system

Abstract

The system according to this embodiment aims to analyze voice call data and messaging app chat data from elderly people to detect and report suspicious conversations and keywords. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a reporting unit, a listing unit, and an aggregation unit. The collection unit collects voice call data and messaging app talk data from elderly people. The analysis unit analyzes the data collected by the collection unit and detects suspicious conversations and those matching keywords. The reporting unit reports the suspicious conversations detected by the analysis unit to the contracting family members or guardians. The listing unit lists conversations that match the keywords detected by the analysis unit and notifies them via email or dashboard. The aggregation unit aggregates the data collected by the collection unit by region.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the risk of the elderly becoming targets of fraud crimes is high and preventive measures are not sufficiently taken.

[0005] The system according to the embodiment aims to analyze voice call data of the elderly and talk data of a messaging app, detect and report suspicious conversations and keywords.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a reporting unit, a listing unit, and an aggregation unit. The collection unit collects voice call data and messaging app chat data from elderly people. The analysis unit analyzes the data collected by the collection unit and detects suspicious conversations and those matching keywords. The reporting unit reports the suspicious conversations detected by the analysis unit to the contracting family members or guardians. The listing unit lists conversations that match the keywords detected by the analysis unit and notifies them via email or a dashboard. The aggregation unit aggregates the data collected by the collection unit by region. [Effects of the Invention]

[0007] The system according to this embodiment can analyze voice call data and messaging app chat data from elderly people to detect and report suspicious conversations and keywords. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The life safety support system according to an embodiment of the present invention is a system that supports the life safety of the elderly by collecting and analyzing voice call data and messaging app talk data of the elderly. The life safety support system supports the life safety of the elderly by using voice call data and digital data from the telephones that the elderly normally use, as well as messaging app talk data. Furthermore, this digital data is secondarily utilized and provided to government agencies and companies as a foundation for improving local public safety. For example, the life safety support system collects voice call data and messaging app talk data of the elderly. This data is collected using the lines of a telecommunications carrier. Next, the collected data is analyzed by AI to detect suspicious conversations and those that match keywords. For example, if an elderly person is having a conversation that may be fraudulent, the AI ​​will detect the conversation and report it directly to the family member or guardian of the person with whom the contract is made. In addition, conversations that match keywords are listed and notified via email and dashboard. Furthermore, the collected data is aggregated by region and provided to government agencies as primary processed data for public safety information such as crime and disaster prevention. Aggregated processed data (secondary processed data) by region is provided to private companies. This contributes to improving local public safety. This service will be a powerful tool for protecting the safety of elderly people, both for their families and guardians. It will also have significance as a socially responsible business for government agencies and private companies, by providing information that contributes to improving local security. Specific products offered include: 1. Directly reporting suspicious conversations involving elderly people to their contracted family members or guardians. 2. Compiling lists of keywords found in elderly people's conversations and disseminating them via email and dashboards. 3. Collecting the above data by region and providing it to government agencies as public safety information, such as crime and disaster prevention data. 4. Providing private companies with aggregated and processed data (secondary processed data) by region. This system will ensure the safety of elderly people who may be targets of fraud and disseminate local security information across Japan. Furthermore, it will promote safety-related actions and realize a smart security system as a foundation for improving public safety. Thus, the life safety support system can support the safety of elderly people and contribute to improving local security.

[0029] The life safety support system according to this embodiment comprises a collection unit, an analysis unit, a reporting unit, a listing unit, and an aggregation unit. The collection unit collects voice call data and messaging app talk data from elderly people. The collection unit collects voice call data, for example, using a telecommunications carrier's network. The collection unit can also collect messaging app talk data. For example, the collection unit obtains talk data using the messaging app's API. Furthermore, the collection unit can also collect digital data. For example, the collection unit collects voice call recordings and call logs. The analysis unit analyzes the data collected by the collection unit and detects suspicious conversations and those matching keywords. The analysis unit analyzes voice call data using AI, for example, to detect suspicious conversations. Furthermore, the analysis unit can analyze messaging app talk data and detect conversations that match keywords. For example, the analysis unit analyzes text data using natural language processing technology to detect conversations containing specific keywords. Furthermore, the analysis unit can convert voice data into text data and analyze the text data. For example, the analysis unit converts audio data into text data using speech recognition technology and then analyzes the text data. The reporting unit reports suspicious conversations detected by the analysis unit to the contracting party's family or guardian. The reporting unit reports suspicious conversations using, for example, email or SMS. The reporting unit can also report suspicious conversations through a dedicated application. For example, the reporting unit reports suspicious conversations in real time using a smartphone app. Furthermore, the reporting unit can also report suspicious conversations using voice calls. For example, the reporting unit reports suspicious conversations using an automated voice call system. The listing unit lists conversations that match keywords detected by the analysis unit and notifies users via email or a dashboard. For example, the listing unit lists conversations containing specific keywords and notifies users via email. The listing unit can also display conversations that match keywords using a dashboard. For example, the listing unit displays a list of conversations that match keywords using a web dashboard. Furthermore, the listing unit can also notify users of conversations that match keywords using an alert function.For example, the listing unit notifies users of conversations matching keywords using real-time alerts. The aggregation unit aggregates the data collected by the collection unit by region. The aggregation unit classifies and aggregates the collected data by region, for example. The aggregation unit can also aggregate data by region and generate statistical information. For example, the aggregation unit aggregates crime rates and disaster prevention information by region. Furthermore, the aggregation unit can visualize the data by region and display it as graphs or charts. For example, the aggregation unit displays the data by region on a map and provides it in a visually easy-to-understand format. As a result, the life safety support system according to this embodiment can support the life safety of the elderly and contribute to improving public safety in the region.

[0030] The data collection unit collects voice call data and messaging app chat data from elderly individuals. Specifically, when collecting voice call data using a telecommunications carrier's network, it simultaneously acquires metadata such as the call start time, end time, and the phone number of the person being called. This allows for analysis of call frequency and patterns of call recipients. When collecting messaging app chat data, it uses the app's API to acquire various data formats such as text messages, images, videos, and voice messages. For example, the data collection unit can filter messages containing specific keywords through the messaging app's API, prioritizing the collection of important conversations. Furthermore, the data collection unit can also collect voice call recordings and call logs, enabling detailed analysis of call content. For example, the data collection unit can save voice call recordings to cloud storage, allowing the analysis unit to access them later. This allows the data collection unit to efficiently collect elderly individuals' communication data from diverse data sources, strengthening the overall system's data infrastructure.

[0031] The analysis unit analyzes the data collected by the collection unit and detects suspicious conversations and keywords. Specifically, it uses AI to analyze voice call data and detect suspicious conversations. For example, it uses speech recognition technology to convert voice data into text data and then analyzes that text data using natural language processing technology. This allows for the rapid detection of conversations containing specific keywords or phrases. The analysis unit can also analyze chat data from messaging apps and detect conversations that match keywords. For example, it uses natural language processing technology to analyze text data and identify conversations containing suspicious keywords such as fraud or extortion. Furthermore, the analysis unit can convert voice data into text data and analyze that text data. For example, it uses speech recognition technology to convert voice data into text data and then analyzes that text data to detect conversations containing specific keywords. This allows the analysis unit to quickly and accurately analyze collected data and detect suspicious conversations in real time. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past suspicious conversation data, the system can predict risk fluctuations during specific time periods or days of the week and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The reporting department reports suspicious conversations detected by the analysis department to the contracting family or guardian. Specifically, it reports suspicious conversations via email or SMS. For example, the reporting department can send the content of suspicious conversations received from the analysis department to the family via email, quickly informing them of the situation. The reporting department can also report suspicious conversations through a dedicated application. For example, it can use a smartphone app to report suspicious conversations in real time, allowing the family to respond immediately. Furthermore, the reporting department can also report suspicious conversations using voice calls. For example, it can use an automated voice call system to report suspicious conversations and notify the family directly. This allows the reporting department to reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using a combination of smartphone notifications, voice calls, SMS, and email. This allows the reporting department to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0033] The listing unit creates a list of conversations that match keywords detected by the analysis unit and notifies recipients via email or dashboard. Specifically, it lists conversations containing specific keywords and notifies recipients via email. For example, the listing unit creates a list of conversations that match keywords received from the analysis unit and sends it via email to family members or guardians. The listing unit can also display conversations that match keywords using a dashboard. For example, it can display a list of conversations that match keywords using a web dashboard, making it easy for family members or guardians to review them. Furthermore, the listing unit can also notify recipients of conversations that match keywords using an alert function. For example, it can notify recipients of conversations that match keywords using real-time alerts, allowing family members or guardians to take immediate action. This enables the listing unit to efficiently list suspicious conversations detected by the analysis unit and quickly disseminate the information to relevant parties.

[0034] The aggregation unit consolidates data collected by the collection unit by region. Specifically, it classifies and aggregates the collected data by region. For example, the aggregation unit classifies collected voice call data and messaging app chat data by region and stores them in a regional database. The aggregation unit can also aggregate regional data and generate statistical information. For example, it aggregates crime rates and disaster prevention information by region to conduct risk assessments for each region. Furthermore, the aggregation unit can visualize regional data and display it as graphs and charts. For example, it can display regional data on a map and provide it in a visually easy-to-understand format. This allows the aggregation unit to efficiently aggregate regional data and provide visually easy-to-understand information to stakeholders. In addition, based on the regional data, the aggregation unit can analyze region-specific risks and trends and formulate future countermeasures. For example, it can analyze the increasing trend in crime rates in a particular region and strengthen crime prevention measures for that region. Furthermore, the aggregation unit can link regional data with other systems and departments to conduct comprehensive risk management. This allows the aggregation unit to efficiently consolidate data from each region, thereby improving the overall performance of the system.

[0035] The aggregation unit provides administrative agencies with public safety information, such as crime and disaster prevention data, as primary processed data from the collected data. For example, the aggregation unit filters the collected data to extract information related to crime and disaster prevention. The aggregation unit can also aggregate the extracted information and organize it as primary processed data. For example, the aggregation unit aggregates crime rates and disaster prevention information and provides it to administrative agencies. Furthermore, the aggregation unit can visualize the primary processed data and display it as graphs and charts. For example, the aggregation unit displays crime rates on a map and provides it in a visually easy-to-understand format. In this way, by providing public safety information to administrative agencies, it can contribute to improving public safety in the region.

[0036] The aggregation unit provides the collected data to private companies as secondary processed data. For example, the aggregation unit analyzes the collected data and generates statistical information. The aggregation unit can also organize the generated statistical information as secondary processed data. For example, the aggregation unit analyzes crime rates and disaster prevention information for each region and provides it to private companies. Furthermore, the aggregation unit can visualize the secondary processed data and display it as graphs and charts. For example, the aggregation unit displays crime rates on a map and provides it in a visually easy-to-understand format. In this way, by providing public safety information to private companies, it can contribute to improving public safety in the region.

[0037] The analysis unit analyzes voice call data and messaging app chat data from elderly people to detect suspicious conversations and keywords. For example, the analysis unit uses AI to analyze voice call data and detect suspicious conversations. For example, the analysis unit uses speech recognition technology to convert voice data into text data and then analyzes the text data. The analysis unit can also analyze messaging app chat data and detect conversations that match keywords. For example, the analysis unit uses natural language processing technology to analyze text data and detect conversations containing specific keywords. Furthermore, the analysis unit can convert voice data into text data and then analyze the text data. For example, the analysis unit uses speech recognition technology to convert voice data into text data and then analyzes the text data. By detecting suspicious conversations and keywords, it is possible to support the safety of elderly people in their daily lives.

[0038] The reporting department directly reports any detected suspicious conversations to the contracting family members or guardians. The reporting department can report suspicious conversations via email or SMS, for example. For instance, it might notify recipients of suspicious conversations via email. The reporting department can also report suspicious conversations through a dedicated application, for example, a smartphone app, providing real-time reports. Furthermore, the reporting department can report suspicious conversations via voice calls, for example, an automated voice call system. This allows for the reporting of suspicious conversations to family members or guardians, thereby supporting the safety of elderly individuals.

[0039] The listing unit creates a list of conversations that match the detected keywords and notifies users via email or a dashboard. For example, the listing unit creates a list of conversations containing a specific keyword and notifies users via email. For example, the listing unit notifies users of conversations that match the keyword via email. The listing unit can also display conversations that match the keyword using a dashboard. For example, the listing unit displays a list of conversations that match the keyword using a web dashboard. Furthermore, the listing unit can also notify users of conversations that match the keyword using an alert function. For example, the listing unit notifies users of conversations that match the keyword using real-time alerts. In this way, by listing and disseminating conversations that match the keyword, it is possible to support the safety of elderly people's lives.

[0040] The data collection unit can analyze the past call history of elderly individuals and select the optimal data collection method. For example, the data collection unit can identify the times of day when elderly individuals frequently make calls and collect data during those times. The data collection unit can also prioritize data collection when elderly individuals call specific individuals. Furthermore, the data collection unit can focus on collecting calls from individuals with a high risk of fraud from the elderly individual's call history. This allows for the selection of the optimal data collection method by analyzing past call history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input call history data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter data based on the elderly person's current living situation and areas of interest during data collection. For example, if an elderly person is interested in health, the data collection unit will prioritize collecting health-related call data. The data collection unit can also collect call data related to a specific hobby if the elderly person has one. Furthermore, if an elderly person is in a specific living situation, the data collection unit can collect call data related to that situation. This allows for the collection of more relevant data by filtering the data based on the elderly person's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input call data into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data based on the geographical location information of elderly individuals. For example, if an elderly person is in a specific area, the data collection unit will prioritize the collection of data related to that area. Furthermore, if an elderly person is traveling, the data collection unit can also collect data related to their travel destination. Additionally, if an elderly person is at home, the data collection unit can prioritize the collection of data around their home. This allows for the collection of more relevant data by basing data collection on the geographical location information of elderly individuals. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze the social media activities of elderly individuals and collect relevant data during data collection. For example, the data collection unit can prioritize collecting call data with people that elderly individuals frequently interact with on social media. The data collection unit can also collect data related to topics that elderly individuals show interest in on social media. Furthermore, the data collection unit can collect data related to groups that elderly individuals participate in on social media. This allows for the collection of more relevant data by collecting data based on the social media activities of elderly individuals. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data into a generating AI and have the generating AI perform an analysis based on importance.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a fraud detection algorithm to data related to fraud. It can also apply a health status analysis algorithm to data related to health. Furthermore, it can apply a social activity analysis algorithm to data related to social activities. By applying analysis algorithms according to the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data into a generating AI and have the generating AI perform analysis according to the category.

[0046] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of recently collected data. Furthermore, the analysis unit can also determine the priority of analysis for historical data based on its importance. Additionally, for data collected during a specific period, the analysis unit can determine the priority of analysis based on the importance of that period. This allows for more appropriate analysis results by prioritizing analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data into a generating AI and have the generating AI perform analysis based on the data collection period.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. It may also analyze data with moderate relevance with an appropriate priority. Furthermore, it may postpone the analysis of data with low relevance. By adjusting the order of analysis based on the relevance of the data, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data into a generating AI and have the generating AI perform analysis based on relevance.

[0048] The reporting unit can adjust the level of detail in its reports based on the importance of the data. For example, it can provide detailed reports for highly important data, and simplified reports for less important data. Furthermore, it can provide reports with a moderate level of detail for data of moderate importance. By adjusting the level of detail based on the importance of the data, more appropriate reporting becomes possible. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input data into a generating AI and have the generating AI generate a report based on importance.

[0049] The reporting unit can apply different reporting algorithms depending on the data category when reporting. For example, the reporting unit can apply a fraud reporting algorithm to data related to fraud. It can also apply a health reporting algorithm to data related to health. Furthermore, it can apply a social activity reporting algorithm to data related to social activities. By applying a reporting algorithm according to the data category, more appropriate reporting becomes possible. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input data into a generating AI and have the generating AI execute a report according to the category.

[0050] The reporting unit can adjust the order of reporting based on when the data was collected. For example, the reporting unit may prioritize reporting recently collected data. It can also determine the order of reporting for historical data based on its importance. Furthermore, for data collected during a specific period, the reporting unit can determine the order of reporting based on the importance of that period. This allows for more appropriate reporting by adjusting the order of reporting based on when the data was collected. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input data into a generating AI and have the generating AI generate reports based on the collection date.

[0051] The reporting unit can adjust the content of its reports based on the relevance of the data. For example, the reporting unit may prioritize reporting data with high relevance. It may also report data with moderate relevance with appropriate priority. Furthermore, it may postpone reporting data with low relevance. By adjusting the content of the reports based on the relevance of the data, more appropriate reporting becomes possible. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input data into a generating AI and have the generating AI generate a report based on relevance.

[0052] The listing unit can adjust the level of detail in the listing based on the importance of the data. For example, the listing unit can create detailed listings for data of high importance. It can also create simplified listings for data of low importance. Furthermore, it can create listings with an appropriate level of detail for data of medium importance. By adjusting the level of detail in the listing based on the importance of the data, more appropriate listings can be created. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input data into a generating AI and have the generating AI perform listing based on importance.

[0053] The listing unit can apply different listing algorithms depending on the data category during the listing process. For example, the listing unit can apply a fraud listing algorithm to data related to fraud. It can also apply a health listing algorithm to data related to health. Furthermore, it can apply a social activity listing algorithm to data related to social activities. By applying a listing algorithm according to the data category, more appropriate listing becomes possible. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input data into a generating AI and have the generating AI perform listing according to the category.

[0054] The listing unit can adjust the listing order based on the data collection timing during the listing process. For example, the listing unit prioritizes listing recently collected data. Furthermore, for historical data, the listing unit can determine the listing order based on importance. Additionally, for data collected during a specific period, the listing unit can determine the listing order based on the importance of that period. This allows for more appropriate listing by adjusting the listing order based on the data collection timing. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input data into a generating AI and have the generating AI perform listing based on the collection timing.

[0055] The listing unit can adjust the content of the list based on the relevance of the data during the listing process. For example, the listing unit can prioritize listing data with high relevance. It can also list data with moderate relevance with an appropriate priority. Furthermore, it can list data with low relevance at a later date. By adjusting the content of the list based on the relevance of the data, a more appropriate list can be created. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input data into a generating AI and have the generating AI perform relevance-based listing.

[0056] The aggregation unit can adjust the level of detail of the aggregation based on the importance of the data. For example, the aggregation unit can perform detailed aggregation for data of high importance. It can also perform simplified aggregation for data of low importance. Furthermore, it can aggregate data of moderate importance with an appropriate level of detail. By adjusting the level of detail of the aggregation based on the importance of the data, more appropriate aggregation becomes possible. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input data into a generating AI and have the generating AI perform aggregation based on importance.

[0057] The aggregation unit can apply different aggregation algorithms depending on the data category during aggregation. For example, the aggregation unit can apply a fraud aggregation algorithm to data related to fraud. It can also apply a health aggregation algorithm to data related to health. Furthermore, it can apply a social activity aggregation algorithm to data related to social activities. By applying an aggregation algorithm according to the data category, more appropriate aggregation becomes possible. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input data into a generating AI and have the generating AI perform aggregation according to the category.

[0058] The aggregation unit can adjust the aggregation order based on the data collection timing during aggregation. For example, the aggregation unit prioritizes the aggregation of recently collected data. Furthermore, for historical data, the aggregation unit can determine the aggregation order based on importance. Additionally, for data collected during a specific period, the aggregation unit can determine the aggregation order based on the importance of that period. This allows for more appropriate aggregation by adjusting the aggregation order based on the data collection timing. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input data into a generating AI and have the generating AI perform aggregation based on the collection timing.

[0059] The aggregation unit can adjust the aggregation process based on the relevance of the data. For example, the aggregation unit prioritizes aggregating data with high relevance. It can also aggregate data with moderate relevance with appropriate priority. Furthermore, it can postpone aggregating data with low relevance. By adjusting the aggregation process based on the relevance of the data, more appropriate aggregation becomes possible. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input data into a generating AI and have the generating AI perform aggregation based on relevance.

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

[0061] The life safety support system can also be equipped with a behavioral analysis unit that learns the behavioral patterns of the elderly and detects abnormal behavior. The behavioral analysis unit can detect abnormalities, for example, when an elderly person deviates from their normal behavioral patterns. For example, if an elderly person who normally goes for a walk at the same time every day suddenly stops doing so, the behavioral analysis unit can detect the abnormality and notify family members or guardians. The behavioral analysis unit can also detect abnormalities when an elderly person moves beyond their normal living area. For example, if an elderly person stays in a place they don't usually go for an extended period of time, the behavioral analysis unit can detect the abnormality and issue a warning. Furthermore, the behavioral analysis unit can detect abnormalities when an elderly person deviates from their normal eating patterns. For example, if the frequency or amount of meals decreases sharply, the behavioral analysis unit can detect the abnormality and prompt appropriate action. In this way, life safety can be further enhanced by monitoring the behavioral patterns of the elderly and detecting abnormalities early.

[0062] The life safety support system can also include a health management unit that monitors the health status of elderly people. The health management unit can, for example, collect vital data such as heart rate, blood pressure, and body temperature using wearable devices. For instance, if the heart rate is abnormally high or low, the health management unit can detect the abnormality and notify family members or medical institutions. The health management unit can also monitor sleep patterns and detect abnormalities. For example, if sleep duration is extremely short or long, the health management unit can detect the abnormality and prompt appropriate action. Furthermore, the health management unit can manage meal records and detect nutritional imbalances. For example, if a specific nutrient is deficient, the health management unit can issue a warning and encourage improvement of nutritional balance. This comprehensive monitoring of the health status of elderly people and the early detection of abnormalities further enhances life safety.

[0063] The life safety support system can also include a social activity support section to assist the social activities of the elderly. This section can, for example, provide information to help the elderly participate in local events and activities. For instance, it can notify them of events held at nearby community centers. It can also provide opportunities for the elderly to participate in volunteer activities. For example, it can provide information on local volunteer activities and encourage participation. Furthermore, the social activity support section can provide a platform to facilitate online interaction among the elderly. For example, it can offer online forums and video chat functions to increase opportunities for the elderly to interact with others. This can improve the quality of life by supporting the social activities of the elderly and preventing isolation.

[0064] The life safety support system can also include a hobby support section to support the hobbies and interests of the elderly. The hobby support section can, for example, provide information related to areas of interest to the elderly. For instance, an elderly person whose hobby is gardening can be provided with information on seasonal gardening tips and how to care for plants. The hobby support section can also suggest new hobbies for the elderly. For example, it can suggest new hobbies such as handicrafts, painting, or music, and provide information on related workshops and classes. Furthermore, the hobby support section can provide opportunities for the elderly to interact with others through their hobbies. For example, it can provide information on hobby-related clubs and groups and encourage participation. This can support the hobbies and interests of the elderly and improve their quality of life.

[0065] The life safety support system can also be equipped with an environmental monitoring unit that monitors the living environment of the elderly and detects abnormalities. The environmental monitoring unit can, for example, monitor indoor temperature, humidity, and air quality and detect abnormalities. For example, if the room temperature is abnormally high or low, the environmental monitoring unit can issue a warning and prompt appropriate action. The environmental monitoring unit can also detect hazards such as fire and gas leaks. For example, it can detect abnormalities using smoke and gas detection sensors and issue an alarm. Furthermore, the environmental monitoring unit can also monitor the usage status of lighting and home appliances and detect abnormalities. For example, if there are home appliances that have not been used for a long time, the environmental monitoring unit can detect the abnormality and prompt appropriate action. In this way, life safety can be further enhanced by comprehensively monitoring the living environment of the elderly and detecting abnormalities early.

[0066] The life safety support system can also include a mobility support unit to assist elderly people with their travel. For example, the mobility support unit can suggest safe routes when elderly people go out. For instance, it can suggest routes with less traffic or barrier-free routes. Furthermore, the mobility support unit can provide support when elderly people use public transportation. For example, it can provide bus and train timetables and transfer information to support smooth travel. In addition, the mobility support unit can respond if an elderly person gets lost. For example, it can use GPS to locate the elderly person and notify family members or guardians. This can improve the quality of life for elderly people by supporting their travel and promoting safe outings.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects voice call data and messaging app chat data from elderly individuals. For example, it collects voice call data using the communication carrier's network and retrieves chat data using the messaging app's API. It also collects voice call recordings and call logs. Step 2: The analysis unit analyzes the data collected by the collection unit and detects suspicious conversations or keywords. For example, it uses AI to analyze voice call data and detect suspicious conversations. It also uses natural language processing technology to analyze text data and detect conversations containing specific keywords. Furthermore, it uses speech recognition technology to convert voice data into text data and analyzes the text data. Step 3: The reporting department reports suspicious conversations detected by the analysis department to the client's family or guardian. For example, suspicious conversations are reported via email or SMS, and real-time reports are made using a dedicated application or automated voice call system. Step 4: The listing unit creates a list of conversations that match the keywords detected by the analysis unit and notifies users via email and dashboards. For example, it lists conversations containing specific keywords, notifies users via email, and displays them in a list using a web dashboard. Furthermore, it uses real-time alerts to notify users of conversations that match the keywords. Step 5: The aggregation unit aggregates the data collected by the collection unit by region. For example, it classifies and aggregates the collected data by region and generates statistical information. Furthermore, it visualizes the data for each region and displays it as graphs and charts.

[0069] (Example of form 2) The life safety support system according to an embodiment of the present invention is a system that supports the life safety of the elderly by collecting and analyzing voice call data and messaging app talk data of the elderly. The life safety support system supports the life safety of the elderly by using voice call data and digital data from the telephones that the elderly normally use, as well as messaging app talk data. Furthermore, this digital data is secondarily utilized and provided to government agencies and companies as a foundation for improving local public safety. For example, the life safety support system collects voice call data and messaging app talk data of the elderly. This data is collected using the lines of a telecommunications carrier. Next, the collected data is analyzed by AI to detect suspicious conversations and those that match keywords. For example, if an elderly person is having a conversation that may be fraudulent, the AI ​​will detect the conversation and report it directly to the family member or guardian of the person with whom the contract is made. In addition, conversations that match keywords are listed and notified via email and dashboard. Furthermore, the collected data is aggregated by region and provided to government agencies as primary processed data for public safety information such as crime and disaster prevention. Aggregated processed data (secondary processed data) by region is provided to private companies. This contributes to improving local public safety. This service will be a powerful tool for protecting the safety of elderly people, both for their families and guardians. It will also have significance as a socially responsible business for government agencies and private companies, by providing information that contributes to improving local security. Specific products offered include: 1. Directly reporting suspicious conversations involving elderly people to their contracted family members or guardians. 2. Compiling lists of keywords found in elderly people's conversations and disseminating them via email and dashboards. 3. Collecting the above data by region and providing it to government agencies as public safety information, such as crime and disaster prevention data. 4. Providing private companies with aggregated and processed data (secondary processed data) by region. This system will ensure the safety of elderly people who may be targets of fraud and disseminate local security information across Japan. Furthermore, it will promote safety-related actions and realize a smart security system as a foundation for improving public safety. Thus, the life safety support system can support the safety of elderly people and contribute to improving local security.

[0070] The life safety support system according to this embodiment comprises a collection unit, an analysis unit, a reporting unit, a listing unit, and an aggregation unit. The collection unit collects voice call data and messaging app talk data from elderly people. The collection unit collects voice call data, for example, using a telecommunications carrier's network. The collection unit can also collect messaging app talk data. For example, the collection unit obtains talk data using the messaging app's API. Furthermore, the collection unit can also collect digital data. For example, the collection unit collects voice call recordings and call logs. The analysis unit analyzes the data collected by the collection unit and detects suspicious conversations and those matching keywords. The analysis unit analyzes voice call data using AI, for example, to detect suspicious conversations. Furthermore, the analysis unit can analyze messaging app talk data and detect conversations that match keywords. For example, the analysis unit analyzes text data using natural language processing technology to detect conversations containing specific keywords. Furthermore, the analysis unit can convert voice data into text data and analyze the text data. For example, the analysis unit converts audio data into text data using speech recognition technology and then analyzes the text data. The reporting unit reports suspicious conversations detected by the analysis unit to the contracting party's family or guardian. The reporting unit reports suspicious conversations using, for example, email or SMS. The reporting unit can also report suspicious conversations through a dedicated application. For example, the reporting unit reports suspicious conversations in real time using a smartphone app. Furthermore, the reporting unit can also report suspicious conversations using voice calls. For example, the reporting unit reports suspicious conversations using an automated voice call system. The listing unit lists conversations that match keywords detected by the analysis unit and notifies users via email or a dashboard. For example, the listing unit lists conversations containing specific keywords and notifies users via email. The listing unit can also display conversations that match keywords using a dashboard. For example, the listing unit displays a list of conversations that match keywords using a web dashboard. Furthermore, the listing unit can also notify users of conversations that match keywords using an alert function.For example, the listing unit notifies users of conversations matching keywords using real-time alerts. The aggregation unit aggregates the data collected by the collection unit by region. The aggregation unit classifies and aggregates the collected data by region, for example. The aggregation unit can also aggregate data by region and generate statistical information. For example, the aggregation unit aggregates crime rates and disaster prevention information by region. Furthermore, the aggregation unit can visualize the data by region and display it as graphs or charts. For example, the aggregation unit displays the data by region on a map and provides it in a visually easy-to-understand format. As a result, the life safety support system according to this embodiment can support the life safety of the elderly and contribute to improving public safety in the region.

[0071] The data collection unit collects voice call data and messaging app chat data from elderly individuals. Specifically, when collecting voice call data using a telecommunications carrier's network, it simultaneously acquires metadata such as the call start time, end time, and the phone number of the person being called. This allows for analysis of call frequency and patterns of call recipients. When collecting messaging app chat data, it uses the app's API to acquire various data formats such as text messages, images, videos, and voice messages. For example, the data collection unit can filter messages containing specific keywords through the messaging app's API, prioritizing the collection of important conversations. Furthermore, the data collection unit can also collect voice call recordings and call logs, enabling detailed analysis of call content. For example, the data collection unit can save voice call recordings to cloud storage, allowing the analysis unit to access them later. This allows the data collection unit to efficiently collect elderly individuals' communication data from diverse data sources, strengthening the overall system's data infrastructure.

[0072] The analysis unit analyzes the data collected by the collection unit and detects suspicious conversations and keywords. Specifically, it uses AI to analyze voice call data and detect suspicious conversations. For example, it uses speech recognition technology to convert voice data into text data and then analyzes that text data using natural language processing technology. This allows for the rapid detection of conversations containing specific keywords or phrases. The analysis unit can also analyze chat data from messaging apps and detect conversations that match keywords. For example, it uses natural language processing technology to analyze text data and identify conversations containing suspicious keywords such as fraud or extortion. Furthermore, the analysis unit can convert voice data into text data and analyze that text data. For example, it uses speech recognition technology to convert voice data into text data and then analyzes that text data to detect conversations containing specific keywords. This allows the analysis unit to quickly and accurately analyze collected data and detect suspicious conversations in real time. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past suspicious conversation data, the system can predict risk fluctuations during specific time periods or days of the week and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0073] The reporting department reports suspicious conversations detected by the analysis department to the contracting family or guardian. Specifically, it reports suspicious conversations via email or SMS. For example, the reporting department can send the content of suspicious conversations received from the analysis department to the family via email, quickly informing them of the situation. The reporting department can also report suspicious conversations through a dedicated application. For example, it can use a smartphone app to report suspicious conversations in real time, allowing the family to respond immediately. Furthermore, the reporting department can also report suspicious conversations using voice calls. For example, it can use an automated voice call system to report suspicious conversations and notify the family directly. This allows the reporting department to reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using a combination of smartphone notifications, voice calls, SMS, and email. This allows the reporting department to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0074] The listing unit creates a list of conversations that match keywords detected by the analysis unit and notifies recipients via email or dashboard. Specifically, it lists conversations containing specific keywords and notifies recipients via email. For example, the listing unit creates a list of conversations that match keywords received from the analysis unit and sends it via email to family members or guardians. The listing unit can also display conversations that match keywords using a dashboard. For example, it can display a list of conversations that match keywords using a web dashboard, making it easy for family members or guardians to review them. Furthermore, the listing unit can also notify recipients of conversations that match keywords using an alert function. For example, it can notify recipients of conversations that match keywords using real-time alerts, allowing family members or guardians to take immediate action. This enables the listing unit to efficiently list suspicious conversations detected by the analysis unit and quickly disseminate the information to relevant parties.

[0075] The aggregation unit consolidates data collected by the collection unit by region. Specifically, it classifies and aggregates the collected data by region. For example, the aggregation unit classifies collected voice call data and messaging app chat data by region and stores them in a regional database. The aggregation unit can also aggregate regional data and generate statistical information. For example, it aggregates crime rates and disaster prevention information by region to conduct risk assessments for each region. Furthermore, the aggregation unit can visualize regional data and display it as graphs and charts. For example, it can display regional data on a map and provide it in a visually easy-to-understand format. This allows the aggregation unit to efficiently aggregate regional data and provide visually easy-to-understand information to stakeholders. In addition, based on the regional data, the aggregation unit can analyze region-specific risks and trends and formulate future countermeasures. For example, it can analyze the increasing trend in crime rates in a particular region and strengthen crime prevention measures for that region. Furthermore, the aggregation unit can link regional data with other systems and departments to conduct comprehensive risk management. This allows the aggregation unit to efficiently consolidate data from each region, thereby improving the overall performance of the system.

[0076] The aggregation unit provides administrative agencies with public safety information, such as crime and disaster prevention data, as primary processed data from the collected data. For example, the aggregation unit filters the collected data to extract information related to crime and disaster prevention. The aggregation unit can also aggregate the extracted information and organize it as primary processed data. For example, the aggregation unit aggregates crime rates and disaster prevention information and provides it to administrative agencies. Furthermore, the aggregation unit can visualize the primary processed data and display it as graphs and charts. For example, the aggregation unit displays crime rates on a map and provides it in a visually easy-to-understand format. In this way, by providing public safety information to administrative agencies, it can contribute to improving public safety in the region.

[0077] The aggregation unit provides the collected data to private companies as secondary processed data. For example, the aggregation unit analyzes the collected data and generates statistical information. The aggregation unit can also organize the generated statistical information as secondary processed data. For example, the aggregation unit analyzes crime rates and disaster prevention information for each region and provides it to private companies. Furthermore, the aggregation unit can visualize the secondary processed data and display it as graphs and charts. For example, the aggregation unit displays crime rates on a map and provides it in a visually easy-to-understand format. In this way, by providing public safety information to private companies, it can contribute to improving public safety in the region.

[0078] The analysis unit analyzes voice call data and messaging app chat data from elderly people to detect suspicious conversations and keywords. For example, the analysis unit uses AI to analyze voice call data and detect suspicious conversations. For example, the analysis unit uses speech recognition technology to convert voice data into text data and then analyzes the text data. The analysis unit can also analyze messaging app chat data and detect conversations that match keywords. For example, the analysis unit uses natural language processing technology to analyze text data and detect conversations containing specific keywords. Furthermore, the analysis unit can convert voice data into text data and then analyze the text data. For example, the analysis unit uses speech recognition technology to convert voice data into text data and then analyzes the text data. By detecting suspicious conversations and keywords, it is possible to support the safety of elderly people in their daily lives.

[0079] The reporting department directly reports any detected suspicious conversations to the contracting family members or guardians. The reporting department can report suspicious conversations via email or SMS, for example. For instance, it might notify recipients of suspicious conversations via email. The reporting department can also report suspicious conversations through a dedicated application, for example, a smartphone app, providing real-time reports. Furthermore, the reporting department can report suspicious conversations via voice calls, for example, an automated voice call system. This allows for the reporting of suspicious conversations to family members or guardians, thereby supporting the safety of elderly individuals.

[0080] The listing unit creates a list of conversations that match the detected keywords and notifies users via email or a dashboard. For example, the listing unit creates a list of conversations containing a specific keyword and notifies users via email. For example, the listing unit notifies users of conversations that match the keyword via email. The listing unit can also display conversations that match the keyword using a dashboard. For example, the listing unit displays a list of conversations that match the keyword using a web dashboard. Furthermore, the listing unit can also notify users of conversations that match the keyword using an alert function. For example, the listing unit notifies users of conversations that match the keyword using real-time alerts. In this way, by listing and disseminating conversations that match the keyword, it is possible to support the safety of elderly people's lives.

[0081] The data collection unit can estimate the emotions of elderly individuals and adjust the timing of data collection based on the estimated emotions. For example, if an elderly individual is stressed, the data collection unit can temporarily stop data collection and resume it once they are relaxed. Conversely, if the elderly individual is relaxed, the data collection unit can collect data more frequently to obtain more detailed information. Furthermore, if the elderly individual is agitated, the data collection unit can reduce data collection and wait until their emotions stabilize. This allows for more appropriate data collection by adjusting the timing of data collection according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input audio data into a generative AI and have the generative AI perform the estimation of the elderly individual's emotions.

[0082] The data collection unit can analyze the past call history of elderly individuals and select the optimal data collection method. For example, the data collection unit can identify the times of day when elderly individuals frequently make calls and collect data during those times. The data collection unit can also prioritize data collection when elderly individuals call specific individuals. Furthermore, the data collection unit can focus on collecting calls from individuals with a high risk of fraud from the elderly individual's call history. This allows for the selection of the optimal data collection method by analyzing past call history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input call history data into a generating AI and have the generating AI select the optimal data collection method.

[0083] The data collection unit can filter data based on the elderly person's current living situation and areas of interest during data collection. For example, if an elderly person is interested in health, the data collection unit will prioritize collecting health-related call data. The data collection unit can also collect call data related to a specific hobby if the elderly person has one. Furthermore, if an elderly person is in a specific living situation, the data collection unit can collect call data related to that situation. This allows for the collection of more relevant data by filtering the data based on the elderly person's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input call data into a generating AI and have the generating AI perform the filtering.

[0084] The data collection unit can estimate the emotions of elderly individuals and determine the priority of data to collect based on the estimated emotions. For example, if an elderly individual is feeling anxious, the data collection unit will prioritize collecting data to alleviate that anxiety. If the elderly individual is feeling at ease, the data collection unit can also perform normal data collection. Furthermore, if an elderly individual is agitated, the data collection unit can prioritize collecting data to calm their agitation. This allows for more appropriate data collection by prioritizing data according to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input audio data into a generative AI and have the generative AI perform the estimation of the elderly individual's emotions.

[0085] The data collection unit can prioritize the collection of highly relevant data based on the geographical location information of elderly individuals. For example, if an elderly person is in a specific area, the data collection unit will prioritize the collection of data related to that area. Furthermore, if an elderly person is traveling, the data collection unit can also collect data related to their travel destination. Additionally, if an elderly person is at home, the data collection unit can prioritize the collection of data around their home. This allows for the collection of more relevant data by basing data collection on the geographical location information of elderly individuals. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0086] The data collection unit can analyze the social media activities of elderly individuals and collect relevant data during data collection. For example, the data collection unit can prioritize collecting call data with people that elderly individuals frequently interact with on social media. The data collection unit can also collect data related to topics that elderly individuals show interest in on social media. Furthermore, the data collection unit can collect data related to groups that elderly individuals participate in on social media. This allows for the collection of more relevant data by collecting data based on the social media activities of elderly individuals. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0087] The analysis unit can estimate the emotions of elderly individuals and adjust the presentation of the analysis based on the estimated emotions. For example, if an elderly individual is feeling anxious, the analysis unit can provide a concise summary of the analysis results. If the elderly individual is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the elderly individual is agitated, the analysis unit can provide the analysis results in a visually calming presentation. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input audio data into a generative AI and have the generative AI perform the estimation of the elderly individual's emotions.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data into a generating AI and have the generating AI perform an analysis based on importance.

[0089] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a fraud detection algorithm to data related to fraud. It can also apply a health status analysis algorithm to data related to health. Furthermore, it can apply a social activity analysis algorithm to data related to social activities. By applying analysis algorithms according to the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data into a generating AI and have the generating AI perform analysis according to the category.

[0090] The analysis unit can estimate the emotions of elderly individuals and adjust the length of the analysis based on the estimated emotions. For example, if an elderly person is in a hurry, the analysis unit can provide a short, concise analysis result. If an elderly person is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if an elderly person is agitated, the analysis unit can provide the analysis result in a visually calming presentation. By adjusting the length of the analysis according to the emotions of the elderly person, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data into a generative AI and have the generative AI perform the estimation of the elderly person's emotions.

[0091] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of recently collected data. Furthermore, the analysis unit can also determine the priority of analysis for historical data based on its importance. Additionally, for data collected during a specific period, the analysis unit can determine the priority of analysis based on the importance of that period. This allows for more appropriate analysis results by prioritizing analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data into a generating AI and have the generating AI perform analysis based on the data collection period.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. It may also analyze data with moderate relevance with an appropriate priority. Furthermore, it may postpone the analysis of data with low relevance. By adjusting the order of analysis based on the relevance of the data, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data into a generating AI and have the generating AI perform analysis based on relevance.

[0093] The reporting unit can estimate the emotions of elderly individuals and adjust the reporting method based on the estimated emotions. For example, if an elderly individual is feeling anxious, the reporting unit can provide a concise and reassuring reporting method. It can also provide a detailed reporting method if the elderly individual is relaxed. Furthermore, if the elderly individual is agitated, the reporting unit can provide a visually calming reporting method. This allows for more appropriate reporting by adjusting the reporting method according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI, or not. For example, the reporting unit can input audio data into a generative AI and have the generative AI perform the estimation of the elderly individual's emotions.

[0094] The reporting unit can adjust the level of detail in its reports based on the importance of the data. For example, it can provide detailed reports for highly important data, and simplified reports for less important data. Furthermore, it can provide reports with a moderate level of detail for data of moderate importance. By adjusting the level of detail based on the importance of the data, more appropriate reporting becomes possible. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input data into a generating AI and have the generating AI generate a report based on importance.

[0095] The reporting unit can apply different reporting algorithms depending on the data category when reporting. For example, the reporting unit can apply a fraud reporting algorithm to data related to fraud. It can also apply a health reporting algorithm to data related to health. Furthermore, it can apply a social activity reporting algorithm to data related to social activities. By applying a reporting algorithm according to the data category, more appropriate reporting becomes possible. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input data into a generating AI and have the generating AI execute a report according to the category.

[0096] The reporting unit can estimate the emotions of elderly individuals and determine the priority of reports based on the estimated emotions. For example, if an elderly individual is feeling anxious, the reporting unit will prioritize reports aimed at reducing anxiety. It can also provide a normal report if the elderly individual is feeling at ease. Furthermore, if an elderly individual is agitated, the reporting unit can prioritize reports aimed at calming their agitation. This allows for more appropriate reporting by prioritizing reports according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input audio data into a generative AI and have the generative AI perform the estimation of the elderly individual's emotions.

[0097] The reporting unit can adjust the order of reporting based on when the data was collected. For example, the reporting unit may prioritize reporting recently collected data. It can also determine the order of reporting for historical data based on its importance. Furthermore, for data collected during a specific period, the reporting unit can determine the order of reporting based on the importance of that period. This allows for more appropriate reporting by adjusting the order of reporting based on when the data was collected. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input data into a generating AI and have the generating AI generate reports based on the collection date.

[0098] The reporting unit can adjust the content of its reports based on the relevance of the data. For example, the reporting unit may prioritize reporting data with high relevance. It may also report data with moderate relevance with appropriate priority. Furthermore, it may postpone reporting data with low relevance. By adjusting the content of the reports based on the relevance of the data, more appropriate reporting becomes possible. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input data into a generating AI and have the generating AI generate a report based on relevance.

[0099] The listing unit can estimate the emotions of elderly individuals and adjust the listing method based on the estimated emotions. For example, if an elderly person is feeling anxious, the listing unit can provide a concise and reassuring listing method. It can also provide a detailed listing method if the elderly person is relaxed. Furthermore, if the elderly person is agitated, the listing unit can provide a visually calming listing method. This allows for more appropriate listing by adjusting the listing method according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the listing unit may be performed using AI, or not. For example, the listing unit can input audio data into a generative AI and have the generative AI perform the estimation of the elderly person's emotions.

[0100] The listing unit can adjust the level of detail in the listing based on the importance of the data. For example, the listing unit can create detailed listings for data of high importance. It can also create simplified listings for data of low importance. Furthermore, it can create listings with an appropriate level of detail for data of medium importance. By adjusting the level of detail in the listing based on the importance of the data, more appropriate listings can be created. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input data into a generating AI and have the generating AI perform listing based on importance.

[0101] The listing unit can apply different listing algorithms depending on the data category during the listing process. For example, the listing unit can apply a fraud listing algorithm to data related to fraud. It can also apply a health listing algorithm to data related to health. Furthermore, it can apply a social activity listing algorithm to data related to social activities. By applying a listing algorithm according to the data category, more appropriate listing becomes possible. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input data into a generating AI and have the generating AI perform listing according to the category.

[0102] The listing unit can estimate the emotions of elderly individuals and determine the priority of listing based on the estimated emotions. For example, if an elderly individual is feeling anxious, the listing unit will prioritize listing data that helps alleviate anxiety. The listing unit can also perform normal listing if the elderly individual is feeling at ease. Furthermore, if an elderly individual is agitated, the listing unit can prioritize listing data that helps calm their agitation. This allows for more appropriate listing by determining the priority of listing according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the listing unit may be performed using AI, or not. For example, the listing unit can input audio data into a generative AI and have the generative AI perform the estimation of the elderly individual's emotions.

[0103] The listing unit can adjust the listing order based on the data collection timing during the listing process. For example, the listing unit prioritizes listing recently collected data. Furthermore, for historical data, the listing unit can determine the listing order based on importance. Additionally, for data collected during a specific period, the listing unit can determine the listing order based on the importance of that period. This allows for more appropriate listing by adjusting the listing order based on the data collection timing. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input data into a generating AI and have the generating AI perform listing based on the collection timing.

[0104] The listing unit can adjust the content of the list based on the relevance of the data during the listing process. For example, the listing unit can prioritize listing data with high relevance. It can also list data with moderate relevance with an appropriate priority. Furthermore, it can list data with low relevance at a later date. By adjusting the content of the list based on the relevance of the data, a more appropriate list can be created. Some or all of the above processing in the listing unit may be performed using AI, for example, or without AI. For example, the listing unit can input data into a generating AI and have the generating AI perform relevance-based listing.

[0105] The aggregation unit can estimate the emotions of elderly individuals and adjust the aggregation method based on the estimated emotions. For example, if an elderly individual is feeling anxious, the aggregation unit can provide a concise and reassuring aggregation method. It can also provide a detailed aggregation method if the elderly individual is relaxed. Furthermore, if the elderly individual is agitated, the aggregation unit can provide a visually calming aggregation method. This allows for more appropriate aggregation by adjusting the aggregation method according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the aggregation unit may be performed using AI, or not. For example, the aggregation unit can input audio data into a generative AI and have the generative AI perform the estimation of the elderly individual's emotions.

[0106] The aggregation unit can adjust the level of detail of the aggregation based on the importance of the data. For example, the aggregation unit can perform detailed aggregation for data of high importance. It can also perform simplified aggregation for data of low importance. Furthermore, it can aggregate data of moderate importance with an appropriate level of detail. By adjusting the level of detail of the aggregation based on the importance of the data, more appropriate aggregation becomes possible. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input data into a generating AI and have the generating AI perform aggregation based on importance.

[0107] The aggregation unit can apply different aggregation algorithms depending on the data category during aggregation. For example, the aggregation unit can apply a fraud aggregation algorithm to data related to fraud. It can also apply a health aggregation algorithm to data related to health. Furthermore, it can apply a social activity aggregation algorithm to data related to social activities. By applying an aggregation algorithm according to the data category, more appropriate aggregation becomes possible. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input data into a generating AI and have the generating AI perform aggregation according to the category.

[0108] The aggregation unit can estimate the emotions of elderly individuals and determine aggregation priorities based on the estimated emotions. For example, if an elderly individual is feeling anxious, the aggregation unit will prioritize aggregating data to alleviate that anxiety. The aggregation unit can also perform normal aggregation if the elderly individual is feeling at ease. Furthermore, if an elderly individual is agitated, the aggregation unit can prioritize aggregating data to suppress that agitation. This allows for more appropriate aggregation by determining aggregation priorities according to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the aggregation unit may be performed using AI, or not. For example, the aggregation unit can input audio data into a generative AI and have the generative AI perform the estimation of the elderly individual's emotions.

[0109] The aggregation unit can adjust the aggregation order based on the data collection timing during aggregation. For example, the aggregation unit prioritizes the aggregation of recently collected data. Furthermore, for historical data, the aggregation unit can determine the aggregation order based on importance. Additionally, for data collected during a specific period, the aggregation unit can determine the aggregation order based on the importance of that period. This allows for more appropriate aggregation by adjusting the aggregation order based on the data collection timing. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input data into a generating AI and have the generating AI perform aggregation based on the collection timing.

[0110] The aggregation unit can adjust the aggregation process based on the relevance of the data. For example, the aggregation unit prioritizes aggregating data with high relevance. It can also aggregate data with moderate relevance with appropriate priority. Furthermore, it can postpone aggregating data with low relevance. By adjusting the aggregation process based on the relevance of the data, more appropriate aggregation becomes possible. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input data into a generating AI and have the generating AI perform aggregation based on relevance.

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

[0112] The life safety support system can also be equipped with a behavioral analysis unit that learns the behavioral patterns of the elderly and detects abnormal behavior. The behavioral analysis unit can detect abnormalities, for example, when an elderly person deviates from their normal behavioral patterns. For example, if an elderly person who normally goes for a walk at the same time every day suddenly stops doing so, the behavioral analysis unit can detect the abnormality and notify family members or guardians. The behavioral analysis unit can also detect abnormalities when an elderly person moves beyond their normal living area. For example, if an elderly person stays in a place they don't usually go for an extended period of time, the behavioral analysis unit can detect the abnormality and issue a warning. Furthermore, the behavioral analysis unit can detect abnormalities when an elderly person deviates from their normal eating patterns. For example, if the frequency or amount of meals decreases sharply, the behavioral analysis unit can detect the abnormality and prompt appropriate action. In this way, life safety can be further enhanced by monitoring the behavioral patterns of the elderly and detecting abnormalities early.

[0113] The life safety support system can also include a health management unit that monitors the health status of elderly people. The health management unit can, for example, collect vital data such as heart rate, blood pressure, and body temperature using wearable devices. For instance, if the heart rate is abnormally high or low, the health management unit can detect the abnormality and notify family members or medical institutions. The health management unit can also monitor sleep patterns and detect abnormalities. For example, if sleep duration is extremely short or long, the health management unit can detect the abnormality and prompt appropriate action. Furthermore, the health management unit can manage meal records and detect nutritional imbalances. For example, if a specific nutrient is deficient, the health management unit can issue a warning and encourage improvement of nutritional balance. This comprehensive monitoring of the health status of elderly people and the early detection of abnormalities further enhances life safety.

[0114] The life safety support system can also include an emotion response unit that estimates the emotions of elderly people and provides appropriate responses based on those estimates. For example, if an elderly person is feeling stressed, the emotion response unit can provide relaxing music or videos. It can also send notifications to encourage communication with family and friends if an elderly person is feeling lonely. For example, it can suggest video calls or encourage sending messages. Furthermore, if an elderly person is feeling joy, the emotion response unit can provide a platform for sharing those emotions. For example, it can record moments of joy in photos or videos and share them with family and friends. This can improve the quality of life for elderly people by providing appropriate responses based on their emotions.

[0115] The life safety support system can also include a social activity support section to assist the social activities of the elderly. This section can, for example, provide information to help the elderly participate in local events and activities. For instance, it can notify them of events held at nearby community centers. It can also provide opportunities for the elderly to participate in volunteer activities. For example, it can provide information on local volunteer activities and encourage participation. Furthermore, the social activity support section can provide a platform to facilitate online interaction among the elderly. For example, it can offer online forums and video chat functions to increase opportunities for the elderly to interact with others. This can improve the quality of life by supporting the social activities of the elderly and preventing isolation.

[0116] The life safety support system can also include a hobby support section to support the hobbies and interests of the elderly. The hobby support section can, for example, provide information related to areas of interest to the elderly. For instance, an elderly person whose hobby is gardening can be provided with information on seasonal gardening tips and how to care for plants. The hobby support section can also suggest new hobbies for the elderly. For example, it can suggest new hobbies such as handicrafts, painting, or music, and provide information on related workshops and classes. Furthermore, the hobby support section can provide opportunities for the elderly to interact with others through their hobbies. For example, it can provide information on hobby-related clubs and groups and encourage participation. This can support the hobbies and interests of the elderly and improve their quality of life.

[0117] The life safety support system may also include a communication adjustment unit that estimates the emotions of elderly people and adjusts the method of communication based on those estimated emotions. For example, if an elderly person is feeling anxious, the communication adjustment unit may encourage communication using gentle language and tone to provide a sense of security. The communication adjustment unit can also engage in normal communication if the elderly person is relaxed. For example, it may encourage conversation about everyday topics or topics of interest. Furthermore, if an elderly person is agitated, the communication adjustment unit may encourage calming communication. For example, it may offer advice such as encouraging deep breathing or provide relaxing topics. In this way, the quality of life can be improved by providing appropriate communication according to the emotions of elderly people.

[0118] The life safety support system can also be equipped with an environmental monitoring unit that monitors the living environment of the elderly and detects abnormalities. The environmental monitoring unit can, for example, monitor indoor temperature, humidity, and air quality and detect abnormalities. For example, if the room temperature is abnormally high or low, the environmental monitoring unit can issue a warning and prompt appropriate action. The environmental monitoring unit can also detect hazards such as fire and gas leaks. For example, it can detect abnormalities using smoke and gas detection sensors and issue an alarm. Furthermore, the environmental monitoring unit can also monitor the usage status of lighting and home appliances and detect abnormalities. For example, if there are home appliances that have not been used for a long time, the environmental monitoring unit can detect the abnormality and prompt appropriate action. In this way, life safety can be further enhanced by comprehensively monitoring the living environment of the elderly and detecting abnormalities early.

[0119] The life safety support system can also include a reminder unit that estimates the emotions of elderly people and provides reminders based on those estimated emotions. For example, if an elderly person is feeling stressed, the reminder unit can provide a reminder to encourage them to take a break to relax. The reminder unit can also remind elderly people of daily tasks that they tend to forget. For example, it can provide reminders for taking medication or regular health checks. Furthermore, the reminder unit can provide reminders for events and activities that elderly people are looking forward to. For example, it can provide reminders for appointments with friends or hobby classes. In this way, by providing appropriate reminders that are tailored to the emotions of elderly people, their quality of life can be improved.

[0120] The life safety support system can also include a mobility support unit to assist elderly people with their travel. For example, the mobility support unit can suggest safe routes when elderly people go out. For instance, it can suggest routes with less traffic or barrier-free routes. Furthermore, the mobility support unit can provide support when elderly people use public transportation. For example, it can provide bus and train timetables and transfer information to support smooth travel. In addition, the mobility support unit can respond if an elderly person gets lost. For example, it can use GPS to locate the elderly person and notify family members or guardians. This can improve the quality of life for elderly people by supporting their travel and promoting safe outings.

[0121] The life safety support system can also include an entertainment section that estimates the emotions of elderly people and provides entertainment based on those estimated emotions. For example, if an elderly person is feeling stressed, the entertainment section can provide relaxing movies or music. If an elderly person is feeling bored, the entertainment section can also provide engaging games or puzzles. For example, it can provide brain training games or crossword puzzles. Furthermore, if an elderly person is feeling happy, the entertainment section can provide entertainment to further enhance that feeling. For example, it can provide concert videos of their favorite artists or photo albums of cherished memories. In this way, by providing appropriate entertainment according to the emotions of elderly people, their quality of life can be improved.

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

[0123] Step 1: The collection unit collects voice call data and messaging app chat data from elderly individuals. For example, it collects voice call data using the communication carrier's network and retrieves chat data using the messaging app's API. It also collects voice call recordings and call logs. Step 2: The analysis unit analyzes the data collected by the collection unit and detects suspicious conversations or keywords. For example, it uses AI to analyze voice call data and detect suspicious conversations. It also uses natural language processing technology to analyze text data and detect conversations containing specific keywords. Furthermore, it uses speech recognition technology to convert voice data into text data and analyzes the text data. Step 3: The reporting department reports suspicious conversations detected by the analysis department to the client's family or guardian. For example, suspicious conversations are reported via email or SMS, and real-time reports are made using a dedicated application or automated voice call system. Step 4: The listing unit creates a list of conversations that match the keywords detected by the analysis unit and notifies users via email and dashboards. For example, it lists conversations containing specific keywords, notifies users via email, and displays them in a list using a web dashboard. Furthermore, it uses real-time alerts to notify users of conversations that match the keywords. Step 5: The aggregation unit aggregates the data collected by the collection unit by region. For example, it classifies and aggregates the collected data by region and generates statistical information. Furthermore, it visualizes the data for each region and displays it as graphs and charts.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, reporting unit, listing unit, and aggregation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects voice call data and messaging app talk data using the communication I / F 44 of the smart device 14. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to detect suspicious conversations or those matching keywords. The reporting unit reports suspicious conversations to the contracting party's family or guardian using, for example, the control unit 46A of the smart device 14. The listing unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and lists conversations matching keywords and notifies them via email or dashboard. The aggregation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and aggregates the collected data by region and provides it to administrative agencies as primary processed data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, reporting unit, listing unit, and aggregation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects voice call data and messaging app talk data using the communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and detects suspicious conversations or those matching keywords. The reporting unit reports suspicious conversations to the contracting party's family or guardian, for example, using the control unit 46A of the smart glasses 214. The listing unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which lists conversations matching keywords and notifies them via email or dashboard. The aggregation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which aggregates the collected data by region and provides it to administrative agencies as primary processed data. The correspondence between each unit and the devices or control units is not limited to the examples described above and can be modified in various ways.

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

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

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

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

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

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

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

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

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

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

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

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, reporting unit, listing unit, and aggregation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects voice call data and messaging application talk data using the communication I / F 44 of the headset terminal 314. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and detects suspicious conversations or those matching keywords. The reporting unit reports suspicious conversations to the contracting party's family or guardian using, for example, the control unit 46A of the headset terminal 314. The listing unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which lists conversations matching keywords and notifies them via email or dashboard. The aggregation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which aggregates the collected data by region and provides it to administrative agencies as primary processed data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, reporting unit, listing unit, and aggregation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects voice call data and messaging app talk data using the communication I / F 44 of the robot 414. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and detects suspicious conversations or those matching keywords. The reporting unit reports suspicious conversations to the contracting party's family or guardian using, for example, the control unit 46A of the robot 414. The listing unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which lists conversations matching keywords and notifies them via email or dashboard. The aggregation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which aggregates the collected data by region and provides it to administrative agencies as primary processed data. The correspondence between each unit and the devices or control units is not limited to the examples described above and can be modified in various ways.

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

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

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

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

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

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

[0186] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0195] (Note 1) A collection unit that collects voice call data and messaging app chat data from elderly people, An analysis unit analyzes the data collected by the aforementioned collection unit and detects items that match suspicious conversations or keywords. A reporting unit reports suspicious conversations detected by the aforementioned analysis unit to the client's family or guardian, The analysis unit creates a list of conversations that match the keywords detected by the analysis unit and notifies users via email or dashboard. The collection unit comprises an aggregation unit that aggregates the data collected by the collection unit for each region. A system characterized by the following features. (Note 2) The aforementioned aggregation unit is The collected data is used as primary processed data to provide public safety information, such as crime and disaster prevention information, to government agencies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned aggregation unit is The collected data will be provided to private companies as secondary processed data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The system analyzes voice call data and messaging app chat data from elderly users to detect suspicious conversations and keywords. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting department, Report any detected suspicious conversations directly to the client's family or guardian. The system described in Appendix 1, characterized by the features described herein. (Note 6) The listing unit, List conversations that match the detected keywords and notify users via email and dashboards. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the emotions of elderly individuals and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the past call history of elderly individuals and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the current living situation and areas of interest of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the emotions of older adults and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, we prioritize the collection of highly relevant data based on the geographical location information of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, analyze the social media activity of older adults and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of elderly people and adjust the representation of the analysis based on the estimated emotions of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the emotions of elderly individuals and adjusts the length of the analysis based on the estimated emotions of these individuals. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reporting department, We estimate the emotions of older adults and adjust the reporting method based on the estimated emotions of older adults. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reporting department, When reporting, adjust the level of detail in the report based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting department, When reporting, different reporting algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reporting department, The system estimates the emotions of older adults and prioritizes reporting based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting department, When reporting, adjust the order of reporting based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting department, When reporting, adjust the content of the report based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The listing unit, We estimate the emotions of older adults and adjust the listing method based on the estimated emotions of older adults. The system described in Appendix 1, characterized by the features described herein. (Note 26) The listing unit, When creating lists, adjust the level of detail in the lists based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The listing unit, When creating lists, different listing algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The listing unit, The system estimates the emotions of elderly individuals and determines the priority of the list based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The listing unit, When creating the list, adjust the order of the list based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The listing unit, When creating lists, adjust the list content based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned aggregation unit is We estimate the emotions of elderly people and adjust the aggregation method based on the estimated emotions of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned aggregation unit is During aggregation, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned aggregation unit is During aggregation, different aggregation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned aggregation unit is The system estimates the emotions of elderly individuals and determines aggregation priorities based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned aggregation unit is During aggregation, adjust the aggregation order based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned aggregation unit is During aggregation, adjust the aggregation content based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A collection unit that collects voice call data and messaging app chat data from elderly people, An analysis unit analyzes the data collected by the aforementioned collection unit and detects items that match suspicious conversations or keywords. A reporting unit reports suspicious conversations detected by the aforementioned analysis unit to the client's family or guardian, The analysis unit creates a list of conversations that match the keywords detected by the analysis unit and notifies users via email or dashboard. The collection unit comprises an aggregation unit that aggregates the data collected by the collection unit for each region. A system characterized by the following features.

2. The aforementioned aggregation unit is The collected data is used as primary processed data to provide public safety information, such as crime and disaster prevention information, to government agencies. The system according to feature 1.

3. The aforementioned aggregation unit is The collected data will be provided to private companies as secondary processed data. The system according to feature 1.

4. The aforementioned analysis unit, The system analyzes voice call data and messaging app chat data from elderly users to detect suspicious conversations and keywords. The system according to feature 1.

5. The aforementioned reporting department, Report any detected suspicious conversations directly to the client's family or guardian. The system according to feature 1.

6. The listing unit, List conversations that match the detected keywords and notify users via email and dashboards. The system according to feature 1.

7. The aforementioned collection unit is We estimate the emotions of elderly individuals and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the past call history of elderly individuals and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the current living situation and areas of interest of elderly individuals. The system according to feature 1.

10. The aforementioned collection unit is We estimate the emotions of older adults and prioritize the data to collect based on those estimated emotions. The system according to feature 1.

Images