system

Abstract

The system according to the embodiment aims to efficiently collect daily business data and infer and organize the next task. [Solution] A system according to an embodiment includes a collection unit, an inference unit, and an organization unit. The collection unit collects daily business data. The inference unit infers the next task based on the data collected by the collection unit. The organization unit organizes and prepares the tasks inferred by the inference unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently collect daily business data and infer and organize the next tasks, so there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect daily business data and infer and organize the next task. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an inference unit, and an organization unit. The collection unit collects daily business data. The inference unit infers the next task based on the data collected by the collection unit. The organization unit organizes and prepares the tasks inferred by the inference unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect daily work data and infer and organize the next task. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business support system according to an embodiment of the present invention collects daily business data, infers the next task, and organizes and prepares it. This business support system collects daily business data (schedules, emails, minutes, etc.) and allows smart subordinates to infer the next task. Specifically, it comprises the following steps: First, collect daily business data. Next, infer the next task based on the collected data. Generative AI technology is used for inference to perform more effective inference. Next, organize and prepare the inferred tasks. Tasks are summarized and preparations are made based on public and internal information. In addition, preparations are enhanced by linking with reliable information sources and noting the source of the information. Linking with reliable information sources is also available as a paid option. For example, daily business data is collected. In this case, data such as schedules, emails, and minutes are collected. Next, the next task is inferred based on the collected data. Generative AI technology is used to infer the next task. For example, the next task to be performed is inferred based on the schedule. Next, organize and prepare the inferred tasks. Tasks are summarized and preparations are made based on public and internal information. For example, preparing materials for the next meeting. Preparation can also be enhanced by linking with reliable information sources and including the source of the information. For example, as a paid option, linking with reliable information sources to obtain the necessary information. This allows the business support system to efficiently collect daily business data, infer, organize, and prepare the next task.

[0029] A business support system according to an embodiment includes a collection unit, an inference unit, and an organization unit. The collection unit collects daily business data. Examples of the daily business data include, but are not limited to, schedules, emails, and minutes. For example, the collection unit can acquire schedules from a calendar, emails from a mail server, and minutes from a document management system. The collection unit can also collect data from external systems using an API. For example, the collection unit acquires schedules using a calendar API, emails using a mail API, and minutes using a document management API. The inference unit uses a generation AI to infer the next task based on the data collected by the collection unit. Inference is performed, for example, based on the schedule, but is not limited to, for example. For example, the inference unit inputs a prompt to the generation AI, such as "Please infer the next task to be performed," and the generation AI infers the next task. The generation AI can perform inference using a text generation AI (e.g., LLM). The inference unit can also perform inference based on not only text data but also image data and audio data using a multimodal generation AI. For example, the inference unit inputs scheduled data into the generation AI and causes it to infer the next task. The organization unit organizes and prepares the tasks inferred by the inference unit. For example, the organization unit summarizes the tasks and prepares them based on public information and internal company information. For example, the organization unit prepares materials for the next meeting. The organization unit also enhances preparation by linking with reliable information sources and including the source of the information. For example, the organization unit, as a paid option, links with reliable information sources to obtain the necessary information. As a result, the business support system according to the embodiment can efficiently collect daily business data, infer the next task, and organize and prepare it.

[0030] The collection unit can collect data on schedules, emails, and minutes. For example, the collection unit acquires schedules from a calendar. For example, the collection unit can acquire schedules using a calendar API. The collection unit can also acquire emails from a mail server. For example, the collection unit can acquire emails using a mail API. The collection unit can also acquire minutes from a document management system. For example, the collection unit can acquire minutes using a document management API. This makes it possible to collect a variety of business data.

[0031] The inference unit can infer the next task based on the schedule. For example, the inference unit uses a generation AI to infer the next task based on the schedule. For example, the inference unit inputs a prompt to the generation AI, such as "Please infer the next task to be performed," and the generation AI infers the next task. The generation AI can perform inference using a text generation AI (for example, LLM). The inference unit can also use a multimodal generation AI to perform inference based on not only text data, but also image data and audio data. For example, the inference unit inputs schedule data to the generation AI and has it infer the next task. This makes it possible to effectively infer the next task based on the schedule.

[0032] The organizing unit can summarize tasks and make preparations based on public information or internal company information. For example, the organizing unit can summarize tasks and make preparations based on public information or internal company information. For example, the organizing unit can prepare materials for an upcoming meeting. The organizing unit can also enhance preparations by linking with reliable information sources and stating the source of the information. For example, as a paid option, the organizing unit can link with reliable information sources to obtain the necessary information. This enables tasks to be summarized and preparations based on the information.

[0033] The organization unit can link with reliable information sources and include the source of the information. For example, the organization unit links with reliable information sources and includes the source of the information. For example, the organization unit links with reliable information sources such as official databases and expert opinions to increase the reliability of the information. Furthermore, the organization unit can further increase the reliability of the information by clearly indicating the source of the information. For example, the organization unit can clearly indicate the source of the information in accordance with a citation format. This allows the organization to link with reliable information sources and increase the reliability of the information.

[0034] The sorting unit can link with highly reliable information sources as a paid option. The sorting unit links with highly reliable information sources as a paid option, for example. For example, the sorting unit links with highly reliable information sources such as the Nikkei Shimbun or SPEEDA to obtain necessary information. The sorting unit also clarifies the fee structure and additional functions provided for the paid option. For example, the sorting unit sets a monthly or annual fee and provides detailed information and expert opinions as additional functions. This allows the paid option to link with even more reliable information sources.

[0035] The collection unit can analyze the user's past data collection history and select an appropriate collection method. The collection unit, for example, analyzes the user's past data collection history and selects the optimal collection method. For example, the collection unit prioritizes collecting types of data that the user has frequently collected in the past. The collection unit can also prioritize and suggest collection methods (manual, API, etc.) that the user has used in the past. The collection unit can also predict and suggest data to be collected in a specific time period based on the user's past collection history. This makes it possible to select the optimal collection method based on the past data collection history.

[0036] The collection unit can filter data based on the user's current project or area of ​​interest when collecting data. For example, the collection unit can preferentially collect data related to a project the user is currently working on. The collection unit can also filter and collect highly relevant data based on the user's area of ​​interest. The collection unit can also filter and collect related data based on keywords set by the user. This makes it possible to filter data based on the user's project or area of ​​interest.

[0037] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data based on the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also collect region-specific data based on the user's geographical location information. Furthermore, when the user is moving, the collection unit can collect optimal data based on the user's current location. This makes it possible to collect highly relevant data based on the user's geographical location information.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activities. The collection unit can also collect related data based on the content posted by accounts the user follows. In this way, related data can be collected based on the user's social media activities.

[0039] The inference unit can adjust the level of detail of the inference based on the importance of the task during inference. The inference unit, for example, uses a generation AI to adjust the level of detail of the inference based on the importance of the task during inference. For example, the inference unit can provide the generation AI with a detailed inference result for a task with high importance. Furthermore, the inference unit can provide the generation AI with a concise inference result for a task with low importance. Furthermore, the inference unit can provide the generation AI with an inference result with an appropriate level of detail for a task with medium importance. This makes it possible to adjust the level of detail of the inference depending on the importance of the task.

[0040] The inference unit can apply different inference algorithms depending on the task category during inference. The inference unit can use, for example, a generative AI to apply different inference algorithms depending on the task category during inference. For example, the inference unit can have the generative AI apply a creative inference algorithm to a creative task. Furthermore, the inference unit can have the generative AI apply a data-driven inference algorithm to an analytical task. Furthermore, the inference unit can have the generative AI apply an efficient inference algorithm to a routine task. This makes it possible to apply the optimal inference algorithm depending on the task category.

[0041] The inference unit can determine the priority of inference based on the submission time of the task during inference. The inference unit, for example, uses a generation AI to determine the priority of inference based on the submission time of the task during inference. For example, the inference unit prioritizes inference for tasks with an approaching submission deadline. Also, the inference unit can postpone inference for tasks with a distant submission deadline. Also, the inference unit can perform inference with a moderate priority for tasks with a medium submission deadline. This makes it possible to determine the priority of inference according to the submission time of the task.

[0042] The inference unit can adjust the order of inference based on the relevance of tasks during inference. The inference unit adjusts the order of inference based on the relevance of tasks during inference, for example, by using a generative AI. For example, the inference unit prioritizes inference for highly relevant tasks. Furthermore, the inference unit can postpone inference for tasks with low relevance. Furthermore, the inference unit can perform inference in an appropriate order for tasks with medium relevance. This makes it possible to adjust the order of inference according to the relevance of tasks.

[0043] When organizing, the organizing unit can analyze the user's past task organization history and select an appropriate organization method. For example, the organizing unit analyzes the user's past task organization history and selects the optimal organization method. For example, the organizing unit preferentially suggests organization methods that the user has used in the past. The organizing unit can also suggest the most efficient organization method from the user's past organization history. The organizing unit can also analyze the user's past organization history and suggest the optimal organization method for a specific time period. This makes it possible to select the optimal organization method based on the user's past task organization history.

[0044] The organizing unit can customize the organizing method based on the user's current living situation when organizing. The organizing unit customizes the organizing method based on the user's current living situation, for example. For example, if the user is busy, the organizing unit can provide a simple organizing method. Also, if the user has time, the organizing unit can provide a detailed organizing method. Also, if the user is concentrating on a specific project, the organizing unit can provide an organizing method that is optimal for that project. In this way, the organizing method can be customized according to the user's living situation.

[0045] When organizing, the organizing unit can select the optimal organizing method taking into account the user's geographical location information. The organizing unit selects the optimal organizing method taking into account, for example, the user's geographical location information. For example, the organizing unit prioritizes organizing tasks related to the user's current location. The organizing unit can also organize region-specific tasks based on the user's geographical location information. Furthermore, when the user is traveling, the organizing unit can organize the optimal tasks based on the user's current location. This makes it possible to select the optimal organizing method based on the user's geographical location information.

[0046] The organizing unit can analyze the user's social media activity and suggest ways to organize it when organizing. For example, the organizing unit analyzes the user's social media activity and suggests ways to organize it. For example, the organizing unit organizes related tasks based on information shared by the user on social media. Furthermore, the organizing unit can organize tasks related to topics of interest from the user's social media activity. Furthermore, the organizing unit can organize related tasks based on the content posted by accounts the user follows. In this way, it is possible to suggest ways to organize it based on the user's social media activity.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The work support system can further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate and sleep patterns, and can adjust the work load based on this. For example, if the user's heart rate is high, tasks can be reallocated to reduce the work load. Also, if the user's sleep pattern is disrupted, important tasks can be postponed to prioritize rest. Furthermore, the health management unit can suggest appropriate times to take breaks based on the user's health condition. This makes it possible to provide work support that takes the user's health condition into consideration.

[0049] The business support system can further include a history analysis unit that analyzes the user's past task completion history and determines the priority of the next task. The history analysis unit, for example, infers that tasks that the user has completed quickly in the past are prioritized. It can also postpone tasks that took the user a long time in the past. Furthermore, the history analysis unit can suggest the best task for a specific time period based on the user's past task completion history. This enables efficient task management based on past history.

[0050] The business support system can further include an activity monitoring unit that monitors the user's current activity status and suggests tasks at appropriate times. For example, if the user is in a meeting, the activity monitoring unit can suggest a task after the meeting ends. Also, if the user is traveling, the activity monitoring unit can suggest a task after the user has completed traveling. Furthermore, if the user is taking a break, the activity monitoring unit can suggest a task after the break ends. This makes it possible to suggest tasks at appropriate times according to the user's activity status.

[0051] The business support system can further include a time prediction unit that analyzes the user's past task completion times and presents an estimated completion time for the next task. The time prediction unit, for example, can set a shorter estimated completion time for a task that the user completed quickly in the past. It can also set a longer estimated completion time for a task that took the user a long time in the past. Furthermore, the time prediction unit can present an optimal estimated completion time for a task in a specific time period based on the user's past task completion times. This enables efficient time management based on past history.

[0052] The business support system can further include a location information utilization unit that proposes optimal tasks by taking into account the user's current geographical location information. For example, if the user is in the office, the location information utilization unit can prioritize and propose tasks to be performed at the office. Also, if the user is on a business trip, the location information utilization unit can propose tasks to be performed at the business trip destination. Furthermore, if the user is at home, the location information utilization unit can also propose tasks to be performed at home. This makes it possible to propose optimal tasks based on 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 daily business data. Daily business data includes, for example, schedules, emails, and meeting minutes. The collection unit can obtain schedules from a calendar, emails from an email server, and meeting minutes from a document management system. The collection unit can also collect data from external systems using APIs. For example, the collection unit can obtain schedules using a calendar API, emails using an email API, and meeting minutes using a document management API. Step 2: The inference unit uses the generation AI to infer the next task based on the data collected by the collection unit. Inference is performed, for example, based on a schedule. The inference unit inputs a prompt to the generation AI, such as "Please infer the next task to be performed," and the generation AI infers the next task. The generation AI can perform inference using a text generation AI (e.g., LLM). The inference unit can also use a multimodal generation AI to perform inference based not only on text data, but also on image data and audio data. Step 3: The organization unit organizes and prepares the tasks inferred by the inference unit. For example, the organization unit summarizes the tasks and prepares them based on public information and internal company information. For example, it prepares materials for the next meeting. The organization unit also enhances preparation by linking with reliable information sources and including the source of the information. For example, as a paid option, it can link with reliable information sources to obtain the necessary information.

[0055] (Example 2) A business support system according to an embodiment of the present invention collects daily business data, infers the next task, and organizes and prepares it. This business support system collects daily business data (schedules, emails, minutes, etc.) and allows smart subordinates to infer the next task. Specifically, it comprises the following steps: First, collect daily business data. Next, infer the next task based on the collected data. Generative AI technology is used for inference to perform more effective inference. Next, organize and prepare the inferred tasks. Tasks are summarized and preparations are made based on public and internal information. In addition, preparations are enhanced by linking with reliable information sources and noting the source of the information. Linking with reliable information sources is also available as a paid option. For example, daily business data is collected. In this case, data such as schedules, emails, and minutes are collected. Next, the next task is inferred based on the collected data. Generative AI technology is used to infer the next task. For example, the next task to be performed is inferred based on the schedule. Next, organize and prepare the inferred tasks. Tasks are summarized and preparations are made based on public and internal information. For example, preparing materials for the next meeting. Preparation can also be enhanced by linking with reliable information sources and including the source of the information. For example, as a paid option, linking with reliable information sources to obtain the necessary information. This allows the business support system to efficiently collect daily business data, infer, organize, and prepare the next task.

[0056] A business support system according to an embodiment includes a collection unit, an inference unit, and an organization unit. The collection unit collects daily business data. Examples of the daily business data include, but are not limited to, schedules, emails, and minutes. For example, the collection unit can acquire schedules from a calendar, emails from a mail server, and minutes from a document management system. The collection unit can also collect data from external systems using an API. For example, the collection unit acquires schedules using a calendar API, emails using a mail API, and minutes using a document management API. The inference unit uses a generation AI to infer the next task based on the data collected by the collection unit. Inference is performed, for example, based on the schedule, but is not limited to, for example. For example, the inference unit inputs a prompt to the generation AI, such as "Please infer the next task to be performed," and the generation AI infers the next task. The generation AI can perform inference using a text generation AI (e.g., LLM). The inference unit can also perform inference based on not only text data but also image data and audio data using a multimodal generation AI. For example, the inference unit inputs scheduled data into the generation AI and causes it to infer the next task. The organization unit organizes and prepares the tasks inferred by the inference unit. For example, the organization unit summarizes the tasks and prepares them based on public information and internal company information. For example, the organization unit prepares materials for the next meeting. The organization unit also enhances preparation by linking with reliable information sources and including the source of the information. For example, the organization unit, as a paid option, links with reliable information sources to obtain the necessary information. As a result, the business support system according to the embodiment can efficiently collect daily business data, infer the next task, and organize and prepare it.

[0057] The collection unit can collect data on schedules, emails, and minutes. For example, the collection unit acquires schedules from a calendar. For example, the collection unit can acquire schedules using a calendar API. The collection unit can also acquire emails from a mail server. For example, the collection unit can acquire emails using a mail API. The collection unit can also acquire minutes from a document management system. For example, the collection unit can acquire minutes using a document management API. This makes it possible to collect a variety of business data.

[0058] The inference unit can infer the next task based on the schedule. For example, the inference unit uses a generation AI to infer the next task based on the schedule. For example, the inference unit inputs a prompt to the generation AI, such as "Please infer the next task to be performed," and the generation AI infers the next task. The generation AI can perform inference using a text generation AI (for example, LLM). The inference unit can also use a multimodal generation AI to perform inference based on not only text data, but also image data and audio data. For example, the inference unit inputs schedule data to the generation AI and has it infer the next task. This makes it possible to effectively infer the next task based on the schedule.

[0059] The organizing unit can summarize tasks and make preparations based on public information or internal company information. For example, the organizing unit can summarize tasks and make preparations based on public information or internal company information. For example, the organizing unit can prepare materials for an upcoming meeting. The organizing unit can also enhance preparations by linking with reliable information sources and stating the source of the information. For example, as a paid option, the organizing unit can link with reliable information sources to obtain the necessary information. This enables tasks to be summarized and preparations based on the information.

[0060] The organization unit can link with reliable information sources and include the source of the information. For example, the organization unit links with reliable information sources and includes the source of the information. For example, the organization unit links with reliable information sources such as official databases and expert opinions to increase the reliability of the information. Furthermore, the organization unit can further increase the reliability of the information by clearly indicating the source of the information. For example, the organization unit can clearly indicate the source of the information in accordance with a citation format. This allows the organization to link with reliable information sources and increase the reliability of the information.

[0061] The sorting unit can link with highly reliable information sources as a paid option. The sorting unit links with highly reliable information sources as a paid option, for example. For example, the sorting unit links with highly reliable information sources such as the Nikkei Shimbun or SPEEDA to obtain necessary information. The sorting unit also clarifies the fee structure and additional functions provided for the paid option. For example, the sorting unit sets a monthly or annual fee and provides detailed information and expert opinions as additional functions. This allows the paid option to link with even more reliable information sources.

[0062] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the timing of collection to reduce the burden. Furthermore, if the user is relaxed, the collection unit can advance the timing of collection to efficiently collect data. Furthermore, if the user is in a hurry, the collection unit can immediately set the timing of collection to quickly collect data. This makes it possible to adjust the timing of data collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0063] The collection unit can analyze the user's past data collection history and select an appropriate collection method. The collection unit, for example, analyzes the user's past data collection history and selects the optimal collection method. For example, the collection unit prioritizes collecting types of data that the user has frequently collected in the past. The collection unit can also prioritize and suggest collection methods (manual, API, etc.) that the user has used in the past. The collection unit can also predict and suggest data to be collected in a specific time period based on the user's past collection history. This makes it possible to select the optimal collection method based on the past data collection history.

[0064] The collection unit can filter data based on the user's current project or area of ​​interest when collecting data. For example, the collection unit can preferentially collect data related to a project the user is currently working on. The collection unit can also filter and collect highly relevant data based on the user's area of ​​interest. The collection unit can also filter and collect related data based on keywords set by the user. This makes it possible to filter data based on the user's project or area of ​​interest.

[0065] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit postpones collection of less important data. When the user is relaxed, the collection unit can prioritize collection of more important data. When the user is in a hurry, the collection unit can immediately collect the most important data. This makes it possible to determine the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data based on the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also collect region-specific data based on the user's geographical location information. Furthermore, when the user is moving, the collection unit can collect optimal data based on the user's current location. This makes it possible to collect highly relevant data based on the user's geographical location information.

[0067] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activities. The collection unit can also collect related data based on the content posted by accounts the user follows. In this way, related data can be collected based on the user's social media activities.

[0068] The inference unit can infer the user's emotion and adjust the expression method of the inference based on the estimated user emotion. The inference unit can, for example, use a generation AI to infer the user's emotion and adjust the expression method of the inference based on the estimated user emotion. For example, if the user is relaxed, the inference unit can provide the generation AI with an inference result that includes a detailed explanation. If the user is in a hurry, the inference unit can provide the generation AI with a concise and to-the-point inference result. If the user is excited, the inference unit can provide the generation AI with an inference result that adds a visually stimulating effect. This makes it possible to adjust the expression method of the inference according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The inference unit can adjust the level of detail of the inference based on the importance of the task during inference. The inference unit, for example, uses a generation AI to adjust the level of detail of the inference based on the importance of the task during inference. For example, the inference unit can provide the generation AI with a detailed inference result for a task with high importance. Furthermore, the inference unit can provide the generation AI with a concise inference result for a task with low importance. Furthermore, the inference unit can provide the generation AI with an inference result with an appropriate level of detail for a task with medium importance. This makes it possible to adjust the level of detail of the inference according to the importance of the task.

[0070] The inference unit can apply different inference algorithms depending on the task category during inference. The inference unit can use, for example, a generative AI to apply different inference algorithms depending on the task category during inference. For example, the inference unit can have the generative AI apply a creative inference algorithm to a creative task. Furthermore, the inference unit can have the generative AI apply a data-driven inference algorithm to an analytical task. Furthermore, the inference unit can have the generative AI apply an efficient inference algorithm to a routine task. This makes it possible to apply the optimal inference algorithm depending on the task category.

[0071] The inference unit can infer the user's emotions and adjust the length of the inference based on the estimated user emotions. The inference unit can, for example, use a generation AI to infer the user's emotions and adjust the length of the inference based on the estimated user emotions. For example, if the user is in a hurry, the inference unit can provide the generation AI with a short, to-the-point inference result. If the user is relaxed, the inference unit can provide the generation AI with a longer inference result including detailed explanations. If the user is excited, the inference unit can provide the generation AI with an inference result that adds a visually stimulating effect. This allows the length of the inference to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The inference unit can determine the priority of inference based on the submission time of the task during inference. The inference unit, for example, uses a generation AI to determine the priority of inference based on the submission time of the task during inference. For example, the inference unit prioritizes inference for tasks with an approaching submission deadline. Also, the inference unit can postpone inference for tasks with a distant submission deadline. Also, the inference unit can perform inference with a moderate priority for tasks with a medium submission deadline. This makes it possible to determine the priority of inference according to the submission time of the task.

[0073] The inference unit can adjust the order of inference based on the relevance of tasks during inference. The inference unit adjusts the order of inference based on the relevance of tasks during inference, for example, by using a generative AI. For example, the inference unit prioritizes inference for highly relevant tasks. Furthermore, the inference unit can postpone inference for tasks with low relevance. Furthermore, the inference unit can perform inference in an appropriate order for tasks with medium relevance. This makes it possible to adjust the order of inference according to the relevance of tasks.

[0074] The organizing unit can estimate the user's emotions and adjust the organizing method based on the estimated user emotions. For example, the organizing unit can estimate the user's emotions and adjust the organizing method based on the estimated user emotions. For example, the organizing unit can provide a simple organizing method when the user is stressed. Furthermore, the organizing unit can provide a detailed organizing method when the user is relaxed. Furthermore, the organizing unit can provide a method that allows the user to organize quickly when the user is in a hurry. This makes it possible to adjust the organizing method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] When organizing, the organizing unit can analyze the user's past task organization history and select an appropriate organization method. For example, the organizing unit analyzes the user's past task organization history and selects the optimal organization method. For example, the organizing unit preferentially suggests organization methods that the user has used in the past. The organizing unit can also suggest the most efficient organization method from the user's past organization history. The organizing unit can also analyze the user's past organization history and suggest the optimal organization method for a specific time period. This makes it possible to select the optimal organization method based on the user's past task organization history.

[0076] The organizing unit can customize the organizing method based on the user's current living situation when organizing. The organizing unit customizes the organizing method based on the user's current living situation, for example. For example, if the user is busy, the organizing unit can provide a simple organizing method. Also, if the user has time, the organizing unit can provide a detailed organizing method. Also, if the user is concentrating on a specific project, the organizing unit can provide an organizing method that is optimal for that project. In this way, the organizing method can be customized according to the user's living situation.

[0077] The organizing unit can estimate the user's emotions and determine the priorities of organizing based on the estimated user emotions. For example, the organizing unit estimates the user's emotions and determines the priorities of organizing based on the estimated user emotions. For example, when the user is feeling stressed, the organizing unit postpones organizing low-importance tasks. Furthermore, when the user is relaxed, the organizing unit can prioritize organizing high-importance tasks. Furthermore, when the user is in a hurry, the organizing unit can immediately organize the most important tasks. This makes it possible to determine the priorities of organizing according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] When organizing, the organizing unit can select the optimal organizing method taking into account the user's geographical location information. The organizing unit selects the optimal organizing method taking into account, for example, the user's geographical location information. For example, the organizing unit prioritizes organizing tasks related to the user's current location. The organizing unit can also organize region-specific tasks based on the user's geographical location information. Furthermore, when the user is traveling, the organizing unit can organize the optimal tasks based on the user's current location. This makes it possible to select the optimal organizing method based on the user's geographical location information.

[0079] The organizing unit can analyze the user's social media activity and suggest ways to organize it when organizing. For example, the organizing unit analyzes the user's social media activity and suggests ways to organize it. For example, the organizing unit organizes related tasks based on information shared by the user on social media. Furthermore, the organizing unit can organize tasks related to topics of interest from the user's social media activity. Furthermore, the organizing unit can organize related tasks based on the content posted by accounts the user follows. In this way, it is possible to suggest ways to organize it based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, inference unit, and organization unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14 and the processor 28 of the data processing device 12, and collects daily business data such as schedules, emails, and minutes. The inference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and infers the next task using a generative AI. The organization unit is realized, for example, by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12, and organizes and prepares the inferred tasks. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, inference unit, and organization unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214 and the processor 28 of the data processing device 12, and collects daily business data such as schedules, emails, and minutes. The inference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and infers the next task using a generative AI. The organization unit is realized, for example, by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12, and organizes and prepares the inferred tasks. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, inference unit, and organization unit 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 is realized by the computer 36 of the headset type terminal 314 and the processor 28 of the data processing device 12, and collects daily business data such as schedules, emails, and minutes. The inference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and infers the next task using a generative AI. The organization unit is realized, for example, by the control unit 46A of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12, and organizes and prepares the inferred tasks. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, inference unit, and organization 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 is realized by the computer 36 of the robot 414 and the processor 28 of the data processing device 12, and collects daily business data such as schedules, emails, and minutes. The inference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and infers the next task using a generative AI. The organization unit is realized, for example, by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12, and organizes and prepares the inferred tasks.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The work support system can further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate and sleep patterns, and can adjust the work load based on this. For example, if the user's heart rate is high, tasks can be reallocated to reduce the work load. Also, if the user's sleep pattern is disrupted, important tasks can be postponed to prioritize rest. Furthermore, the health management unit can suggest appropriate times to take breaks based on the user's health condition. This makes it possible to provide work support that takes the user's health condition into consideration.

[0082] The business support system may further include a notification unit that estimates the user's emotions and adjusts the task notification method based on the estimated emotions. For example, the notification unit may reduce notifications when the user is stressed, or may actively notify the user when the user is relaxed. Furthermore, if the user is in a hurry, the notification unit may prioritize only important notifications. This allows for a flexible notification method that responds to the user's emotions.

[0083] The business support system can further include a history analysis unit that analyzes the user's past task completion history and determines the priority of the next task. The history analysis unit, for example, infers that tasks that the user has completed quickly in the past are prioritized. It can also postpone tasks that took the user a long time in the past. Furthermore, the history analysis unit can suggest the best task for a specific time period based on the user's past task completion history. This enables efficient task management based on past history.

[0084] The business support system may further include a difficulty adjustment unit that estimates the user's emotions and adjusts the difficulty of the tasks based on the estimated emotions. For example, the difficulty adjustment unit may prioritize easy tasks when the user is stressed. Alternatively, the difficulty adjustment unit may prioritize difficult tasks when the user is relaxed. Alternatively, the difficulty adjustment unit may prioritize tasks that can be completed quickly when the user is in a hurry. This allows the difficulty of the tasks to be adjusted according to the user's emotions.

[0085] The business support system can further include an activity monitoring unit that monitors the user's current activity status and suggests tasks at appropriate times. For example, if the user is in a meeting, the activity monitoring unit can suggest a task after the meeting ends. Also, if the user is traveling, the activity monitoring unit can suggest a task after the user has completed traveling. Furthermore, if the user is taking a break, the activity monitoring unit can suggest a task after the break ends. This makes it possible to suggest tasks at appropriate times according to the user's activity status.

[0086] The business support system may further include a progress visualization unit that estimates the user's emotions and visualizes the progress of a task based on the estimated emotions. For example, the progress visualization unit may display a simple progress status when the user is stressed. Alternatively, it may display a detailed progress status when the user is relaxed. Furthermore, it may highlight and display only important progress when the user is in a hurry. This allows for flexible visualization of progress according to the user's emotions.

[0087] The business support system can further include a time prediction unit that analyzes the user's past task completion times and presents an estimated completion time for the next task. The time prediction unit, for example, can set a shorter estimated completion time for a task that the user completed quickly in the past. It can also set a longer estimated completion time for a task that took the user a long time in the past. Furthermore, the time prediction unit can present an optimal estimated completion time for a task in a specific time period based on the user's past task completion times. This enables efficient time management based on past history.

[0088] The business support system may further include a priority adjustment unit that estimates the user's emotions and dynamically changes the priority of tasks based on the estimated emotions. For example, if the user is feeling stressed, the priority adjustment unit postpones tasks with low importance. Also, if the user is relaxed, the priority adjustment unit can infer that tasks with high importance are prioritized. Furthermore, if the user is in a hurry, the most important tasks can be inferred immediately. This allows for flexible adjustment of task priorities according to the user's emotions.

[0089] The business support system can further include a location information utilization unit that proposes optimal tasks by taking into account the user's current geographical location information. For example, if the user is in the office, the location information utilization unit can prioritize and propose tasks to be performed at the office. Also, if the user is on a business trip, the location information utilization unit can propose tasks to be performed at the business trip destination. Furthermore, if the user is at home, the location information utilization unit can also propose tasks to be performed at home. This makes it possible to propose optimal tasks based on the user's geographical location information.

[0090] The business support system may further include a progress reporting unit that estimates the user's emotions and adjusts the task progress reporting method based on the estimated emotions. For example, the progress reporting unit may provide a brief progress report if the user is feeling stressed. Alternatively, the progress reporting unit may provide a detailed progress report if the user is relaxed. Furthermore, if the user is in a hurry, the progress reporting unit may report only important progress. This allows for a flexible progress reporting method that responds to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The collection unit collects daily business data. Daily business data includes, for example, schedules, emails, and meeting minutes. The collection unit can obtain schedules from a calendar, emails from an email server, and meeting minutes from a document management system. The collection unit can also collect data from external systems using APIs. For example, the collection unit can obtain schedules using a calendar API, emails using an email API, and meeting minutes using a document management API. Step 2: The inference unit uses the generation AI to infer the next task based on the data collected by the collection unit. Inference is performed, for example, based on a schedule. The inference unit inputs a prompt to the generation AI, such as "Please infer the next task to be performed," and the generation AI infers the next task. The generation AI can perform inference using a text generation AI (e.g., LLM). The inference unit can also use a multimodal generation AI to perform inference based not only on text data, but also on image data and audio data. Step 3: The organization unit organizes and prepares the tasks inferred by the inference unit. For example, the organization unit summarizes the tasks and prepares them based on public information and internal company information. For example, it prepares materials for the next meeting. The organization unit also enhances preparation by linking with reliable information sources and including the source of the information. For example, as a paid option, it can link with reliable information sources to obtain the necessary information.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 department that collects daily business data; an inference unit that infers a next task based on the data collected by the collection unit; and an organizing unit that organizes and prepares the tasks inferred by the inference unit. A system characterized by:

2. The collecting unit Collect data on appointments, emails, and meeting minutes The system of claim 1 .

3. The inference unit Inferring the next task based on the schedule The system of claim 1 .

4. The organizing unit Summarize tasks and prepare based on public or internal information The system of claim 1 .

5. The organizing unit Work with reliable sources and cite the source of the information The system of claim 1 .

6. The organizing unit Partner with trusted sources as a paid option The system of claim 1 .

7. The collecting unit Estimate user emotions and adjust data collection timing based on the estimated user emotions The system of claim 1 .

8. The collecting unit Analyze the user's past data collection history and select the appropriate collection method The system of claim 1 .

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