system

Abstract

The system according to this embodiment aims to convert messages into voice and notify users at a specified time. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a voice conversion unit, and a notification unit. The reception unit receives a message. The analysis unit analyzes the message received by the reception unit. The voice conversion unit converts the message analyzed by the analysis unit into speech. The notification unit notifies the user of the speech converted by the voice conversion unit at a time specified by the user.

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 the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the vocalization of messages and notifications at specified times are not sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to vocalize messages and notify at a specified time.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a vocalization unit, and a notification unit. The reception unit receives messages. The analysis unit analyzes the messages received by the reception unit. The vocalization unit vocalizes the messages analyzed by the analysis unit. The notification unit notifies the messages vocalized by the vocalization unit at the time specified by the user. [Effects of the Invention]

[0007] The system according to this embodiment can convert messages into voice and notify users at a specified time. [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 notification system according to an embodiment of the present invention is a system in which a user sends a voice message or an SMS text to a specific number, and the content is interpreted and converted into speech by a generative AI. The notification system works as follows: The user sends a message to a specific number. Next, the generative AI analyzes the message and converts it into speech. The generative AI behaves like a human, regardless of gender, age, or language, and speaks in various voices at a time specified by the user, calling the user's smartphone to serve as a notification for appointments or a substitute for notes. For example, if a user enters a message such as, "October 11th is our wedding anniversary, so please buy a cake at the cake shop in front of the station at 6 PM," the generative AI will convert the content into speech and call the user's smartphone at the specified time to notify them. The generative AI can make notifications in a voice that matches the user's preference, such as a young male voice or an elderly female voice. In addition, the generative AI can summarize recorded voice data or text data and convert the summary into speech to remind the user of tasks or appointments they do not want to forget. This allows the user to efficiently manage important tasks and appointments without forgetting them. As a result, the notification system can efficiently convert the user's messages into speech and notify them at a specified time.

[0029] The notification system according to the embodiment comprises a reception unit, an analysis unit, a voice conversion unit, and a notification unit. The reception unit receives messages sent by the user to a specific number. The reception unit can receive, for example, voice messages or text messages. When the reception unit receives a voice message, it can convert the voice data into text data using speech recognition technology. The analysis unit analyzes the message received by the reception unit. The analysis unit can analyze the content of the message using, for example, natural language processing technology. The analysis unit can analyze the content of the message and extract important information. The voice conversion unit converts the message analyzed by the analysis unit into voice. The voice conversion unit can convert the text data into voice data using, for example, speech synthesis technology. The voice conversion unit can convert the message into voice in a voice that matches the user's preference. The notification unit notifies the user of the message converted into voice by the voice conversion unit at a time specified by the user. The notification unit can, for example, make a notification by calling the user's smartphone. The notification unit can play the voice message at a time specified by the user. As a result, the notification system according to the embodiment can efficiently convert the user's message into voice and notify them at a specified time.

[0030] The reception unit receives messages sent by users to a specific number. The reception unit can receive, for example, voice messages and text messages. Specifically, when a user sends a message to a specific number using a smartphone or other device, the reception unit receives the message immediately. In the case of voice messages, the reception unit uses advanced speech recognition technology to convert the voice data into text data. This speech recognition technology can recognize speech with high accuracy even in noisy environments and accurately transcribe the user's speech. For example, by utilizing a deep learning-based speech recognition model and learning the characteristics of the user's speech, it can handle different accents and speaking styles. In the case of text messages, the reception unit receives the message in its original format. This gives users the flexibility to use either voice or text messages. Furthermore, the reception unit has a function to temporarily store received messages and verify data integrity before sending them to the analysis unit. This prevents incorrect data from being sent to the analysis unit and improves the overall reliability of the system.

[0031] The analysis unit analyzes messages received by the reception unit. The analysis unit can analyze message content using, for example, natural language processing (NLP) techniques. Specifically, it uses NLP to analyze the grammatical structure and meaning of messages and extract important information. For example, it utilizes techniques such as tokenization, part-of-speech tagging, and dependency analysis to analyze message content in detail. Furthermore, the analysis unit can use machine learning models to understand the intent of messages and identify what the user wants to convey. For example, if a user sends "Remind me of tomorrow's meeting," the analysis unit extracts keywords such as "tomorrow," "meeting," and "remind," and understands the user's intent as "remind me of tomorrow's meeting." The analysis unit can also refer to past message history and user profile information to perform more accurate analysis. This allows the analysis unit to quickly and accurately analyze user messages and extract important information. Additionally, the analysis unit has a function to verify data integrity and consistency before sending the analysis results to the speech conversion unit. This ensures the accuracy of the analysis results and improves the overall reliability of the system.

[0032] The speech generation unit converts the message analyzed by the analysis unit into speech. The speech generation unit can, for example, convert text data into speech data using speech synthesis technology. Specifically, it uses speech synthesis technology to convert the analyzed text data into natural-sounding speech. The speech synthesis technology utilizes a deep learning-based speech synthesis model, enabling the generation of highly natural and human-like speech. For example, if a user sends "Remind me of tomorrow's meeting," the speech generation unit will generate a voice message such as "Remind me of tomorrow's meeting." Furthermore, the speech generation unit can voice messages in a voice tailored to the user's preferences. For example, by allowing users to select male or female voices, young or old voices, a more personalized experience can be provided. In addition, the speech generation unit has the ability to adjust the speed and tone of the speech, allowing for customization of the voice characteristics according to the user's preferences. This enables the speech generation unit to provide voice messages that are easy for users to hear and understand. Moreover, the speech generation unit has the ability to temporarily store the generated voice data and check the voice quality before sending it to the notification unit. This ensures the quality of voice messages and improves the overall reliability of the system.

[0033] The notification unit delivers voice-generated messages, converted by the voice-generating unit, to the user at a time specified by the user. Specifically, it can make notifications to the user's smartphone. The notification unit can play voice messages at a time specified by the user. For example, if the user specifies, "Please remind me of the meeting at 9 AM tomorrow," the notification unit will send a notification to the user's smartphone at that time and play the voice message. The notification unit can utilize the smartphone's notification function to notify the user in various formats, such as pop-up notifications, banner notifications, and voice notifications. The notification unit also has a function to automatically notify the user of important events and reminders by linking with the user's schedule and calendar. This allows the user to efficiently manage their schedule without forgetting important appointments. Furthermore, the notification unit allows the user to customize the frequency and timing of notifications according to their preferences. For example, if the user has set multiple reminders, the notification unit will notify them at appropriate intervals to avoid placing an excessive burden on the user. The notification unit also has a function to save the notification history and allow the user to refer to past notification content. This allows the user to check past notification content and set up re-notifications as needed. This allows the notification unit to efficiently and effectively notify users and reliably convey important information.

[0034] The speech generation unit can convert messages into voices tailored to the user's preferences. For example, it can convert messages into voices tailored to the user's preferences, such as a young man's voice or an older woman's voice. The speech generation unit can refer to user profiles and past preference data to select a voice tailored to the user's preferences. For example, the speech generation unit can select the optimal voice based on data of voices previously selected by the user. This allows notifications to be delivered in a voice tailored to the user's preferences. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or without AI. For example, the speech generation unit can input a user profile into a generating AI and have the generating AI select the optimal voice.

[0035] The analysis unit can summarize recorded audio and text data. For example, the analysis unit can analyze audio and text data using natural language processing techniques to extract important information. The analysis unit can also summarize audio and text data using summarization algorithms. For example, the analysis unit can convert audio data into text data and then summarize that text data. Furthermore, the analysis unit can directly summarize text data. This enables efficient information management by summarizing recorded audio and text data. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input audio data into a generating AI and have the generating AI perform the summarization.

[0036] The notification unit can make a call to the user's smartphone to send a notification. For example, the notification unit can make a call to the user's smartphone to play a voice message at a time specified by the user. The notification unit can send notifications in various ways, such as phone notifications, app notifications, and SMS notifications. For example, the notification unit can make a phone call to the user's smartphone and play a voice message. The notification unit can also play a voice message through an app. Furthermore, the notification unit can send an SMS to play a voice message. This ensures reliable notification by sending notifications directly to the user's smartphone. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the timing for calling the user's smartphone into a generating AI, and have the generating AI execute the notification.

[0037] The speech generation unit can convert messages into voices in various voices, regardless of gender, age, or language. For example, it can convert messages into voices in various voices, such as a young man's voice, an older woman's voice, or a voice in a different language. The speech generation unit can refer to user profiles and past preference data to select a voice that matches the user's preferences. For example, the speech generation unit can select the optimal voice based on data of voices previously selected by the user. This enables notifications tailored to the user's preferences by converting messages into voices in a variety of voices. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or without AI. For example, the speech generation unit can input a user profile into a generating AI and have the generating AI select the optimal voice.

[0038] The reception unit can analyze the user's past message sending history when receiving a message and select the optimal reception method. For example, the reception unit can prioritize suggesting message formats that the user has frequently used in the past. Furthermore, the reception unit can predict and suggest message formats to be used during specific time periods based on the user's past sending history. In addition, the reception unit can analyze the user's past message sending history and select the most efficient reception method. This allows the reception unit to provide the optimal reception method by analyzing the user's past message sending history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past message sending history into a generating AI and have the generating AI select the optimal reception method.

[0039] The reception unit can filter messages upon receipt based on the user's current situation and areas of interest. For example, the reception unit can prioritize receiving messages that are highly relevant to the user's current situation. It can also prioritize receiving messages containing specific keywords based on the user's areas of interest. Furthermore, the reception unit can filter out unnecessary messages, taking into account the user's current situation and areas of interest. This allows for the priority of receiving highly relevant messages by filtering them based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The reception unit can prioritize receiving messages that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific location, the reception unit can prioritize receiving messages related to that location. It can also prioritize receiving messages related to locations close to the user's current location. Furthermore, the reception unit can filter highly relevant messages based on the user's geographical location information. This allows for the priority reception of highly relevant messages by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the filtering of highly relevant messages.

[0041] The reception unit can analyze the user's social media activity when receiving a message and receive relevant messages. For example, the reception unit can prioritize receiving messages related to topics of interest based on the user's social media activity. It can also consider the user's social media friendships when receiving relevant messages. Furthermore, the reception unit can analyze the user's social media activity and filter highly relevant messages. This allows for the priority reception of highly relevant messages by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of relevant messages.

[0042] The analysis unit can adjust the level of detail of a message analysis based on its importance. For example, for important messages, the analysis unit can perform a detailed analysis to provide abundant information. For general messages, the analysis unit can perform a standard analysis. Furthermore, for low-importance messages, the analysis unit can perform a concise analysis. This enables efficient information provision by performing analysis based on message importance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the message category when analyzing messages. For example, the analysis unit can apply a specialized analysis algorithm to business-related messages. It can also apply a general analysis algorithm to private messages. Furthermore, it can apply a rapid analysis algorithm to urgent messages. This enables the provision of appropriate information by performing analysis according to the message category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of message analysis based on the date the message was submitted. For example, the analysis unit can prioritize the analysis of recently submitted messages. It can also postpone the analysis of older messages. Furthermore, the analysis unit can prioritize the analysis of messages with high urgency based on their submission date. This enables efficient information provision by performing analysis based on the submission date of messages. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of messages. For example, the analysis unit can prioritize the analysis of highly relevant messages. It can also postpone the analysis of less relevant messages. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of messages. This enables efficient information provision by performing analysis based on the relevance of messages. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message relevance data into a generating AI and have the generating AI adjust the order of analysis.

[0046] The speech generation unit can adjust the level of detail in the speech based on the importance of the message. For example, the speech generation unit can perform detailed speech for important messages. It can also perform standard speech for general messages. Furthermore, it can perform concise speech for less important messages. This enables efficient information delivery by performing speech based on the importance of the message. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or without AI. For example, the speech generation unit can input message importance data into a generating AI and have the generating AI adjust the level of detail in the speech.

[0047] The speech generation unit can apply different speech generation algorithms depending on the message category during speech generation. For example, the speech generation unit can apply a specialized speech generation algorithm to business-related messages. It can also apply a general speech generation algorithm to private messages. Furthermore, it can apply a rapid speech generation algorithm to urgent messages. This allows for the provision of appropriate information by performing speech generation according to the message category. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or without AI. For example, the speech generation unit can input message category data into a generating AI and have the generating AI execute the application of a speech generation algorithm.

[0048] The speech-to-speech unit can determine the priority of speech-to-speech based on when the messages were submitted. For example, the speech-to-speech unit can prioritize the speech-to-speech of recently submitted messages. It can also postpone the speech-to-speech of older messages. Furthermore, the speech-to-speech unit can prioritize the speech-to-speech of messages with high urgency based on their submission date. This enables efficient information provision by performing speech-to-speech based on the submission date of the message. Some or all of the above processing in the speech-to-speech unit may be performed using AI, for example, or without AI. For example, the speech-to-speech unit can input message submission date data into a generating AI and have the generating AI determine the priority of speech-to-speech.

[0049] The speech generation unit can adjust the order of speech generation based on the relevance of the messages. For example, the speech generation unit can prioritize the speech generation of highly relevant messages. It can also postpone the speech generation of less relevant messages. Furthermore, the speech generation unit can adjust the order of speech generation based on the relevance of the messages. This enables efficient information provision by performing speech generation based on the relevance of the messages. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or without AI. For example, the speech generation unit can input message relevance data into a generating AI and have the generating AI perform the adjustment of the speech generation order.

[0050] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize suggesting notification methods that the user has preferred to use in the past. Furthermore, the notification unit can predict and suggest notification methods to be used during specific time periods based on the user's past notification history. In addition, the notification unit can analyze the user's past notification history and select the most efficient notification method. This allows the notification unit to provide the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0051] The notification unit can customize the notification method based on the user's current situation when a notification is sent. For example, if the user is in a meeting, the notification unit can prioritize vibration or silent notifications. It can also prioritize voice notifications if the user is driving. Furthermore, if the user is relaxed, the notification unit can use the normal notification method. This improves user convenience by providing notification methods tailored to the user's current situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user's current situation data into a generating AI and have the generating AI perform the customization of the notification method.

[0052] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, if the user is in a specific location, the notification unit can prioritize selecting a notification method related to that location. It can also prioritize selecting a notification method related to a location close to the user's current location. Furthermore, the notification unit can select a highly relevant notification method based on the user's geographical location information. In this way, the notification unit can provide the optimal notification method by taking the user's geographical location information into consideration. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal notification method.

[0053] The notification unit can analyze the user's social media activity and suggest notification methods when sending notifications. For example, the notification unit can prioritize suggesting notification methods related to topics of interest based on the user's social media activity. It can also suggest relevant notification methods considering the user's social media friendships. Furthermore, the notification unit can analyze the user's social media activity and suggest highly relevant notification methods. In this way, by analyzing the user's social media activity, it can provide highly relevant notification methods. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI perform the suggestion of notification methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The notification system can further analyze the user's past behavior patterns and select the optimal notification method. For example, if a user has previously preferred to receive notifications at a specific time, the system can send notifications during that time. Similarly, if a user has preferred to receive notifications at a specific location, the system can send notifications when the user is at that location. Furthermore, if a user has preferred to receive notifications on a specific device, the system can send notifications to that device. This allows the system to provide the optimal notification method based on the user's past behavior patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For instance, the notification unit can input data on the user's past behavior patterns into a generating AI, which can then select the optimal notification method.

[0056] The notification system can further detect the user's current activity status and select the most appropriate notification method. For example, if the user is exercising, voice notifications can be prioritized. If the user is in a meeting, vibration or silent notifications can be prioritized. Furthermore, if the user is relaxed, the normal notification method can be used. This allows the system to provide the most appropriate notification method according to the user's current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current activity status data into a generating AI and have the generating AI select the most appropriate notification method.

[0057] The notification system can further analyze the user's past notification history and select the most appropriate notification content. For example, it can prioritize providing notifications that the user has preferred to receive in the past. It can also provide notifications that are most appropriate for a given time period based on notifications the user has received in the past during specific time periods. Furthermore, it can provide notifications that are most appropriate when the user is in a specific location based on notifications the user has received in the past at that location. In this way, the system can provide notifications that are most appropriate based on the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the most appropriate notification content.

[0058] The notification system can further adjust the content of notifications by taking into account the user's current geographical location. For example, if the user is in a specific location, it can provide notifications related to that location. It can also provide notifications related to locations near the user's current location. Furthermore, if the user is on the move, it can provide notifications related to their destination. This allows for the provision of optimal notifications based on the user's current geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal notification content.

[0059] The notification system can further analyze the user's social media activity and provide relevant notifications. For example, it can provide notifications related to topics the user has shown interest in on social media. It can also provide relevant notifications considering the user's social media friendships. Furthermore, it can analyze the user's social media activity and provide highly relevant notifications. This allows the system to provide optimal notifications based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI select the optimal notification content.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives messages sent by users to a specific number. The reception desk can receive voice messages and text messages, and when receiving voice messages, it uses speech recognition technology to convert the voice data into text data. Step 2: The analysis unit analyzes the message received by the reception unit. The analysis unit can analyze the content of the message using natural language processing technology and extract important information. Step 3: The speech generation unit converts the message analyzed by the analysis unit into speech. The speech generation unit uses speech synthesis technology to convert text data into speech data and can produce speech in a voice tailored to the user's preferences. Step 4: The notification unit notifies the user of the voice-generated message at the time specified by the voice-generating unit. The notification unit can make a notification to the user's smartphone and play the voice message at the specified time.

[0062] (Example of form 2) The notification system according to an embodiment of the present invention is a system in which a user sends a voice message or an SMS text to a specific number, and the content is interpreted and converted into speech by a generative AI. The notification system works as follows: The user sends a message to a specific number. Next, the generative AI analyzes the message and converts it into speech. The generative AI behaves like a human, regardless of gender, age, or language, and speaks in various voices at a time specified by the user, calling the user's smartphone to serve as a notification for appointments or a substitute for notes. For example, if a user enters a message such as, "October 11th is our wedding anniversary, so please buy a cake at the cake shop in front of the station at 6 PM," the generative AI will convert the content into speech and call the user's smartphone at the specified time to notify them. The generative AI can make notifications in a voice that matches the user's preference, such as a young male voice or an elderly female voice. In addition, the generative AI can summarize recorded voice data or text data and convert the summary into speech to remind the user of tasks or appointments they do not want to forget. This allows the user to efficiently manage important tasks and appointments without forgetting them. As a result, the notification system can efficiently convert the user's messages into speech and notify them at a specified time.

[0063] The notification system according to the embodiment comprises a reception unit, an analysis unit, a voice conversion unit, and a notification unit. The reception unit receives messages sent by the user to a specific number. The reception unit can receive, for example, voice messages or text messages. When the reception unit receives a voice message, it can convert the voice data into text data using speech recognition technology. The analysis unit analyzes the message received by the reception unit. The analysis unit can analyze the content of the message using, for example, natural language processing technology. The analysis unit can analyze the content of the message and extract important information. The voice conversion unit converts the message analyzed by the analysis unit into voice. The voice conversion unit can convert the text data into voice data using, for example, speech synthesis technology. The voice conversion unit can convert the message into voice in a voice that matches the user's preference. The notification unit notifies the user of the message converted into voice by the voice conversion unit at a time specified by the user. The notification unit can, for example, make a notification by calling the user's smartphone. The notification unit can play the voice message at a time specified by the user. As a result, the notification system according to the embodiment can efficiently convert the user's message into voice and notify them at a specified time.

[0064] The reception unit receives messages sent by users to a specific number. The reception unit can receive, for example, voice messages and text messages. Specifically, when a user sends a message to a specific number using a smartphone or other device, the reception unit receives the message immediately. In the case of voice messages, the reception unit uses advanced speech recognition technology to convert the voice data into text data. This speech recognition technology can recognize speech with high accuracy even in noisy environments and accurately transcribe the user's speech. For example, by utilizing a deep learning-based speech recognition model and learning the characteristics of the user's speech, it can handle different accents and speaking styles. In the case of text messages, the reception unit receives the message in its original format. This gives users the flexibility to use either voice or text messages. Furthermore, the reception unit has a function to temporarily store received messages and verify data integrity before sending them to the analysis unit. This prevents incorrect data from being sent to the analysis unit and improves the overall reliability of the system.

[0065] The analysis unit analyzes messages received by the reception unit. The analysis unit can analyze message content using, for example, natural language processing (NLP) techniques. Specifically, it uses NLP to analyze the grammatical structure and meaning of messages and extract important information. For example, it utilizes techniques such as tokenization, part-of-speech tagging, and dependency analysis to analyze message content in detail. Furthermore, the analysis unit can use machine learning models to understand the intent of messages and identify what the user wants to convey. For example, if a user sends "Remind me of tomorrow's meeting," the analysis unit extracts keywords such as "tomorrow," "meeting," and "remind," and understands the user's intent as "remind me of tomorrow's meeting." The analysis unit can also refer to past message history and user profile information to perform more accurate analysis. This allows the analysis unit to quickly and accurately analyze user messages and extract important information. Additionally, the analysis unit has a function to verify data integrity and consistency before sending the analysis results to the speech conversion unit. This ensures the accuracy of the analysis results and improves the overall reliability of the system.

[0066] The speech generation unit converts the message analyzed by the analysis unit into speech. The speech generation unit can, for example, convert text data into speech data using speech synthesis technology. Specifically, it uses speech synthesis technology to convert the analyzed text data into natural-sounding speech. The speech synthesis technology utilizes a deep learning-based speech synthesis model, enabling the generation of highly natural and human-like speech. For example, if a user sends "Remind me of tomorrow's meeting," the speech generation unit will generate a voice message such as "Remind me of tomorrow's meeting." Furthermore, the speech generation unit can voice messages in a voice tailored to the user's preferences. For example, by allowing users to select male or female voices, young or old voices, a more personalized experience can be provided. In addition, the speech generation unit has the ability to adjust the speed and tone of the speech, allowing for customization of the voice characteristics according to the user's preferences. This enables the speech generation unit to provide voice messages that are easy for users to hear and understand. Moreover, the speech generation unit has the ability to temporarily store the generated voice data and check the voice quality before sending it to the notification unit. This ensures the quality of voice messages and improves the overall reliability of the system.

[0067] The notification unit delivers voice-generated messages, converted by the voice-generating unit, to the user at a time specified by the user. Specifically, it can make notifications to the user's smartphone. The notification unit can play voice messages at a time specified by the user. For example, if the user specifies, "Please remind me of the meeting at 9 AM tomorrow," the notification unit will send a notification to the user's smartphone at that time and play the voice message. The notification unit can utilize the smartphone's notification function to notify the user in various formats, such as pop-up notifications, banner notifications, and voice notifications. The notification unit also has a function to automatically notify the user of important events and reminders by linking with the user's schedule and calendar. This allows the user to efficiently manage their schedule without forgetting important appointments. Furthermore, the notification unit allows the user to customize the frequency and timing of notifications according to their preferences. For example, if the user has set multiple reminders, the notification unit will notify them at appropriate intervals to avoid placing an excessive burden on the user. The notification unit also has a function to save the notification history and allow the user to refer to past notification content. This allows the user to check past notification content and set up re-notifications as needed. This allows the notification unit to efficiently and effectively notify users and reliably convey important information.

[0068] The speech generation unit can convert messages into voices tailored to the user's preferences. For example, it can convert messages into voices tailored to the user's preferences, such as a young man's voice or an older woman's voice. The speech generation unit can refer to user profiles and past preference data to select a voice tailored to the user's preferences. For example, the speech generation unit can select the optimal voice based on data of voices previously selected by the user. This allows notifications to be delivered in a voice tailored to the user's preferences. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or without AI. For example, the speech generation unit can input a user profile into a generating AI and have the generating AI select the optimal voice.

[0069] The analysis unit can summarize recorded audio and text data. For example, the analysis unit can analyze audio and text data using natural language processing techniques to extract important information. The analysis unit can also summarize audio and text data using summarization algorithms. For example, the analysis unit can convert audio data into text data and then summarize that text data. Furthermore, the analysis unit can directly summarize text data. This enables efficient information management by summarizing recorded audio and text data. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input audio data into a generating AI and have the generating AI perform the summarization.

[0070] The notification unit can make a call to the user's smartphone to send a notification. For example, the notification unit can make a call to the user's smartphone to play a voice message at a time specified by the user. The notification unit can send notifications in various ways, such as phone notifications, app notifications, and SMS notifications. For example, the notification unit can make a phone call to the user's smartphone and play a voice message. The notification unit can also play a voice message through an app. Furthermore, the notification unit can send an SMS to play a voice message. This ensures reliable notification by sending notifications directly to the user's smartphone. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the timing for calling the user's smartphone into a generating AI, and have the generating AI execute the notification.

[0071] The speech generation unit can convert messages into voices in various voices, regardless of gender, age, or language. For example, it can convert messages into voices in various voices, such as a young man's voice, an older woman's voice, or a voice in a different language. The speech generation unit can refer to user profiles and past preference data to select a voice that matches the user's preferences. For example, the speech generation unit can select the optimal voice based on data of voices previously selected by the user. This enables notifications tailored to the user's preferences by converting messages into voices in a variety of voices. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or without AI. For example, the speech generation unit can input a user profile into a generating AI and have the generating AI select the optimal voice.

[0072] The reception unit can estimate the user's emotions and adjust how messages are received based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and receive messages quickly. This improves user convenience by providing a message receiving method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception unit can analyze the user's past message sending history when receiving a message and select the optimal reception method. For example, the reception unit can prioritize suggesting message formats that the user has frequently used in the past. Furthermore, the reception unit can predict and suggest message formats to be used during specific time periods based on the user's past sending history. In addition, the reception unit can analyze the user's past message sending history and select the most efficient reception method. This allows the reception unit to provide the optimal reception method by analyzing the user's past message sending history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past message sending history into a generating AI and have the generating AI select the optimal reception method.

[0074] The reception unit can filter messages upon receipt based on the user's current situation and areas of interest. For example, the reception unit can prioritize receiving messages that are highly relevant to the user's current situation. It can also prioritize receiving messages containing specific keywords based on the user's areas of interest. Furthermore, the reception unit can filter out unnecessary messages, taking into account the user's current situation and areas of interest. This allows for the priority of receiving highly relevant messages by filtering them based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0075] The reception unit can estimate the user's emotions and determine the priority of messages to receive based on the estimated emotions. For example, if the user is stressed, the reception unit can prioritize important messages. If the user is relaxed, the reception unit can receive all messages equally. Furthermore, if the user is in a hurry, the reception unit can prioritize urgent messages. This allows for the priority of important messages by providing message prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception unit can prioritize receiving messages that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific location, the reception unit can prioritize receiving messages related to that location. It can also prioritize receiving messages related to locations close to the user's current location. Furthermore, the reception unit can filter highly relevant messages based on the user's geographical location information. This allows for the priority reception of highly relevant messages by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the filtering of highly relevant messages.

[0077] The reception unit can analyze the user's social media activity when receiving a message and receive relevant messages. For example, the reception unit can prioritize receiving messages related to topics of interest based on the user's social media activity. It can also consider the user's social media friendships when receiving relevant messages. Furthermore, the reception unit can analyze the user's social media activity and filter highly relevant messages. This allows for the priority reception of highly relevant messages by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the filtering of relevant messages.

[0078] The analysis unit can estimate the user's emotions and adjust the message analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide abundant information. If the user is in a hurry, the analysis unit can perform a concise analysis that gets straight to the point. Furthermore, if the user is stressed, the analysis unit can perform a simple and easy-to-understand analysis. This improves user convenience by providing a message analysis method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit can adjust the level of detail of a message analysis based on its importance. For example, for important messages, the analysis unit can perform a detailed analysis to provide abundant information. For general messages, the analysis unit can perform a standard analysis. Furthermore, for low-importance messages, the analysis unit can perform a concise analysis. This enables efficient information provision by performing analysis based on message importance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the message category when analyzing messages. For example, the analysis unit can apply a specialized analysis algorithm to business-related messages. It can also apply a general analysis algorithm to private messages. Furthermore, it can apply a rapid analysis algorithm to urgent messages. This enables the provision of appropriate information by performing analysis according to the message category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0081] The analysis unit can estimate the user's emotions 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 provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This improves user convenience by providing a display method of analysis results that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The analysis unit can determine the priority of message analysis based on the date the message was submitted. For example, the analysis unit can prioritize the analysis of recently submitted messages. It can also postpone the analysis of older messages. Furthermore, the analysis unit can prioritize the analysis of messages with high urgency based on their submission date. This enables efficient information provision by performing analysis based on the submission date of messages. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of messages. For example, the analysis unit can prioritize the analysis of highly relevant messages. It can also postpone the analysis of less relevant messages. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of messages. This enables efficient information provision by performing analysis based on the relevance of messages. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input message relevance data into a generating AI and have the generating AI adjust the order of analysis.

[0084] The speech generation unit can estimate the user's emotions and adjust the speech expression based on the estimated emotions. For example, if the user is relaxed, the speech generation unit can produce speech in a calm voice. If the user is in a hurry, the speech generation unit can produce speech quickly and concisely. Furthermore, if the user is stressed, the speech generation unit can produce speech in a calm voice. This improves user convenience by providing a speech expression that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or without AI. For example, the speech generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The speech generation unit can adjust the level of detail in the speech based on the importance of the message. For example, the speech generation unit can perform detailed speech for important messages. It can also perform standard speech for general messages. Furthermore, it can perform concise speech for less important messages. This enables efficient information delivery by performing speech based on the importance of the message. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or without AI. For example, the speech generation unit can input message importance data into a generating AI and have the generating AI adjust the level of detail in the speech.

[0086] The speech generation unit can apply different speech generation algorithms depending on the message category during speech generation. For example, the speech generation unit can apply a specialized speech generation algorithm to business-related messages. It can also apply a general speech generation algorithm to private messages. Furthermore, it can apply a rapid speech generation algorithm to urgent messages. This allows for the provision of appropriate information by performing speech generation according to the message category. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or without AI. For example, the speech generation unit can input message category data into a generating AI and have the generating AI execute the application of a speech generation algorithm.

[0087] The speech generation unit can estimate the user's emotions and adjust the length of the speech based on the estimated emotions. For example, if the user is in a hurry, the speech generation unit can produce a short, concise speech. If the user is relaxed, the speech generation unit can produce a longer speech with detailed explanations. Furthermore, if the user is stressed, the speech generation unit can produce a simple and easy-to-understand speech. This improves user convenience by providing speech lengths that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or not using AI. For example, the speech generation unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0088] The speech-to-speech unit can determine the priority of speech-to-speech based on when the messages were submitted. For example, the speech-to-speech unit can prioritize the speech-to-speech of recently submitted messages. It can also postpone the speech-to-speech of older messages. Furthermore, the speech-to-speech unit can prioritize the speech-to-speech of messages with high urgency based on their submission date. This enables efficient information provision by performing speech-to-speech based on the submission date of the message. Some or all of the above processing in the speech-to-speech unit may be performed using AI, for example, or without AI. For example, the speech-to-speech unit can input message submission date data into a generating AI and have the generating AI determine the priority of speech-to-speech.

[0089] The speech generation unit can adjust the order of speech generation based on the relevance of the messages. For example, the speech generation unit can prioritize the speech generation of highly relevant messages. It can also postpone the speech generation of less relevant messages. Furthermore, the speech generation unit can adjust the order of speech generation based on the relevance of the messages. This enables efficient information provision by performing speech generation based on the relevance of the messages. Some or all of the above processing in the speech generation unit may be performed using AI, for example, or without AI. For example, the speech generation unit can input message relevance data into a generating AI and have the generating AI perform the adjustment of the speech generation order.

[0090] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is tense, the notification unit can make a notification in a calm voice. If the user is relaxed, the notification unit can make a notification in a cheerful voice. Furthermore, if the user is in a hurry, the notification unit can make a quick and concise notification. This improves user convenience by providing a notification method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize suggesting notification methods that the user has preferred to use in the past. Furthermore, the notification unit can predict and suggest notification methods to be used during specific time periods based on the user's past notification history. In addition, the notification unit can analyze the user's past notification history and select the most efficient notification method. This allows the notification unit to provide the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0092] The notification unit can customize the notification method based on the user's current situation when a notification is sent. For example, if the user is in a meeting, the notification unit can prioritize vibration or silent notifications. It can also prioritize voice notifications if the user is driving. Furthermore, if the user is relaxed, the notification unit can use the normal notification method. This improves user convenience by providing notification methods tailored to the user's current situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user's current situation data into a generating AI and have the generating AI perform the customization of the notification method.

[0093] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is stressed, the notification unit can prioritize important notifications. If the user is relaxed, the notification unit can distribute all notifications equally. Furthermore, if the user is in a hurry, the notification unit can prioritize urgent notifications. This allows for prioritizing important notifications by providing notification priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, if the user is in a specific location, the notification unit can prioritize selecting a notification method related to that location. It can also prioritize selecting a notification method related to a location close to the user's current location. Furthermore, the notification unit can select a highly relevant notification method based on the user's geographical location information. In this way, the notification unit can provide the optimal notification method by taking the user's geographical location information into consideration. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal notification method.

[0095] The notification unit can analyze the user's social media activity and suggest notification methods when sending notifications. For example, the notification unit can prioritize suggesting notification methods related to topics of interest based on the user's social media activity. It can also suggest relevant notification methods considering the user's social media friendships. Furthermore, the notification unit can analyze the user's social media activity and suggest highly relevant notification methods. In this way, by analyzing the user's social media activity, it can provide highly relevant notification methods. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI perform the suggestion of notification methods.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The notification system can further estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification can be delayed. If the user is relaxed, the notification can be sent immediately. Furthermore, if the user is in a hurry, important notifications can be prioritized. This allows notifications to be sent at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The notification system can further analyze the user's past behavior patterns and select the optimal notification method. For example, if a user has previously preferred to receive notifications at a specific time, the system can send notifications during that time. Similarly, if a user has preferred to receive notifications at a specific location, the system can send notifications when the user is at that location. Furthermore, if a user has preferred to receive notifications on a specific device, the system can send notifications to that device. This allows the system to provide the optimal notification method based on the user's past behavior patterns. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For instance, the notification unit can input data on the user's past behavior patterns into a generating AI, which can then select the optimal notification method.

[0099] The notification system can further detect the user's current activity status and select the most appropriate notification method. For example, if the user is exercising, voice notifications can be prioritized. If the user is in a meeting, vibration or silent notifications can be prioritized. Furthermore, if the user is relaxed, the normal notification method can be used. This allows the system to provide the most appropriate notification method according to the user's current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current activity status data into a generating AI and have the generating AI select the most appropriate notification method.

[0100] The notification system can further estimate the user's emotions and adjust the notification content based on the estimated emotions. For example, if the user is stressed, a simple and easy-to-understand notification can be provided. If the user is relaxed, a detailed notification can be provided. Furthermore, if the user is in a hurry, a concise notification that gets straight to the point can be provided. This allows for the provision of optimal notification content tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0101] The notification system can further estimate the user's emotions and adjust the notification frequency based on the estimated emotions. For example, if the user is stressed, the notification frequency can be reduced. Conversely, if the user is relaxed, the notification frequency can be increased. Furthermore, if the user is in a hurry, important notifications can be prioritized. This allows for the provision of an optimal notification frequency tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The notification system can further analyze the user's past notification history and select the most appropriate notification content. For example, it can prioritize providing notifications that the user has preferred to receive in the past. It can also provide notifications that are most appropriate for a given time period based on notifications the user has received in the past during specific time periods. Furthermore, it can provide notifications that are most appropriate when the user is in a specific location based on notifications the user has received in the past at that location. In this way, the system can provide notifications that are most appropriate based on the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the most appropriate notification content.

[0103] The notification system can further adjust the content of notifications by taking into account the user's current geographical location. For example, if the user is in a specific location, it can provide notifications related to that location. It can also provide notifications related to locations near the user's current location. Furthermore, if the user is on the move, it can provide notifications related to their destination. This allows for the provision of optimal notifications based on the user's current geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal notification content.

[0104] The notification system can further estimate the user's emotions and adjust the notification voice based on the estimated emotions. For example, if the user is stressed, the notification can be delivered in a calm voice. If the user is relaxed, the notification can be delivered in a cheerful voice. Furthermore, if the user is in a hurry, the notification can be delivered in a quick and concise voice. This allows for notifications to be delivered in the most appropriate voice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The notification system can further analyze the user's social media activity and provide relevant notifications. For example, it can provide notifications related to topics the user has shown interest in on social media. It can also provide relevant notifications considering the user's social media friendships. Furthermore, it can analyze the user's social media activity and provide highly relevant notifications. This allows the system to provide optimal notifications based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI select the optimal notification content.

[0106] The notification system can further estimate the user's emotions and adjust the priority of notifications based on the estimated emotions. For example, if the user is stressed, important notifications can be prioritized. If the user is relaxed, all notifications can be distributed equally. Furthermore, if the user is in a hurry, urgent notifications can be prioritized. This allows for the provision of optimal notification priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives messages sent by users to a specific number. The reception desk can receive voice messages and text messages, and when receiving voice messages, it uses speech recognition technology to convert the voice data into text data. Step 2: The analysis unit analyzes the message received by the reception unit. The analysis unit can analyze the content of the message using natural language processing technology and extract important information. Step 3: The speech generation unit converts the message analyzed by the analysis unit into speech. The speech generation unit uses speech synthesis technology to convert text data into speech data and can produce speech in a voice tailored to the user's preferences. Step 4: The notification unit notifies the user of the voice-generated message at the time specified by the voice-generating unit. The notification unit can make a notification to the user's smartphone and play the voice message at the specified time.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, voice conversion unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives messages sent by the user to a specific number. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received message. The voice conversion unit is implemented by the identification processing unit 290 of the data processing unit 12 and converts the analyzed message into voice. The notification unit is implemented by the output device 40 of the smart device 14 and notifies the user of the voiced message at a time specified by the user. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, voice conversion unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives messages sent by the user to a specific number. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received message. The voice conversion unit is implemented by the identification processing unit 290 of the data processing unit 12 and converts the analyzed message into voice. The notification unit is implemented by the speaker 240 of the smart glasses 214 and notifies the user of the voiced message at a time specified by the user. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, voice conversion unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives messages sent by the user to a specific number. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received message. The voice conversion unit is implemented by the identification processing unit 290 of the data processing unit 12 and converts the analyzed message into voice. The notification unit is implemented by the speaker 240 of the headset terminal 314 and notifies the user of the voiced message at a time specified by the user. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, voice conversion unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives messages sent by the user to a specific number. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received message. The voice conversion unit is implemented by the identification processing unit 290 of the data processing unit 12 and converts the analyzed message into speech. The notification unit is implemented by the speaker 240 of the robot 414 and notifies the user of the speech-converted message at a time specified by the user. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The reception desk that receives messages, An analysis unit that analyzes the message received by the aforementioned reception unit, A speech conversion unit that converts the message analyzed by the analysis unit into speech, The system includes a notification unit that notifies the user of the voiced message produced by the voice-generating unit at a time specified by the user. A system characterized by the following features. (Note 2) The aforementioned speech conversion unit, The system will convert messages into voices tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Summarize recorded audio and text data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Send notifications to the user's smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned speech conversion unit, Messages can be voiced in a variety of voices, regardless of gender, age, or language. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts how messages are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving a message, the system analyzes the user's past message sending history and selects the most suitable receiving method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving messages, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of messages to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving messages, the system prioritizes receiving messages that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a message, the system analyzes the user's social media activity and selects relevant messages. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the message analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing messages, adjust the level of detail based on the importance of the message. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing messages, different analysis algorithms are applied depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing messages, the priority of analysis is determined based on when the messages were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When parsing messages, the order of parsing is adjusted based on the relevance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned speech conversion unit, The system estimates the user's emotions and adjusts the voice representation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned speech conversion unit, When converting to speech, adjust the level of detail in the speech based on the importance of the message. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned speech conversion unit, When converting to speech, different speech algorithms are applied depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned speech conversion unit, It estimates the user's emotions and adjusts the length of the speech based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned speech conversion unit, When converting messages to audio, the priority of the audio conversion is determined based on when the messages were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned speech conversion unit, During the speech generation process, the order of speech is adjusted based on the relevance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When sending notifications, customize the notification method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending notifications, we analyze the user's social media activity and suggest notification methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 reception desk that receives messages, An analysis unit that analyzes the message received by the aforementioned reception unit, A speech conversion unit that converts the message analyzed by the analysis unit into speech, The system includes a notification unit that notifies the user of the voiced message produced by the voice-generating unit at a time specified by the user. A system characterized by the following features.

2. The aforementioned speech conversion unit, The system will convert messages into voices tailored to the user's preferences. The system according to feature 1.

3. The aforementioned analysis unit, Summarize recorded audio and text data. The system according to feature 1.

4. The aforementioned notification unit, Send notifications to the user's smartphone. The system according to feature 1.

5. The aforementioned speech conversion unit, Messages can be voiced in a variety of voices, regardless of gender, age, or language. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts how messages are received based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is When receiving a message, the system analyzes the user's past message sending history and selects the most suitable receiving method. The system according to feature 1.

8. The aforementioned reception unit is When receiving messages, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of messages to accept based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving messages, the system prioritizes receiving messages that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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