system

Abstract

The system according to the embodiment aims to enable users to efficiently check calendar information and provide summarized information. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a summarization unit, and a provision unit. The reception unit receives instructions from a user. The analysis unit analyzes calendar information based on the instructions received by the reception unit. The summarization unit summarizes the information analyzed by the analysis unit. The provision unit provides the information summarized by the summarization unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to efficiently check and summarize calendar information.

[0005] The system according to the embodiment aims to enable users to efficiently check calendar information and provide summarized information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a summarization unit, and a provision unit. The reception unit receives instructions from a user. The analysis unit analyzes calendar information based on the instructions received by the reception unit. The summarization unit summarizes the information analyzed by the analysis unit. The provision unit provides the information summarized by the summarization unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently check calendar information and provides summarized information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A schedule confirmation assistance system according to an embodiment of the present invention allows users to check their schedules for past and future dates and to provide instructions for searching for specific events. The system summarizes information entered by the user on a calendar, such as dates, times, and locations, and then provides an AI response. The response is provided not only in text format but also as voice guidance. First, a user specifies a past or future date and instructs the system to search for a specific event. For example, the system may issue an instruction such as, "Tell me about next week's meeting schedule." This instruction is then input to an AI assistant. The AI ​​assistant then analyzes the information entered on the calendar and extracts information related to the specified date or event. For example, it extracts information such as the date, time, location, and participants of next week's meeting. The extracted information is then summarized and provided to the user. The system provides this information not only in text format but also as voice guidance. For example, it may report, "Next week's meeting will be held in a conference room in our office on Monday at 10:00." This system allows users to easily check their schedule and quickly obtain information about specific events. Voice guidance also allows users to obtain information even when visual confirmation is difficult. This allows the schedule confirmation assistance system to allow the user to easily check schedules for past and future dates and quickly obtain information about specific events.

[0029] A schedule confirmation assistance system according to an embodiment includes a reception unit, an analysis unit, a summarization unit, and a provision unit. The reception unit receives instructions from a user. The instructions from the user include, but are not limited to, voice instructions, text instructions, and gesture instructions. The analysis unit analyzes calendar information based on the instructions received by the reception unit. The calendar information includes, but is not limited to, appointments, events, and reminders. The analysis unit performs analysis using methods such as natural language processing, data mining, and machine learning algorithms. The summarization unit summarizes the information analyzed by the analysis unit. The summarization is performed based on, for example, the length of the sentence and the importance of the information to be summarized, but is not limited to, examples. The provision unit provides the information summarized by the summarization unit. Methods of providing the information include, but are not limited to, screen display, audio output, and email transmission. As a result, the schedule confirmation assistance system according to an embodiment analyzes calendar information based on a user's instructions, summarizes it, and provides it, making it easier for the user to check their schedule.

[0030] The schedule confirmation assistance system includes a voice reception unit that receives voice input. The voice reception unit receives voice input from a user. Voice input includes, but is not limited to, input using a microphone. The voice reception unit converts voice into text using voice recognition technology. For example, when a user gives a voice instruction such as "Tell me about next week's meeting schedule," the voice reception unit converts the voice into text and sends it to the analysis unit. This allows the user to give voice instructions by responding to the voice input. Some or all of the above-described processing in the voice reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice reception unit may input voice data to a generation AI and have the generation AI convert the voice data into text data.

[0031] The schedule confirmation assistance system includes a text accepting unit that accepts text input. The text accepting unit accepts text input from a user. Examples of text input include, but are not limited to, keyboard input and touchscreen input. When a user gives a text instruction such as "Tell me about next week's meeting schedule," the text accepting unit transmits the text to an analysis unit. This allows the user to give instructions in text by responding to the text input. Some or all of the above-described processing in the text accepting unit may be performed using, for example, AI, or may be performed without using AI. For example, the text accepting unit may input text data to a generation AI and have the generation AI analyze the text data.

[0032] The schedule confirmation assistance system includes a natural language processing unit that performs natural language processing. The natural language processing unit analyzes a user's instructions using natural language processing technology. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The natural language processing unit analyzes the user's instructions and extracts calendar information. For example, if the user instructs the natural language processing unit to "tell me about next week's meeting schedules," the natural language processing unit analyzes the instruction and extracts information about next week's meetings from the calendar. This allows the natural language processing unit to more accurately understand the user's instructions. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit may input the user's instruction data into a generation AI and have the generation AI analyze the instruction.

[0033] The schedule confirmation assistance system includes an update unit that periodically updates calendar data. The update unit periodically updates the calendar data. The update frequency may be, for example, daily, weekly, or real-time, but is not limited to these examples. The update unit periodically acquires and updates the calendar data to keep it up to date. For example, the update unit checks the calendar data daily and reflects new or changed schedules. By periodically updating the calendar data, the latest schedule information can be maintained. Some or all of the above-described processing in the update unit may be performed using, or without, AI. For example, the update unit may input calendar data to a generation AI and have the generation AI update the data.

[0034] The schedule confirmation assistance system includes a customization unit that customizes information according to the user's preferences. The customization unit customizes the information according to the user's preferences. Examples of customization include, but are not limited to, color selection and notification method selection. The customization unit adjusts the information display method and notification method based on the user's settings. For example, the customization unit displays a calendar in the user's preferred color and allows the user to select the notification method from email, push notification, etc. This enables more personalized information to be provided by customizing the information according to the user's preferences. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit may input the user's setting data into a generation AI and have the generation AI adjust the customization.

[0035] The reception unit can analyze the user's past instruction history and select the optimal reception method. For example, the reception unit preferentially suggests instruction methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an instruction method to be used in a specific time period based on the user's past instruction history. Furthermore, the reception unit can automatically select the optimal reception method based on the content previously input by the user. In this way, by analyzing the past instruction history, the optimal reception method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's instruction history data into a generation AI and have the generation AI select the optimal reception method.

[0036] When receiving an instruction, the reception unit can perform filtering based on the user's current situation and areas of interest. For example, the reception unit preferentially receives relevant instructions based on the user's current situation (at work, on vacation, etc.). The reception unit can also filter instructions based on the user's areas of interest (meeting, private event, etc.). Furthermore, the reception unit can also receive optimal instructions based on the user's current activity (moving, stationary, etc.). In this way, by filtering instructions based on the user's situation and areas of interest, more relevant instructions can be received. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's situation data and area of ​​interest data to the generation AI and cause the generation AI to perform filtering.

[0037] When receiving an instruction, the reception unit can prioritize receiving highly relevant instructions by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize receiving instructions related to that location. Furthermore, when the user is traveling, the reception unit can also prioritize receiving instructions related to the user's destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving instructions related to the user's home. In this way, highly relevant instructions can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant instructions.

[0038] When receiving an instruction, the reception unit can analyze the user's social media activity and receive related instructions. For example, the reception unit can prioritize receiving instructions related to events shared by the user on social media. The reception unit can also prioritize receiving instructions related to accounts the user follows on social media. Furthermore, the reception unit can also prioritize receiving instructions related to events the user plans to attend on social media. In this way, by analyzing social media activity, related instructions can be received preferentially. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related instructions.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the calendar. For example, the analysis unit performs a detailed analysis on important events. The analysis unit can also perform a standard analysis on normal events. Furthermore, the analysis unit can perform a simplified analysis on events with low importance. In this way, by adjusting the level of detail of the analysis based on the importance of the calendar, detailed analysis can be performed on important events. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input calendar importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the calendar category. For example, the analysis unit can apply a business-oriented analysis algorithm to business events. The analysis unit can also apply a private-oriented analysis algorithm to private events. The analysis unit can also apply a social-oriented analysis algorithm to social events. This allows for more appropriate analysis by applying different analysis algorithms depending on the calendar category. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input calendar category data into the generation AI and have the generation AI select an analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the calendar. For example, the analysis unit prioritizes analysis of upcoming events. The analysis unit can also postpone analysis of events in the distant future. Furthermore, the analysis unit can analyze past events as needed. In this way, by determining the priority of analysis based on the submission time of the calendar, it is possible to prioritize analysis of upcoming events. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input calendar submission time data to the generation AI and have the generation AI determine the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on calendar relevance. For example, the analysis unit prioritizes analysis of events with important relevance. The analysis unit can also perform standard analysis of events with normal relevance. Furthermore, the analysis unit can postpone analysis of events with low relevance. In this way, by adjusting the order of analysis based on calendar relevance, events with important relevance can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input calendar relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0043] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the calendar. For example, the summarization unit provides a detailed summary for important events. The summarization unit can also provide a standard summary for ordinary events. Furthermore, the summarization unit can provide a simplified summary for events with low importance. In this way, by adjusting the level of detail of the summary based on the importance of the calendar, a detailed summary is provided for important events. Some or all of the above-mentioned processing in the summarization unit may be performed using AI, for example, or may be performed without using AI. For example, the summarization unit can input calendar importance data to the generation AI and cause the generation AI to adjust the level of detail of the summary.

[0044] When generating a summary, the summarization unit can apply different summarization algorithms depending on the calendar category. For example, the summarization unit can apply a business-oriented summarization algorithm to business events. The summarization unit can also apply a private-oriented summarization algorithm to private events. The summarization unit can also apply a social-oriented summarization algorithm to social events. By applying different summarization algorithms depending on the calendar category, more appropriate summaries can be provided. Some or all of the above-described processing in the summarization unit can be performed using, for example, AI, or can be performed without using AI. For example, the summarization unit can input calendar category data into the generation AI and have the generation AI select a summarization algorithm.

[0045] When generating summaries, the summarization unit can determine the priority of summaries based on the submission time of the calendar. For example, the summarization unit can provide summaries preferentially for upcoming events. The summarization unit can also provide summaries later for events in the distant future. Furthermore, the summarization unit can provide summaries for past events as needed. In this way, by determining the priority of summaries based on the submission time of the calendar, summaries are provided preferentially for upcoming events. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input calendar submission time data into the generation AI and have the generation AI determine the priority of summaries.

[0046] The summarization unit can adjust the order of summaries based on calendar relevance when generating summaries. For example, the summarization unit can provide summaries preferentially for events with important relevance. The summarization unit can also provide summaries as a standard for events with normal relevance. Furthermore, the summarization unit can provide summaries later for events with low relevance. In this way, by adjusting the order of summaries based on calendar relevance, summaries are provided preferentially for events with important relevance. Some or all of the above-described processing in the summarization unit may be performed using, or without, AI. For example, the summarization unit can input calendar relevance data to the generation AI and cause the generation AI to adjust the order of summaries.

[0047] The providing unit can adjust the level of detail provided based on the importance of the calendar when providing the information. For example, the providing unit provides detailed information for important events. The providing unit can also provide standard information for ordinary events. Furthermore, the providing unit can provide simplified information for events with low importance. In this way, by adjusting the level of detail provided based on the importance of the calendar, detailed information is provided for important events. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input calendar importance data to the generating AI and cause the generating AI to adjust the level of detail provided.

[0048] The providing unit can apply different providing algorithms depending on the calendar category when providing the information. For example, the providing unit can apply a business-oriented providing algorithm to a business event. The providing unit can also apply a private-oriented providing algorithm to a private event. The providing unit can also apply a social-oriented providing algorithm to a social event. This allows more appropriate information to be provided by applying different providing algorithms depending on the calendar category. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input calendar category data to the generation AI and cause the generation AI to select a providing algorithm.

[0049] The providing unit can determine the priority of provision based on the submission time of the calendar at the time of provision. For example, the providing unit provides information preferentially for upcoming events. The providing unit can also provide information later for events in the distant future. Furthermore, the providing unit can provide information for past events as needed. In this way, by determining the priority of provision based on the submission time of the calendar, information is provided preferentially for upcoming events. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input calendar submission time data to the generation AI and cause the generation AI to determine the priority of provision.

[0050] The providing unit can adjust the order of provision based on the relevance of the calendar when providing the information. For example, the providing unit provides information preferentially for events with important relevance. The providing unit can also provide information in a standard manner for events with normal relevance. Furthermore, the providing unit can also provide information later for events with low relevance. In this way, by adjusting the order of provision based on the relevance of the calendar, information is provided preferentially for events with important relevance. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input calendar relevance data to the generating AI and cause the generating AI to adjust the order of provision.

[0051] When receiving a voice instruction, the voice receiving unit can analyze the user's past voice instruction history and select the optimal reception method. For example, the voice receiving unit can prioritize and suggest a voice instruction method that the user has frequently used in the past. The voice receiving unit can also predict and suggest a voice instruction method to be used in a specific time period based on the user's past voice instruction history. Furthermore, the voice receiving unit can automatically select the optimal reception method based on the voice content previously input by the user. In this way, by analyzing the past voice instruction history, the optimal reception method for the user can be provided. Some or all of the above-described processing in the voice receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice receiving unit can input the user's voice instruction history data to a generation AI and have the generation AI select the optimal reception method.

[0052] When receiving a voice instruction, the voice receiving unit can prioritize receiving a highly relevant voice instruction by taking into account the user's geographical location information. For example, when the user is in a specific location, the voice receiving unit can prioritize receiving a voice instruction related to that location. Furthermore, when the user is traveling, the voice receiving unit can also prioritize receiving a voice instruction related to the user's destination. Furthermore, when the user is at home, the voice receiving unit can also prioritize receiving a voice instruction related to the user's home. In this way, by taking the user's geographical location information into account, highly relevant voice instructions can be prioritized. Some or all of the above-described processing in the voice receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the voice receiving unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant voice instructions.

[0053] When receiving a text instruction, the text receiving unit can analyze the user's past text instruction history and select the optimal reception method. For example, the text receiving unit can preferentially suggest text instruction methods that the user has frequently used in the past. The text receiving unit can also predict and suggest a text instruction method to be used during a specific time period based on the user's past text instruction history. Furthermore, the text receiving unit can automatically select the optimal reception method based on text content previously input by the user. In this way, by analyzing the past text instruction history, the optimal reception method for a text instruction can be provided to the user. Some or all of the above-described processing in the text receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the text receiving unit can input the user's text instruction history data to a generation AI and cause the generation AI to select the optimal reception method.

[0054] When receiving a text instruction, the text receiving unit can prioritize receiving a highly relevant text instruction by taking into account the user's geographical location information. For example, when the user is in a specific location, the text receiving unit can prioritize receiving a text instruction related to that location. Furthermore, when the user is traveling, the text receiving unit can also prioritize receiving a text instruction related to the user's destination. Furthermore, when the user is at home, the text receiving unit can also prioritize receiving a text instruction related to the user's home. In this way, by taking the user's geographical location information into account, highly relevant text instructions can be prioritized. Some or all of the above-described processing in the text receiving unit can be performed using AI, for example, or without using AI. For example, the text receiving unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant text instructions.

[0055] During natural language processing, the natural language processing unit can select the optimal processing method by referring to the user's past instruction history. For example, the natural language processing unit preferentially suggests natural language processing methods that the user has frequently used in the past. The natural language processing unit can also predict and suggest natural language processing methods to be used during a specific time period based on the user's past instruction history. Furthermore, the natural language processing unit can automatically select the optimal natural language processing method based on content previously input by the user. In this way, by referring to the past instruction history, the optimal natural language processing method can be provided to the user. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit can input the user's instruction history data into a generation AI and cause the generation AI to select the optimal processing method.

[0056] During natural language processing, the natural language processing unit can select the optimal processing method by taking into account the user's geographical location information. For example, when the user is in a specific location, the natural language processing unit can prioritize natural language processing related to that location. Furthermore, when the user is on the move, the natural language processing unit can also prioritize natural language processing related to the user's destination. Furthermore, when the user is at home, the natural language processing unit can also prioritize natural language processing related to the user's home. In this way, by taking the user's geographical location information into account, highly relevant natural language processing can be prioritized. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit can input the user's geographical location data into a generation AI and cause the generation AI to select the optimal processing method.

[0057] When updating the calendar, the update unit can select the optimal update method by referring to the user's past update history. For example, the update unit can prioritize and suggest update methods that the user has frequently used in the past. The update unit can also predict and suggest update methods to be used during specific time periods based on the user's past update history. Furthermore, the update unit can automatically select the optimal update method based on content previously input by the user. In this way, by referring to the past update history, the optimal calendar update method can be provided to the user. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the user's update history data into a generation AI and have the generation AI select the optimal update method.

[0058] When updating the calendar, the update unit can select the optimal update method by taking into account the user's geographical location information. For example, when the user is in a specific location, the update unit can prioritize updates related to that location. Furthermore, when the user is traveling, the update unit can also prioritize updates related to the user's destination. Furthermore, when the user is at home, the update unit can also prioritize updates related to the user's home. In this way, by taking the user's geographical location information into account, calendar updates with high relevance can be prioritized. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal update method.

[0059] During customization, the customization unit can select the optimal customization method by referring to the user's past customization history. For example, the customization unit can prioritize and suggest customization methods that the user has frequently used in the past. The customization unit can also predict and suggest a customization method to be used during a specific time period based on the user's past customization history. Furthermore, the customization unit can automatically select the optimal customization method based on content previously input by the user. In this way, by referring to the past customization history, the optimal customization method can be provided to the user. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's customization history data into a generation AI and cause the generation AI to select the optimal customization method.

[0060] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. For example, when the user is in a specific location, the customization unit can prioritize customization related to that location. Furthermore, when the user is traveling, the customization unit can also prioritize customization related to the user's destination. Furthermore, when the user is at home, the customization unit can also prioritize customization related to the user's home. In this way, by taking the user's geographical location information into account, highly relevant customization can be prioritized. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal customization method.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The analysis unit can analyze the user's past schedule history and make future schedule suggestions. For example, it can analyze patterns of events and meetings that the user frequently attended in the past and predict and suggest similar events in the future. The analysis unit can also analyze the reasons why the user canceled an event in the past and make suggestions to avoid similar situations. Furthermore, the analysis unit can also suggest the optimal schedule for a specific time period based on the user's past schedule history. This makes it possible to suggest schedules that take into account the user's past behavioral patterns.

[0063] The natural language processor can provide additional related information based on the user's instructions. For example, if the user instructs, "Tell me about next week's meeting schedules," the natural language processor can provide detailed information about the meeting (date, time, location, and participants), as well as related materials and minutes of past meetings. Also, if the user instructs, "Tell me about my next vacation schedule," the natural language processor can provide detailed information about the vacation (start date, end date, and destination), as well as related travel information and weather forecasts. Furthermore, if the user instructs, "Tell me about this week's tasks," the natural language processor can provide detailed information about the task (task name, deadline, and priority), as well as related resources and reference materials. This makes it possible to provide more comprehensive information based on the user's instructions.

[0064] The update unit can automatically set reminders based on the user's schedule. For example, if the user schedules an important meeting, the update unit can set a reminder the day before the meeting. If the user schedules a trip, the update unit can set a reminder one week before departure. Furthermore, if the user schedules a recurring task, the update unit can also set a reminder the day before the task. This allows the user to receive reminders at the appropriate time so that they do not forget important events or tasks.

[0065] The reception unit can suggest an optimal instruction reception method based on the user's current activity status. For example, if the user is driving, the reception unit can preferentially suggest voice input. If the user is in a meeting, the reception unit can preferentially suggest text input. Furthermore, if the user is relaxed, the reception unit can also provide detailed input options. This makes it possible to provide an optimal instruction reception method according to the user's current activity status.

[0066] The providing unit can provide relevant news and information based on the user's schedule. For example, if the user has a business meeting scheduled, the providing unit can provide relevant industry news and market trends. If the user has a trip scheduled, the providing unit can provide tourist information and weather forecasts for the destination. Furthermore, if the user has a health checkup scheduled, the providing unit can also provide the latest health information and advice. This makes it possible to provide relevant information based on the user's schedule.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit receives instructions from the user, including voice instructions, text instructions, gesture instructions, and the like. Step 2: The analysis unit analyzes the calendar information based on the instructions received by the reception unit. The calendar information includes appointments, events, reminders, etc., and the analysis is performed using methods such as natural language processing, data mining, and machine learning algorithms. Step 3: The summarization unit summarizes the information analyzed by the analysis unit. The summarization is based on the length of the text and the importance of the information being summarized. Step 4: The providing unit provides the information summarized by the summarizing unit by a method such as displaying the information on a screen, outputting the information by voice, or sending an email.

[0069] (Example 2) A schedule confirmation assistance system according to an embodiment of the present invention allows users to check their schedules for past and future dates and to provide instructions for searching for specific events. The system summarizes information entered by the user on a calendar, such as dates, times, and locations, and then provides an AI response. The response is provided not only in text format but also as voice guidance. First, a user specifies a past or future date and instructs the system to search for a specific event. For example, the system may issue an instruction such as, "Tell me about next week's meeting schedule." This instruction is then input to an AI assistant. The AI ​​assistant then analyzes the information entered on the calendar and extracts information related to the specified date or event. For example, it extracts information such as the date, time, location, and participants of next week's meeting. The extracted information is then summarized and provided to the user. The system provides this information not only in text format but also as voice guidance. For example, it may report, "Next week's meeting will be held in a conference room in our office on Monday at 10:00." This system allows users to easily check their schedule and quickly obtain information about specific events. Voice guidance also allows users to obtain information even when visual confirmation is difficult. This allows the schedule confirmation assistance system to allow the user to easily check schedules for past and future dates and quickly obtain information about specific events.

[0070] A schedule confirmation assistance system according to an embodiment includes a reception unit, an analysis unit, a summarization unit, and a provision unit. The reception unit receives instructions from a user. The instructions from the user include, but are not limited to, voice instructions, text instructions, and gesture instructions. The analysis unit analyzes calendar information based on the instructions received by the reception unit. The calendar information includes, but is not limited to, appointments, events, and reminders. The analysis unit performs analysis using methods such as natural language processing, data mining, and machine learning algorithms. The summarization unit summarizes the information analyzed by the analysis unit. The summarization is performed based on, for example, the length of the sentence and the importance of the information to be summarized, but is not limited to, examples. The provision unit provides the information summarized by the summarization unit. Methods of providing the information include, but are not limited to, screen display, audio output, and email transmission. As a result, the schedule confirmation assistance system according to an embodiment analyzes calendar information based on a user's instructions, summarizes it, and provides it, making it easier for the user to check their schedule.

[0071] The schedule confirmation assistance system includes a voice reception unit that receives voice input. The voice reception unit receives voice input from a user. Voice input includes, but is not limited to, input using a microphone. The voice reception unit converts voice into text using voice recognition technology. For example, when a user gives a voice instruction such as "Tell me about next week's meeting schedule," the voice reception unit converts the voice into text and sends it to the analysis unit. This allows the user to give voice instructions by responding to the voice input. Some or all of the above-described processing in the voice reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice reception unit may input voice data to a generation AI and have the generation AI convert the voice data into text data.

[0072] The schedule confirmation assistance system includes a text accepting unit that accepts text input. The text accepting unit accepts text input from a user. Examples of text input include, but are not limited to, keyboard input and touchscreen input. When a user gives a text instruction such as "Tell me about next week's meeting schedule," the text accepting unit transmits the text to an analysis unit. This allows the user to give instructions in text by responding to the text input. Some or all of the above-described processing in the text accepting unit may be performed using, for example, AI, or may be performed without using AI. For example, the text accepting unit may input text data to a generation AI and have the generation AI analyze the text data.

[0073] The schedule confirmation assistance system includes a natural language processing unit that performs natural language processing. The natural language processing unit analyzes a user's instructions using natural language processing technology. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The natural language processing unit analyzes the user's instructions and extracts calendar information. For example, if the user instructs the natural language processing unit to "tell me about next week's meeting schedules," the natural language processing unit analyzes the instruction and extracts information about next week's meetings from the calendar. This allows the natural language processing unit to more accurately understand the user's instructions. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit may input the user's instruction data into a generation AI and have the generation AI analyze the instruction.

[0074] The schedule confirmation assistance system includes an update unit that periodically updates calendar data. The update unit periodically updates the calendar data. The update frequency may be, for example, daily, weekly, or real-time, but is not limited to these examples. The update unit periodically acquires and updates the calendar data to keep it up to date. For example, the update unit checks the calendar data daily and reflects new or changed schedules. By periodically updating the calendar data, the latest schedule information can be maintained. Some or all of the above-described processing in the update unit may be performed using, or without, AI. For example, the update unit may input calendar data to a generation AI and have the generation AI update the data.

[0075] The schedule confirmation assistance system includes a customization unit that customizes information according to the user's preferences. The customization unit customizes the information according to the user's preferences. Examples of customization include, but are not limited to, color selection and notification method selection. The customization unit adjusts the information display method and notification method based on the user's settings. For example, the customization unit displays a calendar in the user's preferred color and allows the user to select the notification method from email, push notification, etc. This enables more personalized information to be provided by customizing the information according to the user's preferences. Some or all of the above-described processing in the customization unit may be performed using, or without, AI. For example, the customization unit may input the user's setting data into a generation AI and have the generation AI adjust the customization.

[0076] The reception unit can estimate the user's emotions and adjust the timing of instruction acceptance based on the estimated user emotions. For example, when the user is stressed, the reception unit can provide a simple interface and speed up the timing of instruction acceptance. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and flexibly adjust the timing of instruction acceptance. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and quickly accept instructions. This allows instructions to be accepted at a more appropriate time by adjusting the timing of instruction acceptance according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI execute emotion estimation.

[0077] The reception unit can analyze the user's past instruction history and select the optimal reception method. For example, the reception unit preferentially suggests instruction methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an instruction method to be used in a specific time period based on the user's past instruction history. Furthermore, the reception unit can automatically select the optimal reception method based on the content previously input by the user. In this way, by analyzing the past instruction history, the optimal reception method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's instruction history data into a generation AI and have the generation AI select the optimal reception method.

[0078] When receiving an instruction, the reception unit can perform filtering based on the user's current situation and areas of interest. For example, the reception unit preferentially receives relevant instructions based on the user's current situation (at work, on vacation, etc.). The reception unit can also filter instructions based on the user's areas of interest (meeting, private event, etc.). Furthermore, the reception unit can also receive optimal instructions based on the user's current activity (moving, stationary, etc.). In this way, by filtering instructions based on the user's situation and areas of interest, more relevant instructions can be received. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's situation data and area of ​​interest data to the generation AI and cause the generation AI to perform filtering.

[0079] The reception unit can estimate the user's emotions and determine the priority of instructions to be received based on the estimated user's emotions. For example, when the user is nervous, the reception unit can prioritize receiving important instructions. Furthermore, when the user is relaxed, the reception unit can prioritize receiving normal instructions. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving urgent instructions. In this way, by determining the priority of instructions according to the user's emotions, important instructions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0080] When receiving an instruction, the reception unit can prioritize receiving highly relevant instructions by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize receiving instructions related to that location. Furthermore, when the user is traveling, the reception unit can also prioritize receiving instructions related to the user's destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving instructions related to the user's home. In this way, highly relevant instructions can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant instructions.

[0081] When receiving an instruction, the reception unit can analyze the user's social media activity and receive related instructions. For example, the reception unit can prioritize receiving instructions related to events shared by the user on social media. The reception unit can also prioritize receiving instructions related to accounts the user follows on social media. Furthermore, the reception unit can also prioritize receiving instructions related to events the user plans to attend on social media. In this way, by analyzing social media activity, related instructions can be received preferentially. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related instructions.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is in a hurry. Furthermore, the analysis unit can perform an analysis that focuses on important information when the user is stressed. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the calendar. For example, the analysis unit performs a detailed analysis on important events. The analysis unit can also perform a standard analysis on normal events. Furthermore, the analysis unit can perform a simplified analysis on events with low importance. In this way, by adjusting the level of detail of the analysis based on the importance of the calendar, detailed analysis can be performed on important events. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input calendar importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the calendar category. For example, the analysis unit can apply a business-oriented analysis algorithm to business events. The analysis unit can also apply a private-oriented analysis algorithm to private events. The analysis unit can also apply a social-oriented analysis algorithm to social events. This allows for more appropriate analysis by applying different analysis algorithms depending on the calendar category. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input calendar category data into the generation AI and have the generation AI select an analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize important analyses. Furthermore, if the user is relaxed, the analysis unit can prioritize normal analyses. Furthermore, if the user is in a hurry, the analysis unit can prioritize urgent analyses. Thus, by determining the analysis priority according to the user's emotions, important analyses can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the calendar. For example, the analysis unit prioritizes analysis of upcoming events. The analysis unit can also postpone analysis of events in the distant future. Furthermore, the analysis unit can analyze past events as needed. In this way, by determining the priority of analysis based on the submission time of the calendar, it is possible to prioritize analysis of upcoming events. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input calendar submission time data to the generation AI and have the generation AI determine the analysis priority.

[0087] During analysis, the analysis unit can adjust the order of analysis based on calendar relevance. For example, the analysis unit prioritizes analysis of events with important relevance. The analysis unit can also perform standard analysis of events with normal relevance. Furthermore, the analysis unit can postpone analysis of events with low relevance. In this way, by adjusting the order of analysis based on calendar relevance, events with important relevance can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input calendar relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0088] The summarization unit can estimate the user's emotions and adjust the summary presentation method based on the estimated user emotions. For example, the summarization unit can provide a detailed summary when the user is relaxed. The summarization unit can also provide a concise summary when the user is in a hurry. Furthermore, the summarization unit can provide a summary that focuses on important information when the user is stressed. By adjusting the summary presentation method according to the user's emotions, a more appropriate summary can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the summarization unit can be performed using, for example, AI, or without AI. For example, the summarization unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0089] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the calendar. For example, the summarization unit provides a detailed summary for important events. The summarization unit can also provide a standard summary for ordinary events. Furthermore, the summarization unit can provide a simplified summary for events with low importance. In this way, by adjusting the level of detail of the summary based on the importance of the calendar, a detailed summary is provided for important events. Some or all of the above-mentioned processing in the summarization unit may be performed using AI, for example, or may be performed without using AI. For example, the summarization unit can input calendar importance data to the generation AI and cause the generation AI to adjust the level of detail of the summary.

[0090] When generating a summary, the summarization unit can apply different summarization algorithms depending on the calendar category. For example, the summarization unit can apply a business-oriented summarization algorithm to business events. The summarization unit can also apply a private-oriented summarization algorithm to private events. The summarization unit can also apply a social-oriented summarization algorithm to social events. By applying different summarization algorithms depending on the calendar category, more appropriate summaries can be provided. Some or all of the above-described processing in the summarization unit can be performed using, for example, AI, or can be performed without using AI. For example, the summarization unit can input calendar category data into the generation AI and have the generation AI select a summarization algorithm.

[0091] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. For example, if the user is in a hurry, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can also provide a longer summary with detailed explanations. Furthermore, if the user is stressed, the summarization unit can also provide a summary that focuses on important information. By adjusting the length of the summary according to the user's emotions, a more appropriate summary can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the summarization unit can be performed using, for example, AI, or without AI. For example, the summarization unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0092] When generating summaries, the summarization unit can determine the priority of summaries based on the submission time of the calendar. For example, the summarization unit can provide summaries preferentially for upcoming events. The summarization unit can also provide summaries later for events in the distant future. Furthermore, the summarization unit can provide summaries for past events as needed. In this way, by determining the priority of summaries based on the submission time of the calendar, summaries are provided preferentially for upcoming events. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input calendar submission time data into the generation AI and have the generation AI determine the priority of summaries.

[0093] The summarization unit can adjust the order of summaries based on calendar relevance when generating summaries. For example, the summarization unit can provide summaries preferentially for events with important relevance. The summarization unit can also provide summaries as a standard for events with normal relevance. Furthermore, the summarization unit can provide summaries later for events with low relevance. In this way, by adjusting the order of summaries based on calendar relevance, summaries are provided preferentially for events with important relevance. Some or all of the above-described processing in the summarization unit may be performed using, or without, AI. For example, the summarization unit can input calendar relevance data to the generation AI and cause the generation AI to adjust the order of summaries.

[0094] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, the providing unit can provide detailed information when the user is relaxed. The providing unit can also provide concise information when the user is in a hurry. Furthermore, the providing unit can provide information that focuses on important information when the user is stressed. This allows for more appropriate information provision by adjusting the method of providing information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0095] The providing unit can adjust the level of detail provided based on the importance of the calendar when providing the information. For example, the providing unit provides detailed information for important events. The providing unit can also provide standard information for ordinary events. Furthermore, the providing unit can provide simplified information for events with low importance. In this way, by adjusting the level of detail provided based on the importance of the calendar, detailed information is provided for important events. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input calendar importance data to the generating AI and cause the generating AI to adjust the level of detail provided.

[0096] The providing unit can apply different providing algorithms depending on the calendar category when providing the information. For example, the providing unit can apply a business-oriented providing algorithm to a business event. The providing unit can also apply a private-oriented providing algorithm to a private event. The providing unit can also apply a social-oriented providing algorithm to a social event. This allows more appropriate information to be provided by applying different providing algorithms depending on the calendar category. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input calendar category data to the generation AI and cause the generation AI to select a providing algorithm.

[0097] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize providing important information. Furthermore, if the user is relaxed, the providing unit can prioritize providing normal information. Furthermore, if the user is in a hurry, the providing unit can prioritize providing urgent information. Thus, by determining the priority of information provision according to the user's emotions, important information can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0098] The providing unit can determine the priority of provision based on the submission time of the calendar at the time of provision. For example, the providing unit provides information preferentially for upcoming events. The providing unit can also provide information later for events in the distant future. Furthermore, the providing unit can provide information for past events as needed. In this way, by determining the priority of provision based on the submission time of the calendar, information is provided preferentially for upcoming events. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input calendar submission time data to the generation AI and cause the generation AI to determine the priority of provision.

[0099] The providing unit can adjust the order of provision based on the relevance of the calendar when providing the information. For example, the providing unit provides information preferentially for events with important relevance. The providing unit can also provide information in a standard manner for events with normal relevance. Furthermore, the providing unit can also provide information later for events with low relevance. In this way, by adjusting the order of provision based on the relevance of the calendar, information is provided preferentially for events with important relevance. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input calendar relevance data to the generating AI and cause the generating AI to adjust the order of provision.

[0100] The voice receiving unit can estimate the user's emotions and adjust the timing of receiving voice instructions based on the estimated user emotions. For example, when the user is stressed, the voice receiving unit can provide a simple voice interface and speed up the timing of receiving voice instructions. Furthermore, when the user is relaxed, the voice receiving unit can provide detailed voice input options and flexibly adjust the timing of receiving voice instructions. Furthermore, when the user is in a hurry, the voice receiving unit can prioritize voice input and quickly accept voice instructions. This allows voice instructions to be accepted at a more appropriate time by adjusting the timing of receiving voice instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the voice receiving unit can be performed using AI, for example, or without AI. For example, the voice receiving unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0101] When receiving a voice instruction, the voice receiving unit can analyze the user's past voice instruction history and select the optimal reception method. For example, the voice receiving unit can prioritize and suggest a voice instruction method that the user has frequently used in the past. The voice receiving unit can also predict and suggest a voice instruction method to be used in a specific time period based on the user's past voice instruction history. Furthermore, the voice receiving unit can automatically select the optimal reception method based on the voice content previously input by the user. In this way, by analyzing the past voice instruction history, the optimal reception method for the user can be provided. Some or all of the above-described processing in the voice receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice receiving unit can input the user's voice instruction history data to a generation AI and have the generation AI select the optimal reception method.

[0102] The voice receiving unit can estimate the user's emotions and determine the priority of voice instructions based on the estimated user emotions. For example, when the user is nervous, the voice receiving unit can prioritize important voice instructions. Furthermore, when the user is relaxed, the voice receiving unit can also prioritize normal voice instructions. Furthermore, when the user is in a hurry, the voice receiving unit can also prioritize urgent voice instructions. Thus, by determining the priority of voice instructions according to the user's emotions, important voice instructions can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the voice receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice receiving unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0103] When receiving a voice instruction, the voice receiving unit can prioritize receiving a highly relevant voice instruction by taking into account the user's geographical location information. For example, when the user is in a specific location, the voice receiving unit can prioritize receiving a voice instruction related to that location. Furthermore, when the user is traveling, the voice receiving unit can also prioritize receiving a voice instruction related to the user's destination. Furthermore, when the user is at home, the voice receiving unit can also prioritize receiving a voice instruction related to the user's home. In this way, by taking the user's geographical location information into account, highly relevant voice instructions can be prioritized. Some or all of the above-described processing in the voice receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the voice receiving unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant voice instructions.

[0104] The text accepting unit can estimate the user's emotions and adjust the timing of accepting text instructions based on the estimated user emotions. For example, when the user is stressed, the text accepting unit can provide a simple text interface and speed up the timing of accepting text instructions. Furthermore, when the user is relaxed, the text accepting unit can provide detailed text input options and flexibly adjust the timing of accepting text instructions. Furthermore, when the user is in a hurry, the text accepting unit can prioritize text input and quickly accept text instructions. This allows the timing of accepting text instructions to be adjusted according to the user's emotions, thereby accepting text instructions at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the text accepting unit can be performed using, for example, an AI. For example, the text accepting unit can input the user's emotion data into the generation AI and have the generation AI execute emotion estimation.

[0105] When receiving a text instruction, the text receiving unit can analyze the user's past text instruction history and select the optimal reception method. For example, the text receiving unit can preferentially suggest text instruction methods that the user has frequently used in the past. The text receiving unit can also predict and suggest a text instruction method to be used during a specific time period based on the user's past text instruction history. Furthermore, the text receiving unit can automatically select the optimal reception method based on text content previously input by the user. In this way, by analyzing the past text instruction history, the optimal reception method for a text instruction can be provided to the user. Some or all of the above-described processing in the text receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the text receiving unit can input the user's text instruction history data to a generation AI and cause the generation AI to select the optimal reception method.

[0106] The text accepting unit can estimate the user's emotions and determine the priority of text instructions based on the estimated user emotions. For example, when the user is nervous, the text accepting unit can prioritize receiving important text instructions. Furthermore, when the user is relaxed, the text accepting unit can also prioritize receiving normal text instructions. Furthermore, when the user is in a hurry, the text accepting unit can also prioritize receiving urgent text instructions. Thus, by determining the priority of text instructions according to the user's emotions, important text instructions can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the text accepting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the text accepting unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0107] When receiving a text instruction, the text receiving unit can prioritize receiving a highly relevant text instruction by taking into account the user's geographical location information. For example, when the user is in a specific location, the text receiving unit can prioritize receiving a text instruction related to that location. Furthermore, when the user is traveling, the text receiving unit can also prioritize receiving a text instruction related to the user's destination. Furthermore, when the user is at home, the text receiving unit can also prioritize receiving a text instruction related to the user's home. In this way, by taking the user's geographical location information into account, highly relevant text instructions can be prioritized. Some or all of the above-described processing in the text receiving unit can be performed using AI, for example, or without using AI. For example, the text receiving unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant text instructions.

[0108] The natural language processing unit can estimate the user's emotions and adjust the natural language processing method based on the estimated user emotions. For example, when the user is relaxed, the natural language processing unit can perform detailed natural language processing. Furthermore, when the user is in a hurry, the natural language processing unit can also perform simplified natural language processing. Furthermore, when the user is stressed, the natural language processing unit can also perform natural language processing that focuses on important information. This allows for more appropriate natural language processing by adjusting the natural language processing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the natural language processing unit can be performed using, for example, an AI, or without an AI. For example, the natural language processing unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0109] During natural language processing, the natural language processing unit can select the optimal processing method by referring to the user's past instruction history. For example, the natural language processing unit preferentially suggests natural language processing methods that the user has frequently used in the past. The natural language processing unit can also predict and suggest natural language processing methods to be used during a specific time period based on the user's past instruction history. Furthermore, the natural language processing unit can automatically select the optimal natural language processing method based on content previously input by the user. In this way, by referring to the past instruction history, the optimal natural language processing method can be provided to the user. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit can input the user's instruction history data into a generation AI and cause the generation AI to select the optimal processing method.

[0110] The natural language processing unit can estimate the user's emotions and determine the priority of natural language processing based on the estimated user emotions. For example, if the user is nervous, the natural language processing unit can prioritize important natural language processing. Furthermore, if the user is relaxed, the natural language processing unit can also prioritize normal natural language processing. Furthermore, if the user is in a hurry, the natural language processing unit can also prioritize urgent natural language processing. Thus, by determining the priority of natural language processing according to the user's emotions, important natural language processing can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the natural language processing unit can be performed using, for example, an AI, or without an AI. For example, the natural language processing unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0111] During natural language processing, the natural language processing unit can select the optimal processing method by taking into account the user's geographical location information. For example, when the user is in a specific location, the natural language processing unit can prioritize natural language processing related to that location. Furthermore, when the user is on the move, the natural language processing unit can also prioritize natural language processing related to the user's destination. Furthermore, when the user is at home, the natural language processing unit can also prioritize natural language processing related to the user's home. In this way, by taking the user's geographical location information into account, highly relevant natural language processing can be prioritized. Some or all of the above-mentioned processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit can input the user's geographical location data into a generation AI and cause the generation AI to select the optimal processing method.

[0112] The update unit can estimate the user's emotions and adjust the timing of calendar updates based on the estimated user emotions. For example, the update unit can perform detailed updates when the user is relaxed. Furthermore, the update unit can also perform simplified updates when the user is in a hurry. Furthermore, the update unit can perform updates that focus on important information when the user is stressed. By adjusting the calendar update timing according to the user's emotions, the calendar can be updated at a more appropriate time. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using, for example, an AI, or without an AI. For example, the update unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0113] When updating the calendar, the update unit can select the optimal update method by referring to the user's past update history. For example, the update unit can prioritize and suggest update methods that the user has frequently used in the past. The update unit can also predict and suggest update methods to be used during specific time periods based on the user's past update history. Furthermore, the update unit can automatically select the optimal update method based on content previously input by the user. In this way, by referring to the past update history, the optimal calendar update method can be provided to the user. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the user's update history data into a generation AI and have the generation AI select the optimal update method.

[0114] The update unit can estimate the user's emotions and determine the priority of calendar updates based on the estimated user emotions. For example, if the user is nervous, the update unit can prioritize important updates. Furthermore, if the user is relaxed, the update unit can prioritize regular updates. Furthermore, if the user is in a hurry, the update unit can prioritize urgent updates. Thus, by determining the priority of calendar updates according to the user's emotions, important updates can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using, for example, an AI, or without an AI. For example, the update unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0115] When updating the calendar, the update unit can select the optimal update method by taking into account the user's geographical location information. For example, when the user is in a specific location, the update unit can prioritize updates related to that location. Furthermore, when the user is traveling, the update unit can also prioritize updates related to the user's destination. Furthermore, when the user is at home, the update unit can also prioritize updates related to the user's home. In this way, by taking the user's geographical location information into account, calendar updates with high relevance can be prioritized. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal update method.

[0116] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user emotions. For example, the customization unit can provide detailed customization when the user is relaxed. The customization unit can also provide simplified customization when the user is in a hurry. Furthermore, the customization unit can provide customization that focuses on important information when the user is stressed. This allows for more appropriate customization by adjusting the customization method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the customization unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0117] During customization, the customization unit can select the optimal customization method by referring to the user's past customization history. For example, the customization unit can prioritize and suggest customization methods that the user has frequently used in the past. The customization unit can also predict and suggest a customization method to be used during a specific time period based on the user's past customization history. Furthermore, the customization unit can automatically select the optimal customization method based on content previously input by the user. In this way, by referring to the past customization history, the optimal customization method can be provided to the user. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's customization history data into a generation AI and cause the generation AI to select the optimal customization method.

[0118] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user emotions. For example, if the user is nervous, the customization unit can prioritize important customization. Furthermore, if the user is relaxed, the customization unit can prioritize normal customization. Furthermore, if the user is in a hurry, the customization unit can prioritize urgent customization. Thus, by determining the priority of customization according to the user's emotions, important customization can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the customization unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0119] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. For example, when the user is in a specific location, the customization unit can prioritize customization related to that location. Furthermore, when the user is traveling, the customization unit can also prioritize customization related to the user's destination. Furthermore, when the user is at home, the customization unit can also prioritize customization related to the user's home. In this way, by taking the user's geographical location information into account, highly relevant customization can be prioritized. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal customization method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, summarization unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user instructions using the microphone 38B or touch panel 38A of the smart device 14. The analysis unit analyzes calendar information using the specific processing unit 290 of the data processing device 12. The summarization unit summarizes the information analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides information using the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, summarization unit, and provision unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's instruction using the microphone 238 of the smart glasses 214. The analysis unit analyzes calendar information using the specific processing unit 290 of the data processing device 12. The summarization unit summarizes the information analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides the information using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, summarization unit, and provision unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a user's instruction using the microphone 238 of the headset type terminal 314. The analysis unit analyzes calendar information using the specific processing unit 290 of the data processing device 12. The summarization unit summarizes the information analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides the information using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, summarization unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's instruction using the microphone 238 of the robot 414. The analysis unit analyzes calendar information using the specific processing unit 290 of the data processing device 12. The summarization unit summarizes the information analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides information using the speaker 240 of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The analysis unit can analyze the user's past schedule history and make future schedule suggestions. For example, it can analyze patterns of events and meetings that the user frequently attended in the past and predict and suggest similar events in the future. The analysis unit can also analyze the reasons why the user canceled an event in the past and make suggestions to avoid similar situations. Furthermore, the analysis unit can also suggest the optimal schedule for a specific time period based on the user's past schedule history. This makes it possible to suggest schedules that take into account the user's past behavioral patterns.

[0122] The voice receiving unit can analyze the user's voice tone and speed to estimate the user's emotional state. For example, if the user is in a hurry, the voice tone is likely to be high and the speed is likely to be fast. In this case, the voice receiving unit adjusts the voice to quickly accept instructions. Also, if the user is relaxed, the voice tone is likely to be low and the speed is likely to be slow. In this case, the voice receiving unit can provide detailed input options. Furthermore, if the user is stressed, the voice tone is likely to be unstable. In this case, the voice receiving unit can provide a simple interface and quickly accept instructions. This enables more appropriate voice instructions to be accepted based on the user's voice tone and speed.

[0123] The text accepting unit can analyze the user's input speed and input content to estimate the user's emotional state. For example, if the user is in a hurry, the input speed is fast and instructions are often given in short sentences. In this case, the text accepting unit adjusts to accept instructions quickly. Also, if the user is relaxed, the input speed is slow and instructions are often given in detailed sentences. In this case, the text accepting unit can provide detailed input options. Furthermore, if the user is stressed, the input content is often unstable. In this case, the text accepting unit provides a simple interface and can accept instructions quickly. This makes it possible to accept more appropriate text instructions based on the user's input speed and content.

[0124] The natural language processor can provide additional related information based on the user's instructions. For example, if the user instructs, "Tell me about next week's meeting schedules," the natural language processor can provide detailed information about the meeting (date, time, location, and participants), as well as related materials and minutes of past meetings. Also, if the user instructs, "Tell me about my next vacation schedule," the natural language processor can provide detailed information about the vacation (start date, end date, and destination), as well as related travel information and weather forecasts. Furthermore, if the user instructs, "Tell me about this week's tasks," the natural language processor can provide detailed information about the task (task name, deadline, and priority), as well as related resources and reference materials. This makes it possible to provide more comprehensive information based on the user's instructions.

[0125] The update unit can automatically set reminders based on the user's schedule. For example, if the user schedules an important meeting, the update unit can set a reminder the day before the meeting. If the user schedules a trip, the update unit can set a reminder one week before departure. Furthermore, if the user schedules a recurring task, the update unit can also set a reminder the day before the task. This allows the user to receive reminders at the appropriate time so that they do not forget important events or tasks.

[0126] The customization unit can adjust the design of the interface based on the emotional state of the user. For example, if the user is relaxed, the customization unit can provide a colorful and detailed interface. If the user is in a hurry, the customization unit can provide a simple and intuitive interface. Furthermore, if the user is stressed, the customization unit can provide an interface with calm colors and a design that focuses on important information. This makes it possible to provide a more comfortable interface depending on the emotional state of the user.

[0127] The reception unit can suggest an optimal instruction reception method based on the user's current activity status. For example, if the user is driving, the reception unit can preferentially suggest voice input. If the user is in a meeting, the reception unit can preferentially suggest text input. Furthermore, if the user is relaxed, the reception unit can also provide detailed input options. This makes it possible to provide an optimal instruction reception method according to the user's current activity status.

[0128] The analysis unit can suggest optimal break times based on the user's schedule. For example, if the user has scheduled consecutive meetings, the analysis unit can suggest appropriate break times. Also, if the user plans to work for a long period of time, the analysis unit can suggest regular breaks. Furthermore, if the user is feeling stressed, the analysis unit can also suggest break times for relaxation. In this way, appropriate break times can be provided based on the user's schedule.

[0129] The providing unit can provide relevant news and information based on the user's schedule. For example, if the user has a business meeting scheduled, the providing unit can provide relevant industry news and market trends. If the user has a trip scheduled, the providing unit can provide tourist information and weather forecasts for the destination. Furthermore, if the user has a health checkup scheduled, the providing unit can also provide the latest health information and advice. This makes it possible to provide relevant information based on the user's schedule.

[0130] The providing unit can adjust the way information is provided based on the emotional state of the user. For example, if the user is relaxed, the providing unit can provide detailed information. If the user is in a hurry, the providing unit can provide concise information. Furthermore, if the user is feeling stressed, the providing unit can provide information that focuses on important information. This allows more appropriate information to be provided according to the emotional state of the user.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit receives instructions from the user, including voice instructions, text instructions, gesture instructions, and the like. Step 2: The analysis unit analyzes the calendar information based on the instructions received by the reception unit. The calendar information includes appointments, events, reminders, etc., and the analysis is performed using methods such as natural language processing, data mining, and machine learning algorithms. Step 3: The summarization unit summarizes the information analyzed by the analysis unit. The summarization is based on the length of the text and the importance of the information being summarized. Step 4: The providing unit provides the information summarized by the summarizing unit by a method such as displaying the information on a screen, outputting the information by voice, or sending an email.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a 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.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives instructions from a user; an analysis unit that analyzes calendar information based on the instruction received by the reception unit; a summarizing unit that summarizes the information analyzed by the analyzing unit; a providing unit that provides the information summarized by the summarizing unit; Equipped with A system characterized by:

2. A voice input receiving unit is provided.

2. The system of claim 1.

3. A text input unit is provided to accept text input.

2. The system of claim 1.

4. Equipped with a natural language processing unit that processes natural language 2. The system of claim 1.

5. Equipped with an update section that periodically updates calendar data 2. The system of claim 1.

6. It has a customization section that customizes information according to the user's preferences.

2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of accepting instructions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past instruction history and select the optimal reception method 2. The system of claim 1.

9. The reception unit When receiving instructions, filter them based on the user's current situation and interests.

2. The system of claim 1.

Images