system

Abstract

The system according to this embodiment aims to efficiently coordinate schedules. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a provision unit, and a reflection unit. The collection unit collects calendar information for each user. The analysis unit analyzes the calendar information collected by the collection unit and extracts common free time. The proposal unit proposes adjustable time slots based on the common free time extracted by the analysis unit. The provision unit provides the user with the adjustable time slots proposed by the proposal unit. The reflection unit automatically reflects the adjusted schedule provided by the provision unit in the calendar.

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 technologies presented challenges in scheduling, as it was time-consuming and difficult to do efficiently.

[0005] The system according to this embodiment aims to efficiently coordinate schedules. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a provision unit, and a reflection unit. The collection unit collects calendar information for each user. The analysis unit analyzes the calendar information collected by the collection unit and extracts common free time. The proposal unit proposes adjustable time slots based on the common free time extracted by the analysis unit. The provision unit provides the user with the adjustable time slots proposed by the proposal unit. The reflection unit automatically reflects the adjusted schedule provided by the provision unit in the calendar. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently adjust schedules. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A schedule adjustment system according to an embodiment of the present invention utilizes AI to streamline schedule adjustments. This schedule adjustment system collects each user's calendar information, analyzes the collected calendar information, and extracts common free time slots. Furthermore, even if a schedule appears to be full, the AI ​​analyzes whether it is possible to reschedule and suggests possible time slots. This allows users to efficiently adjust their schedules. For example, when finding free time for everyone to attend a meeting, the schedule adjustment system collects each user's calendar information and extracts common free time slots. Even if no common free time slots are found, the AI ​​analyzes each user's schedule and suggests possible time slots. This allows users to reduce unnecessary communication and efficiently adjust their schedules. Furthermore, even if a schedule is private, the AI ​​analyzes the schedule content and determines whether it is possible to reschedule. For example, if a task slot is set, the AI ​​determines whether the task can be moved to another time slot and suggests possible time slots. This allows users to efficiently adjust their schedules, regardless of whether the schedule is public or private. In this way, the schedule adjustment system utilizes AI to streamline schedule adjustments and frees users from tedious tasks. This frees users from the stressful task of scheduling, allowing them to focus on more important work. It also enables the scheduling system to efficiently collect, analyze, suggest, provide, and incorporate users' calendar information.

[0029] The scheduling system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and a reflection unit. The collection unit collects calendar information for each user. Calendar information includes, but is not limited to, the title of an appointment, date and time, location, and participants. The collection unit can acquire data using, for example, an API. The collection unit can also collect calendar information manually entered by the user. Furthermore, the collection unit can also collect calendar information using sensor data. For example, the collection unit can acquire location information from the user's smartphone and collect calendar information based on that information. The analysis unit analyzes the calendar information collected by the collection unit and extracts common free time slots. The analysis unit can, for example, use a clustering algorithm to extract common free time slots. The analysis unit can also use regression analysis to extract common free time slots. Furthermore, the analysis unit can also use a classification algorithm to extract common free time slots. The proposal unit proposes adjustable time slots based on the common free time slots extracted by the analysis unit. The proposal unit can, for example, use a recommendation system to select adjustable time slots. Furthermore, the proposal unit may use an optimization algorithm to select adjustable time slots. In addition, the proposal unit may use a heuristic algorithm to select adjustable time slots. The delivery unit provides the user with the adjustable time slots proposed by the proposal unit. The delivery unit may use push notifications as a notification method, for example. The delivery unit may also use email as a notification method. In addition, the delivery unit may also use SMS as a notification method. The reflection unit automatically reflects the adjusted schedule provided by the delivery unit into the calendar. The reflection unit may use an API as a method for automatic calendar entry, for example. The reflection unit may also assist the user with manual entry as a method for automatic calendar entry. In addition, the reflection unit may also use sensor data as a method for automatic calendar entry.As a result, the schedule adjustment system according to the embodiment can efficiently collect, analyze, propose, provide, and reflect the user's calendar information.

[0030] The data collection unit can analyze the user's past calendar information and select the optimal collection method. For example, the data collection unit can prioritize collecting information from calendar apps that the user has frequently used in the past. The data collection unit can also select a method for collecting data at specific time periods based on the user's past calendar information. Furthermore, the data collection unit can analyze the user's past calendar information and propose the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past calendar information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past calendar information into a generating AI and have the generating AI select the optimal collection method.

[0031] The data collection unit can filter calendar information based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting calendar information related to projects the user is currently working on. The data collection unit can also filter and collect relevant calendar information based on the user's areas of interest. Furthermore, if the user is focused on a specific project, the data collection unit can collect only information related to that project. This allows for the collection of highly relevant information by filtering calendar information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's project information and areas of interest into a generating AI and have the generating AI perform the filtering.

[0032] When collecting calendar information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting calendar information related to the user's current location. Furthermore, if the user plans to move to a specific area, the collection unit can also collect information related to that area. Furthermore, the collection unit can also collect the most relevant calendar information based on the user's geographical location information. In this way, highly relevant calendar information can be collected by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0033] The collection unit can analyze the user's social media activities and collect related information when collecting calendar information. For example, the collection unit reflects event information shared by the user on social media in the calendar. The collection unit can also collect related calendar information from the user's social media activities. The collection unit can also preferentially collect event information that the user plans to attend on social media. This makes it possible to collect related calendar information by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the calendar information. For example, the analysis unit performs a detailed analysis of calendar information with high importance. The analysis unit can also perform a simplified analysis of calendar information with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the calendar information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the calendar information. 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 the importance of the calendar information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of calendar information. For example, the analysis unit can apply an analysis algorithm depending on the importance of a meeting to meeting information. The analysis unit can also apply an analysis algorithm depending on the progress of a project to project information. The analysis unit can also apply an analysis algorithm that takes privacy into consideration to personal schedules. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the category of calendar information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the category of calendar information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the calendar information. For example, the analysis unit prioritizes analysis of the most recent calendar information. The analysis unit can also postpone calendar information submitted earlier. The analysis unit can also dynamically adjust the priority of analysis based on the submission time. This enables efficient analysis by determining the priority of analysis based on the submission time of the calendar information. 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 the submission time of the calendar information to the generation AI and have the generation AI determine the priority of analysis.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the calendar information. For example, the analysis unit prioritizes analysis of highly relevant calendar information. The analysis unit can also postpone analysis of less relevant calendar information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the calendar information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the calendar information. 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 the relevance of the calendar information to the generation AI and have the generation AI adjust the order of analysis.

[0038] The suggestion unit can adjust the level of detail of the proposal based on the importance of the adjustable time slot when making a proposal. For example, the suggestion unit makes a detailed proposal for a time slot with high importance. The suggestion unit can also make a simplified proposal for a time slot with low importance. The suggestion unit can also dynamically adjust the level of detail of the proposal according to the importance of the adjustable time slot. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the adjustable time slot. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the importance of the adjustable time slot to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0039] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the category of the adjustable time slot. For example, the suggestion unit can apply a proposal algorithm based on the importance of a meeting to a meeting time slot. The suggestion unit can also apply a proposal algorithm based on the progress of a project to a project time slot. The suggestion unit can also apply a proposal algorithm that takes privacy into consideration to personal schedules. This enables highly accurate proposals by applying an appropriate proposal algorithm depending on the category of the adjustable time slot. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the category of the adjustable time slot into a generation AI and cause the generation AI to apply a proposal algorithm.

[0040] The proposal department can determine the priority of proposals based on the submission timing of the available time slots. For example, the proposal department will prioritize proposals for the most recent available time slots. It can also postpone proposals for older available time slots. Furthermore, the proposal department can dynamically adjust the priority of proposals based on the submission timing. This enables efficient proposals by determining the priority of proposals based on the submission timing of the available time slots. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of the available time slots into a generating AI and have the generating AI determine the priority of proposals.

[0041] The proposal unit can adjust the order of proposals based on the relevance of the adjustable time slots. For example, the proposal unit will prioritize proposals for highly relevant adjustable time slots. It can also postpone proposals for less relevant adjustable time slots. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the adjustable time slots. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the adjustable time slots. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the adjustable time slots into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0042] The service provider can select an appropriate display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods previously used by the user. The service provider can also select the optimal display method from the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and propose the most efficient display method. This allows the service provider to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's operation history data into a generating AI and have the generating AI perform the selection of the display method.

[0043] The service provider can select an appropriate display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Also, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the display method.

[0044] When reflecting, the reflection unit can select an appropriate reflection method by referring to the user's past calendar information. For example, the reflection unit can prioritize the reflection method that the user has used in the past. The reflection unit can also select the optimal reflection method from the user's past calendar information. The reflection unit can also analyze the user's past calendar information and suggest the most efficient reflection method. In this way, the optimal reflection method can be selected by referring to the user's past calendar information. Some or all of the above-mentioned processing in the reflection unit can be performed using, for example, AI, or can be performed without using AI. For example, the reflection unit can input the user's past calendar information to the generation AI and have the generation AI select the reflection method.

[0045] When reflecting, the reflection unit can select an appropriate reflection method by taking into account the user's geographical location information. For example, the reflection unit prioritizes reflecting calendar information related to the user's current location. Furthermore, if the user plans to move to a specific area, the reflection unit can also reflect information related to that area. Furthermore, the reflection unit can also reflect the most relevant calendar information based on the user's geographical location information. This makes it possible to provide an optimal reflection method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input the user's geographical location information to the generation AI and cause the generation AI to select the reflection method.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection unit can collect user health data and adjust the timing of calendar information collection based on the user's health status. For example, if the user is tired, the collection timing can be delayed until the user has rested. Conversely, if the user is in good health, calendar information can be collected immediately, and analysis can begin quickly. Furthermore, if the user is ill, the collection timing can be adjusted to minimize the user's burden. In this way, the user's burden can be reduced by adjusting the timing of calendar information collection according to the user's health status.

[0048] The proposal department can also analyze users' past behavior patterns and select the most suitable proposal method. For example, it can prioritize providing proposal methods that users have frequently used in the past. It can also select proposal methods for specific time periods based on users' past behavior patterns. Furthermore, it can analyze users' past behavior patterns and propose the most efficient proposal method. In this way, the optimal proposal method can be selected by analyzing users' past behavior patterns.

[0049] The update function can analyze the user's past update history and select the optimal update method. For example, it can prioritize providing update methods that the user has frequently used in the past. It can also select update methods for specific time periods based on the user's past update history. Furthermore, it can analyze the user's past update history and suggest the most efficient update method. In this way, the optimal update method can be selected by analyzing the user's past update history.

[0050] The data collection unit can also analyze the user's device usage and select the optimal data collection method. For example, if a user frequently uses a smartphone, it can prioritize collecting information from the smartphone. Similarly, if the user uses a tablet, it can prioritize collecting information from the tablet. Furthermore, if the user uses a desktop computer, it can prioritize collecting information from the desktop. This allows the system to select the most suitable data collection method by analyzing the user's device usage.

[0051] The suggestion unit can also analyze the user's social media activity and select the optimal suggestion method. For example, suggestions can be made based on event information shared by the user on social media. It can also make related suggestions based on the user's social media activity. It can also prioritize suggestions based on event information that the user plans to attend on social media. In this way, the optimal suggestion method can be selected by analyzing the user's social media activity.

[0052] The reflection unit can also determine the priority of reflection by taking into account the user's geographical location information. For example, calendar information related to the user's current location can be reflected with priority. Also, if the user plans to move to a specific area, information related to that area can be reflected with priority. Furthermore, the most relevant calendar information can be reflected with priority based on the user's geographical location information. In this way, an optimal reflection method can be provided by taking into account the user's geographical location information.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection unit collects calendar information for each user. Calendar information includes the title, date, time, location, and participants of events. The collection unit can acquire data using an API, or it can collect calendar information using calendar information manually entered by the user or sensor data. For example, it can acquire location information from the user's smartphone and collect calendar information based on that information. Step 2: The analysis unit analyzes the calendar information collected by the collection unit and extracts common free time. The analysis unit can use a clustering algorithm, regression analysis, classification algorithm, etc. to extract common free time. Step 3: The suggestion unit suggests adjustable time slots based on the common available time slots extracted by the analysis unit. The suggestion unit may use a recommendation system, an optimization algorithm, a heuristic algorithm, or the like to select adjustable time slots. Step 4: The providing unit provides the user with the adjustable time slot proposed by the proposing unit. The providing unit may use a push notification, email, SMS, or the like as a notification method. Step 5: The reflecting unit automatically reflects the adjusted schedule provided by the providing unit in the calendar. The reflecting unit can use an API as a method for automatically inputting the schedule into the calendar, or can support manual input by the user. Sensor data can also be used.

[0055] (Example 2) A schedule adjustment system according to an embodiment of the present invention utilizes AI to streamline schedule adjustments. This schedule adjustment system collects each user's calendar information, analyzes the collected calendar information, and extracts common free time slots. Furthermore, even if a schedule appears to be full, the AI ​​analyzes whether it is possible to reschedule and suggests possible time slots. This allows users to efficiently adjust their schedules. For example, when finding free time for everyone to attend a meeting, the schedule adjustment system collects each user's calendar information and extracts common free time slots. Even if no common free time slots are found, the AI ​​analyzes each user's schedule and suggests possible time slots. This allows users to reduce unnecessary communication and efficiently adjust their schedules. Furthermore, even if a schedule is private, the AI ​​analyzes the schedule content and determines whether it is possible to reschedule. For example, if a task slot is set, the AI ​​determines whether the task can be moved to another time slot and suggests possible time slots. This allows users to efficiently adjust their schedules, regardless of whether the schedule is public or private. In this way, the schedule adjustment system utilizes AI to streamline schedule adjustments and frees users from tedious tasks. This frees users from the stressful task of scheduling, allowing them to focus on more important work. It also enables the scheduling system to efficiently collect, analyze, suggest, provide, and incorporate users' calendar information.

[0056] The scheduling system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and a reflection unit. The collection unit collects calendar information for each user. Calendar information includes, but is not limited to, the title of an appointment, date and time, location, and participants. The collection unit can acquire data using, for example, an API. The collection unit can also collect calendar information manually entered by the user. Furthermore, the collection unit can also collect calendar information using sensor data. For example, the collection unit can acquire location information from the user's smartphone and collect calendar information based on that information. The analysis unit analyzes the calendar information collected by the collection unit and extracts common free time slots. The analysis unit can, for example, use a clustering algorithm to extract common free time slots. The analysis unit can also use regression analysis to extract common free time slots. Furthermore, the analysis unit can also use a classification algorithm to extract common free time slots. The proposal unit proposes adjustable time slots based on the common free time slots extracted by the analysis unit. The proposal unit can, for example, use a recommendation system to select adjustable time slots. Furthermore, the proposal unit may use an optimization algorithm to select adjustable time slots. In addition, the proposal unit may use a heuristic algorithm to select adjustable time slots. The delivery unit provides the user with the adjustable time slots proposed by the proposal unit. The delivery unit may use push notifications as a notification method, for example. The delivery unit may also use email as a notification method. In addition, the delivery unit may also use SMS as a notification method. The reflection unit automatically reflects the adjusted schedule provided by the delivery unit into the calendar. The reflection unit may use an API as a method for automatic calendar entry, for example. The reflection unit may also assist the user with manual entry as a method for automatic calendar entry. In addition, the reflection unit may also use sensor data as a method for automatic calendar entry.As a result, the schedule adjustment system according to the embodiment can efficiently collect, analyze, propose, provide, and reflect the user's calendar information.

[0057] The collection unit can estimate the user's emotions and adjust the timing of collecting calendar information based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit delays the collection timing and collects calendar information during a time when the user is relaxed. Furthermore, when the user is relaxed, the collection unit can immediately collect calendar information and quickly start analysis. Furthermore, when the user is busy, the collection unit can adjust the collection timing to minimize the burden on the user. This reduces the burden on the user by adjusting the timing of collecting calendar information according to the user's emotions. 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0058] The data collection unit can analyze the user's past calendar information and select the optimal collection method. For example, the data collection unit can prioritize collecting information from calendar apps that the user has frequently used in the past. The data collection unit can also select a method for collecting data at specific time periods based on the user's past calendar information. Furthermore, the data collection unit can analyze the user's past calendar information and propose the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past calendar information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past calendar information into a generating AI and have the generating AI select the optimal collection method.

[0059] The data collection unit can filter calendar information based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting calendar information related to projects the user is currently working on. The data collection unit can also filter and collect relevant calendar information based on the user's areas of interest. Furthermore, if the user is focused on a specific project, the data collection unit can collect only information related to that project. This allows for the collection of highly relevant information by filtering calendar information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's project information and areas of interest into a generating AI and have the generating AI perform the filtering.

[0060] The collection unit can estimate the user's emotions and determine the priority of calendar information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit postpones collecting calendar information of low importance. Furthermore, when the user is relaxed, the collection unit can collect all calendar information evenly. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting calendar information of high importance. Thus, by determining the priority of calendar information according to the user's emotions, important information can be collected preferentially. 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0061] When collecting calendar information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting calendar information related to the user's current location. Furthermore, if the user plans to move to a specific area, the collection unit can also collect information related to that area. Furthermore, the collection unit can also collect the most relevant calendar information based on the user's geographical location information. In this way, highly relevant calendar information can be collected by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0062] The collection unit can analyze the user's social media activities and collect related information when collecting calendar information. For example, the collection unit reflects event information shared by the user on social media in the calendar. The collection unit can also collect related calendar information from the user's social media activities. The collection unit can also preferentially collect event information that the user plans to attend on social media. This makes it possible to collect related calendar information by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0063] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the calendar information. For example, the analysis unit performs a detailed analysis of calendar information with high importance. The analysis unit can also perform a simplified analysis of calendar information with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the calendar information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the calendar information. 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 the importance of the calendar information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0065] During analysis, the analysis unit can apply different analysis algorithms depending on the category of calendar information. For example, the analysis unit can apply an analysis algorithm depending on the importance of a meeting to meeting information. The analysis unit can also apply an analysis algorithm depending on the progress of a project to project information. The analysis unit can also apply an analysis algorithm that takes privacy into consideration to personal schedules. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the category of calendar information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the category of calendar information to the generation AI and cause the generation AI to apply the analysis algorithm.

[0066] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the calendar information. For example, the analysis unit prioritizes analysis of the most recent calendar information. The analysis unit can also postpone calendar information submitted earlier. The analysis unit can also dynamically adjust the priority of analysis based on the submission time. This enables efficient analysis by determining the priority of analysis based on the submission time of the calendar information. 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 the submission time of the calendar information to the generation AI and have the generation AI determine the priority of analysis.

[0068] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the calendar information. For example, the analysis unit prioritizes analysis of highly relevant calendar information. The analysis unit can also postpone analysis of less relevant calendar information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the calendar information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the calendar information. 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 the relevance of the calendar information to the generation AI and have the generation AI adjust the order of analysis.

[0069] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that are easy to understand by adjusting the way the suggestions are expressed according to the user's emotions. The 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-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0070] The suggestion unit can adjust the level of detail of the proposal based on the importance of the adjustable time slot when making a proposal. For example, the suggestion unit makes a detailed proposal for a time slot with high importance. The suggestion unit can also make a simplified proposal for a time slot with low importance. The suggestion unit can also dynamically adjust the level of detail of the proposal according to the importance of the adjustable time slot. This enables efficient proposals by adjusting the level of detail of the proposal according to the importance of the adjustable time slot. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the importance of the adjustable time slot to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0071] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the category of the adjustable time slot. For example, the suggestion unit can apply a proposal algorithm based on the importance of a meeting to a meeting time slot. The suggestion unit can also apply a proposal algorithm based on the progress of a project to a project time slot. The suggestion unit can also apply a proposal algorithm that takes privacy into consideration to personal schedules. This enables highly accurate proposals by applying an appropriate proposal algorithm depending on the category of the adjustable time slot. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the category of the adjustable time slot into a generation AI and cause the generation AI to apply a proposal algorithm.

[0072] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, the suggestion unit can provide appropriate suggestions to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The proposal department can determine the priority of proposals based on the submission timing of the available time slots. For example, the proposal department will prioritize proposals for the most recent available time slots. It can also postpone proposals for older available time slots. Furthermore, the proposal department can dynamically adjust the priority of proposals based on the submission timing. This enables efficient proposals by determining the priority of proposals based on the submission timing of the available time slots. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of the available time slots into a generating AI and have the generating AI determine the priority of proposals.

[0074] The proposal unit can adjust the order of proposals based on the relevance of the adjustable time slots. For example, the proposal unit will prioritize proposals for highly relevant adjustable time slots. It can also postpone proposals for less relevant adjustable time slots. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the adjustable time slots. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the adjustable time slots. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the adjustable time slots into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0075] The providing unit can estimate the user's emotions and adjust the display method of the provided information based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. By adjusting the display method of the provided information according to the user's emotions, it is possible to provide a display that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 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.

[0076] The service provider can select an appropriate display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods previously used by the user. The service provider can also select the optimal display method from the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and propose the most efficient display method. This allows the service provider to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's operation history data into a generating AI and have the generating AI perform the selection of the display method.

[0077] The providing unit can estimate the user's emotions and adjust the provided operation procedures based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the providing unit can also provide detailed operation procedures. Furthermore, if the user is in a hurry, the providing unit can also provide procedures that can be quickly operated. By adjusting the provided operation procedures according to the user's emotions, it is possible to provide operation procedures that are easy for the user to use. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 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.

[0078] The service provider can select an appropriate display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Also, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the display method.

[0079] The reflection unit can estimate the user's emotions and adjust the reflection method based on the estimated user's emotions. For example, if the user is nervous, the reflection unit can provide a simple and intuitive reflection method. Furthermore, if the user is relaxed, the reflection unit can also provide a detailed reflection method. Furthermore, if the user is in a hurry, the reflection unit can also provide a quick reflection method. By adjusting the reflection method according to the user's emotions, a reflection method that is easy for the user to use 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 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 reflection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reflection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0080] When reflecting, the reflection unit can select an appropriate reflection method by referring to the user's past calendar information. For example, the reflection unit can prioritize the reflection method that the user has used in the past. The reflection unit can also select the optimal reflection method from the user's past calendar information. The reflection unit can also analyze the user's past calendar information and suggest the most efficient reflection method. In this way, the optimal reflection method can be selected by referring to the user's past calendar information. Some or all of the above-mentioned processing in the reflection unit can be performed using, for example, AI, or can be performed without using AI. For example, the reflection unit can input the user's past calendar information to the generation AI and have the generation AI select the reflection method.

[0081] The reflection unit can estimate the user's emotions and determine the priority of reflection based on the estimated user emotions. For example, if the user is nervous, the reflection unit postpones reflection of less important information. Furthermore, if the user is relaxed, the reflection unit can also perform all reflections equally. Furthermore, if the user is in a hurry, the reflection unit can also prioritize reflection of more important information. Thus, by determining the priority of reflections according to the user's emotions, important information can be reflected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reflection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reflection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0082] When reflecting, the reflection unit can select an appropriate reflection method by taking into account the user's geographical location information. For example, the reflection unit prioritizes reflecting calendar information related to the user's current location. Furthermore, if the user plans to move to a specific area, the reflection unit can also reflect information related to that area. Furthermore, the reflection unit can also reflect the most relevant calendar information based on the user's geographical location information. This makes it possible to provide an optimal reflection method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input the user's geographical location information to the generation AI and cause the generation AI to select the reflection method. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, and reflection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects calendar information via the communication I / F 44 of the smart device 14 and analyzes it with the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented with the identification processing unit 290 of the data processing unit 12 and extracts common free time. The proposal unit is implemented with the identification processing unit 290 of the data processing unit 12 and proposes adjustable time slots. The provision unit notifies the user via the output device 40 of the smart device 14. The reflection unit reflects the information in the calendar with the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, and reflection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects calendar information via the communication I / F 44 of the smart glasses 214 and analyzes it by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts common free time. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes adjustable time slots. The provision unit notifies the user via the speaker 240 of the smart glasses 214. The reflection unit reflects the information in the calendar by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, provision unit, and reflection unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects calendar information via the communication I / F 44 of the headset type terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts common free time. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests adjustable time slots. The provision unit notifies the user via the speaker 240 of the headset type terminal 314. The reflection unit reflects the information on the calendar by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, provision unit, and reflection unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects calendar information via the communication I / F 44 of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts common free time. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes adjustable time slots. The provision unit notifies the user via the speaker 240 of the robot 414. The reflection unit reflects the information on the calendar by the control unit 46A of the robot 414.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The data collection unit can collect user health data and adjust the timing of calendar information collection based on the user's health status. For example, if the user is tired, the collection timing can be delayed until the user has rested. Conversely, if the user is in good health, calendar information can be collected immediately, and analysis can begin quickly. Furthermore, if the user is ill, the collection timing can be adjusted to minimize the user's burden. In this way, the user's burden can be reduced by adjusting the timing of calendar information collection according to the user's health status.

[0085] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, it will prioritize analyzing high-priority calendar information. If the user is relaxed, it can analyze all calendar information equally. Furthermore, if the user is in a hurry, it can prioritize analyzing high-priority calendar information. In this way, by determining the priority of analysis according to the user's emotions, important information can be analyzed preferentially.

[0086] The proposal department can also analyze users' past behavior patterns and select the most suitable proposal method. For example, it can prioritize providing proposal methods that users have frequently used in the past. It can also select proposal methods for specific time periods based on users' past behavior patterns. Furthermore, it can analyze users' past behavior patterns and propose the most efficient proposal method. In this way, the optimal proposal method can be selected by analyzing users' past behavior patterns.

[0087] The providing unit can also estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, if the user is feeling stressed, the timing of providing information can be delayed so that the information is provided during a time period when the user is relaxed. Also, if the user is relaxed, the information can be provided immediately. Furthermore, if the user is busy, the timing of providing information can be adjusted to minimize the burden on the user. In this way, the burden on the user can be reduced by adjusting the timing of providing information according to the user's emotions.

[0088] The update function can analyze the user's past update history and select the optimal update method. For example, it can prioritize providing update methods that the user has frequently used in the past. It can also select update methods for specific time periods based on the user's past update history. Furthermore, it can analyze the user's past update history and suggest the most efficient update method. In this way, the optimal update method can be selected by analyzing the user's past update history.

[0089] The data collection unit can also analyze the user's device usage and select the optimal data collection method. For example, if a user frequently uses a smartphone, it can prioritize collecting information from the smartphone. Similarly, if the user uses a tablet, it can prioritize collecting information from the tablet. Furthermore, if the user uses a desktop computer, it can prioritize collecting information from the desktop. This allows the system to select the most suitable data collection method by analyzing the user's device usage.

[0090] The analysis unit can also estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that focus on the main points. In this way, by adjusting the accuracy of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0091] The suggestion unit can also analyze the user's social media activity and select the optimal suggestion method. For example, suggestions can be made based on event information shared by the user on social media. It can also make related suggestions based on the user's social media activity. It can also prioritize suggestions based on event information that the user plans to attend on social media. In this way, the optimal suggestion method can be selected by analyzing the user's social media activity.

[0092] The providing unit can also estimate the user's emotions and adjust the content to be provided based on the estimated user's emotions. For example, if the user is nervous, simple, highly visible content can be provided. If the user is relaxed, detailed content can be provided. Furthermore, if the user is in a hurry, content that focuses on the main points can be provided. In this way, by adjusting the content to be provided according to the user's emotions, it is possible to provide content that is easy for the user to understand.

[0093] The reflection unit can also determine the priority of reflection by taking into account the user's geographical location information. For example, calendar information related to the user's current location can be reflected with priority. Also, if the user plans to move to a specific area, information related to that area can be reflected with priority. Furthermore, the most relevant calendar information can be reflected with priority based on the user's geographical location information. In this way, an optimal reflection method can be provided by taking into account the user's geographical location information.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The collection unit collects calendar information for each user. Calendar information includes the title, date, time, location, and participants of events. The collection unit can acquire data using an API, or it can collect calendar information using calendar information manually entered by the user or sensor data. For example, it can acquire location information from the user's smartphone and collect calendar information based on that information. Step 2: The analysis unit analyzes the calendar information collected by the collection unit and extracts common free time. The analysis unit can use a clustering algorithm, regression analysis, classification algorithm, etc. to extract common free time. Step 3: The suggestion unit suggests adjustable time slots based on the common available time slots extracted by the analysis unit. The suggestion unit may use a recommendation system, an optimization algorithm, a heuristic algorithm, or the like to select adjustable time slots. Step 4: The providing unit provides the user with the adjustable time slot proposed by the proposing unit. The providing unit may use a push notification, email, SMS, or the like as a notification method. Step 5: The reflecting unit automatically reflects the adjusted schedule provided by the providing unit in the calendar. The reflecting unit can use an API as a method for automatically inputting the schedule into the calendar, or can support manual input by the user. Sensor data can also be used.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] 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.

[0167] [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects calendar information of each user; an analysis unit that analyzes the calendar information collected by the collection unit and extracts common free time; a suggestion unit that suggests an adjustable time slot based on the common free time extracted by the analysis unit; a providing unit that provides the user with the adjustable time slot proposed by the proposing unit; a reflection unit that automatically reflects the adjusted schedule provided by the provision unit on a calendar; A system characterized by:

2. The collecting unit To estimate a user's emotion and adjust the timing of collecting calendar information based on the estimated user's emotion. The system of claim 1 .

3. The collecting unit Analyze users' past calendar information and select the appropriate collection method The system of claim 1 .

4. The collecting unit When collecting calendar information, filter it based on the user's current projects and interests. The system of claim 1 .

5. The collecting unit The user's emotions are estimated, and the priority of calendar information to be collected is determined based on the estimated user's emotions. The system of claim 1 .

6. The collecting unit When collecting calendar information, prioritize collecting relevant information based on the user's geographic location. The system of claim 1 .

7. The collecting unit When collecting calendar information, analyze users' social media activity and collect related information The system of claim 1 .

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions The system of claim 1 .

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