system

Abstract

The system according to this embodiment aims to integrate data from multiple tools and determine the priority of tasks. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a determination unit, and a notification unit. The collection unit collects data such as the user's emails, calendars, and chat tools. The analysis unit analyzes the data collected by the collection unit and determines the importance and urgency of each case. The determination unit prioritizes the cases based on the results determined by the analysis unit. The notification unit aggregates and notifies the deadlines of the cases that have been prioritized by the determination unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to integrate data from a plurality of tools to determine the priority of tasks, and there is a risk of omission of tasks.

[0005] The system according to the embodiment aims to integrate data from a plurality of tools and determine the priority of tasks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a determination unit, and a notification unit. The data collection unit collects data from the user's emails, calendars, chat tools, etc. The analysis unit analyzes the data collected by the data collection unit and determines the importance and urgency of each case. The determination unit prioritizes the cases based on the results determined by the analysis unit. The notification unit aggregates and notifies the deadlines for the cases that have been prioritized by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate data from multiple tools and determine the priority of tasks. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​system according to an embodiment of the present invention is a system that tells users the priority of tasks by linking with email, calendar, chat tools, etc. This AI system analyzes data from Gmail, calendar, chat tools, etc., to analyze the importance and urgency of the tasks the user has. Based on this, it prioritizes tasks and notifies the user. It also aggregates and informs the user of task deadlines, so it is possible to prevent tasks from being overlooked. In particular, it can increase the productivity of people who are too busy to organize tasks or who have difficulty prioritizing. This AI system consists of the following steps. First, it collects data from the user's email, calendar, chat tools, etc. Next, the AI ​​analyzes the collected data and determines the importance and urgency of each task. Furthermore, it prioritizes tasks based on the determination results and notifies the user. Finally, it aggregates the task deadlines and notifies the user, preventing tasks from being overlooked. In addition, because task deadlines are aggregated and notified, the user can prevent tasks from being overlooked. This AI system targets all business people. Even people who can prioritize on their own can gain new insights by having others suggest ideas. This system is particularly useful for people who are too busy to organize their tasks or who tend to tackle tasks haphazardly. By implementing this system, users can free themselves from the burden of schedule management and dedicate more time to their core work. By focusing on essential tasks, productivity can be increased, contributing to the growth of Japanese companies. Furthermore, sharing project progress with managers reduces unnecessary communication and can help mitigate issues like power harassment. The AI ​​system can then guide users in prioritizing their tasks, preventing them from missing or overlooking important tasks.

[0029] The AI ​​system according to this embodiment comprises a data collection unit, an analysis unit, a determination unit, and a notification unit. The data collection unit collects data from the user's emails, calendar, chat tools, etc. The data collection unit can collect data from, for example, Gmail, calendar, and chat tools. The data collection unit can acquire data using, for example, an API. The data collection unit can also collect email subjects and bodies, calendar events, chat message content, etc., with the user's permission. The analysis unit analyzes the data collected by the data collection unit and determines the importance and urgency of each case. The analysis unit can analyze email content using, for example, text analysis technology. The analysis unit can also analyze calendar events using data mining technology. Furthermore, the analysis unit can also analyze chat message content using machine learning algorithms. The determination unit prioritizes cases based on the results determined by the analysis unit. The determination unit can evaluate the importance and urgency of each case using, for example, a scoring system. The determination unit can also determine priorities using a rule-based determination method. The notification unit aggregates and notifies the user of the deadlines for tasks that have been prioritized by the judgment unit. The notification unit notifies the user, for example, via email or push notification. The notification unit can also add the deadlines for tasks to a calendar and notify the user. In this way, the AI ​​system according to the embodiment can inform the user of task priorities and prevent tasks from being missed or overlooked.

[0030] The data collection unit collects user data from emails, calendars, chat tools, and other sources. Specifically, the unit obtains data from Gmail, Calendar, and chat tools using APIs. For example, it can use the Gmail API to collect detailed data from a user's mailbox, such as subject, body, sender information, and received date and time. It can use the Calendar API to obtain detailed information about the user's appointments and events, and the chat tool API to collect data such as message content, sender, and timestamp. This data collection is done with the user's permission, and encryption technology is used to protect privacy. The data collection unit can flexibly set the frequency and timing of data collection and supports real-time data updates. For example, when a user receives a new email or adds a new appointment, data can be collected immediately and reflected throughout the system. Furthermore, the data collection unit has the function to integrate and centrally manage data from different data sources. This allows for a comprehensive understanding of diverse user activity information and efficiently provides the data necessary for subsequent analysis and decision-making.

[0031] The analysis unit analyzes the data collected by the data collection unit to determine the importance and urgency of each case. Specifically, it uses text analysis technology to analyze the content of emails and extract important keywords and phrases. For example, it uses natural language processing (NLP) technology to detect keywords such as "urgent" and "important" from the body of an email and evaluate the importance of that email. It also uses data mining technology to analyze calendar events and determines the urgency by considering the type of event, the importance of the participants, and the time of the event. Furthermore, it uses machine learning algorithms to analyze the content of chat messages and evaluate the importance of the messages by performing sentiment analysis and topic modeling. The analysis unit integrates these analysis results to determine the overall importance and urgency of each case. For example, if the content of an email is urgent and the calendar event is an important meeting, the priority of that case will be high. In addition, the analysis unit can learn from past data and user behavior patterns to perform more accurate analysis. As a result, the analysis unit can quickly and accurately analyze diverse user data and grasp the importance and urgency of each case in real time.

[0032] The judgment unit prioritizes cases based on the results determined by the analysis unit. Specifically, it uses a scoring system to evaluate the importance and urgency of each case. For example, it assigns a score to each case based on data provided by the analysis unit and determines the priority based on that score. The scoring system performs a comprehensive evaluation by combining multiple evaluation criteria. For example, it calculates a score for each case by comprehensively considering factors such as the importance of emails, the urgency of calendar events, and the sentiment analysis results of chat messages. The judgment unit can also determine priorities using rule-based judgment methods. For example, it can set a rule to automatically assign a high priority to emails containing specific keywords. Furthermore, the judgment unit can learn the user's past behavior patterns and priority history to perform more accurate prioritization. As a result, the judgment unit can provide information to efficiently manage the user's tasks and prioritize important cases.

[0033] The notification unit aggregates and notifies users of the deadlines for tasks prioritized by the judgment unit. Specifically, it notifies users using email or push notifications. For example, it can send push notifications to users' smartphones to inform them of the deadlines and urgency of important tasks. The notification unit can also add task deadlines to the calendar and notify users of those deadlines. For example, it can automatically add events to the user's calendar and use a reminder function to notify them as the deadline approaches. Furthermore, the notification unit can customize notification methods according to user preferences. For example, it can send email notifications to users who prefer email notifications and smartphone notifications to users who prefer push notifications. The notification unit can also utilize visual interfaces and voice assistants to display notification content in an easy-to-understand manner. This ensures that the notification unit reliably informs users of important task deadlines and prevents tasks from being overlooked. In addition, the notification unit can collect user feedback and continuously improve notification content and methods. This allows the notification unit to provide flexible notification functions that meet user needs and improve the efficiency of task management.

[0034] The judgment unit can determine the importance and urgency of each case. For example, the judgment unit can evaluate importance based on the proximity of the case's deadline. It can also evaluate urgency based on the impact of the case. Furthermore, the judgment unit can use a scoring system to determine the priority of cases. This allows for the appropriate determination of task priorities by determining the importance and urgency of each case. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can perform judgments using an AI model that takes the case's deadline and impact as input and outputs importance and urgency.

[0035] The notification unit can aggregate and notify users of the deadlines for each project. For example, the notification unit can add project deadlines to a calendar and notify users. The notification unit can also notify users via email or push notifications. Furthermore, the notification unit can display a list of project deadlines and notify users of that list. By aggregating and notifying users of project deadlines, it is possible to prevent tasks from being missed or overlooked. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can send notifications using an AI model that takes project deadlines as input and outputs notification content.

[0036] The data collection unit can collect data from sources such as Gmail, Calendar, and chat tools. For example, the data collection unit can collect email subjects and body text using the Gmail API. It can also collect appointments using the Calendar API. Furthermore, it can collect message content using the chat tool API. By collecting data from sources such as Gmail, Calendar, and chat tools, user task information can be managed centrally. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes data from Gmail, Calendar, and chat tools as input and outputs the collection results.

[0037] The analysis unit can analyze the collected data and determine the importance and urgency of each case. For example, the analysis unit can analyze the content of emails using text analysis technology. It can also analyze calendar schedules using data mining technology. Furthermore, the analysis unit can analyze the content of chat messages using machine learning algorithms. By analyzing the collected data and determining the importance and urgency of each case, it is possible to appropriately determine the priority of tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the collected data as input and outputs importance and urgency.

[0038] The judgment unit can prioritize cases based on the judgment results. For example, the judgment unit can use a scoring system to evaluate the importance and urgency of each case. The judgment unit can also determine priorities using a rule-based judgment method. This allows users to efficiently manage tasks by prioritizing cases based on the judgment results. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can determine priorities using an AI model that takes the judgment results as input and outputs priorities.

[0039] The notification unit can notify the user. For example, the notification unit can notify the user using email or push notifications. The notification unit can also notify the user by adding the due date of an item to the calendar. Furthermore, the notification unit can notify the user by displaying a list of item due dates. This allows the user to properly understand the priority and due dates of tasks by notifying them. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that takes the notification content as input and outputs the notification method.

[0040] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from data sources that the user has frequently accessed in the past. The data collection unit can also analyze the user's past data collection patterns and suggest the optimal collection timing. Furthermore, the data collection unit can select the optimal collection method by referring to data collection methods used by the user in the past (API, manual input, etc.). This enables efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can select a collection method using an AI model that takes the user's past data collection history as input and outputs the optimal collection method.

[0041] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can collect relevant data based on keywords set by the user. This allows for the efficient collection of highly relevant data by filtering data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can filter data using an AI model that takes the user's projects and areas of interest as input and outputs the filtered results.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect region-specific data based on the user's geographical location information. Furthermore, if the user is on the move, the data collection unit can collect data related to their destination. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes the user's geographical location information as input and outputs highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze a user's social media activity history and collect data based on their interests. Furthermore, the data collection unit can collect relevant data based on information about accounts that the user follows. This allows for the efficient collection of highly relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes user social media activity data as input and outputs relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes data importance as input and outputs the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to email data. It can also apply a schedule analysis algorithm to calendar data. Furthermore, it can apply a dialogue analysis algorithm to chat data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can select an analysis algorithm using an AI model that takes the data category as input and outputs the analysis algorithm to be applied.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. It can also prioritize the analysis of data with an approaching submission deadline. Furthermore, the analysis unit can adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority of analysis using an AI model that takes the data submission date as input and outputs the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. Furthermore, the analysis unit can postpone the analysis of less relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order of analysis using an AI model that takes the relevance of the data as input and outputs the order of analysis.

[0048] The judgment unit can improve the accuracy of its judgment by considering the interrelationships between data during the judgment process. For example, the judgment unit can perform a judgment by combining email and calendar data. It can also perform a judgment by associating chat tool exchanges with email content. Furthermore, the judgment unit can analyze the interrelationships between data to perform a more accurate judgment. This makes it possible to perform a more accurate judgment by considering the interrelationships between data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can improve the accuracy of its judgment by using an AI model that takes the interrelationships between data as input and outputs a judgment result.

[0049] The judgment unit can make a judgment by considering the attribute information of the data submitter. For example, the judgment unit can determine the priority of the judgment based on the submitter's job title. The judgment unit can also make a judgment by considering the submitter's past performance. Furthermore, the judgment unit can make a judgment based on the submitter's field of expertise. This makes it possible to make a more appropriate judgment by considering the attribute information of the data submitter. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can make a judgment using an AI model that takes the submitter's attribute information as input and outputs a judgment result.

[0050] The determination unit can perform determinations while considering the geographical distribution of the data. For example, the determination unit may prioritize determining data that is geographically close. The determination unit can also adjust the order of determinations based on the geographical distribution. Furthermore, the determination unit may postpone determining data that is geographically far away. This allows for more appropriate determinations by considering the geographical distribution of the data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can perform determinations using an AI model that takes the geographical distribution of the data as input and outputs a determination result.

[0051] The judgment unit can improve the accuracy of its judgment by referring to relevant literature for the data during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by referring to relevant literature. The judgment unit can also make a more accurate judgment based on the relevant literature for the data. Furthermore, the judgment unit can analyze the relevant literature to strengthen the basis for its judgment. This makes it possible to make a more accurate judgment by referring to relevant literature for the data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can improve the accuracy of its judgment by using an AI model that takes relevant literature as input and outputs a judgment result.

[0052] The notification unit can select the optimal display method by referring to the user's past operation history when sending a notification. For example, the notification unit may prioritize providing notification formats that the user has previously preferred. The notification unit can also analyze the user's past operation history and suggest the optimal notification method. Furthermore, the notification unit can avoid notification formats that the user has previously ignored and provide new notification methods. In this way, the optimal notification method can be provided by referring to the user's past operation history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can select a display method using an AI model that takes the user's past operation history as input and outputs the optimal display method.

[0053] The notification unit can select the optimal display method when a notification is sent, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the notification unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the notification unit can provide a concise and highly visible display method. This enables optimal notification display by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can select a display method using an AI model that takes the user's device information as input and outputs the optimal display method.

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

[0055] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize collecting data from data sources that the user has frequently accessed in the past. It can also analyze the user's past data collection patterns and suggest the optimal collection timing. Furthermore, it can select the optimal collection method by referring to data collection methods the user has used in the past (API, manual input, etc.). This enables efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can select a collection method using an AI model that takes the user's past data collection history as input and outputs the optimal collection method.

[0056] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. Furthermore, it can adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes data importance as input and outputs the level of detail of the analysis.

[0057] The judgment unit can improve the accuracy of its judgment by considering the interrelationships between data during the judgment process. For example, it can perform a judgment by combining email and calendar data. It can also perform a judgment by associating chat tool exchanges with email content. Furthermore, it can analyze the interrelationships between data to perform a more accurate judgment. Thus, by considering the interrelationships between data, a more accurate judgment becomes possible. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can improve the accuracy of its judgment by using an AI model that takes the interrelationships between data as input and outputs a judgment result.

[0058] The notification unit can select the optimal display method by referring to the user's past operation history when sending a notification. For example, it can prioritize providing notification formats that the user has previously preferred. It can also analyze the user's past operation history and suggest the optimal notification method. Furthermore, it can avoid notification formats that the user has previously ignored and provide new notification methods. In this way, the optimal notification method can be provided by referring to the user's past operation history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can select a display method using an AI model that takes the user's past operation history as input and outputs the optimal display method.

[0059] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a natural language processing algorithm can be applied to email data. A schedule analysis algorithm can also be applied to calendar data. Furthermore, a dialogue analysis algorithm can be applied to chat data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can select an analysis algorithm using an AI model that takes the data category as input and outputs the analysis algorithm to be applied.

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

[0061] Step 1: The data collection unit collects data from the user's emails, calendars, chat tools, etc. For example, it retrieves data from Gmail, calendars, chat tools, etc., using APIs. With the user's permission, the data collection unit collects email subjects and bodies, calendar events, chat message content, etc. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the importance and urgency of each case. For example, it analyzes email content using text analysis technology, analyzes calendar schedules using data mining technology, and analyzes chat message content using machine learning algorithms. Step 3: The judgment unit prioritizes cases based on the results determined by the analysis unit. For example, it uses a scoring system to evaluate the importance and urgency of each case and determines the priority using a rule-based judgment method. Step 4: The notification unit aggregates and notifies users of the deadlines for cases that have been prioritized by the judgment unit. For example, it notifies users via email or push notification and adds the case deadlines to their calendar.

[0062] (Example of form 2) The AI ​​system according to an embodiment of the present invention is a system that tells users the priority of tasks by linking with email, calendar, chat tools, etc. This AI system analyzes data from Gmail, calendar, chat tools, etc., to analyze the importance and urgency of the tasks the user has. Based on this, it prioritizes tasks and notifies the user. It also aggregates and informs the user of task deadlines, so it is possible to prevent tasks from being overlooked. In particular, it can increase the productivity of people who are too busy to organize tasks or who have difficulty prioritizing. This AI system consists of the following steps. First, it collects data from the user's email, calendar, chat tools, etc. Next, the AI ​​analyzes the collected data and determines the importance and urgency of each task. Furthermore, it prioritizes tasks based on the determination results and notifies the user. Finally, it aggregates the task deadlines and notifies the user, preventing tasks from being overlooked. In addition, because task deadlines are aggregated and notified, the user can prevent tasks from being overlooked. This AI system targets all business people. Even people who can prioritize on their own can gain new insights by having others suggest ideas. This system is particularly useful for people who are too busy to organize their tasks or who tend to tackle tasks haphazardly. By implementing this system, users can free themselves from the burden of schedule management and dedicate more time to their core work. By focusing on essential tasks, productivity can be increased, contributing to the growth of Japanese companies. Furthermore, sharing project progress with managers reduces unnecessary communication and can help mitigate issues like power harassment. The AI ​​system can then guide users in prioritizing their tasks, preventing them from missing or overlooking important tasks.

[0063] The AI ​​system according to this embodiment comprises a data collection unit, an analysis unit, a determination unit, and a notification unit. The data collection unit collects data from the user's emails, calendar, chat tools, etc. The data collection unit can collect data from, for example, Gmail, calendar, and chat tools. The data collection unit can acquire data using, for example, an API. The data collection unit can also collect email subjects and bodies, calendar events, chat message content, etc., with the user's permission. The analysis unit analyzes the data collected by the data collection unit and determines the importance and urgency of each case. The analysis unit can analyze email content using, for example, text analysis technology. The analysis unit can also analyze calendar events using data mining technology. Furthermore, the analysis unit can also analyze chat message content using machine learning algorithms. The determination unit prioritizes cases based on the results determined by the analysis unit. The determination unit can evaluate the importance and urgency of each case using, for example, a scoring system. The determination unit can also determine priorities using a rule-based determination method. The notification unit aggregates and notifies the user of the deadlines for tasks that have been prioritized by the judgment unit. The notification unit notifies the user, for example, via email or push notification. The notification unit can also add the deadlines for tasks to a calendar and notify the user. In this way, the AI ​​system according to the embodiment can inform the user of task priorities and prevent tasks from being missed or overlooked.

[0064] The data collection unit collects user data from emails, calendars, chat tools, and other sources. Specifically, the unit obtains data from Gmail, Calendar, and chat tools using APIs. For example, it can use the Gmail API to collect detailed data from a user's mailbox, such as subject, body, sender information, and received date and time. It can use the Calendar API to obtain detailed information about the user's appointments and events, and the chat tool API to collect data such as message content, sender, and timestamp. This data collection is done with the user's permission, and encryption technology is used to protect privacy. The data collection unit can flexibly set the frequency and timing of data collection and supports real-time data updates. For example, when a user receives a new email or adds a new appointment, data can be collected immediately and reflected throughout the system. Furthermore, the data collection unit has the function to integrate and centrally manage data from different data sources. This allows for a comprehensive understanding of diverse user activity information and efficiently provides the data necessary for subsequent analysis and decision-making.

[0065] The analysis unit analyzes the data collected by the data collection unit to determine the importance and urgency of each case. Specifically, it uses text analysis technology to analyze the content of emails and extract important keywords and phrases. For example, it uses natural language processing (NLP) technology to detect keywords such as "urgent" and "important" from the body of an email and evaluate the importance of that email. It also uses data mining technology to analyze calendar events and determines the urgency by considering the type of event, the importance of the participants, and the time of the event. Furthermore, it uses machine learning algorithms to analyze the content of chat messages and evaluate the importance of the messages by performing sentiment analysis and topic modeling. The analysis unit integrates these analysis results to determine the overall importance and urgency of each case. For example, if the content of an email is urgent and the calendar event is an important meeting, the priority of that case will be high. In addition, the analysis unit can learn from past data and user behavior patterns to perform more accurate analysis. As a result, the analysis unit can quickly and accurately analyze diverse user data and grasp the importance and urgency of each case in real time.

[0066] The judgment unit prioritizes cases based on the results determined by the analysis unit. Specifically, it uses a scoring system to evaluate the importance and urgency of each case. For example, it assigns a score to each case based on data provided by the analysis unit and determines the priority based on that score. The scoring system performs a comprehensive evaluation by combining multiple evaluation criteria. For example, it calculates a score for each case by comprehensively considering factors such as the importance of emails, the urgency of calendar events, and the sentiment analysis results of chat messages. The judgment unit can also determine priorities using rule-based judgment methods. For example, it can set a rule to automatically assign a high priority to emails containing specific keywords. Furthermore, the judgment unit can learn the user's past behavior patterns and priority history to perform more accurate prioritization. As a result, the judgment unit can provide information to efficiently manage the user's tasks and prioritize important cases.

[0067] The notification unit aggregates and notifies users of the deadlines for tasks prioritized by the judgment unit. Specifically, it notifies users using email or push notifications. For example, it can send push notifications to users' smartphones to inform them of the deadlines and urgency of important tasks. The notification unit can also add task deadlines to the calendar and notify users of those deadlines. For example, it can automatically add events to the user's calendar and use a reminder function to notify them as the deadline approaches. Furthermore, the notification unit can customize notification methods according to user preferences. For example, it can send email notifications to users who prefer email notifications and smartphone notifications to users who prefer push notifications. The notification unit can also utilize visual interfaces and voice assistants to display notification content in an easy-to-understand manner. This ensures that the notification unit reliably informs users of important task deadlines and prevents tasks from being overlooked. In addition, the notification unit can collect user feedback and continuously improve notification content and methods. This allows the notification unit to provide flexible notification functions that meet user needs and improve the efficiency of task management.

[0068] The judgment unit can determine the importance and urgency of each case. For example, the judgment unit can evaluate importance based on the proximity of the case's deadline. It can also evaluate urgency based on the impact of the case. Furthermore, the judgment unit can use a scoring system to determine the priority of cases. This allows for the appropriate determination of task priorities by determining the importance and urgency of each case. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can perform judgments using an AI model that takes the case's deadline and impact as input and outputs importance and urgency.

[0069] The notification unit can aggregate and notify users of the deadlines for each project. For example, the notification unit can add project deadlines to a calendar and notify users. The notification unit can also notify users via email or push notifications. Furthermore, the notification unit can display a list of project deadlines and notify users of that list. By aggregating and notifying users of project deadlines, it is possible to prevent tasks from being missed or overlooked. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can send notifications using an AI model that takes project deadlines as input and outputs notification content.

[0070] The data collection unit can collect data from sources such as Gmail, Calendar, and chat tools. For example, the data collection unit can collect email subjects and body text using the Gmail API. It can also collect appointments using the Calendar API. Furthermore, it can collect message content using the chat tool API. By collecting data from sources such as Gmail, Calendar, and chat tools, user task information can be managed centrally. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes data from Gmail, Calendar, and chat tools as input and outputs the collection results.

[0071] The analysis unit can analyze the collected data and determine the importance and urgency of each case. For example, the analysis unit can analyze the content of emails using text analysis technology. It can also analyze calendar schedules using data mining technology. Furthermore, the analysis unit can analyze the content of chat messages using machine learning algorithms. By analyzing the collected data and determining the importance and urgency of each case, it is possible to appropriately determine the priority of tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the collected data as input and outputs importance and urgency.

[0072] The judgment unit can prioritize cases based on the judgment results. For example, the judgment unit can use a scoring system to evaluate the importance and urgency of each case. The judgment unit can also determine priorities using a rule-based judgment method. This allows users to efficiently manage tasks by prioritizing cases based on the judgment results. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can determine priorities using an AI model that takes the judgment results as input and outputs priorities.

[0073] The notification unit can notify the user. For example, the notification unit can notify the user using email or push notifications. The notification unit can also notify the user by adding the due date of an item to the calendar. Furthermore, the notification unit can notify the user by displaying a list of item due dates. This allows the user to properly understand the priority and due dates of tasks by notifying them. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send notifications using an AI model that takes the notification content as input and outputs the notification method.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to gather more detailed information. Furthermore, if the user is busy, the data collection unit can prioritize the collection of only important data, enabling efficient data collection. This reduces the user's burden and enables efficient data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can adjust the timing of data collection using an AI model that takes user emotion data as input and outputs the collection timing.

[0075] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from data sources that the user has frequently accessed in the past. The data collection unit can also analyze the user's past data collection patterns and suggest the optimal collection timing. Furthermore, the data collection unit can select the optimal collection method by referring to data collection methods used by the user in the past (API, manual input, etc.). This enables efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can select a collection method using an AI model that takes the user's past data collection history as input and outputs the optimal collection method.

[0076] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can collect relevant data based on keywords set by the user. This allows for the efficient collection of highly relevant data by filtering data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can filter data using an AI model that takes the user's projects and areas of interest as input and outputs the filtered results.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can also prioritize collecting detailed data. Furthermore, if the user is busy, the data collection unit can prioritize collecting high-urgency data. This allows for efficient collection of important data by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can determine data priority using an AI model that takes user emotion data as input and outputs data priority.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect region-specific data based on the user's geographical location information. Furthermore, if the user is on the move, the data collection unit can collect data related to their destination. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes the user's geographical location information as input and outputs highly relevant data.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze a user's social media activity history and collect data based on their interests. Furthermore, the data collection unit can collect relevant data based on information about accounts that the user follows. This allows for the efficient collection of highly relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes user social media activity data as input and outputs relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the presentation using an AI model that takes user emotion data as input and outputs a presentation method for the analysis results.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes data importance as input and outputs the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to email data. It can also apply a schedule analysis algorithm to calendar data. Furthermore, it can apply a dialogue analysis algorithm to chat data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can select an analysis algorithm using an AI model that takes the data category as input and outputs the analysis algorithm to be applied.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the length of the analysis using an AI model that takes user emotion data as input and outputs the length of the analysis result.

[0084] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. It can also prioritize the analysis of data with an approaching submission deadline. Furthermore, the analysis unit can adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority of analysis using an AI model that takes the data submission date as input and outputs the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. Furthermore, the analysis unit can postpone the analysis of less relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order of analysis using an AI model that takes the relevance of the data as input and outputs the order of analysis.

[0086] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated emotions. For example, if the user is stressed, the judgment unit may relax the judgment criteria. Conversely, if the user is relaxed, the judgment unit may tighten the judgment criteria. Furthermore, if the user is in a hurry, the judgment unit may make a quick judgment. By adjusting the judgment criteria based on the user's emotions, it becomes possible to make a judgment appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or not using AI. For example, the judgment unit can adjust the judgment criteria using an AI model that takes user emotion data as input and outputs judgment criteria.

[0087] The judgment unit can improve the accuracy of its judgment by considering the interrelationships between data during the judgment process. For example, the judgment unit can perform a judgment by combining email and calendar data. It can also perform a judgment by associating chat tool exchanges with email content. Furthermore, the judgment unit can analyze the interrelationships between data to perform a more accurate judgment. This makes it possible to perform a more accurate judgment by considering the interrelationships between data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can improve the accuracy of its judgment by using an AI model that takes the interrelationships between data as input and outputs a judgment result.

[0088] The judgment unit can make a judgment by considering the attribute information of the data submitter. For example, the judgment unit can determine the priority of the judgment based on the submitter's job title. The judgment unit can also make a judgment by considering the submitter's past performance. Furthermore, the judgment unit can make a judgment based on the submitter's field of expertise. This makes it possible to make a more appropriate judgment by considering the attribute information of the data submitter. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can make a judgment using an AI model that takes the submitter's attribute information as input and outputs a judgment result.

[0089] The judgment unit can estimate the user's emotions and adjust the order in which the judgment results are displayed based on the estimated emotions. For example, if the user is feeling stressed, the judgment unit can display important results first. If the user is relaxed, the judgment unit can also display detailed results sequentially. Furthermore, if the user is in a hurry, the judgment unit can also display concise results first. By adjusting the order in which the judgment results are displayed based on the user's emotions, it becomes possible to display results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can adjust the order in which results are displayed using an AI model that takes user emotion data as input and outputs the order in which results are displayed.

[0090] The determination unit can perform determinations while considering the geographical distribution of the data. For example, the determination unit may prioritize determining data that is geographically close. The determination unit can also adjust the order of determinations based on the geographical distribution. Furthermore, the determination unit may postpone determining data that is geographically far away. This allows for more appropriate determinations by considering the geographical distribution of the data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can perform determinations using an AI model that takes the geographical distribution of the data as input and outputs a determination result.

[0091] The judgment unit can improve the accuracy of its judgment by referring to relevant literature for the data during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by referring to relevant literature. The judgment unit can also make a more accurate judgment based on the relevant literature for the data. Furthermore, the judgment unit can analyze the relevant literature to strengthen the basis for its judgment. This makes it possible to make a more accurate judgment by referring to relevant literature for the data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can improve the accuracy of its judgment by using an AI model that takes relevant literature as input and outputs a judgment result.

[0092] The notification unit can estimate the user's emotions and adjust the way notifications are displayed based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible notification. If the user is relaxed, the notification unit can also provide a detailed notification. Furthermore, if the user is in a hurry, the notification unit can provide a concise notification. By adjusting the way notifications are displayed based on the user's emotions, it becomes possible to provide notifications that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can adjust the display method using an AI model that takes user emotion data as input and outputs the way notifications are displayed.

[0093] The notification unit can select the optimal display method by referring to the user's past operation history when sending a notification. For example, the notification unit may prioritize providing notification formats that the user has previously preferred. The notification unit can also analyze the user's past operation history and suggest the optimal notification method. Furthermore, the notification unit can avoid notification formats that the user has previously ignored and provide new notification methods. In this way, the optimal notification method can be provided by referring to the user's past operation history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can select a display method using an AI model that takes the user's past operation history as input and outputs the optimal display method.

[0094] The notification unit can estimate the user's emotions and adjust the notification's operation steps based on the estimated emotions. For example, if the user is stressed, the notification unit can simplify the operation steps to allow the user to quickly check the notification content. If the user is relaxed, the notification unit can also provide detailed operation steps to allow for a deeper understanding of the notification content. Furthermore, if the user is in a hurry, the notification unit can provide the shortest possible operation steps to allow the user to quickly check the notification content. This makes it possible to provide user-friendly notifications by adjusting the notification's operation steps based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can adjust the operation steps using an AI model that takes user emotion data as input and outputs operation steps.

[0095] The notification unit can select the optimal display method when a notification is sent, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the notification unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the notification unit can provide a concise and highly visible display method. This enables optimal notification display by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can select a display method using an AI model that takes the user's device information as input and outputs the optimal display method.

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

[0097] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, it can prioritize analyzing high-priority items. If the user is relaxed, it can perform a more detailed analysis. Furthermore, if the user is in a hurry, it can provide analysis results quickly. In this way, by adjusting the analysis priority based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can adjust the analysis priority using an AI model that takes user emotion data as input and outputs the analysis priority.

[0098] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated emotions. For example, if the user is stressed, the judgment criteria can be relaxed. Conversely, if the user is relaxed, the judgment criteria can be made stricter. Furthermore, if the user is in a hurry, a judgment can be made quickly. In this way, by adjusting the judgment criteria based on the user's emotions, it becomes possible to make a judgment appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or not using AI. For example, the judgment unit can adjust the judgment criteria using an AI model that takes user emotion data as input and outputs judgment criteria.

[0099] The notification unit can estimate the user's emotions and adjust the way notifications are displayed based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible notification. If the user is relaxed, it can provide a detailed notification. Furthermore, if the user is in a hurry, it can provide a concise notification. By adjusting the way notifications are displayed based on the user's emotions, it becomes possible to provide notifications that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can adjust the display method using an AI model that takes user emotion data as input and outputs the way notifications are displayed.

[0100] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to collect more detailed information. Furthermore, if the user is busy, only important data can be prioritized for efficient data collection. In this way, adjusting the timing of data collection based on the user's emotions reduces the user's burden and enables efficient data collection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can adjust the timing of data collection using an AI model that takes user emotion data as input and outputs the collection timing.

[0101] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a concise analysis result. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the presentation using an AI model that takes user emotion data as input and outputs a presentation method for the analysis result.

[0102] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize collecting data from data sources that the user has frequently accessed in the past. It can also analyze the user's past data collection patterns and suggest the optimal collection timing. Furthermore, it can select the optimal collection method by referring to data collection methods the user has used in the past (API, manual input, etc.). This enables efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can select a collection method using an AI model that takes the user's past data collection history as input and outputs the optimal collection method.

[0103] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. Furthermore, it can adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes data importance as input and outputs the level of detail of the analysis.

[0104] The judgment unit can improve the accuracy of its judgment by considering the interrelationships between data during the judgment process. For example, it can perform a judgment by combining email and calendar data. It can also perform a judgment by associating chat tool exchanges with email content. Furthermore, it can analyze the interrelationships between data to perform a more accurate judgment. Thus, by considering the interrelationships between data, a more accurate judgment becomes possible. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can improve the accuracy of its judgment by using an AI model that takes the interrelationships between data as input and outputs a judgment result.

[0105] The notification unit can select the optimal display method by referring to the user's past operation history when sending a notification. For example, it can prioritize providing notification formats that the user has previously preferred. It can also analyze the user's past operation history and suggest the optimal notification method. Furthermore, it can avoid notification formats that the user has previously ignored and provide new notification methods. In this way, the optimal notification method can be provided by referring to the user's past operation history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can select a display method using an AI model that takes the user's past operation history as input and outputs the optimal display method.

[0106] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a natural language processing algorithm can be applied to email data. A schedule analysis algorithm can also be applied to calendar data. Furthermore, a dialogue analysis algorithm can be applied to chat data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can select an analysis algorithm using an AI model that takes the data category as input and outputs the analysis algorithm to be applied.

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

[0108] Step 1: The data collection unit collects data from the user's emails, calendars, chat tools, etc. For example, it retrieves data from Gmail, calendars, chat tools, etc., using APIs. With the user's permission, the data collection unit collects email subjects and bodies, calendar events, chat message content, etc. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the importance and urgency of each case. For example, it analyzes email content using text analysis technology, analyzes calendar schedules using data mining technology, and analyzes chat message content using machine learning algorithms. Step 3: The judgment unit prioritizes cases based on the results determined by the analysis unit. For example, it uses a scoring system to evaluate the importance and urgency of each case and determines the priority using a rule-based judgment method. Step 4: The notification unit aggregates and notifies users of the deadlines for cases that have been prioritized by the judgment unit. For example, it notifies users via email or push notification and adds the case deadlines to their calendar.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 or the processor 28 of the data processing unit 12, and collects data such as the user's emails, calendar, and chat tools. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to determine the importance and urgency of each case. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12, and prioritizes the cases based on the analysis results. The notification unit is implemented by the control unit 46A of the smart device 14, and notifies the user based on the determined priority. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, determination unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 or the processor 28 of the data processing unit 12, and collects data such as the user's emails, calendar, and chat tools. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to determine the importance and urgency of each case. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12, and prioritizes the cases based on the analysis results. The notification unit is implemented by the control unit 46A of the smart glasses 214, and notifies the user based on the determined priority. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 or the processor 28 of the data processing unit 12, and collects data such as the user's emails, calendars, and chat tools. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to determine the importance and urgency of each case. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12, and prioritizes the cases based on the analysis results. The notification unit is implemented by the control unit 46A of the headset terminal 314, and notifies the user based on the determined priority. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 or the processor 28 of the data processing unit 12, and collects data such as the user's emails, calendars, and chat tools. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to determine the importance and urgency of each case. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12, and prioritizes the cases based on the analysis results. The notification unit is implemented by the control unit 46A of the robot 414, and notifies the user based on the determined priority. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The data collection unit collects data from users' emails, calendars, chat tools, etc. An analysis unit analyzes the data collected by the aforementioned collection unit and determines the importance and urgency of each case, A determination unit that prioritizes cases based on the results determined by the aforementioned analysis unit, The system includes a notification unit that aggregates and notifies the deadlines of cases that have been given priority by the determination unit. A system characterized by the following features. (Note 2) The determination unit, Determine the importance and urgency of each case. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Consolidate and notify the deadlines for each project. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It collects data from Gmail, Calendar, chat tools, etc. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The collected data is analyzed to determine the importance and urgency of each case. The system described in Appendix 1, characterized by the features described herein. (Note 6) The determination unit, Prioritize cases based on the assessment results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification unit, Notify the user The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, The system estimates the user's emotions and adjusts the criteria for judgment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, When making a judgment, the accuracy of the judgment is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, When making a decision, the attribute information of the data submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, It estimates the user's emotions and adjusts the order in which the judgment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The determination unit, When making a decision, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The determination unit, When making a judgment, we refer to relevant literature to improve the accuracy of the judgment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the user's emotions and adjusts how notifications are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When a notification is sent, the system will refer to the user's past activity history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification procedure based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When a notification is sent, the system selects the optimal display method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection unit collects data from users' emails, calendars, chat tools, etc. An analysis unit analyzes the data collected by the aforementioned collection unit and determines the importance and urgency of each case, A determination unit that prioritizes cases based on the results determined by the aforementioned analysis unit, The system includes a notification unit that aggregates and notifies the deadlines of cases that have been given priority by the determination unit. A system characterized by the following features.

2. The determination unit, Determine the importance and urgency of each case. The system according to feature 1.

3. The aforementioned notification unit, Consolidate and notify the deadlines for each project. The system according to feature 1.

4. The aforementioned collection unit is It collects data from Gmail, Calendar, chat tools, etc. The system according to feature 1.

5. The aforementioned analysis unit, The collected data is analyzed to determine the importance and urgency of each case. The system according to feature 1.

6. The determination unit, Prioritize cases based on the assessment results. The system according to feature 1.

7. The aforementioned notification unit, Notify the user The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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