system

Abstract

The system according to this embodiment aims to ensure that elderly people can only receive safe phone calls. [Solution] The system according to the embodiment comprises an analysis unit, a determination unit, and a ringing unit. The analysis unit analyzes the content of the phone call. The determination unit determines whether or not the call is secure based on the content analyzed by the analysis unit. The ringing unit sounds a ringtone if the determination unit determines that the call is secure.

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 and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the elderly are defenseless against fraud calls and it is difficult to receive only safe calls.

[0005] The system according to the embodiment aims to enable the elderly to receive only safe calls.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a determination unit, and a ringing unit. The analysis unit analyzes the content of the call. The determination unit determines whether the call is a safe call based on the content analyzed by the analysis unit. The ringing unit rings a ringing tone when the determination unit determines that the call is a safe call.

Effects of the Invention

[0007] The system according to this embodiment allows elderly people to receive only safe phone calls. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 telephone call receiving system according to an embodiment of the present invention is a system that uses AI to ensure that only safe calls are received at the homes of elderly people. When a call comes in, the telephone call receiving system uses AI to analyze the content of the call and determine whether it is a safe call. Next, the ringtone is sounded only if the call is determined to be safe. This allows elderly people to answer calls with peace of mind. For example, when a call comes in, the AI ​​uses speech recognition technology to convert the content of the call into text. For example, a call containing "keywords that may indicate fraud" is determined to be unsafe. This reduces the risk of fraud. Next, the AI ​​analyzes the content of the call and determines whether it is a safe call. For example, a call from family or a friend is determined to be safe, while a call from an unknown number or a call containing keywords that may indicate fraud is determined to be unsafe. This allows elderly people to answer calls with peace of mind. Furthermore, the ringtone is sounded only if the call is determined to be safe. This allows elderly people to answer calls with peace of mind. For example, the ringtone will only sound when a call comes in from family or a friend, allowing elderly people to answer calls with peace of mind. This mechanism allows elderly people to answer calls with peace of mind. This system can reduce the risk of fraud and ensure that only safe calls are received. For example, by reducing the risk of fraud, elderly people can receive calls with peace of mind. Also, because only calls from family and friends are received, elderly people can receive calls with peace of mind. In this way, the phone receiving system allows elderly people to receive calls with peace of mind.

[0029] The telephone call receiving system according to this embodiment comprises an analysis unit, a determination unit, and a ringing unit. The analysis unit analyzes the content of the telephone call. The analysis unit converts the content of the telephone call into text using, for example, speech recognition technology. The analysis unit can convert the content of the telephone call into text with high accuracy using, for example, deep learning-based speech recognition technology. The analysis unit can also convert the content of the telephone call into text using HMM-based speech recognition technology. The analysis unit can convert the content of the telephone call into text in real time using, for example, speech recognition technology. The determination unit determines whether or not a call is safe based on the content analyzed by the analysis unit. The determination unit determines, for example, that a call from family or a friend is safe. The determination unit can determine, for example, that a call from a person registered in the contact list is safe. The determination unit can also determine that a call from a specific telephone number is safe. The determination unit can prioritize determining that a call from family or a friend is safe. The determination unit determines that a call containing potentially fraudulent keywords is unsafe. The determination unit can determine that calls containing keywords such as "money," "bank transfer," and "urgent" are unsafe. The determination unit can also update potentially fraudulent keywords by referring to the latest information on fraud methods. For example, the determination unit can automatically extract potentially fraudulent keywords and determine them to be unsafe. The ringing unit sounds a ringtone when the determination unit determines the call is safe. The ringing unit can, for example, only sound the ringtone when the call is safe. The ringing unit can, for example, only sound the ringtone when a call comes in from family or a friend. Furthermore, the ringing unit can sound the ringtone only when a call comes in from a specific phone number. The ringing unit can, for example, sound a specific ringtone when the call is determined to be safe. As a result, the telephone receiving system according to this embodiment allows elderly people to receive calls with peace of mind.

[0030] The analysis unit analyzes the content of phone calls. For example, the analysis unit converts the content of phone calls into text using speech recognition technology. Specifically, by using deep learning-based speech recognition technology, the content of phone calls can be converted into text with high accuracy. Deep learning technology can accurately capture the characteristics of speech by learning from a large amount of speech data, making it possible to accurately recognize speech even in noisy environments. It is also possible to use HMM (Hidden Markov Model)-based speech recognition technology. HMMs can efficiently analyze continuous speech data by modeling the temporal changes in speech. By combining these technologies, the analysis unit can convert the content of phone calls into text in real time. Furthermore, the analysis unit can analyze the meaning of the text using not only speech recognition technology but also natural language processing technology. For example, it can extract important keywords from the text and summarize the content of the phone call. As a result, the analysis unit can analyze the content of phone calls in detail and generate highly accurate data to provide to the judgment unit.

[0031] The judgment unit determines whether a call is safe or not based on the information analyzed by the analysis unit. For example, the judgment unit can determine that a call from family or friends is safe. Specifically, it can determine that a call from someone registered in the contact list is safe. The contact list is a database containing phone numbers and names that the user has registered in advance, and the judgment unit refers to this list to verify the origin of the call. It can also determine that a call from a specific phone number is safe. For example, by registering phone numbers from trusted institutions such as banks or medical institutions in advance, these calls can be determined to be safe. Furthermore, the judgment unit determines that a call containing keywords that may indicate fraud is unsafe. For example, it can determine that a call containing keywords such as "money," "bank transfer," or "emergency" is unsafe. The judgment unit can update keywords that may indicate fraud by referring to the latest information on fraud methods. This allows the judgment unit to always determine safety based on the latest fraud information. In addition, the judgment unit can use AI to analyze the content of a call and automatically extract keywords that may indicate fraud. By learning from past fraudulent call data, the AI ​​can recognize fraud patterns and respond to new fraud methods. This allows the judgment unit to determine safety with high accuracy, providing users with an environment where they can receive calls with peace of mind.

[0032] The ringing unit sounds the ringtone when the detection unit determines that the call is safe. Specifically, it can only ring the ringtone when it is a safe call. For example, it can only ring the ringtone when a call comes in from family or a friend. The ringing unit can also ring the ringtone only when a call comes in from a specific phone number. This ensures that the user does not miss important calls. Furthermore, the ringing unit can play a specific ringtone when it determines that the call is safe. For example, by setting a specific melody for calls from family and a different melody for calls from friends, the user can intuitively determine who is calling. In addition, the ringing unit can customize the ringtone according to the user's preferences. For example, it can set a quiet ringtone at certain times and a loud notification in emergencies. This allows the ringing unit to provide a flexible notification method that suits the user's lifestyle. Furthermore, the ringing unit can use other notification methods such as vibration and flashing lights in addition to ringtones. This allows it to accommodate users with hearing impairments or users who need to receive calls in a quiet environment. As a result, the telephone receiving system according to this embodiment not only allows elderly people to receive calls with peace of mind, but also can meet the needs of various users.

[0033] The analysis unit can convert the content of a phone call into text using speech recognition technology. The analysis unit can convert the content of a phone call into text with high accuracy using, for example, deep learning-based speech recognition technology. The analysis unit can also convert the content of a phone call into text using, for example, HMM-based speech recognition technology. The analysis unit can convert the content of a phone call into text in real time using, for example, speech recognition technology. This improves the accuracy of the analysis by converting the content of the phone call into text. Speech recognition technology includes, but is not limited to, deep learning-based speech recognition technology and HMM-based speech recognition technology. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the phone audio data into a generative AI and have the generative AI perform the conversion from audio data to text data.

[0034] The determination unit can determine that calls from family and friends are safe. The determination unit can determine that calls from people registered in the contact list are safe, for example. The determination unit can also determine that calls from specific phone numbers are safe, for example. The determination unit can prioritize determining that calls from family and friends are safe, for example. This allows elderly people to receive calls with peace of mind by determining that calls from family and friends are safe. Family and friends include, but are not limited to, people registered in the contact list and calls from specific phone numbers. Some or all of the above processing in the determination unit may be performed using, for example, a generating AI, or without a generating AI. For example, the determination unit can input caller information into a generating AI and have the generating AI determine whether or not the call is safe based on the caller information.

[0035] The determination unit can determine that a phone call containing potentially fraudulent keywords is unsafe. For example, the determination unit can determine that a phone call containing keywords such as "money," "bank transfer," and "urgent" is unsafe. The determination unit can update potentially fraudulent keywords by referring to the latest information on fraud methods, for example. The determination unit can automatically extract potentially fraudulent keywords and determine that they are unsafe, for example. This can reduce the risk of fraud. Potentially fraudulent keywords include, but are not limited to, "money," "bank transfer," and "urgent." Some or all of the above processing in the determination unit may be performed using, for example, a generating AI, or without a generating AI. For example, the determination unit can input the content of a phone call into a generating AI and have the generating AI extract potentially fraudulent keywords.

[0036] The ringing unit can only ring the ringtone if the call is safe. For example, the ringing unit can only ring the ringtone if the call is safe. For example, the ringing unit can only ring the ringtone if the call is from family or a friend. The ringing unit can also ring the ringtone only if the call is from a specific phone number. For example, the ringing unit can ring a specific ringtone if it is determined to be a safe call. This allows elderly people to answer calls with peace of mind. Safe calls include, but are not limited to, calls from family or friends or calls from specific phone numbers. Some or all of the above processing in the ringing unit may be performed using, for example, a generating AI, or without a generating AI. For example, the ringing unit can input information that the determination unit has determined to be a safe call into the generating AI, and cause the generating AI to ring the ringtone.

[0037] The analysis unit can handle different languages ​​and dialects when converting telephone content to text using speech recognition technology. For example, the analysis unit can use AI to automatically detect different languages, select an appropriate speech recognition model, and convert to text. For example, the analysis unit can use dictionaries to recognize dialects and regional words and convert them to standard languages. For example, the analysis unit can adjust the accuracy of speech recognition and perform text conversion based on a language set by the user. This improves the accuracy of the analysis by supporting different languages ​​and dialects. Different languages ​​and dialects include, but are not limited to, the use of a multilingual speech recognition engine or a dialect dictionary. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input telephone audio data into a generative AI and have the generative AI perform text conversion that supports different languages ​​and dialects.

[0038] The analysis unit can improve the accuracy of its analysis by removing background noise and other noise when analyzing the content of a phone call. For example, the analysis unit can use AI to filter out background noise in real time and extract and analyze only the main voice. The analysis unit can use noise cancellation technology to clarify the content of a phone call. The analysis unit can, for example, emphasize specific frequency bands and reduce noise to improve the accuracy of speech recognition. This improves the accuracy of the analysis by removing background noise and other noise. Background noise and other noise include, but are not limited to, noise cancellation technology and filtering technology. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input phone audio data into a generative AI and have the generative AI remove background noise and other noise.

[0039] The analysis unit can highlight specific keywords or phrases when converting phone content to text using speech recognition technology. For example, the analysis unit can automatically detect potentially fraudulent keywords and highlight them in the text. The analysis unit can highlight important phrases, such as the names of family and friends. The analysis unit can highlight specific keywords set by the user. This makes it less likely that important information will be overlooked by highlighting specific keywords or phrases. Specific keywords or phrases include, but are not limited to, "important," "urgent," and "confirm." Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input phone audio data into a generative AI and have the generative AI perform the highlighting of specific keywords or phrases.

[0040] The analysis unit can improve the accuracy of its analysis by referring to past call history when analyzing the content of a phone call. For example, the analysis unit can analyze past call history to identify frequently used phrases and keywords. For example, the analysis unit can learn specific patterns from past call history to improve the accuracy of its analysis. For example, the analysis unit can identify calls that are likely to be fraudulent based on past call history to improve the accuracy of its analysis. Thus, the accuracy of the analysis is improved by referring to past call history. Past call history includes, but is not limited to, the retention period and reference frequency of call content. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past call history data into a generative AI and have the generative AI perform the analysis of the call history.

[0041] The determination unit can improve the accuracy of its determination when determining whether a call from family or friends is safe by referring to call history and contact lists. For example, the determination unit can analyze call history and determine that calls from frequently called individuals are safe. For example, the determination unit can refer to contact lists and determine that calls from registered family and friends are safe. For example, the determination unit can improve the accuracy of its determination by combining call history and contact lists. This improves the accuracy of the determination by referring to call history and contact lists. Call history and contact lists include, but are not limited to, call frequency and contact reliability. Some or all of the above processing in the determination unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the determination unit can input call history data and contact lists into a generative AI and have the generative AI perform analysis of the call history and contact lists.

[0042] The judgment unit can update its criteria by referring to the latest information on fraudulent practices when determining that a phone call containing potentially fraudulent keywords is unsafe. For example, the judgment unit can periodically update the latest information on fraudulent practices and reflect it in the criteria. For example, the judgment unit can automatically update the list of potentially fraudulent keywords and incorporate it into the criteria. For example, the judgment unit can adjust the criteria by referring to news and reports on fraudulent practices. This ensures that the criteria are always up-to-date by referring to the latest information on fraudulent practices. The latest information on fraudulent practices includes, but is not limited to, police databases and security company reports. Some or all of the above processes in the judgment unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the judgment unit can input the latest information on fraudulent practices into a generating AI and have the generating AI perform the updating of the criteria.

[0043] The determination unit can improve the accuracy of its determination when determining whether a call from family or friends is safe by considering the frequency and time of the call. For example, the determination unit can analyze the frequency of calls and determine that calls from frequently spoken individuals are safe. For example, the determination unit can consider the time of the call and determine that calls received during normal hours are safe. For example, the determination unit can improve the accuracy of its determination by combining the frequency and time of the call. This improves the accuracy of the determination by considering the frequency and time of the call. The frequency and time of the call include, but are not limited to, the number of calls and the time of the call. Some or all of the above processing in the determination unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the determination unit can input call history data into a generative AI and have the generative AI perform an analysis of the frequency and time of the call.

[0044] The determination unit can consider not only the content of the call but also the caller's information when determining that a call containing potentially fraudulent keywords is unsafe. For example, the determination unit can analyze the caller's phone number to identify numbers that are likely to be fraudulent. For example, the determination unit can refer to the caller's regional information to identify calls from regions that are likely to be fraudulent. For example, the determination unit can combine the caller's information with the content of the call to improve the accuracy of the determination. This improves the accuracy of the determination by considering the caller's information. The caller's information includes, but is not limited to, the caller's phone number and the caller's address. Some or all of the above processing in the determination unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the determination unit can input the caller's information into a generating AI and have the generating AI perform an analysis of the caller's information.

[0045] The ringing unit can adjust the ringing timing considering the user's schedule and activity status when ringing only for secure calls. For example, the ringing unit can refer to the user's calendar information and prevent the ringing from sounding during important meetings. For example, the ringing unit can monitor the user's activity status in real time and prevent the ringing from sounding during exercise or sleep. For example, the ringing unit can ring the ring at the optimal time based on the user's schedule. This allows the ringing to sound at the optimal time according to the user's schedule and activity status. The user's schedule and activity status include, but are not limited to, calendar apps and activity trackers. Some or all of the above processing in the ringing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the ringing unit can input the user's schedule information into a generative AI and have the generative AI adjust the ringing timing.

[0046] The ringing unit can provide customizable ringtones according to the user's preferences when sounding an incoming call. For example, the ringing unit can set music or sound effects selected by the user as the ringtone. For example, the ringing unit can provide ringtones that reflect the volume and tone customized by the user. For example, the ringing unit can set different ringtones for specific contacts set by the user. This allows for a more comfortable user experience by setting ringtones according to the user's preferences. User preferences include, but are not limited to, user settings and past selection history. Some or all of the above processing in the ringing unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the ringing unit can input user preference data into a generation AI and have the generation AI perform the task of providing customizable ringtones.

[0047] The ringing unit can set an optimal volume considering the user's device settings and ambient noise when ringing a call only if it is a secure call. For example, the ringing unit can automatically set an appropriate volume based on the user's device settings. For example, the ringing unit can detect ambient noise in real time and set an optimal volume. For example, the ringing unit can customize the volume according to the user's preference. This results in a more appropriate ringtone being set by setting an optimal volume according to the user's device settings and ambient noise. User's device settings and ambient noise include, but are not limited to, device volume settings and ambient noise levels. Some or all of the above processing in the ringing unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the ringing unit can input user device settings and ambient noise data into a generation AI and have the generation AI set an optimal volume.

[0048] The ringing unit can adjust the ringing pattern by referring to the user's past response history when sounding the ringtone. For example, the ringing unit can analyze the time periods when the user has previously responded and adjust the ringing pattern accordingly. For example, the ringing unit can learn and apply the optimal ringing pattern from the user's past response history. For example, the ringing unit can set a ringing pattern that the user is more likely to respond to during specific time periods. This allows the optimal ringing pattern to be set by referring to the user's past response history. The user's past response history includes, but is not limited to, the frequency and time periods of responses. Some or all of the above processing in the ringing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ringing unit can input the user's past response history data into a generative AI and have the generative AI perform the adjustment of the ringing pattern.

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

[0050] The analysis unit can handle different languages ​​and dialects when converting telephone content to text using speech recognition technology. For example, the analysis unit can use AI to automatically detect different languages, select an appropriate speech recognition model, and convert to text. For example, the analysis unit can use dictionaries to recognize dialects and regional words and convert them to standard languages. For example, the analysis unit can adjust the accuracy of speech recognition and perform text conversion based on a language set by the user. This improves the accuracy of the analysis by supporting different languages ​​and dialects. Different languages ​​and dialects include, but are not limited to, the use of a multilingual speech recognition engine or a dialect dictionary. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input telephone audio data into a generative AI and have the generative AI perform text conversion that supports different languages ​​and dialects.

[0051] The analysis unit can improve the accuracy of its analysis by removing background noise and other noise when analyzing the content of a phone call. For example, the analysis unit can use AI to filter out background noise in real time and extract and analyze only the main voice. The analysis unit can use noise cancellation technology to clarify the content of a phone call. The analysis unit can, for example, emphasize specific frequency bands and reduce noise to improve the accuracy of speech recognition. This improves the accuracy of the analysis by removing background noise and other noise. Background noise and other noise include, but are not limited to, noise cancellation technology and filtering technology. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input phone audio data into a generative AI and have the generative AI remove background noise and other noise.

[0052] The judgment unit can update its criteria by referring to the latest information on fraudulent practices when determining that a phone call containing potentially fraudulent keywords is unsafe. For example, the judgment unit can periodically update the latest information on fraudulent practices and reflect it in the criteria. For example, the judgment unit can automatically update the list of potentially fraudulent keywords and incorporate it into the criteria. For example, the judgment unit can adjust the criteria by referring to news and reports on fraudulent practices. This ensures that the criteria are always up-to-date by referring to the latest information on fraudulent practices. The latest information on fraudulent practices includes, but is not limited to, police databases and security company reports. Some or all of the above processes in the judgment unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the judgment unit can input the latest information on fraudulent practices into a generating AI and have the generating AI perform the updating of the criteria.

[0053] The ringing unit can adjust the ringing timing considering the user's schedule and activity status when ringing only for secure calls. For example, the ringing unit can refer to the user's calendar information and prevent the ringing from sounding during important meetings. For example, the ringing unit can monitor the user's activity status in real time and prevent the ringing from sounding during exercise or sleep. For example, the ringing unit can ring the ring at the optimal time based on the user's schedule. This allows the ringing to sound at the optimal time according to the user's schedule and activity status. The user's schedule and activity status include, but are not limited to, calendar apps and activity trackers. Some or all of the above processing in the ringing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the ringing unit can input the user's schedule information into a generative AI and have the generative AI adjust the ringing timing.

[0054] The analysis unit can improve the accuracy of its analysis by referring to past call history when analyzing the content of a phone call. For example, the analysis unit can analyze past call history to identify frequently used phrases and keywords. For example, the analysis unit can learn specific patterns from past call history to improve the accuracy of its analysis. For example, the analysis unit can identify calls that are likely to be fraudulent based on past call history to improve the accuracy of its analysis. Thus, the accuracy of the analysis is improved by referring to past call history. Past call history includes, but is not limited to, the retention period and reference frequency of call content. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past call history data into a generative AI and have the generative AI perform the analysis of the call history.

[0055] The ringing unit can set an optimal volume considering the user's device settings and ambient noise when ringing a call only if it is a secure call. For example, the ringing unit can automatically set an appropriate volume based on the user's device settings. For example, the ringing unit can detect ambient noise in real time and set an optimal volume. For example, the ringing unit can customize the volume according to the user's preference. This results in a more appropriate ringtone being set by setting an optimal volume according to the user's device settings and ambient noise. User's device settings and ambient noise include, but are not limited to, device volume settings and ambient noise levels. Some or all of the above processing in the ringing unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the ringing unit can input user device settings and ambient noise data into a generation AI and have the generation AI set an optimal volume.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The analysis unit analyzes the content of the phone call. The analysis unit converts the content of the phone call into text using speech recognition technology. For example, deep learning-based speech recognition technology or HMM-based speech recognition technology can be used to convert the content of the phone call into text in real time with high accuracy. Step 2: The determination unit determines whether a call is safe or not based on the information analyzed by the analysis unit. The determination unit determines that calls from family, friends, or people registered in the contact list are safe. It also determines that calls containing potentially fraudulent keywords are unsafe and can update the keywords by referring to the latest information on fraudulent methods. Step 3: The ringing unit will sound the ringtone if the detection unit determines that the call is secure. For example, the ringtone can be set to sound only when a call comes in from family or a friend, or from a specific phone number. Alternatively, a specific ringtone can be set to sound when the call is determined to be secure.

[0058] (Example of form 2) The telephone call receiving system according to an embodiment of the present invention is a system that uses AI to ensure that only safe calls are received at the homes of elderly people. When a call comes in, the telephone call receiving system uses AI to analyze the content of the call and determine whether it is a safe call. Next, the ringtone is sounded only if the call is determined to be safe. This allows elderly people to answer calls with peace of mind. For example, when a call comes in, the AI ​​uses speech recognition technology to convert the content of the call into text. For example, a call containing "keywords that may indicate fraud" is determined to be unsafe. This reduces the risk of fraud. Next, the AI ​​analyzes the content of the call and determines whether it is a safe call. For example, a call from family or a friend is determined to be safe, while a call from an unknown number or a call containing keywords that may indicate fraud is determined to be unsafe. This allows elderly people to answer calls with peace of mind. Furthermore, the ringtone is sounded only if the call is determined to be safe. This allows elderly people to answer calls with peace of mind. For example, the ringtone will only sound when a call comes in from family or a friend, allowing elderly people to answer calls with peace of mind. This mechanism allows elderly people to answer calls with peace of mind. This system can reduce the risk of fraud and ensure that only safe calls are received. For example, by reducing the risk of fraud, elderly people can receive calls with peace of mind. Also, because only calls from family and friends are received, elderly people can receive calls with peace of mind. In this way, the phone receiving system allows elderly people to receive calls with peace of mind.

[0059] The telephone call receiving system according to this embodiment comprises an analysis unit, a determination unit, and a ringing unit. The analysis unit analyzes the content of the telephone call. The analysis unit converts the content of the telephone call into text using, for example, speech recognition technology. The analysis unit can convert the content of the telephone call into text with high accuracy using, for example, deep learning-based speech recognition technology. The analysis unit can also convert the content of the telephone call into text using HMM-based speech recognition technology. The analysis unit can convert the content of the telephone call into text in real time using, for example, speech recognition technology. The determination unit determines whether or not a call is safe based on the content analyzed by the analysis unit. The determination unit determines, for example, that a call from family or a friend is safe. The determination unit can determine, for example, that a call from a person registered in the contact list is safe. The determination unit can also determine that a call from a specific telephone number is safe. The determination unit can prioritize determining that a call from family or a friend is safe. The determination unit determines that a call containing potentially fraudulent keywords is unsafe. The determination unit can determine that calls containing keywords such as "money," "bank transfer," and "urgent" are unsafe. The determination unit can also update potentially fraudulent keywords by referring to the latest information on fraud methods. For example, the determination unit can automatically extract potentially fraudulent keywords and determine them to be unsafe. The ringing unit sounds a ringtone when the determination unit determines the call is safe. The ringing unit can, for example, only sound the ringtone when the call is safe. The ringing unit can, for example, only sound the ringtone when a call comes in from family or a friend. Furthermore, the ringing unit can sound the ringtone only when a call comes in from a specific phone number. The ringing unit can, for example, sound a specific ringtone when the call is determined to be safe. As a result, the telephone receiving system according to this embodiment allows elderly people to receive calls with peace of mind.

[0060] The analysis unit analyzes the content of phone calls. For example, the analysis unit converts the content of phone calls into text using speech recognition technology. Specifically, by using deep learning-based speech recognition technology, the content of phone calls can be converted into text with high accuracy. Deep learning technology can accurately capture the characteristics of speech by learning from a large amount of speech data, making it possible to accurately recognize speech even in noisy environments. It is also possible to use HMM (Hidden Markov Model)-based speech recognition technology. HMMs can efficiently analyze continuous speech data by modeling the temporal changes in speech. By combining these technologies, the analysis unit can convert the content of phone calls into text in real time. Furthermore, the analysis unit can analyze the meaning of the text using not only speech recognition technology but also natural language processing technology. For example, it can extract important keywords from the text and summarize the content of the phone call. As a result, the analysis unit can analyze the content of phone calls in detail and generate highly accurate data to provide to the judgment unit.

[0061] The judgment unit determines whether a call is safe or not based on the information analyzed by the analysis unit. For example, the judgment unit can determine that a call from family or friends is safe. Specifically, it can determine that a call from someone registered in the contact list is safe. The contact list is a database containing phone numbers and names that the user has registered in advance, and the judgment unit refers to this list to verify the origin of the call. It can also determine that a call from a specific phone number is safe. For example, by registering phone numbers from trusted institutions such as banks or medical institutions in advance, these calls can be determined to be safe. Furthermore, the judgment unit determines that a call containing keywords that may indicate fraud is unsafe. For example, it can determine that a call containing keywords such as "money," "bank transfer," or "emergency" is unsafe. The judgment unit can update keywords that may indicate fraud by referring to the latest information on fraud methods. This allows the judgment unit to always determine safety based on the latest fraud information. In addition, the judgment unit can use AI to analyze the content of a call and automatically extract keywords that may indicate fraud. By learning from past fraudulent call data, the AI ​​can recognize fraud patterns and respond to new fraud methods. This allows the judgment unit to determine safety with high accuracy, providing users with an environment where they can receive calls with peace of mind.

[0062] The ringing unit sounds the ringtone when the detection unit determines that the call is safe. Specifically, it can only ring the ringtone when it is a safe call. For example, it can only ring the ringtone when a call comes in from family or a friend. The ringing unit can also ring the ringtone only when a call comes in from a specific phone number. This ensures that the user does not miss important calls. Furthermore, the ringing unit can play a specific ringtone when it determines that the call is safe. For example, by setting a specific melody for calls from family and a different melody for calls from friends, the user can intuitively determine who is calling. In addition, the ringing unit can customize the ringtone according to the user's preferences. For example, it can set a quiet ringtone at certain times and a loud notification in emergencies. This allows the ringing unit to provide a flexible notification method that suits the user's lifestyle. Furthermore, the ringing unit can use other notification methods such as vibration and flashing lights in addition to ringtones. This allows it to accommodate users with hearing impairments or users who need to receive calls in a quiet environment. As a result, the telephone receiving system according to this embodiment not only allows elderly people to receive calls with peace of mind, but also can meet the needs of various users.

[0063] The analysis unit can convert the content of a phone call into text using speech recognition technology. The analysis unit can convert the content of a phone call into text with high accuracy using, for example, deep learning-based speech recognition technology. The analysis unit can also convert the content of a phone call into text using, for example, HMM-based speech recognition technology. The analysis unit can convert the content of a phone call into text in real time using, for example, speech recognition technology. This improves the accuracy of the analysis by converting the content of the phone call into text. Speech recognition technology includes, but is not limited to, deep learning-based speech recognition technology and HMM-based speech recognition technology. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the phone audio data into a generative AI and have the generative AI perform the conversion from audio data to text data.

[0064] The determination unit can determine that calls from family and friends are safe. The determination unit can determine that calls from people registered in the contact list are safe, for example. The determination unit can also determine that calls from specific phone numbers are safe, for example. The determination unit can prioritize determining that calls from family and friends are safe, for example. This allows elderly people to receive calls with peace of mind by determining that calls from family and friends are safe. Family and friends include, but are not limited to, people registered in the contact list and calls from specific phone numbers. Some or all of the above processing in the determination unit may be performed using, for example, a generating AI, or without a generating AI. For example, the determination unit can input caller information into a generating AI and have the generating AI determine whether or not the call is safe based on the caller information.

[0065] The determination unit can determine that a phone call containing potentially fraudulent keywords is unsafe. For example, the determination unit can determine that a phone call containing keywords such as "money," "bank transfer," and "urgent" is unsafe. The determination unit can update potentially fraudulent keywords by referring to the latest information on fraud methods, for example. The determination unit can automatically extract potentially fraudulent keywords and determine that they are unsafe, for example. This can reduce the risk of fraud. Potentially fraudulent keywords include, but are not limited to, "money," "bank transfer," and "urgent." Some or all of the above processing in the determination unit may be performed using, for example, a generating AI, or without a generating AI. For example, the determination unit can input the content of a phone call into a generating AI and have the generating AI extract potentially fraudulent keywords.

[0066] The ringing unit can only ring the ringtone if the call is safe. For example, the ringing unit can only ring the ringtone if the call is safe. For example, the ringing unit can only ring the ringtone if the call is from family or a friend. The ringing unit can also ring the ringtone only if the call is from a specific phone number. For example, the ringing unit can ring a specific ringtone if it is determined to be a safe call. This allows elderly people to answer calls with peace of mind. Safe calls include, but are not limited to, calls from family or friends or calls from specific phone numbers. Some or all of the above processing in the ringing unit may be performed using, for example, a generating AI, or without a generating AI. For example, the ringing unit can input information that the determination unit has determined to be a safe call into the generating AI, and cause the generating AI to ring the ringtone.

[0067] The analysis unit can estimate the user's emotions when analyzing the content of a phone call and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is nervous, the AI ​​can detect the emotion and focus on specific keywords to improve the accuracy of the analysis. For example, if the user is relaxed, the AI ​​can detect the emotion and adjust the accuracy of the analysis to analyze a wider range of content. For example, if the user is excited, the AI ​​can detect the emotion and adjust the accuracy of the analysis to highlight keywords that are likely to be fraudulent. This improves the accuracy of the analysis by adjusting it according to the user's emotions. The user's emotions are estimated based on, for example, voice tone, word choice, and speaking patterns. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input telephone voice data into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0068] The analysis unit can handle different languages ​​and dialects when converting telephone content to text using speech recognition technology. For example, the analysis unit can use AI to automatically detect different languages, select an appropriate speech recognition model, and convert to text. For example, the analysis unit can use dictionaries to recognize dialects and regional words and convert them to standard languages. For example, the analysis unit can adjust the accuracy of speech recognition and perform text conversion based on a language set by the user. This improves the accuracy of the analysis by supporting different languages ​​and dialects. Different languages ​​and dialects include, but are not limited to, the use of a multilingual speech recognition engine or a dialect dictionary. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input telephone audio data into a generative AI and have the generative AI perform text conversion that supports different languages ​​and dialects.

[0069] The analysis unit can improve the accuracy of its analysis by removing background noise and other noise when analyzing the content of a phone call. For example, the analysis unit can use AI to filter out background noise in real time and extract and analyze only the main voice. The analysis unit can use noise cancellation technology to clarify the content of a phone call. The analysis unit can, for example, emphasize specific frequency bands and reduce noise to improve the accuracy of speech recognition. This improves the accuracy of the analysis by removing background noise and other noise. Background noise and other noise include, but are not limited to, noise cancellation technology and filtering technology. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input phone audio data into a generative AI and have the generative AI remove background noise and other noise.

[0070] The analysis unit can estimate the user's emotions when analyzing the content of a phone call and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can display the analysis results concisely and highlight only the important information. For example, if the user is relaxed, the analysis unit can display detailed analysis results and provide additional information. For example, if the user is excited, the analysis unit can display the analysis results visually in an easy-to-understand manner and highlight important keywords. This allows for a deeper understanding of the analysis results by adjusting the display method according to the user's emotions. The user's emotions are estimated based on, for example, voice tone, word choice, and speaking patterns, but are not limited to such examples. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input telephone voice data into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0071] The analysis unit can highlight specific keywords or phrases when converting phone content to text using speech recognition technology. For example, the analysis unit can automatically detect potentially fraudulent keywords and highlight them in the text. The analysis unit can highlight important phrases, such as the names of family and friends. The analysis unit can highlight specific keywords set by the user. This makes it less likely that important information will be overlooked by highlighting specific keywords or phrases. Specific keywords or phrases include, but are not limited to, "important," "urgent," and "confirm." Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input phone audio data into a generative AI and have the generative AI perform the highlighting of specific keywords or phrases.

[0072] The analysis unit can improve the accuracy of its analysis by referring to past call history when analyzing the content of a phone call. For example, the analysis unit can analyze past call history to identify frequently used phrases and keywords. For example, the analysis unit can learn specific patterns from past call history to improve the accuracy of its analysis. For example, the analysis unit can identify calls that are likely to be fraudulent based on past call history to improve the accuracy of its analysis. Thus, the accuracy of the analysis is improved by referring to past call history. Past call history includes, but is not limited to, the retention period and reference frequency of call content. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past call history data into a generative AI and have the generative AI perform the analysis of the call history.

[0073] The judgment unit can estimate the user's emotions and adjust the criteria for determining whether a call is safe or not based on the estimated user emotions. For example, if the user is nervous, the judgment unit can tighten the criteria and more strictly determine whether a call is potentially fraudulent. For example, if the user is relaxed, the judgment unit can loosen the criteria and prioritize determining calls from family and friends as safe. For example, if the user is excited, the judgment unit can adjust the criteria and make a determination based on important keywords. This improves the accuracy of the determination by adjusting the criteria according to the user's emotions. The user's emotions are estimated based on, for example, voice tone, word choice, and speaking patterns. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input the phone's voice data into the generating AI, allowing the generating AI to estimate the user's emotions.

[0074] The determination unit can improve the accuracy of its determination when determining whether a call from family or friends is safe by referring to call history and contact lists. For example, the determination unit can analyze call history and determine that calls from frequently called individuals are safe. For example, the determination unit can refer to contact lists and determine that calls from registered family and friends are safe. For example, the determination unit can improve the accuracy of its determination by combining call history and contact lists. This improves the accuracy of the determination by referring to call history and contact lists. Call history and contact lists include, but are not limited to, call frequency and contact reliability. Some or all of the above processing in the determination unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the determination unit can input call history data and contact lists into a generative AI and have the generative AI perform analysis of the call history and contact lists.

[0075] The judgment unit can update its criteria by referring to the latest information on fraudulent practices when determining that a phone call containing potentially fraudulent keywords is unsafe. For example, the judgment unit can periodically update the latest information on fraudulent practices and reflect it in the criteria. For example, the judgment unit can automatically update the list of potentially fraudulent keywords and incorporate it into the criteria. For example, the judgment unit can adjust the criteria by referring to news and reports on fraudulent practices. This ensures that the criteria are always up-to-date by referring to the latest information on fraudulent practices. The latest information on fraudulent practices includes, but is not limited to, police databases and security company reports. Some or all of the above processes in the judgment unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the judgment unit can input the latest information on fraudulent practices into a generating AI and have the generating AI perform the updating of the criteria.

[0076] The judgment unit can estimate the user's emotions and adjust the notification method of the judgment result based on the estimated user emotions. For example, if the user is nervous, the judgment unit can provide a concise and clear notification method. For example, if the user is relaxed, the judgment unit can provide a detailed notification method and also display additional information. For example, if the user is excited, the judgment unit can provide a visually easy-to-understand notification method. This improves the understanding of notifications by adjusting the notification method according to the user's emotions. The user's emotions are estimated based on, for example, voice tone, word choice, and speaking patterns, but are not limited to such examples. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the judgment unit can input telephone audio data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0077] The determination unit can improve the accuracy of its determination when determining whether a call from family or friends is safe by considering the frequency and time of the call. For example, the determination unit can analyze the frequency of calls and determine that calls from frequently spoken individuals are safe. For example, the determination unit can consider the time of the call and determine that calls received during normal hours are safe. For example, the determination unit can improve the accuracy of its determination by combining the frequency and time of the call. This improves the accuracy of the determination by considering the frequency and time of the call. The frequency and time of the call include, but are not limited to, the number of calls and the time of the call. Some or all of the above processing in the determination unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the determination unit can input call history data into a generative AI and have the generative AI perform an analysis of the frequency and time of the call.

[0078] The determination unit can consider not only the content of the call but also the caller's information when determining that a call containing potentially fraudulent keywords is unsafe. For example, the determination unit can analyze the caller's phone number to identify numbers that are likely to be fraudulent. For example, the determination unit can refer to the caller's regional information to identify calls from regions that are likely to be fraudulent. For example, the determination unit can combine the caller's information with the content of the call to improve the accuracy of the determination. This improves the accuracy of the determination by considering the caller's information. The caller's information includes, but is not limited to, the caller's phone number and the caller's address. Some or all of the above processing in the determination unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the determination unit can input the caller's information into a generating AI and have the generating AI perform an analysis of the caller's information.

[0079] The ringing unit can estimate the user's emotions and adjust the type and volume of the ringtone based on the estimated emotions. For example, if the user is nervous, the ringing unit can set a calm-sounding ringtone. For example, if the user is relaxed, the ringing unit can set a bright-sounding ringtone. For example, if the user is excited, the ringing unit can adjust the volume to set an appropriate ringtone. This allows for a more appropriate ringtone to be set by adjusting the type and volume of the ringtone according to the user's emotions. The user's emotions are estimated based on, for example, voice tone, word choice, and speaking patterns, but are not limited to such examples. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the ringing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ringing unit can input user emotion data into a generating AI, which can then adjust the type and volume of the ringtone.

[0080] The ringing unit can adjust the ringing timing considering the user's schedule and activity status when ringing only for secure calls. For example, the ringing unit can refer to the user's calendar information and prevent the ringing from sounding during important meetings. For example, the ringing unit can monitor the user's activity status in real time and prevent the ringing from sounding during exercise or sleep. For example, the ringing unit can ring the ring at the optimal time based on the user's schedule. This allows the ringing to sound at the optimal time according to the user's schedule and activity status. The user's schedule and activity status include, but are not limited to, calendar apps and activity trackers. Some or all of the above processing in the ringing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the ringing unit can input the user's schedule information into a generative AI and have the generative AI adjust the ringing timing.

[0081] The ringing unit can provide customizable ringtones according to the user's preferences when sounding an incoming call. For example, the ringing unit can set music or sound effects selected by the user as the ringtone. For example, the ringing unit can provide ringtones that reflect the volume and tone customized by the user. For example, the ringing unit can set different ringtones for specific contacts set by the user. This allows for a more comfortable user experience by setting ringtones according to the user's preferences. User preferences include, but are not limited to, user settings and past selection history. Some or all of the above processing in the ringing unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the ringing unit can input user preference data into a generation AI and have the generation AI perform the task of providing customizable ringtones.

[0082] The ringing unit can estimate the user's emotions and adjust the ringing duration of the ringtone based on the estimated emotions. For example, if the user is nervous, the ringing unit can set a shorter ringing duration. For example, if the user is relaxed, the ringing unit can set a longer ringing duration. For example, if the user is excited, the ringing unit can set an appropriate ringing duration. By adjusting the ringing duration of the ringtone according to the user's emotions, a more appropriate ringtone is set. The user's emotions are estimated based on, for example, voice tone, word choice, and speaking patterns, but are not limited to such examples. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the ringing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ringing unit can input user emotion data into a generating AI, which can then adjust the ringtone duration.

[0083] The ringing unit can set an optimal volume considering the user's device settings and ambient noise when ringing a call only if it is a secure call. For example, the ringing unit can automatically set an appropriate volume based on the user's device settings. For example, the ringing unit can detect ambient noise in real time and set an optimal volume. For example, the ringing unit can customize the volume according to the user's preference. This results in a more appropriate ringtone being set by setting an optimal volume according to the user's device settings and ambient noise. User's device settings and ambient noise include, but are not limited to, device volume settings and ambient noise levels. Some or all of the above processing in the ringing unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the ringing unit can input user device settings and ambient noise data into a generation AI and have the generation AI set an optimal volume.

[0084] The ringing unit can adjust the ringing pattern by referring to the user's past response history when sounding the ringtone. For example, the ringing unit can analyze the time periods when the user has previously responded and adjust the ringing pattern accordingly. For example, the ringing unit can learn and apply the optimal ringing pattern from the user's past response history. For example, the ringing unit can set a ringing pattern that the user is more likely to respond to during specific time periods. This allows the optimal ringing pattern to be set by referring to the user's past response history. The user's past response history includes, but is not limited to, the frequency and time periods of responses. Some or all of the above processing in the ringing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ringing unit can input the user's past response history data into a generative AI and have the generative AI perform the adjustment of the ringing pattern.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The analysis unit can estimate the user's emotions when analyzing the content of a phone call and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is nervous, the AI ​​can detect the emotion and focus on specific keywords to improve the accuracy of the analysis. For example, if the user is relaxed, the AI ​​can detect the emotion and adjust the accuracy of the analysis to analyze a wider range of content. For example, if the user is excited, the AI ​​can detect the emotion and adjust the accuracy of the analysis to highlight keywords that are likely to be fraudulent. This improves the accuracy of the analysis by adjusting it according to the user's emotions. The user's emotions are estimated based on, for example, voice tone, word choice, and speaking patterns. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input telephone voice data into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0087] The analysis unit can handle different languages ​​and dialects when converting telephone content to text using speech recognition technology. For example, the analysis unit can use AI to automatically detect different languages, select an appropriate speech recognition model, and convert to text. For example, the analysis unit can use dictionaries to recognize dialects and regional words and convert them to standard languages. For example, the analysis unit can adjust the accuracy of speech recognition and perform text conversion based on a language set by the user. This improves the accuracy of the analysis by supporting different languages ​​and dialects. Different languages ​​and dialects include, but are not limited to, the use of a multilingual speech recognition engine or a dialect dictionary. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input telephone audio data into a generative AI and have the generative AI perform text conversion that supports different languages ​​and dialects.

[0088] The judgment unit can estimate the user's emotions and adjust the criteria for determining whether a call is safe or not based on the estimated user emotions. For example, if the user is nervous, the judgment unit can tighten the criteria and more strictly determine whether a call is potentially fraudulent. For example, if the user is relaxed, the judgment unit can loosen the criteria and prioritize determining calls from family and friends as safe. For example, if the user is excited, the judgment unit can adjust the criteria and make a determination based on important keywords. This improves the accuracy of the determination by adjusting the criteria according to the user's emotions. The user's emotions are estimated based on, for example, voice tone, word choice, and speaking patterns. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the judgment unit can input the phone's voice data into the generating AI, allowing the generating AI to estimate the user's emotions.

[0089] The analysis unit can improve the accuracy of its analysis by removing background noise and other noise when analyzing the content of a phone call. For example, the analysis unit can use AI to filter out background noise in real time and extract and analyze only the main voice. The analysis unit can use noise cancellation technology to clarify the content of a phone call. The analysis unit can, for example, emphasize specific frequency bands and reduce noise to improve the accuracy of speech recognition. This improves the accuracy of the analysis by removing background noise and other noise. Background noise and other noise include, but are not limited to, noise cancellation technology and filtering technology. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input phone audio data into a generative AI and have the generative AI remove background noise and other noise.

[0090] The judgment unit can update its criteria by referring to the latest information on fraudulent practices when determining that a phone call containing potentially fraudulent keywords is unsafe. For example, the judgment unit can periodically update the latest information on fraudulent practices and reflect it in the criteria. For example, the judgment unit can automatically update the list of potentially fraudulent keywords and incorporate it into the criteria. For example, the judgment unit can adjust the criteria by referring to news and reports on fraudulent practices. This ensures that the criteria are always up-to-date by referring to the latest information on fraudulent practices. The latest information on fraudulent practices includes, but is not limited to, police databases and security company reports. Some or all of the above processes in the judgment unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the judgment unit can input the latest information on fraudulent practices into a generating AI and have the generating AI perform the updating of the criteria.

[0091] The ringing unit can estimate the user's emotions and adjust the type and volume of the ringtone based on the estimated emotions. For example, if the user is nervous, the ringing unit can set a calm-sounding ringtone. For example, if the user is relaxed, the ringing unit can set a bright-sounding ringtone. For example, if the user is excited, the ringing unit can adjust the volume to set an appropriate ringtone. This allows for a more appropriate ringtone to be set by adjusting the type and volume of the ringtone according to the user's emotions. The user's emotions are estimated based on, for example, voice tone, word choice, and speaking patterns, but are not limited to such examples. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the ringing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the ringing unit can input user emotion data into a generating AI, which can then adjust the type and volume of the ringtone.

[0092] The ringing unit can adjust the ringing timing considering the user's schedule and activity status when ringing only for secure calls. For example, the ringing unit can refer to the user's calendar information and prevent the ringing from sounding during important meetings. For example, the ringing unit can monitor the user's activity status in real time and prevent the ringing from sounding during exercise or sleep. For example, the ringing unit can ring the ring at the optimal time based on the user's schedule. This allows the ringing to sound at the optimal time according to the user's schedule and activity status. The user's schedule and activity status include, but are not limited to, calendar apps and activity trackers. Some or all of the above processing in the ringing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the ringing unit can input the user's schedule information into a generative AI and have the generative AI adjust the ringing timing.

[0093] The analysis unit can improve the accuracy of its analysis by referring to past call history when analyzing the content of a phone call. For example, the analysis unit can analyze past call history to identify frequently used phrases and keywords. For example, the analysis unit can learn specific patterns from past call history to improve the accuracy of its analysis. For example, the analysis unit can identify calls that are likely to be fraudulent based on past call history to improve the accuracy of its analysis. Thus, the accuracy of the analysis is improved by referring to past call history. Past call history includes, but is not limited to, the retention period and reference frequency of call content. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past call history data into a generative AI and have the generative AI perform the analysis of the call history.

[0094] The judgment unit can estimate the user's emotions and adjust the notification method of the judgment result based on the estimated user emotions. For example, if the user is nervous, the judgment unit can provide a concise and clear notification method. For example, if the user is relaxed, the judgment unit can provide a detailed notification method and also display additional information. For example, if the user is excited, the judgment unit can provide a visually easy-to-understand notification method. This improves the understanding of notifications by adjusting the notification method according to the user's emotions. The user's emotions are estimated based on, for example, voice tone, word choice, and speaking patterns, but are not limited to such examples. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the judgment unit can input telephone audio data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0095] The ringing unit can set an optimal volume considering the user's device settings and ambient noise when ringing a call only if it is a secure call. For example, the ringing unit can automatically set an appropriate volume based on the user's device settings. For example, the ringing unit can detect ambient noise in real time and set an optimal volume. For example, the ringing unit can customize the volume according to the user's preference. This results in a more appropriate ringtone being set by setting an optimal volume according to the user's device settings and ambient noise. User's device settings and ambient noise include, but are not limited to, device volume settings and ambient noise levels. Some or all of the above processing in the ringing unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the ringing unit can input user device settings and ambient noise data into a generation AI and have the generation AI set an optimal volume.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The analysis unit analyzes the content of the phone call. The analysis unit converts the content of the phone call into text using speech recognition technology. For example, deep learning-based speech recognition technology or HMM-based speech recognition technology can be used to convert the content of the phone call into text in real time with high accuracy. Step 2: The determination unit determines whether a call is safe or not based on the information analyzed by the analysis unit. The determination unit determines that calls from family, friends, or people registered in the contact list are safe. It also determines that calls containing potentially fraudulent keywords are unsafe and can update the keywords by referring to the latest information on fraudulent methods. Step 3: The ringing unit will sound the ringtone if the detection unit determines that the call is secure. For example, the ringtone can be set to sound only when a call comes in from family or a friend, or from a specific phone number. Alternatively, a specific ringtone can be set to sound when the call is determined to be secure.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements, including the analysis unit, determination unit, and ringing unit described above, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and converts the content of a phone call into text using speech recognition technology. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines whether or not it is a secure phone call based on the analyzed content. The ringing unit is implemented by the control unit 46A of the smart device 14 and sounds a ringtone if it is determined to be a secure phone call. 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.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements, including the analysis unit, determination unit, and ringing unit described above, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and converts the content of a phone call into text using speech recognition technology. The determination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines whether or not the call is secure based on the analyzed content. The ringing unit is implemented by the control unit 46A of the smart glasses 214 and sounds a ringtone if it is determined that the call is secure. 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.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements, including the analysis unit, determination unit, and ringing unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and converts the content of the call into text using speech recognition technology. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines whether or not it is a secure call based on the analyzed content. The ringing unit is implemented by the control unit 46A of the headset terminal 314 and rings a ringtone if it is determined to be a secure call. 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.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements, including the analysis unit, determination unit, and ringing unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and converts the content of a phone call into text using speech recognition technology. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines whether or not it is a safe phone call based on the analyzed content. The ringing unit is implemented by the control unit 46A of the robot 414 and rings a ringtone if it is determined to be a safe phone call. 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) The analysis unit analyzes the content of the phone call, A determination unit that determines whether or not a phone call is secure based on the content analyzed by the analysis unit, The system includes a ringing unit that sounds a ringtone when the determination unit determines that it is a safe phone call. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Speech recognition technology is used to convert the content of a phone call into text. The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, Determine that calls from family and friends are safe. The system described in Appendix 1, characterized by the features described herein. (Note 4) The determination unit, The system identifies phone calls containing keywords that may indicate fraud as unsafe. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned vibrating part is, Only ring if the call is secure. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, When analyzing the content of a phone call, the system estimates the user's emotions and adjusts the accuracy of the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, When using speech recognition technology to convert phone calls into text, we want to be able to handle different languages ​​and dialects. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing phone calls, background noise and other sounds are removed to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing the content of a phone call, the system estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When converting phone calls to text using speech recognition technology, specific keywords or phrases can be highlighted. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing the content of phone calls, past call history is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The determination unit, The system estimates the user's emotions and adjusts the criteria for determining whether a call is safe based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The determination unit, When determining whether a call from family or friends is safe, the system improves accuracy by referencing call history and contact lists. The system described in Appendix 1, characterized by the features described herein. (Note 14) The determination unit, When determining that a phone call containing potentially fraudulent keywords is unsafe, we update our criteria by referring to the latest information on fraud tactics. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, The system estimates the user's emotions and adjusts the notification method of the judgment result based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, When determining whether calls from family and friends are safe, the accuracy of the determination will be improved by considering the frequency and time of the call. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, When determining that a phone call containing potentially fraudulent keywords is unsafe, the caller's information, as well as the content of the call, should be considered. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned vibrating part is, It estimates the user's emotions and adjusts the ringtone type and volume based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned vibrating part is, When ringing only for secure calls, the ringing timing is adjusted considering the user's schedule and activity status. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned vibrating part is, When a ringtone sounds, it provides a customizable ringtone according to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned vibrating part is, It estimates the user's emotions and adjusts the ringtone duration based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned vibrating part is, When ringing only for secure calls, the system will set the optimal volume considering the user's device settings and ambient noise. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned vibrating part is, When ringing, the system adjusts the ringing pattern by referring to the user's past answering history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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. The analysis unit analyzes the content of the phone call, A determination unit that determines whether or not a phone call is secure based on the content analyzed by the analysis unit, The system includes a ringing unit that sounds a ringtone when the determination unit determines that it is a safe phone call. A system characterized by the following features.

2. The aforementioned analysis unit, Speech recognition technology is used to convert the content of a phone call into text. The system according to feature 1.

3. The determination unit, Determine that calls from family and friends are safe. The system according to feature 1.

4. The determination unit, The system identifies phone calls containing keywords that may indicate fraud as unsafe. The system according to feature 1.

5. The aforementioned vibrating part is, Only ring if the call is secure. The system according to feature 1.

6. The aforementioned analysis unit, When analyzing the content of a phone call, the system estimates the user's emotions and adjusts the accuracy of the analysis based on those estimated emotions. The system according to feature 1.

7. The aforementioned analysis unit, When using speech recognition technology to convert phone calls into text, we want to be able to handle different languages ​​and dialects. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing phone calls, background noise and other sounds are removed to improve the accuracy of the analysis. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing the content of a phone call, the system estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit, When converting phone calls to text using speech recognition technology, specific keywords or phrases can be highlighted. The system according to feature 1.

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