System

Abstract

A system according to an embodiment is directed to automatically summarizing and notifying the requirements of the other party when an incoming call is missed.SOLUTION: A system according to an embodiment includes a collection unit, a summarization unit, and a notification unit. The collection unit extracts a requirement from the incoming call log. The summarizing unit summarizes the requirements collected by the collecting unit by using natural language processing. The notification unit notifies the requirements summarized by the summarization unit based on a notification setting that can be customized by the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was no way to understand the other party's requirements when a call was missed, making it difficult to contact them in a timely manner.

[0005] The system according to the embodiment aims to automatically summarize and notify the other party's requirements when a call is missed. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a summarization unit, and a notification unit. The collection unit extracts requirements from an incoming call log. The summarization unit summarizes the requirements collected by the collection unit using natural language processing. The notification unit notifies the user of the requirements summarized by the summarization unit based on notification settings that can be customized by the user. [Effects of the Invention]

[0007] The system according to the embodiment can automatically summarize and notify the other party of their requirements when they miss a call. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automatic requirements summarization system according to an embodiment of the present invention automatically summarizes and notifies the other party of their requirements when a call is received and missed. This automatic requirements summarization system extracts requirements from an incoming call log, summarizes them using natural language processing, and notifies the user based on notification settings customizable by the user. For example, the automatic requirements summarization system collects information such as the caller's phone number, call duration, and call content from the incoming call log. Next, the automatic requirements summarization system analyzes the call content using natural language processing, extracts important information, and generates a summary. Finally, the automatic requirements summarization system notifies the user of the summarized requirements based on the notification method and timing set by the user. This enables timely information sharing and efficient communication, reducing stress in work and personal life. This allows the automatic requirements summarization system to quickly grasp the other party's requirements even when the user is away and take necessary measures. For example, it can instantly grasp and respond to requirements for changes to the time of an important meeting. Furthermore, the automatic requirements summarization system reduces unnecessary communication by allowing the user to understand the other party's requirements simply by checking the summarized requirements. Furthermore, since the user can understand the other party's requirements even when they are away, they can continue their work and life without stress and with peace of mind. For example, you will have peace of mind knowing that you will not miss any important communications.

[0029] An automatic requirements summarization system according to an embodiment includes a collection unit, a summarization unit, and a notification unit. The collection unit extracts requirements from an incoming call log. The incoming call log includes information such as the caller's phone number, call duration, and call content. For example, if a caller leaves a requirement such as "I would like to change the meeting time," the collection unit extracts the content from the incoming call log. The collection unit can also analyze the incoming call log using AI. The summarization unit summarizes the requirements collected by the collection unit using natural language processing. Natural language processing analyzes the call content using techniques such as morphological analysis, grammatical analysis, and semantic analysis to extract important information. For example, the summarization unit summarizes the requirement "I would like to change the meeting time" as "change meeting time." Some or all of the above-described processing in the summarization unit may be performed using generation AI (e.g., text generation AI or multimodal generation AI). The notification unit notifies the user of the requirements summarized by the summarization unit based on user-customizable notification settings. The notification unit can notify the user via methods such as email, push notification, and SMS. In addition, the notification unit can also provide immediate notification or notification after a certain period of time based on the notification timing set by the user. As a result, the automatic requirements summarization system according to the embodiment can automatically summarize and notify requirements when a call is missed, thereby achieving timely information sharing and efficient communication.

[0030] The collection unit can collect the caller's telephone number, call duration, call content, and other information. The collection unit, for example, collects the caller's telephone number. The caller's telephone number is information recorded in an incoming call log, and the collection unit can extract this information. The collection unit can also collect call duration. The call duration includes information such as the date and time the call was made and the duration of the call. The collection unit can also collect call content. The call content includes messages and requirements left by the caller. For example, if a caller leaves a requirement such as "I would like to change the meeting time," this content is recorded as the call content. Other information includes the caller's location information and device information. For example, the caller's location information is obtained using GPS data, IP address, etc. In this way, the collection unit can collect detailed information about the caller and accurately understand their requirements.

[0031] The summarization unit can analyze the call content recorded in the incoming call log and extract important information. The summarization unit analyzes the call content using, for example, natural language processing. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The summarization unit extracts important information from the call content. For example, if the call content includes "I would like to change the meeting time," the summarization unit generates a summary such as "Meeting time changed." Criteria for extracting important information include the frequency of keyword appearances and specific phrases. For example, the summarization unit analyzes the frequency of keywords such as "meeting" and "change" in the call content and extracts information containing these keywords as important information. The summarization unit can also extract important information based on specific phrases. For example, call content containing a phrase such as "I would like to change the time" is extracted as important information. In this way, the summarization unit can analyze the call content and extract important information, thereby improving the accuracy of the summary. Some or all of the above-mentioned processing in the summarization unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0032] The notification unit can notify the user of the summarized requirements based on the notification method and timing set by the user. The notification unit can provide notifications via email, push notification, SMS, or other methods. The user can freely set the notification method. For example, if the user wants to receive notifications via email, the user can register an email address and select email as the notification method. If the user wants to receive push notifications, the user can install a dedicated app and enable notifications. If the user wants to receive notifications via SMS, the user can register a phone number and select SMS as the notification method. The notification unit can also provide immediate notification or notification after a specified time has elapsed based on the notification timing set by the user. For example, if the user wants immediate notification, the notification can be provided immediately as soon as the summarized requirements are generated. If the user wants notification after a specified time has elapsed, the notification can be provided after the specified time has elapsed. This allows the notification unit to provide information tailored to the user's needs by providing notifications based on the user's settings. Some or all of the above-described processing by the notification unit may be performed using AI.

[0033] The notification unit can provide notifications via email, push notification, and SMS. For example, the notification unit provides notifications via email. Email notification is a method of sending summarized requirements to an email address registered by the user. The notification unit can set the email address registration method and the notification content format. The notification unit can also provide notifications via push notification. Push notification is a method of sending notifications directly to the user's device through a dedicated app. The notification unit can set the app installation method and notification permission settings. The notification unit can also provide notifications via SMS. SMS notification is a method of sending summarized requirements as a text message to a phone number registered by the user. The notification unit can set the phone number registration method and the notification content format. This allows the notification unit to provide a variety of notification methods, allowing the user to receive information in the most optimal way. Some or all of the above-mentioned processing in the notification unit may be performed using AI.

[0034] The notification unit can provide notifications immediately or after a certain time, depending on the user's needs. The notification unit, for example, provides immediate notification. Immediate notification is a method of providing notification immediately after summarized requirements are generated. The notification unit can set notification trigger conditions and notification delay times. The notification unit can also provide notification after a certain time. Notification after a certain time is a method of providing notification after a specified time has elapsed. The notification unit can set notification delay times and re-notification conditions. For example, if a user desires immediate notification, the notification can be provided immediately after summarized requirements are generated. Also, if a user desires notification after a certain time has elapsed, the notification can be provided after the specified time has elapsed. In this way, the notification unit can adjust the timing of notifications to improve user convenience. Some or all of the above-described processing in the notification unit may be performed using AI.

[0035] The collection unit can analyze a caller's past call history and prioritize collection of requirements with high importance. The collection unit, for example, analyzes a caller's past call history. The past call history includes information such as the content, frequency, and importance of calls made by the caller in the past. If a caller has had many important requirements in the past, the collection unit can prioritize collection of the call log of that caller. In addition, if a caller has had urgent requirements in the past, the collection unit can immediately collect the call log of that caller. Furthermore, the collection unit can prioritize collection of the call log of a caller with whom the caller has frequently been in contact in the past. Criteria for extracting requirements with high importance include the frequency of keyword appearances and specific phrases. For example, the collection unit analyzes the frequency of appearances of keywords such as "urgent" and "important" in the past call history and extracts call content containing these keywords as important requirements. The collection unit can also extract important requirements based on specific phrases. For example, call content containing a phrase such as "urgent response required" can be extracted as an important requirement. This allows the collection unit to analyze the caller's past call history and prioritize collection of important requirements, thereby enabling important information to be quickly grasped. Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0036] When collecting call logs, the collection unit can filter the call logs based on the caller's current situation and areas of interest. For example, the collection unit identifies the caller's current situation. The caller's current situation is identified using location information, device status, etc. The collection unit can postpone the call log if the caller is currently in a meeting. The collection unit can also identify the caller's areas of interest. The caller's areas of interest are identified based on past call content, social media activity, etc. The collection unit can prioritize collecting call logs if the caller has requirements related to a particular area of ​​interest. For example, if the caller is currently traveling, the call log can be postponed. This allows the collection unit to prioritize collecting highly relevant information by filtering based on the caller's situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0037] When collecting call logs, the collection unit can select an appropriate collection means depending on the caller's input method. For example, if the caller leaves a voice message, the collection unit collects call logs using voice recognition technology. Voice recognition technology is technology that converts the caller's voice into text data. If the caller leaves a text message, the collection unit can collect call logs using text analysis technology. Text analysis technology is technology that analyzes the caller's text message and extracts important information. Furthermore, if the caller sends an image, the collection unit can also collect call logs using image analysis technology. Image analysis technology is technology that analyzes the caller's image and extracts important information. This allows the collection unit to select the optimal collection means depending on the caller's input method, enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0038] When collecting call logs, the collection unit can prioritize collection of highly relevant logs based on the caller's geographical location information. The collection unit, for example, acquires the caller's geographical location information. The geographical location information is acquired using GPS data, an IP address, or the like. The collection unit can prioritize collection of call logs when the caller is close to the user. The collection unit can also postpone collection of call logs when the caller is far from the user. Furthermore, when the caller is in a specific geographical area, the collection unit can also prioritize collection of call logs related to that area. In this way, the collection unit can prioritize collection of highly relevant information by taking the caller's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI.

[0039] When collecting call logs, the collection unit can analyze the caller's social media activity and collect related logs. The collection unit, for example, analyzes the caller's social media activity. Social media activity includes information such as the content of posts made by the caller on social media, the number of likes, and the number of followers. If the caller posts important information on social media, the collection unit can prioritize collecting the call logs. In addition, if the caller is participating in a specific event on social media, the collection unit can prioritize collecting the call logs. Furthermore, if the caller posts information related to users on social media, the collection unit can prioritize collecting the call logs. In this way, the collection unit can prioritize collecting highly relevant information by analyzing the caller's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0040] When collecting call logs, the collection unit can adjust the collection method based on the caller's past feedback. The collection unit, for example, analyzes the caller's past feedback. The past feedback includes information such as ratings, comments, and survey results provided by the caller in the past. If the caller has provided important feedback in the past, the collection unit can prioritize collecting the call logs. In addition, if the caller has provided negative feedback in the past, the collection unit can postpone collecting the call logs. Furthermore, if the caller has provided feedback frequently in the past, the collection unit can prioritize collecting the call logs. In this way, the collection unit can optimize the collection method by reflecting the caller's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0041] When generating a summary, the summarization unit can adjust the specificity of the summary based on the importance of the requirements. The summarization unit, for example, evaluates the importance of the requirements. The importance of the requirements is evaluated based on the frequency of keyword appearance, specific phrases, etc. The summarization unit can provide a detailed summary for requirements of high importance. The summarization unit can also provide a concise summary for requirements of low importance. Furthermore, the summarization unit can provide a summary with appropriate detail for requirements of medium importance. In this way, the summarization unit can appropriately convey important information by adjusting the detail level of the summary based on the importance of the requirements. Some or all of the above-mentioned processing in the summarization unit may be performed using generation AI (e.g., text generation AI or multimodal generation AI).

[0042] The summarization unit can apply different summarization algorithms depending on the category of requirements when generating a summary. For example, the summarization unit classifies the categories of requirements. The categories of requirements are classified into business requirements, technical requirements, personal requirements, etc. The summarization unit can apply a business summarization algorithm to business-related requirements. The summarization unit can also apply a private summarization algorithm to private-related requirements. Furthermore, the summarization unit can apply an emergency summarization algorithm to urgent requirements. In this way, the summarization unit can provide an appropriate summary by applying different summarization algorithms depending on the category of requirements. Some or all of the above-mentioned processing in the summarization unit may be performed using generation AI (e.g., text generation AI or multimodal generation AI).

[0043] When generating a summary, the summarization unit can improve the accuracy of the summary based on the user's past summarization results. The summarization unit, for example, analyzes the user's past summarization results. The past summarization results include information such as the user's previously preferred summarization style and feedback. The summarization unit can generate a summary by referring to the user's previously preferred summarization style. The summarization unit can also generate a summary by referring to summarization results for which the user has provided feedback in the past. Furthermore, the summarization unit can improve the accuracy of the summary based on the summarization results used by the user in the past. In this way, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. Some or all of the above-mentioned processing in the summarization unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0044] When generating summaries, the summarization unit can set the priority of summaries based on the submission time of the requirements. The summarization unit, for example, acquires the submission time of the requirements. The submission time includes information such as the submission date and time and the submission frequency. The summarization unit can prioritize summarizing recently submitted requirements. The summarization unit can also postpone requirements that were submitted recently. Furthermore, the summarization unit can appropriately summarize requirements that were submitted recently. In this way, the summarization unit can prioritize providing the latest information by determining the priority of summaries based on the submission time of the requirements. Some or all of the above-mentioned processing in the summarization unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0045] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of requirements. The summarization unit, for example, evaluates the relevance of requirements. The relevance of requirements is evaluated based on the co-occurrence of keywords, similarity of content, etc. The summarization unit can prioritize summarizing highly relevant requirements. The summarization unit can also postpone requirements with low relevance. Furthermore, the summarization unit can appropriately summarize requirements with medium relevance. In this way, the summarization unit can prioritize providing important information by adjusting the order of summaries based on the relevance of requirements. Some or all of the above-mentioned processing in the summarization unit may be performed using generation AI (e.g., text generation AI or multimodal generation AI).

[0046] When generating a summary, the summarization unit can adjust the use of technical terms in the summary based on the user's level of expertise. The summarization unit, for example, evaluates the user's level of expertise. The level of expertise is evaluated based on survey results, past usage history, etc. The summarization unit can provide a summary that uses a lot of technical terms if the user has technical knowledge. The summarization unit can also provide a summary in simple language if the user does not have technical knowledge. Furthermore, the summarization unit can provide a summary that uses appropriate technical terms depending on the user's level of expertise. In this way, the summarization unit can provide a summary that is easy for the user to understand by adjusting the use of technical terms in the summary depending on the user's level of expertise. Some or all of the above-mentioned processing in the summarization unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0047] The notification unit can adjust the specificity of the notification based on the importance of the requirement when notifying. The notification unit, for example, evaluates the importance of the requirement. The importance of the requirement is evaluated based on the frequency of appearance of keywords, specific phrases, etc. The notification unit can provide a detailed notification for a requirement of high importance. The notification unit can also provide a concise notification for a requirement of low importance. Furthermore, the notification unit can provide a notification with appropriate detail for a requirement of medium importance. In this way, the notification unit can appropriately convey important information by adjusting the detail of the notification based on the importance of the requirement. Some or all of the above-mentioned processing in the notification unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0048] The notification unit can apply different notification algorithms depending on the category of the requirement when providing a notification. For example, the notification unit classifies the category of the requirement. The requirement categories are classified into business requirements, technical requirements, personal requirements, and the like. The notification unit can apply a business notification algorithm to a business-related requirement. Furthermore, the notification unit can apply a private notification algorithm to a private-related requirement. Furthermore, the notification unit can apply an emergency notification algorithm to an emergency-related requirement. In this way, the notification unit can provide an appropriate notification by applying different notification algorithms depending on the category of the requirement. Some or all of the above-described processing in the notification unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0049] The notification unit can improve the accuracy of notification based on the user's past notification results when providing notification. The notification unit, for example, analyzes the user's past notification results. The past notification results include information such as the user's previously preferred notification method and feedback. The notification unit can provide notification by referring to the user's previously preferred notification method. The notification unit can also provide notification by referring to notification results for which the user has provided feedback in the past. Furthermore, the notification unit can improve the accuracy of notification based on the notification results used by the user in the past. In this way, the notification unit can improve the accuracy of notification by referring to the user's past notification results. Some or all of the above-described processing in the notification unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0050] At the time of notification, the notification unit can set a priority of notification based on the submission time of the requirement. The notification unit, for example, acquires the submission time of the requirement. The submission time includes information such as the submission date and time and the submission frequency. The notification unit can prioritize notifying recently submitted requirements. The notification unit can also postpone requirements that were submitted recently. Furthermore, the notification unit can also appropriately notify requirements that were submitted recently. In this way, the notification unit can prioritize providing the latest information by determining the priority of notification based on the submission time of the requirement. Some or all of the above-mentioned processing in the notification unit may be performed using a generation AI (for example, a text generation AI or a multimodal generation AI).

[0051] The notification unit can adjust the order of notifications based on the relevance of the requirements at the time of notification. The notification unit, for example, evaluates the relevance of the requirements. The relevance of the requirements is evaluated based on the co-occurrence of keywords, the similarity of content, etc. The notification unit can prioritize notifying highly relevant requirements. The notification unit can also postpone requirements with low relevance. Furthermore, the notification unit can also appropriately notify requirements with medium relevance. In this way, the notification unit can prioritize providing important information by adjusting the order of notifications based on the relevance of the requirements. Some or all of the above-mentioned processing in the notification unit may be performed using a generation AI (for example, a text generation AI or a multimodal generation AI).

[0052] The notification unit can select an appropriate notification method based on the user's device information when providing notification. The notification unit, for example, acquires the user's device information. The device information includes information such as the device type and OS version. If the user is using a smartphone, the notification unit can provide a notification method tailored to the screen size. Furthermore, if the user is using a tablet, the notification unit can also provide a notification method optimized for a large screen. Furthermore, if the user is using a smartwatch, the notification unit can also provide a simple and highly visible notification method. This allows the notification unit to provide the optimal notification method by taking the user's device information into consideration. Some or all of the above-described processing in the notification unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When generating a summary, the summarization unit can analyze the user's past behavioral patterns and customize the content of the summary. For example, if the user has been sensitive to a particular keyword in the past, it can provide a summary that emphasizes that keyword. Also, if the user has preferred a particular format of summaries in the past, it can provide a summary that matches that format. Furthermore, if the user has frequently checked particular information in the past, it can provide a summary that includes that information preferentially. In this way, the summarization unit can provide the most suitable summary for the user by customizing the content of the summary based on the user's past behavioral patterns.

[0055] The collection unit can analyze the caller's past call content and evaluate the caller's reliability. For example, if the caller has provided accurate information in the past, the collection of the caller's requirements can be prioritized. Also, if the caller has provided incorrect information in the past, the collection of the caller's requirements can be postponed. Furthermore, if the caller has provided important information in the past, the collection unit can immediately collect the caller's requirements. This allows the collection unit to quickly grasp important information by adjusting the collection priority based on the caller's reliability.

[0056] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the urgency of the requirement. For example, a detailed summary can be provided for a requirement with a high urgency. A concise summary can be provided for a requirement with a low urgency. Furthermore, a summary with an appropriate level of detail can be provided for a requirement with a medium urgency. This allows the summarization unit to appropriately convey important information by adjusting the level of detail of the summary based on the urgency of the requirement.

[0057] The collection unit can adjust the collection priority based on the caller's current situation. For example, if the caller is in an emergency, the collection of those requirements can be prioritized. Also, if the caller is in a normal situation, the collection of those requirements can be postponed. Furthermore, if the caller is in a specific situation, collection can be performed with a priority according to that situation. In this way, the collection unit can quickly grasp important information by adjusting the collection priority based on the caller's current situation.

[0058] The notification unit can analyze the user's past notification history and adjust the frequency of notifications. For example, if the user frequently received notifications in the past, the notification frequency can be increased. Also, if the user did not receive many notifications in the past, the notification frequency can be decreased. Furthermore, if the user previously received notifications during a specific time period, notifications can be sent according to that time period. In this way, the notification unit can provide optimal notifications for the user by adjusting the notification frequency based on the user's past notification history.

[0059] The collection unit can analyze the sender's past feedback and improve the accuracy of collection. For example, the collection method can be adjusted based on feedback provided by the sender in the past. The collection priority can also be adjusted based on the content of feedback provided by the sender in the past. Furthermore, the collection frequency can be adjusted based on the frequency of feedback provided by the sender in the past. In this way, the collection unit can quickly grasp important information by improving the accuracy of collection based on the sender's past feedback.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit extracts requirements from the incoming call log. The incoming call log contains information such as the caller's phone number, call duration, and call content. For example, if the caller leaves a requirement such as "I would like to change the meeting time," the collection unit extracts that content from the incoming call log. The collection unit can also use AI to analyze the incoming call log. Step 2: The summarization unit summarizes the requirements collected by the collection unit using natural language processing. Natural language processing analyzes the content of the call using techniques such as morphological analysis, grammatical analysis, and semantic analysis to extract important information. For example, the summarization unit summarizes the requirement "I would like to change the meeting time" as "Change meeting time." Some or all of the above-mentioned processing in the summarization unit may be performed using generation AI (e.g., text generation AI or multimodal generation AI). Step 3: The notification unit notifies the user of the requirements summarized by the summarization unit based on notification settings that the user can customize. The notification unit can notify the user by email, push notification, SMS, etc. The notification unit can also notify the user immediately or after a certain period of time based on the notification timing set by the user.

[0062] (Example 2) An automatic requirements summarization system according to an embodiment of the present invention automatically summarizes and notifies the other party of their requirements when a call is received and missed. This automatic requirements summarization system extracts requirements from an incoming call log, summarizes them using natural language processing, and notifies the user based on notification settings customizable by the user. For example, the automatic requirements summarization system collects information such as the caller's phone number, call duration, and call content from the incoming call log. Next, the automatic requirements summarization system analyzes the call content using natural language processing, extracts important information, and generates a summary. Finally, the automatic requirements summarization system notifies the user of the summarized requirements based on the notification method and timing set by the user. This enables timely information sharing and efficient communication, reducing stress in work and personal life. This allows the automatic requirements summarization system to quickly grasp the other party's requirements even when the user is away and take necessary measures. For example, it can instantly grasp and respond to requirements for changes to the time of an important meeting. Furthermore, the automatic requirements summarization system reduces unnecessary communication by allowing the user to understand the other party's requirements simply by checking the summarized requirements. Furthermore, since the user can understand the other party's requirements even when they are away, they can continue their work and life without stress and with peace of mind. For example, you will have peace of mind knowing that you will not miss any important communications.

[0063] An automatic requirements summarization system according to an embodiment includes a collection unit, a summarization unit, and a notification unit. The collection unit extracts requirements from an incoming call log. The incoming call log includes information such as the caller's phone number, call duration, and call content. For example, if a caller leaves a requirement such as "I would like to change the meeting time," the collection unit extracts the content from the incoming call log. The collection unit can also analyze the incoming call log using AI. The summarization unit summarizes the requirements collected by the collection unit using natural language processing. Natural language processing analyzes the call content using techniques such as morphological analysis, grammatical analysis, and semantic analysis to extract important information. For example, the summarization unit summarizes the requirement "I would like to change the meeting time" as "change meeting time." Some or all of the above-described processing in the summarization unit may be performed using generation AI (e.g., text generation AI or multimodal generation AI). The notification unit notifies the user of the requirements summarized by the summarization unit based on user-customizable notification settings. The notification unit can notify the user via methods such as email, push notification, and SMS. In addition, the notification unit can also provide immediate notification or notification after a certain period of time based on the notification timing set by the user. As a result, the automatic requirements summarization system according to the embodiment can automatically summarize and notify requirements when a call is missed, thereby achieving timely information sharing and efficient communication.

[0064] The collection unit can collect the caller's telephone number, call duration, call content, and other information. The collection unit, for example, collects the caller's telephone number. The caller's telephone number is information recorded in an incoming call log, and the collection unit can extract this information. The collection unit can also collect call duration. The call duration includes information such as the date and time the call was made and the duration of the call. The collection unit can also collect call content. The call content includes messages and requirements left by the caller. For example, if a caller leaves a requirement such as "I would like to change the meeting time," this content is recorded as the call content. Other information includes the caller's location information and device information. For example, the caller's location information is obtained using GPS data, IP address, etc. In this way, the collection unit can collect detailed information about the caller and accurately understand their requirements.

[0065] The summarization unit can analyze the call content recorded in the incoming call log and extract important information. The summarization unit analyzes the call content using, for example, natural language processing. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The summarization unit extracts important information from the call content. For example, if the call content includes "I would like to change the meeting time," the summarization unit generates a summary such as "Meeting time changed." Criteria for extracting important information include the frequency of keyword appearances and specific phrases. For example, the summarization unit analyzes the frequency of keywords such as "meeting" and "change" in the call content and extracts information containing these keywords as important information. The summarization unit can also extract important information based on specific phrases. For example, call content containing a phrase such as "I would like to change the time" is extracted as important information. In this way, the summarization unit can analyze the call content and extract important information, thereby improving the accuracy of the summary. Some or all of the above-mentioned processing in the summarization unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0066] The notification unit can notify the user of the summarized requirements based on the notification method and timing set by the user. The notification unit can provide notifications via email, push notification, SMS, or other methods. The user can freely set the notification method. For example, if the user wants to receive notifications via email, the user can register an email address and select email as the notification method. If the user wants to receive push notifications, the user can install a dedicated app and enable notifications. If the user wants to receive notifications via SMS, the user can register a phone number and select SMS as the notification method. The notification unit can also provide immediate notification or notification after a specified time has elapsed based on the notification timing set by the user. For example, if the user wants immediate notification, the notification can be provided immediately as soon as the summarized requirements are generated. If the user wants notification after a specified time has elapsed, the notification can be provided after the specified time has elapsed. This allows the notification unit to provide information tailored to the user's needs by providing notifications based on the user's settings. Some or all of the above-described processing by the notification unit may be performed using AI.

[0067] The notification unit can provide notifications via email, push notification, and SMS. For example, the notification unit provides notifications via email. Email notification is a method of sending summarized requirements to an email address registered by the user. The notification unit can set the email address registration method and the notification content format. The notification unit can also provide notifications via push notification. Push notification is a method of sending notifications directly to the user's device through a dedicated app. The notification unit can set the app installation method and notification permission settings. The notification unit can also provide notifications via SMS. SMS notification is a method of sending summarized requirements as a text message to a phone number registered by the user. The notification unit can set the phone number registration method and the notification content format. This allows the notification unit to provide a variety of notification methods, allowing the user to receive information in the most optimal way. Some or all of the above-mentioned processing in the notification unit may be performed using AI.

[0068] The notification unit can provide notifications immediately or after a certain time, depending on the user's needs. The notification unit, for example, provides immediate notification. Immediate notification is a method of providing notification immediately after summarized requirements are generated. The notification unit can set notification trigger conditions and notification delay times. The notification unit can also provide notification after a certain time. Notification after a certain time is a method of providing notification after a specified time has elapsed. The notification unit can set notification delay times and re-notification conditions. For example, if a user desires immediate notification, the notification can be provided immediately after summarized requirements are generated. Also, if a user desires notification after a certain time has elapsed, the notification can be provided after the specified time has elapsed. In this way, the notification unit can adjust the timing of notifications to improve user convenience. Some or all of the above-described processing in the notification unit may be performed using AI.

[0069] The collection unit can estimate the user's emotions and adjust the timing of collecting call logs based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. The user's emotions are estimated using technologies such as voice analysis, facial expression recognition, and text analysis. If the user is feeling stressed, the collection unit can delay collection of call logs and start collection when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately collect call logs to quickly understand the requirements. Furthermore, if the user is in a hurry, the collection unit can collect call logs in real time and analyze the requirements immediately. This allows the collection unit to reduce the user's stress by adjusting the collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI (e.g., a text generation AI or a multimodal generation AI). Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0070] The collection unit can analyze a caller's past call history and prioritize collection of requirements with high importance. The collection unit, for example, analyzes a caller's past call history. The past call history includes information such as the content, frequency, and importance of calls made by the caller in the past. If a caller has had many important requirements in the past, the collection unit can prioritize collection of the call log of that caller. In addition, if a caller has had urgent requirements in the past, the collection unit can immediately collect the call log of that caller. Furthermore, the collection unit can prioritize collection of the call log of a caller with whom the caller has frequently been in contact in the past. Criteria for extracting requirements with high importance include the frequency of keyword appearances and specific phrases. For example, the collection unit analyzes the frequency of appearances of keywords such as "urgent" and "important" in the past call history and extracts call content containing these keywords as important requirements. The collection unit can also extract important requirements based on specific phrases. For example, call content containing a phrase such as "urgent response required" can be extracted as an important requirement. This allows the collection unit to analyze the caller's past call history and prioritize collection of important requirements, thereby enabling important information to be quickly grasped. Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0071] When collecting call logs, the collection unit can filter the call logs based on the caller's current situation and areas of interest. For example, the collection unit identifies the caller's current situation. The caller's current situation is identified using location information, device status, etc. The collection unit can postpone the call log if the caller is currently in a meeting. The collection unit can also identify the caller's areas of interest. The caller's areas of interest are identified based on past call content, social media activity, etc. The collection unit can prioritize collecting call logs if the caller has requirements related to a particular area of ​​interest. For example, if the caller is currently traveling, the call log can be postponed. This allows the collection unit to prioritize collecting highly relevant information by filtering based on the caller's situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0072] When collecting call logs, the collection unit can select an appropriate collection means depending on the caller's input method. For example, if the caller leaves a voice message, the collection unit collects call logs using voice recognition technology. Voice recognition technology is technology that converts the caller's voice into text data. If the caller leaves a text message, the collection unit can collect call logs using text analysis technology. Text analysis technology is technology that analyzes the caller's text message and extracts important information. Furthermore, if the caller sends an image, the collection unit can also collect call logs using image analysis technology. Image analysis technology is technology that analyzes the caller's image and extracts important information. This allows the collection unit to select the optimal collection means depending on the caller's input method, enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0073] The collection unit can estimate the user's emotions and determine the priority of the call logs to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. The user's emotions are estimated using technologies such as voice analysis, facial expression recognition, and text analysis. When the user is feeling stressed, the collection unit can postpone call logs of lower importance. When the user is relaxed, the collection unit can also collect all call logs equally. Furthermore, when the user is in a hurry, the collection unit can prioritize the collection of call logs of higher importance. This allows the collection unit to reduce the user's stress by determining the priority of call logs to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI (e.g., text generation AI or multimodal generation AI). Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0074] When collecting call logs, the collection unit can prioritize collection of highly relevant logs based on the caller's geographical location information. The collection unit, for example, acquires the caller's geographical location information. The geographical location information is acquired using GPS data, an IP address, or the like. The collection unit can prioritize collection of call logs when the caller is close to the user. The collection unit can also postpone collection of call logs when the caller is far from the user. Furthermore, when the caller is in a specific geographical area, the collection unit can also prioritize collection of call logs related to that area. In this way, the collection unit can prioritize collection of highly relevant information by taking the caller's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI.

[0075] When collecting call logs, the collection unit can analyze the caller's social media activity and collect related logs. The collection unit, for example, analyzes the caller's social media activity. Social media activity includes information such as the content of posts made by the caller on social media, the number of likes, and the number of followers. If the caller posts important information on social media, the collection unit can prioritize collecting the call logs. In addition, if the caller is participating in a specific event on social media, the collection unit can prioritize collecting the call logs. Furthermore, if the caller posts information related to users on social media, the collection unit can prioritize collecting the call logs. In this way, the collection unit can prioritize collecting highly relevant information by analyzing the caller's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0076] When collecting call logs, the collection unit can adjust the collection method based on the caller's past feedback. The collection unit, for example, analyzes the caller's past feedback. The past feedback includes information such as ratings, comments, and survey results provided by the caller in the past. If the caller has provided important feedback in the past, the collection unit can prioritize collecting the call logs. In addition, if the caller has provided negative feedback in the past, the collection unit can postpone collecting the call logs. Furthermore, if the caller has provided feedback frequently in the past, the collection unit can prioritize collecting the call logs. In this way, the collection unit can optimize the collection method by reflecting the caller's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI.

[0077] The summarization unit can estimate the user's emotion and adjust the summary presentation method based on the estimated user's emotion. The summarization unit, for example, estimates the user's emotion. The user's emotion is estimated using technologies such as voice analysis, facial expression recognition, and text analysis. The summarization unit can provide a concise and easy-to-understand summary when the user is stressed. The summarization unit can also provide a detailed summary when the user is relaxed. Furthermore, the summarization unit can provide a short summary that covers the main points when the user is in a hurry. In this way, the summarization unit can adjust the summary presentation method according to the user's emotion, thereby providing a summary that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI (e.g., text generation AI or multimodal generation AI). Some or all of the above-mentioned processing in the summarization unit may be performed using AI.

[0078] When generating a summary, the summarization unit can adjust the specificity of the summary based on the importance of the requirements. The summarization unit, for example, evaluates the importance of the requirements. The importance of the requirements is evaluated based on the frequency of keyword appearance, specific phrases, etc. The summarization unit can provide a detailed summary for requirements of high importance. The summarization unit can also provide a concise summary for requirements of low importance. Furthermore, the summarization unit can provide a summary with appropriate detail for requirements of medium importance. In this way, the summarization unit can appropriately convey important information by adjusting the detail level of the summary based on the importance of the requirements. Some or all of the above-mentioned processing in the summarization unit may be performed using generation AI (e.g., text generation AI or multimodal generation AI).

[0079] The summarization unit can apply different summarization algorithms depending on the category of requirements when generating a summary. For example, the summarization unit classifies the categories of requirements. The categories of requirements are classified into business requirements, technical requirements, personal requirements, etc. The summarization unit can apply a business summarization algorithm to business-related requirements. The summarization unit can also apply a private summarization algorithm to private-related requirements. Furthermore, the summarization unit can apply an emergency summarization algorithm to urgent requirements. In this way, the summarization unit can provide an appropriate summary by applying different summarization algorithms depending on the category of requirements. Some or all of the above-mentioned processing in the summarization unit may be performed using generation AI (e.g., text generation AI or multimodal generation AI).

[0080] When generating a summary, the summarization unit can improve the accuracy of the summary based on the user's past summarization results. The summarization unit, for example, analyzes the user's past summarization results. The past summarization results include information such as the user's previously preferred summarization style and feedback. The summarization unit can generate a summary by referring to the user's previously preferred summarization style. The summarization unit can also generate a summary by referring to summarization results for which the user has provided feedback in the past. Furthermore, the summarization unit can improve the accuracy of the summary based on the summarization results used by the user in the past. In this way, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. Some or all of the above-mentioned processing in the summarization unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0081] The summarization unit can estimate the user's emotion and adjust the length of the summary based on the estimated user's emotion. The summarization unit, for example, estimates the user's emotion. The user's emotion is estimated using technologies such as voice analysis, facial expression recognition, and text analysis. The summarization unit can provide a short summary when the user is stressed. The summarization unit can also provide a long summary when the user is relaxed. Furthermore, the summarization unit can provide a short summary that covers the main points when the user is in a hurry. In this way, the summarization unit can adjust the length of the summary according to the user's emotion, thereby providing an optimal summary for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI (e.g., text generation AI or multimodal generation AI). Some or all of the above-mentioned processing in the summarization unit may be performed using AI.

[0082] When generating summaries, the summarization unit can set the priority of summaries based on the submission time of the requirements. The summarization unit, for example, acquires the submission time of the requirements. The submission time includes information such as the submission date and time and the submission frequency. The summarization unit can prioritize summarizing recently submitted requirements. The summarization unit can also postpone requirements that were submitted recently. Furthermore, the summarization unit can appropriately summarize requirements that were submitted recently. In this way, the summarization unit can prioritize providing the latest information by determining the priority of summaries based on the submission time of the requirements. Some or all of the above-mentioned processing in the summarization unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0083] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of requirements. The summarization unit, for example, evaluates the relevance of requirements. The relevance of requirements is evaluated based on the co-occurrence of keywords, similarity of content, etc. The summarization unit can prioritize summarizing highly relevant requirements. The summarization unit can also postpone requirements with low relevance. Furthermore, the summarization unit can appropriately summarize requirements with medium relevance. In this way, the summarization unit can prioritize providing important information by adjusting the order of summaries based on the relevance of requirements. Some or all of the above-mentioned processing in the summarization unit may be performed using generation AI (e.g., text generation AI or multimodal generation AI).

[0084] When generating a summary, the summarization unit can adjust the use of technical terms in the summary based on the user's level of expertise. The summarization unit, for example, evaluates the user's level of expertise. The level of expertise is evaluated based on survey results, past usage history, etc. The summarization unit can provide a summary that uses a lot of technical terms if the user has technical knowledge. The summarization unit can also provide a summary in simple language if the user does not have technical knowledge. Furthermore, the summarization unit can provide a summary that uses appropriate technical terms depending on the user's level of expertise. In this way, the summarization unit can provide a summary that is easy for the user to understand by adjusting the use of technical terms in the summary depending on the user's level of expertise. Some or all of the above-mentioned processing in the summarization unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0085] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. The notification unit, for example, estimates the user's emotion. The user's emotion is estimated using technologies such as voice analysis, facial expression recognition, and text analysis. The notification unit can select a quiet notification method when the user is stressed. The notification unit can also select a normal notification method when the user is relaxed. Furthermore, the notification unit can select a more noticeable notification method when the user is in a hurry. In this way, the notification unit can provide the optimal notification method for the user by adjusting the notification method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI (e.g., text generation AI or multimodal generation AI). Some or all of the above-mentioned processing in the notification unit may be performed using AI.

[0086] The notification unit can adjust the specificity of the notification based on the importance of the requirement when notifying. The notification unit, for example, evaluates the importance of the requirement. The importance of the requirement is evaluated based on the frequency of appearance of keywords, specific phrases, etc. The notification unit can provide a detailed notification for a requirement of high importance. The notification unit can also provide a concise notification for a requirement of low importance. Furthermore, the notification unit can provide a notification with appropriate detail for a requirement of medium importance. In this way, the notification unit can appropriately convey important information by adjusting the detail of the notification based on the importance of the requirement. Some or all of the above-mentioned processing in the notification unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0087] The notification unit can apply different notification algorithms depending on the category of the requirement when providing a notification. For example, the notification unit classifies the category of the requirement. The requirement categories are classified into business requirements, technical requirements, personal requirements, and the like. The notification unit can apply a business notification algorithm to a business-related requirement. Furthermore, the notification unit can apply a private notification algorithm to a private-related requirement. Furthermore, the notification unit can apply an emergency notification algorithm to an emergency-related requirement. In this way, the notification unit can provide an appropriate notification by applying different notification algorithms depending on the category of the requirement. Some or all of the above-described processing in the notification unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0088] The notification unit can improve the accuracy of notification based on the user's past notification results when providing notification. The notification unit, for example, analyzes the user's past notification results. The past notification results include information such as the user's previously preferred notification method and feedback. The notification unit can provide notification by referring to the user's previously preferred notification method. The notification unit can also provide notification by referring to notification results for which the user has provided feedback in the past. Furthermore, the notification unit can improve the accuracy of notification based on the notification results used by the user in the past. In this way, the notification unit can improve the accuracy of notification by referring to the user's past notification results. Some or all of the above-described processing in the notification unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI).

[0089] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. The notification unit, for example, estimates the user's emotion. The user's emotion is estimated using technologies such as voice analysis, facial expression recognition, and text analysis. If the user is feeling stressed, the notification unit can delay the notification and provide the notification when the user is relaxed. If the user is relaxed, the notification unit can also provide the notification immediately. Furthermore, if the user is in a hurry, the notification unit can provide the notification in real time. In this way, the notification unit can adjust the timing of the notification according to the user's emotion, thereby providing the notification at the optimal timing for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI (e.g., text generation AI or multimodal generation AI). Some or all of the above-mentioned processing in the notification unit may be performed using AI.

[0090] At the time of notification, the notification unit can set a priority of notification based on the submission time of the requirement. The notification unit, for example, acquires the submission time of the requirement. The submission time includes information such as the submission date and time and the submission frequency. The notification unit can prioritize notifying recently submitted requirements. The notification unit can also postpone requirements that were submitted recently. Furthermore, the notification unit can also appropriately notify requirements that were submitted recently. In this way, the notification unit can prioritize providing the latest information by determining the priority of notification based on the submission time of the requirement. Some or all of the above-mentioned processing in the notification unit may be performed using a generation AI (for example, a text generation AI or a multimodal generation AI).

[0091] The notification unit can adjust the order of notifications based on the relevance of the requirements at the time of notification. The notification unit, for example, evaluates the relevance of the requirements. The relevance of the requirements is evaluated based on the co-occurrence of keywords, the similarity of content, etc. The notification unit can prioritize notifying highly relevant requirements. The notification unit can also postpone requirements with low relevance. Furthermore, the notification unit can also appropriately notify requirements with medium relevance. In this way, the notification unit can prioritize providing important information by adjusting the order of notifications based on the relevance of the requirements. Some or all of the above-mentioned processing in the notification unit may be performed using a generation AI (for example, a text generation AI or a multimodal generation AI).

[0092] The notification unit can select an appropriate notification method based on the user's device information when providing notification. The notification unit, for example, acquires the user's device information. The device information includes information such as the device type and OS version. If the user is using a smartphone, the notification unit can provide a notification method tailored to the screen size. Furthermore, if the user is using a tablet, the notification unit can also provide a notification method optimized for a large screen. Furthermore, if the user is using a smartwatch, the notification unit can also provide a simple and highly visible notification method. This allows the notification unit to provide the optimal notification method by taking the user's device information into consideration. Some or all of the above-described processing in the notification unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, summarization unit, and notification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects incoming call logs using the camera 42 and microphone 38B of the smart device 14 and analyzes them using the control unit 46A. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the collected requirements using natural language processing. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and notifies the user of the summarized requirements based on the notification method and timing set by the user. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the summarization unit and notification unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, summarization unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects an incoming call log using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the log using the control unit 46A. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the collected requirements using natural language processing. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the user of the summarized requirements based on the notification method and timing set by the user. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the summarization unit and notification unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, summarization unit, and notification unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects incoming call logs using the camera 42 and microphone 238 of the headset type terminal 314 and analyzes them using the control unit 46A. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the collected requirements using natural language processing. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314 and notifies the user of the summarized requirements based on the notification method and timing set by the user. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the summarization unit and notification unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, summarization unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects incoming call logs using the camera 42 and microphone 238 of the robot 414 and analyzes them using the control unit 46A. The summarization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and summarizes the collected requirements using natural language processing. The notification unit is realized, for example, by the control unit 46A of the robot 414 and notifies the user of the summarized requirements based on the notification method and timing set by the user. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the summarization unit and notification unit may be realized, for example, by the control unit 46A of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The collection unit can analyze the caller's tone of voice and speaking style to estimate the caller's emotions. For example, if the caller is nervous, it can prioritize collecting those requirements. If the caller is relaxed, it can postpone collecting those requirements. Furthermore, if the caller is angry, it can collect those requirements immediately and respond quickly. This allows the collection unit to quickly grasp important requirements by adjusting collection priorities based on the caller's emotions.

[0095] When generating a summary, the summarization unit can analyze the user's past behavioral patterns and customize the content of the summary. For example, if the user has been sensitive to a particular keyword in the past, it can provide a summary that emphasizes that keyword. Also, if the user has preferred a particular format of summaries in the past, it can provide a summary that matches that format. Furthermore, if the user has frequently checked particular information in the past, it can provide a summary that includes that information preferentially. In this way, the summarization unit can provide the most suitable summary for the user by customizing the content of the summary based on the user's past behavioral patterns.

[0096] The notification unit can adjust the notification method based on the user's current activity status. For example, if the user is in a meeting, a quiet notification method can be selected. If the user is driving, a voice notification can be selected. Furthermore, if the user is relaxed, a normal notification method can be selected. In this way, the notification unit can provide the optimal notification method for the user by adjusting the notification method according to the user's activity status.

[0097] The collection unit can analyze the caller's past call content and evaluate the caller's reliability. For example, if the caller has provided accurate information in the past, the collection of the caller's requirements can be prioritized. Also, if the caller has provided incorrect information in the past, the collection of the caller's requirements can be postponed. Furthermore, if the caller has provided important information in the past, the collection unit can immediately collect the caller's requirements. This allows the collection unit to quickly grasp important information by adjusting the collection priority based on the caller's reliability.

[0098] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the urgency of the requirement. For example, a detailed summary can be provided for a requirement with a high urgency. A concise summary can be provided for a requirement with a low urgency. Furthermore, a summary with an appropriate level of detail can be provided for a requirement with a medium urgency. This allows the summarization unit to appropriately convey important information by adjusting the level of detail of the summary based on the urgency of the requirement.

[0099] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, a concise 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 short notification that focuses on the main points can be provided. In this way, the notification unit can provide the optimal notification for the user by adjusting the content of the notification according to the user's emotions.

[0100] The collection unit can adjust the collection priority based on the caller's current situation. For example, if the caller is in an emergency, the collection of those requirements can be prioritized. Also, if the caller is in a normal situation, the collection of those requirements can be postponed. Furthermore, if the caller is in a specific situation, collection can be performed with a priority according to that situation. In this way, the collection unit can quickly grasp important information by adjusting the collection priority based on the caller's current situation.

[0101] When generating a summary, the summarization unit can adjust the tone of the summary based on the user's current emotional state. For example, if the user is feeling stressed, the summary can be provided in a calm tone. If the user is relaxed, the summary can be provided in a friendly tone. If the user is in a hurry, the summary can be provided in a concise and direct tone. In this way, the summarization unit can provide a summary that is easy for the user to understand by adjusting the tone of the summary according to the user's emotional state.

[0102] The notification unit can analyze the user's past notification history and adjust the frequency of notifications. For example, if the user frequently received notifications in the past, the notification frequency can be increased. Also, if the user did not receive many notifications in the past, the notification frequency can be decreased. Furthermore, if the user previously received notifications during a specific time period, notifications can be sent according to that time period. In this way, the notification unit can provide optimal notifications for the user by adjusting the notification frequency based on the user's past notification history.

[0103] The collection unit can analyze the sender's past feedback and improve the accuracy of collection. For example, the collection method can be adjusted based on feedback provided by the sender in the past. The collection priority can also be adjusted based on the content of feedback provided by the sender in the past. Furthermore, the collection frequency can be adjusted based on the frequency of feedback provided by the sender in the past. In this way, the collection unit can quickly grasp important information by improving the accuracy of collection based on the sender's past feedback.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit extracts requirements from the incoming call log. The incoming call log contains information such as the caller's phone number, call duration, and call content. For example, if the caller leaves a requirement such as "I would like to change the meeting time," the collection unit extracts that content from the incoming call log. The collection unit can also use AI to analyze the incoming call log. Step 2: The summarization unit summarizes the requirements collected by the collection unit using natural language processing. Natural language processing analyzes the content of the call using techniques such as morphological analysis, grammatical analysis, and semantic analysis to extract important information. For example, the summarization unit summarizes the requirement "I would like to change the meeting time" as "Change meeting time." Some or all of the above-mentioned processing in the summarization unit may be performed using generation AI (e.g., text generation AI or multimodal generation AI). Step 3: The notification unit notifies the user of the requirements summarized by the summarization unit based on notification settings that the user can customize. The notification unit can notify the user by email, push notification, SMS, etc. The notification unit can also notify the user immediately or after a certain period of time based on the notification timing set by the user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that extracts requirements from the incoming logs; a summarizing unit that summarizes the requirements collected by the collecting unit using natural language processing; a notification unit that notifies the requirements summarized by the summarization unit based on notification settings that can be customized by a user; Equipped with A system characterized by:

2. The collecting unit Collect caller's phone number, call duration, call content and other information 2. The system of claim 1.

3. The summary section Analyze the call contents recorded in the call log and extract important information 2. The system of claim 1.

4. The notification unit Communicate summarized requirements based on the notification method and timing you set 2. The system of claim 1.

5. The notification unit Notify via email, push notification, and SMS 2. The system of claim 1.

6. The notification unit Immediate notification and notification after a certain time, tailored to the user's needs 2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting incoming call logs based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the caller's past call history and prioritize the collection of important requests 2. The system of claim 1.

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