtained, for example, using GPS data or a location information service. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input geographical location information data to a generation AI and have the generation AI select the optimal notification method.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The access unit opens various functions of the smartphone. For example, the access unit obtains access permissions for the smartphone's microphone, speaker, call history, etc. The access unit can configure settings on the smartphone's settings screen to allow access to voice analysis and call history. Step 2: The analysis unit uses AI to analyze the received call. For example, the analysis unit analyzes the tone of the voice, speaking style, and specific keywords to determine whether there is a possibility of fraud. The analysis unit analyzes the characteristics of the voice and the content of the conversation. Step 3: The notification unit notifies the family if there is a possible problem based on the results of the analysis by the analysis unit. For example, if the notification unit determines that there is a possibility of fraud, it will send a notification to the family via SMS or email. The notification unit sends a notification including information such as the content of the call and the analysis results.

[0059] (Example 2) A system according to an embodiment of the present invention is a smartphone system designed to protect elderly people from telephone fraud. With the elderly's prior consent, the smartphone system unlocks various smartphone functions, analyzes received calls, and notifies family members and other relevant parties if a problem is suspected. This reduces the risk of elderly people becoming victims of fraud. For example, the smartphone system obtains the elderly's prior consent and obtains access permissions to the smartphone's microphone, speaker, call history, and other devices. The smartphone system then analyzes the received calls. This analysis uses AI to analyze the voice characteristics and content of the conversation. For example, the AI ​​analyzes the tone of voice, speaking style, and specific keywords to determine whether there is a possibility of fraud. Furthermore, if there is a possibility of fraud, the smartphone system notifies family members and other relevant parties. For example, if a possible fraud is detected, a notification is sent to the family via SMS or email. This notification includes information such as the content of the call and the analysis results, allowing family members to respond promptly. This allows the smartphone system to provide an environment in which elderly people can use their smartphones with peace of mind.

[0060] A smartphone system according to an embodiment includes an unlocking unit, an analysis unit, and a notification unit. The unlocking unit unlocks various smartphone functions. For example, the unlocking unit obtains access permissions for the smartphone's microphone, speaker, call history, and the like. The unlocking unit can configure settings on the smartphone's settings screen to allow voice analysis and access to the call history. The analysis unit uses AI to analyze received calls. For example, the analysis unit analyzes the tone of voice, speaking style, and specific keywords to determine whether there is a possibility of fraud. The analysis unit analyzes the voice characteristics and the content of the conversation. For example, the analysis unit analyzes the tone of voice, speaking style, and specific keywords to determine whether there is a possibility of fraud. The notification unit notifies family members if there is a possible problem based on the analysis results by the analysis unit. For example, if the notification unit determines there is a possibility of fraud, it sends a notification to family members via SMS or email. The notification unit sends a notification including information such as the content of the call and the analysis results. This enables the smartphone system according to an embodiment to provide smartphones that protect elderly people from "it's my son" scams.

[0061] The opening unit can obtain access rights to the smartphone's microphone or speaker, call history, etc. The access rights include, but are not limited to, microphone access, speaker access, and call history access, for example. The opening unit can, for example, configure settings on the smartphone's settings screen to allow access to voice analysis and call history. The opening unit can also open various functions of the smartphone with the user's consent. This allows the authority to open various functions of the smartphone to be obtained.

[0062] The analysis unit can use AI to analyze voice tone, speaking style, and specific keywords. The analysis unit, for example, analyzes voice tone. For example, the analysis unit analyzes voice pitch, intensity, emotional tone, etc. The analysis unit can also analyze speaking style. For example, the analysis unit analyzes speaking speed, intonation, rhythm, etc. The analysis unit can also analyze specific keywords. For example, the analysis unit analyzes keywords related to fraud and warning words, etc. In this way, using AI improves the accuracy of analyzing fraudulent calls. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input voice data into a generation AI and have the generation AI analyze voice tone, speaking style, and specific keywords.

[0063] The notification unit can notify the family via SMS, email, or a dedicated app. For example, if it determines that there is a possibility of fraud, the notification unit sends a notification to the family via SMS. The notification unit can also send a notification by email. The notification unit can also send a notification via a dedicated app. This allows the family to be promptly notified if there is a possibility of fraud. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the results of the analysis by the analysis unit into a generation AI and have the generation AI generate the notification content.

[0064] The release unit can estimate the user's emotions and adjust the timing of smartphone function release based on the estimated user emotions. For example, when the user is relaxed, the release unit can smoothly release the smartphone functions. Furthermore, when the user is stressed, the release unit can also simplify the function release procedure so that it can be completed in a short time. Furthermore, when the user is in a hurry, the release unit can quickly release the functions using audio guidance. This allows the smartphone functions to be released at the optimal timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the release unit can be performed using AI, for example, or without AI. For example, the release unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing based on the emotion.

[0065] The unlocking unit can analyze the user's past smartphone usage history and select an appropriate function unlocking method. For example, the unlocking unit prioritizes unlocking functions that the user has frequently used in the past. The unlocking unit can also predict and unlock functions that will be used during specific time periods based on the user's past usage history. The unlocking unit can also suggest an optimal unlocking method based on the user's past feedback. This makes it possible to provide an optimal function unlocking method based on the user's past usage history. Some or all of the above-described processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input the user's usage history data into a generation AI and have the generation AI select an optimal unlocking method.

[0066] When releasing functions, the release unit can perform filtering based on the user's current living situation and areas of interest. For example, when the user is out, the release unit releases only the minimum necessary functions. Furthermore, when the user is at home, the release unit can also release all functions. Furthermore, the release unit can preferentially release related functions based on the user's areas of interest. This makes it possible to release functions according to the user's living situation and areas of interest. Some or all of the above-mentioned processing in the release unit may be performed using AI, for example, or may be performed without using AI. For example, the release unit can input the user's living situation data into the generation AI and have the generation AI perform filtering.

[0067] When releasing a function, the release unit can select an appropriate release means according to the user's input method. For example, if the user prefers voice input, the release unit can release the function with a voice command. Furthermore, if the user prefers text input, the release unit can also release the function with a simple text input. Furthermore, if the user prefers gesture input, the release unit can also release the function with a specific gesture. This makes it possible to provide an optimal release means according to the user's input method. Some or all of the above-described processing in the release unit may be performed using, for example, AI, or may be performed without using AI. For example, the release unit can input the user's input data into a generation AI and have the generation AI select the optimal release means.

[0068] The release unit can estimate the user's emotions and determine the priority of functions to be released based on the estimated user emotions. For example, if the user is nervous, the release unit can prioritize the emergency contact function. Furthermore, if the user is relaxed, the release unit can prioritize the entertainment function. Furthermore, if the user is in a hurry, the release unit can prioritize the navigation function. This allows the optimal function to be released preferentially according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the release unit can be performed using, for example, an AI, or without an AI. For example, the release unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority based on the emotion.

[0069] When releasing functions, the release unit can prioritize releasing highly relevant functions based on the user's geographical location information. For example, when the user is at home, the release unit can prioritize releasing a home automation function. Furthermore, when the user is out, the release unit can prioritize releasing a navigation function. Furthermore, when the user is in a specific facility, the release unit can prioritize releasing functions related to the facility. This makes it possible to release optimal functions based on the user's geographical location information. The geographical location information is obtained, for example, using GPS data or a location information service. Some or all of the above-mentioned processing in the release unit may be performed using, for example, AI, or may be performed without using AI. For example, the release unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant functions.

[0070] When releasing a function, the release unit can analyze the user's social media activity and release the related function. For example, the release unit can release a function related to a location where the user checked in on social media. The release unit can also analyze the content of the user's social media posts and release the related function. The release unit can also release the related function based on the activity of the user's friends on social media. This makes it possible to release the optimal function based on the user's social media activity. Social media activity can be analyzed, for example, by analyzing the content of posts, the number of likes, the number of followers, etc. Some or all of the above-mentioned processing in the release unit can be performed using, or without, AI. For example, the release unit can input the user's social media data into a generation AI and have the generation AI select the related function.

[0071] When unlocking a function, the unlocking unit can customize the unlocking method by reflecting the user's past feedback. For example, the unlocking unit may prioritize the unlocking method that the user previously preferred. The unlocking unit can also improve the unlocking procedure based on the user's past feedback. The unlocking unit can also suggest a new unlocking method by referring to the user's past feedback. This makes it possible to provide an optimal unlocking method based on the user's past feedback. Feedback is analyzed based on, for example, user reviews and survey results. Some or all of the above-described processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input user feedback data into a generation AI and have the generation AI select a customized unlocking method.

[0072] The analysis unit can estimate the user's emotions and adjust the expression method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows for optimal analysis results to be provided according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the expression method based on the emotion.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the call. For example, in the case of an important call, the analysis unit performs a detailed analysis. In addition, the analysis unit can also perform a simplified analysis in the case of a general call. In addition, the analysis unit can also perform a quick analysis in the case of an emergency call. This allows optimal analysis to be performed depending on the importance of the call. The importance of the call is evaluated based on, for example, the content of the call and the importance of the caller. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input call data to a generation AI and have the generation AI adjust the level of detail based on the importance.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the call category. For example, in the case of a business call, the analysis unit applies a business analysis algorithm. Furthermore, in the case of a private call, the analysis unit can also apply a private analysis algorithm. Furthermore, in the case of an emergency call, the analysis unit can also apply an emergency analysis algorithm. This allows optimal analysis to be performed depending on the call category. Call categories are classified into, for example, business calls, personal calls, emergency calls, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input call data into a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also introduce new analysis methods by referring to the user's past analysis results. This allows the analysis accuracy to be improved based on the user's past analysis results. Past analysis results are analyzed based on, for example, past call analysis data and analysis history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is excited, the analysis unit can also perform an analysis with visually stimulating effects. This allows the analysis length to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the analysis based on the emotion.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time when the call was received. For example, in the case of an emergency call, the analysis unit performs analysis with the highest priority. In addition, in the case of a normal call, the analysis unit can also perform analysis with normal priority. In addition, in the case of a past call, the analysis unit can postpone analysis. This makes it possible to provide optimal analysis priority based on the time when the call was received. The time when the call was received is evaluated based on, for example, the date and time of the call and the frequency of the calls. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input call reception time data into the generation AI and have the generation AI determine the priority.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the calls. For example, in the case of an important call, the analysis unit performs analysis with the highest priority. In addition, in the case of a general call, the analysis unit can also perform analysis in the normal order. In addition, in the case of a call with low relevance, the analysis unit can postpone analysis. This makes it possible to provide an optimal analysis order based on the relevance of the calls. The relevance of the calls is evaluated based on, for example, the similarity of the call content and the relationship between the call partners. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input call relevance data to a generation AI and have the generation AI adjust the order.

[0079] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user has general knowledge, the analysis unit can also provide analysis results that use simple terminology. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results that avoid technical terminology. This makes it possible to provide optimal analysis results according to the user's level of expertise. The level of expertise is evaluated based on, for example, the user's occupation, educational background, past experience, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI to adjust the use of technical terminology.

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

[0081] At the time of notification, the notification unit can select the optimal notification method by analyzing the user's past notification history. For example, the notification unit prioritizes the use of a notification method that the user previously preferred. The notification unit can also suggest the optimal notification method for a specific time period based on the user's past notification history. The notification unit can also improve the notification method based on the user's past feedback. This makes it possible to provide the optimal notification method based on the user's past notification history. The notification history is analyzed based on, for example, the content of past notifications and the frequency of notifications. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input notification history data into a generation AI and have the generation AI select the optimal notification method.

[0082] The notification unit can customize the notification method based on the user's current living situation when notifying. For example, if the user is out, the notification unit can notify by SMS. If the user is at home, the notification unit can also notify by voice. If the user is in a meeting, the notification unit can also notify by vibration. This makes it possible to provide the optimal notification method according to the user's living situation. The living situation is evaluated based on, for example, the user's current activity status and daily routine. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input living situation data into a generation AI and have the generation AI customize the notification method.

[0083] The notification unit can improve the notification method by reflecting user feedback at the time of notification. For example, the notification unit prioritizes the adoption of a notification method that the user has previously preferred. The notification unit can also improve the notification procedure based on the user's past feedback. The notification unit can also suggest a new notification method by referring to the user's past feedback. This makes it possible to provide an optimal notification method based on user feedback. The feedback is analyzed based on, for example, user reviews and survey results. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input feedback data into a generation AI and have the generation AI improve the notification method.

[0084] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. For example, if the user is nervous, the notification unit can prioritize emergency notifications. Furthermore, if the user is relaxed, the notification unit can also prioritize regular notifications. Furthermore, if the user is in a hurry, the notification unit can also prioritize important notifications. This allows optimal notification priorities to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of notifications based on the emotion.

[0085] The notification unit can select the optimal notification method based on the user's geographical location information when notifying the user. For example, if the user is at home, the notification unit can provide a voice notification. If the user is out, the notification unit can also provide an SMS notification. If the user is in a specific facility, the notification unit can also select a notification method appropriate for that facility. This makes it possible to provide the optimal notification method based on the user's geographical location information. The geographical location information is obtained, for example, using GPS data or a location information service. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input geographical location information data to a generation AI and have the generation AI select the optimal notification method.

[0086] The notification unit can analyze the user's social media activity and suggest a notification method when providing a notification. For example, if the user is active on social media, the notification unit can provide an in-app notification. The notification unit can also analyze the user's social media posts and provide related notifications. The notification unit can also provide related notifications based on the activity of the user's friends on social media. This makes it possible to provide the optimal notification method based on the user's social media activity. Social media activity is analyzed based on, for example, the post content, the number of likes, the number of followers, etc. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input social media data into a generation AI and have the generation AI suggest a notification method.

[0087] The notification unit can customize the notification method by reflecting the user's past feedback when providing a notification. For example, the notification unit prioritizes the use of a notification method that the user previously preferred. The notification unit can also improve the notification procedure based on the user's past feedback. The notification unit can also suggest a new notification method by referring to the user's past feedback. This makes it possible to provide an optimal notification method based on the user's past feedback. The feedback is analyzed based on, for example, user reviews and survey results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input feedback data into a generation AI and have the generation AI customize the notification method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned opening unit, analysis unit, and notification unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the opening unit is realized by the control unit 46A of the smart device 14 and obtains access rights to the smartphone's microphone, speaker, call history, etc. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes received phone calls using AI, analyzing the tone of voice, speaking style, and specific keywords. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and sends a notification to family members via SMS or email if it determines that there is a possibility of fraud. === Hard Collateral 1-2 === Each of the multiple elements, including the opening unit, analysis unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the opening unit is realized by the control unit 46A of the smart glasses 214 and acquires access rights to the smartphone's microphone, speaker, call history, etc. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes received phone calls using AI, analyzing the tone of voice, speaking style, and specific keywords. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and sends a notification to family members via SMS or email if it determines that there is a possibility of fraud. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned opening unit, analysis unit, and notification unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the opening unit is realized by the control unit 46A of the headset-type terminal 314 and obtains access rights to the smartphone's microphone, speaker, call history, etc. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes received phone calls using AI, analyzing the tone of voice, speaking style, and specific keywords. The notification unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and sends a notification to family members via SMS or email if it is determined that there is a possibility of fraud. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned opening unit, analysis unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the opening unit is realized by the control unit 46A of the robot 414 and obtains access rights to the smartphone's microphone, speaker, call history, etc. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes received phone calls using AI, analyzing the tone of voice, speaking style, and specific keywords. The notification unit is realized, for example, by the control unit 46A of the robot 414 and sends a notification to family members via SMS or email if it is determined that there is a possibility of fraud.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is nervous, urgent calls can be analyzed first. Alternatively, if the user is relaxed, regular calls can be analyzed first. Alternatively, if the user is in a hurry, calls that can be analyzed in a short time can be analyzed first. This allows optimal analysis priorities to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine priorities based on emotions.

[0090] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user emotions. For example, if the user is nervous, the notification can be concise and reassuring. If the user is relaxed, the notification can include detailed information. If the user is in a hurry, the notification can be short and to the point. This allows optimal notification content to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification content based on the emotion.

[0091] The release unit can estimate the user's emotions and adjust the order of function activation based on the estimated user emotions. For example, if the user is nervous, the emergency contact function can be prioritized. Also, if the user is relaxed, the entertainment function can be prioritized. Also, if the user is in a hurry, the navigation function can be prioritized. This makes it possible to provide an optimal function activation order according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the release unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the release unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the order based on the emotion.

[0092] The analysis unit can estimate the user's emotions and adjust the expression method of the analysis based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible analysis result. If the user is relaxed, it can provide a detailed analysis result. If the user is in a hurry, it can provide a summary analysis result. This allows the optimal analysis result to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the expression method based on the emotion.

[0093] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user emotions. For example, if the user is nervous, the notification can be made in a calm voice. If the user is relaxed, the notification can be made in a cheerful voice. If the user is in a hurry, the notification can be made quickly and concisely. This makes it possible to provide an optimal notification method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification method based on the emotion.

[0094] The analysis unit can evaluate the importance of a call based on the content of the call and determine the priority of analysis according to the importance. For example, for a highly urgent call, analysis is given top priority. For a business call, analysis can be performed with normal priority. For a private call, analysis can be postponed. This makes it possible to provide optimal analysis priority according to the importance of the call. The importance of a call is evaluated based on, for example, the content of the call and the importance of the caller. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input call data into a generation AI and have the generation AI determine the priority based on the importance.

[0095] The notification unit can analyze the user's past notification history and select the optimal notification method. For example, it can prioritize the notification method that the user previously preferred. It can also suggest the optimal notification method for a specific time period based on the user's past notification history. It can also improve the notification method based on the user's past feedback. This makes it possible to provide the optimal notification method based on the user's past notification history. The notification history is analyzed based on, for example, the content of past notifications and the frequency of notifications. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input notification history data into a generation AI and have the generation AI select the optimal notification method.

[0096] The opening unit can perform filtering based on the user's current living situation and areas of interest. For example, when the user is out, only the minimum necessary functions can be opened. Also, when the user is at home, all functions can be opened. Also, based on the user's areas of interest, related functions can be opened preferentially. This makes it possible to open functions according to the user's living situation and areas of interest. Some or all of the above-mentioned processing in the opening unit may be performed using AI, for example, or may be performed without using AI. For example, the opening unit can input the user's living situation data into the generation AI and have the generation AI perform filtering.

[0097] The analysis unit can apply different analysis algorithms depending on the call category. For example, in the case of a business call, a business analysis algorithm can be applied. In addition, in the case of a private call, a private analysis algorithm can be applied. In addition, in the case of an emergency call, an emergency analysis algorithm can be applied. This allows optimal analysis to be performed depending on the call category. Call categories are classified into, for example, business calls, personal calls, emergency calls, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input call data into a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0098] The notification unit can select the optimal notification method based on the user's geographical location information. For example, if the user is at home, a voice notification can be sent. If the user is out, an SMS notification can be sent. If the user is in a specific facility, a notification method appropriate for that facility can be selected. This makes it possible to provide the optimal notification method based on the user's geographical location information. The geographical location information is obtained, for example, using GPS data or a location information service. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input geographical location information data to a generation AI and have the generation AI select the optimal notification method.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The access unit opens various functions of the smartphone. For example, the access unit obtains access permissions for the smartphone's microphone, speaker, call history, etc. The access unit can configure settings on the smartphone's settings screen to allow access to voice analysis and call history. Step 2: The analysis unit uses AI to analyze the received call. For example, the analysis unit analyzes the tone of the voice, speaking style, and specific keywords to determine whether there is a possibility of fraud. The analysis unit analyzes the characteristics of the voice and the content of the conversation. Step 3: The notification unit notifies the family if there is a possible problem based on the results of the analysis by the analysis unit. For example, if the notification unit determines that there is a possibility of fraud, it will send a notification to the family via SMS or email. The notification unit sends a notification including information such as the content of the call and the analysis results.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An opening that unlocks various smartphone functions, an analysis unit that analyzes a received call using the function released by the release unit; a notification unit that notifies a family member if there is a problem based on the results of the analysis by the analysis unit. A system characterized by:

2. The opening is Obtaining permission to access the smartphone's microphone or speaker, call history, etc.

2. The system of claim 1.

3. The analysis unit Uses AI to analyze voice tone, speaking style, and specific keywords 2. The system of claim 1.

4. The notification unit Notify family members via SMS, email, or a dedicated app 2. The system of claim 1.

5. The opening is Estimates the user's emotions and adjusts the timing of smartphone function release based on the estimated user emotions.

2. The system of claim 1.

6. The opening is Analyze the user's past smartphone usage history and select the appropriate method to unlock features 2. The system of claim 1.

7. The opening is When the feature is released, it will filter based on the user's current lifestyle and interests.

2. The system of claim 1.

8. The opening is When releasing a function, select the appropriate release method according to the user's input method.

2. The system of claim 1.

Images