System

Abstract

An object of a system according to an embodiment is to efficiently identify tasks and assemble a schedule plan when a new business is examined.SOLUTION: A system according to an embodiment includes a goal input unit, a task start unit, and an application task organization unit. The goal input unit allows the user to input a goal and an achievement date. The task sorting unit sorts out tasks to be done for each period on the basis of the objective of the goal and the achievement date input by the objective input unit. An application task arrangement part arranges in-house applications necessary for achieving a goal, including in-house rules.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently identify tasks and create schedule plans when considering new businesses, so there is room for improvement.

[0005] The system according to the embodiment aims to efficiently identify tasks and assemble schedule plans when considering a new business venture. [Means for solving the problem]

[0006] The system according to the embodiment includes a goal input unit, a task identification unit, and a requested task organization unit. The goal input unit allows a user to input a goal target and a completion date. The task identification unit identifies tasks to be done by period based on the goal target and completion date input by the goal input unit. The requested task organization unit organizes internal requests required to achieve the goal, including internal company rules. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently identify tasks and create schedule plans when considering a new business venture. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The in-house system according to the embodiment of the present invention is a system that automatically identifies tasks to be done by period and creates a schedule when the user inputs a goal and a target date for achieving the goal. This allows the in-house system to efficiently identify tasks and manage schedules.

[0029] The internal system according to the embodiment includes a goal input unit, a task identification unit, and an application task organization unit. The goal input unit allows a user to input a goal target and a target achievement date. For example, the user inputs a specific goal and date, such as "launching a new product to market" and "December 31, 2024." The task identification unit identifies tasks to be performed by time period based on the goal target and target achievement date input by the goal input unit. For example, the generation AI lists tasks such as "market research," "product development," "formulation of a marketing plan," and "securing sales channels" for each time period. The generation AI identifies tasks based on prompts containing instructions from the user about what the user wants the generation AI to do. The application task organization unit organizes internal applications required to achieve the goal, including internal rules. For example, it lists specific application tasks such as "requesting the legal department to review the contract," "preparing and submitting a request for approval," and "obtaining budget approval." The generation AI learns internal rules and procedures in advance and suggests necessary application tasks at the appropriate time. As a result, the in-house system according to the embodiment makes it easier to identify tasks and manage schedules when considering new businesses or planning projects, thereby improving the success rate of projects.

[0030] The goal input unit can refer to data on similar past projects based on the goal and achievement date entered by the user and propose an optimal achievement date. For example, the goal input unit refers to data on similar past projects based on the goal and achievement date entered by the user and evaluates whether the achievement date is realistic. For example, the goal input unit adjusts the achievement date based on the average duration of past projects. The goal input unit also analyzes data on similar past projects and proposes an optimal achievement date if the achievement date is not realistic. For example, the goal input unit resets the achievement date depending on the scale and complexity of the project. The goal input unit also refers to past successes and failures based on the goal and achievement date entered by the user and proposes an optimal achievement date. For example, the goal input unit determines whether to advance or extend the achievement date based on past data. In this way, by proposing a realistic achievement date based on past data, the success rate of the project is improved.

[0031] When the goal input unit inputs the goal and achievement date, the generation AI can automatically analyze the project's risk factors and propose risk mitigation measures. For example, the goal input unit automatically analyzes the project's risk factors based on the goal and achievement date input by the user. For example, it identifies risk factors based on past data and proposes risk mitigation measures. The goal input unit also analyzes the project's risk factors and proposes risk mitigation measures. For example, it lists specific measures for risk factors and presents them to the user. The goal input unit also analyzes the project's risk factors and proposes risk mitigation measures based on the goal and achievement date input by the user. For example, it identifies risk factors in advance and creates a schedule for implementing risk mitigation measures. In this way, the project's success rate is improved by identifying project risk factors in advance and proposing risk mitigation measures.

[0032] The goal input unit can provide an interface that allows more intuitive input using voice input or gesture input when inputting a goal and a completion date. The goal input unit provides an interface that allows more intuitive input using voice input when inputting a goal and a completion date, for example. For example, the user inputs the goal and the completion date by voice. The goal input unit also provides an interface that allows more intuitive input using gesture input when inputting a goal and a completion date. For example, the user inputs the goal and the completion date by gesture. The goal input unit also provides an interface that allows more intuitive input using voice input or gesture input when inputting a goal and a completion date. For example, the user inputs the goal and the completion date by voice or gesture. This allows the user to intuitively input the goal and the completion date by using voice input or gesture input.

[0033] The goal input unit can refer to goal setting examples from different industries or fields and provide the user with new goal setting ideas. The goal input unit, for example, refers to goal setting examples from different industries or fields and provides the user with new goal setting ideas. For example, goals are set based on successful examples from other industries. The goal input unit also refers to goal setting examples from different industries or fields and provides the user with new goal setting ideas. For example, goal setting methods from other fields are adopted. The goal input unit also refers to goal setting examples from different industries or fields and provides the user with new goal setting ideas. For example, goal setting examples from different industries are used as reference. In this way, by referring to examples from different industries or fields, the user can be provided with new goal setting ideas.

[0034] The task identification unit allows the generation AI to learn past project data and propose the optimal task order or duration. For example, the task identification unit allows the generation AI to learn past project data and propose the optimal task order and duration. For example, it determines the task order based on past success stories. The task identification unit also allows the generation AI to learn past project data and propose the optimal task order and duration. For example, it sets the task duration based on past data. The task identification unit also allows the generation AI to learn past project data and propose the optimal task order and duration. For example, it adjusts the task order based on the progress of past projects. This improves project efficiency by proposing the optimal task order and duration based on past project data.

[0035] In the task identification section, the generation AI automatically analyzes the dependencies between each task and can propose an efficient task schedule. In the task identification section, for example, the generation AI automatically analyzes the dependencies between tasks and generates an efficient task schedule. For example, it assembles a schedule taking into account the dependencies between tasks. In addition, when identifying tasks, the generation AI automatically analyzes the dependencies between each task and generates an efficient task schedule. For example, it determines the order of tasks based on the dependencies. In addition, in the task identification section, the generation AI automatically analyzes the dependencies between tasks and generates an efficient task schedule. For example, it sets the duration of tasks taking into account the dependencies. In this way, analyzing task dependencies and generating an efficient task schedule smooths the progress of the project.

[0036] The task identification unit allows the generation AI to refer to different project management methods and propose the optimal task identification method. For example, the task identification unit allows the generation AI to refer to different project management methods and propose the optimal task identification method. For example, it may identify tasks based on the agile method. The task identification unit also allows the generation AI to refer to different project management methods and propose the optimal task identification method. For example, it may identify tasks based on the waterfall method. The task identification unit also allows the generation AI to refer to different project management methods and propose the optimal task identification method. For example, it may propose a method that combines agile and waterfall. In this way, the optimal task identification method can be proposed by referring to different project management methods.

[0037] The task identification unit allows the generation AI to incorporate the opinions of external experts and consultants, thereby generating a more accurate task list. For example, the task identification unit allows the generation AI to incorporate the opinions of external experts and consultants, thereby generating a more accurate task list. For example, it identifies tasks based on the opinions of experts. Furthermore, when identifying tasks, the task identification unit allows the generation AI to incorporate the opinions of external experts and consultants, thereby generating a more accurate task list. For example, it reflects the advice of consultants. Furthermore, the task identification unit allows the generation AI to incorporate the opinions of external experts and consultants, thereby generating a more accurate task list. For example, it adjusts tasks based on the knowledge of experts. In this way, by incorporating the opinions of external experts and consultants, a more accurate task list can be generated.

[0038] The application task organizing unit allows the generation AI to learn from past internal application data and propose the optimal application procedure or timing. For example, the application task organizing unit allows the generation AI to learn from past internal application data and propose the optimal application procedure and timing. For example, it determines the application procedure based on past success stories. The application task organizing unit also allows the generation AI to learn from past internal application data and propose the optimal application procedure and timing. For example, it sets the application timing based on past data. The application task organizing unit also allows the generation AI to learn from past internal application data and propose the optimal application procedure and timing. For example, it adjusts the application procedure based on the progress of past applications. This improves the efficiency of the application process by proposing the optimal application procedure and timing based on past application data.

[0039] The application task organizing unit uses a generation AI to automatically analyze the dependencies of each application and propose an efficient application schedule. For example, the application task organizing unit uses a generation AI to automatically analyze the dependencies of internal applications and generate an efficient application schedule. For example, it creates a schedule taking into account the dependencies between applications. Furthermore, when organizing tasks for internal applications, the application task organizing unit uses a generation AI to automatically analyze the dependencies of each application and generate an efficient application schedule. For example, it determines the order of applications based on the dependencies. Furthermore, the application task organizing unit uses a generation AI to automatically analyze the dependencies of internal applications and generate an efficient application schedule. For example, it sets the application period taking into account the dependencies. In this way, the application process is smoothed by analyzing the dependencies of applications and generating an efficient application schedule.

[0040] The application task organizing unit allows the generation AI to refer to application procedures from different companies or industries and propose the optimal application procedure. For example, the application task organizing unit allows the generation AI to refer to application procedures from different companies or industries and propose the optimal application procedure. For example, it sets application procedures based on success stories from other companies. The application task organizing unit also allows the generation AI to refer to application procedures from different companies or industries and propose the optimal application procedure. For example, it incorporates application procedures from other industries. The application task organizing unit also allows the generation AI to refer to application procedures from different companies or industries and propose the optimal application procedure. For example, it refers to application procedures from different industries. In this way, it is possible to propose the optimal application procedure by referring to application procedures from different companies or industries.

[0041] The application task organizing unit allows the generation AI to incorporate the opinions of external legal experts or consultants, thereby generating more accurate application tasks. For example, the application task organizing unit allows the generation AI to incorporate the opinions of external legal experts or consultants, thereby generating more accurate application tasks. For example, it identifies application tasks based on the opinions of experts. Furthermore, when organizing tasks for internal applications, the application task organizing unit allows the generation AI to incorporate the opinions of external legal experts or consultants, thereby generating more accurate application tasks. For example, it reflects the advice of consultants. Furthermore, the application task organizing unit allows the generation AI to incorporate the opinions of external legal experts or consultants, thereby generating more accurate application tasks. For example, it adjusts application tasks based on the knowledge of experts. In this way, by incorporating the opinions of external legal experts or consultants, more accurate application tasks can be generated.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The goal input unit can refer to data on similar past projects based on the goal and completion date entered by the user and suggest the optimal completion date. For example, it can adjust the completion date based on the average duration of past projects. The goal input unit also refers to past successes and failures and suggests the optimal completion date if the completion date is not realistic. For example, it can reset the completion date depending on the scale and complexity of the project. This improves the success rate of projects by suggesting realistic completion dates based on past data.

[0044] When the goal input section inputs the goal and achievement date, the generation AI can automatically analyze the project's risk factors and propose risk mitigation measures. For example, it can identify risk factors based on past data and propose risk mitigation measures. It can also list specific countermeasures for risk factors and present them to the user. This allows the system to identify project risk factors in advance and propose risk mitigation measures, thereby improving the project's success rate.

[0045] The goal input unit can provide an interface that allows a user to input a goal and a completion date more intuitively using voice input or gesture input. For example, the user can input the goal and the completion date by voice. Alternatively, the user can input the goal and the completion date by gesture using gesture input. This allows the user to intuitively input the goal and the completion date by using voice input or gesture input.

[0046] The goal input unit can refer to goal setting examples from different industries or fields and provide the user with new goal setting ideas. For example, goals can be set based on successful examples from other industries. It can also incorporate goal setting methods from other fields. In this way, by referring to examples from different industries or fields, the user can be provided with new goal setting ideas.

[0047] The task identification part uses the generation AI to learn from past project data and propose the optimal task order or duration. For example, it can determine the task order based on past success stories. It can also set task durations based on past data. This improves project efficiency by proposing the optimal task order and duration based on past project data.

[0048] The task identification part uses a generation AI to automatically analyze the dependencies between tasks and propose an efficient task schedule. For example, it can create a schedule taking into account the dependencies between tasks. It can also determine the order of tasks based on the dependencies. This allows the system to analyze task dependencies and generate an efficient task schedule, helping to smooth the progress of the project.

[0049] The application task organization unit uses a generation AI to learn from the company's past application data and propose optimal application procedures or timing. For example, it can determine application procedures based on past success stories. It can also set application timing based on past data. This improves the efficiency of the application process by proposing optimal application procedures and timing based on past application data.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: In the goal input section, the user inputs the goal and the date of achievement. For example, the user inputs a specific goal and date, such as "launching a new product to market" and "December 31, 2024." Step 2: The task identification section identifies tasks to be done by period based on the goal and achievement date entered by the goal input section. For example, the generation AI will list tasks such as "market research," "product development," "formulation of marketing plan," and "securing sales channels" for each period. The generation AI will identify tasks based on prompts that include instructions on what the user wants the generation AI to do. Step 3: The Request Task Organizer organizes the internal requests required to achieve the goal, including company rules. For example, it lists specific request tasks such as "requesting the legal department to review the contract," "creating and submitting a request form," and "obtaining budget approval." The Generator AI has learned company rules and procedures in advance and suggests the necessary request tasks at the appropriate time.

[0052] (Example 2) The in-house system according to the embodiment of the present invention is a system that automatically identifies tasks to be done by period and creates a schedule when the user inputs a goal and a target date for achieving the goal. This allows the in-house system to efficiently identify tasks and manage schedules.

[0053] The internal system according to the embodiment includes a goal input unit, a task identification unit, and an application task organization unit. The goal input unit allows a user to input a goal target and a target achievement date. For example, the user inputs a specific goal and date, such as "launching a new product to market" and "December 31, 2024." The task identification unit identifies tasks to be performed by time period based on the goal target and target achievement date input by the goal input unit. For example, the generation AI lists tasks such as "market research," "product development," "formulation of a marketing plan," and "securing sales channels" for each time period. The generation AI identifies tasks based on prompts containing instructions from the user about what the user wants the generation AI to do. The application task organization unit organizes internal applications required to achieve the goal, including internal rules. For example, it lists specific application tasks such as "requesting the legal department to review the contract," "preparing and submitting a request for approval," and "obtaining budget approval." The generation AI learns internal rules and procedures in advance and suggests necessary application tasks at the appropriate time. As a result, the in-house system according to the embodiment makes it easier to identify tasks and manage schedules when considering new businesses or planning projects, thereby improving the success rate of projects.

[0054] The goal input unit can refer to data on similar past projects based on the goal and achievement date entered by the user and propose an optimal achievement date. For example, the goal input unit refers to data on similar past projects based on the goal and achievement date entered by the user and evaluates whether the achievement date is realistic. For example, the goal input unit adjusts the achievement date based on the average duration of past projects. The goal input unit also analyzes data on similar past projects and proposes an optimal achievement date if the achievement date is not realistic. For example, the goal input unit resets the achievement date depending on the scale and complexity of the project. The goal input unit also refers to past successes and failures based on the goal and achievement date entered by the user and proposes an optimal achievement date. For example, the goal input unit determines whether to advance or extend the achievement date based on past data. In this way, by proposing a realistic achievement date based on past data, the success rate of the project is improved.

[0055] When the goal input unit inputs the goal and achievement date, the generation AI can automatically analyze the project's risk factors and propose risk mitigation measures. For example, the goal input unit automatically analyzes the project's risk factors based on the goal and achievement date input by the user. For example, it identifies risk factors based on past data and proposes risk mitigation measures. The goal input unit also analyzes the project's risk factors and proposes risk mitigation measures. For example, it lists specific measures for risk factors and presents them to the user. The goal input unit also analyzes the project's risk factors and proposes risk mitigation measures based on the goal and achievement date input by the user. For example, it identifies risk factors in advance and creates a schedule for implementing risk mitigation measures. In this way, the project's success rate is improved by identifying project risk factors in advance and proposing risk mitigation measures.

[0056] The goal input unit can use the emotion estimation function to analyze the emotion regarding the goal entered by the user and suggest goal setting that will elicit positive emotions. The goal input unit, for example, uses the emotion estimation function to analyze the emotion regarding the goal entered by the user. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The goal input unit also analyzes the emotion regarding the goal entered by the user and suggests goal setting that will elicit positive emotions. For example, if the emotion score is low, it resets the goal. The goal input unit also uses the emotion estimation function to analyze the emotion regarding the goal entered by the user and suggests goal setting that will elicit positive emotions. For example, it adjusts the goal according to the user's emotions. In this way, adjusting the goal setting based on the user's emotions improves motivation for the project.

[0057] The goal input unit can provide an interface that allows more intuitive input using voice input or gesture input when inputting a goal and a completion date. The goal input unit provides an interface that allows more intuitive input using voice input when inputting a goal and a completion date, for example. For example, the user inputs the goal and the completion date by voice. The goal input unit also provides an interface that allows more intuitive input using gesture input when inputting a goal and a completion date. For example, the user inputs the goal and the completion date by gesture. The goal input unit also provides an interface that allows more intuitive input using voice input or gesture input when inputting a goal and a completion date. For example, the user inputs the goal and the completion date by voice or gesture. This allows the user to intuitively input the goal and the completion date by using voice input or gesture input.

[0058] The goal input unit can refer to goal setting examples from different industries or fields and provide the user with new goal setting ideas. The goal input unit, for example, refers to goal setting examples from different industries or fields and provides the user with new goal setting ideas. For example, goals are set based on successful examples from other industries. The goal input unit also refers to goal setting examples from different industries or fields and provides the user with new goal setting ideas. For example, goal setting methods from other fields are adopted. The goal input unit also refers to goal setting examples from different industries or fields and provides the user with new goal setting ideas. For example, goal setting examples from different industries are used as reference. In this way, by referring to examples from different industries or fields, the user can be provided with new goal setting ideas.

[0059] The goal input unit uses the emotion estimation function to analyze the emotion regarding the goal entered by the user in real time, thereby increasing motivation to achieve the goal. The goal input unit, for example, uses the emotion estimation function to provide feedback on the emotion regarding the goal entered by the user in real time. For example, it analyzes the user's facial expression and voice and displays an emotion score. The goal input unit also provides feedback on the emotion regarding the goal entered by the user in real time, thereby increasing motivation to achieve the goal. For example, it displays a message that elicits positive emotions. The goal input unit also uses the emotion estimation function to provide feedback on the emotion regarding the goal entered by the user in real time, thereby increasing motivation to achieve the goal. For example, it adjusts the goal according to the emotion score. In this way, feedback is provided in real time based on the user's emotion, thereby increasing motivation to achieve the goal.

[0060] The task identification unit allows the generation AI to learn past project data and propose the optimal task order or duration. For example, the task identification unit allows the generation AI to learn past project data and propose the optimal task order and duration. For example, it determines the task order based on past success stories. The task identification unit also allows the generation AI to learn past project data and propose the optimal task order and duration. For example, it sets the task duration based on past data. The task identification unit also allows the generation AI to learn past project data and propose the optimal task order and duration. For example, it adjusts the task order based on the progress of past projects. This improves project efficiency by proposing the optimal task order and duration based on past project data.

[0061] In the task identification section, the generation AI automatically analyzes the dependencies between each task and can propose an efficient task schedule. In the task identification section, for example, the generation AI automatically analyzes the dependencies between tasks and generates an efficient task schedule. For example, it assembles a schedule taking into account the dependencies between tasks. In addition, when identifying tasks, the generation AI automatically analyzes the dependencies between each task and generates an efficient task schedule. For example, it determines the order of tasks based on the dependencies. In addition, in the task identification section, the generation AI automatically analyzes the dependencies between tasks and generates an efficient task schedule. For example, it sets the duration of tasks taking into account the dependencies. In this way, analyzing task dependencies and generating an efficient task schedule smooths the progress of the project.

[0062] The task identification unit allows the generation AI to refer to different project management methods and propose the optimal task identification method. For example, the task identification unit allows the generation AI to refer to different project management methods and propose the optimal task identification method. For example, it may identify tasks based on the agile method. The task identification unit also allows the generation AI to refer to different project management methods and propose the optimal task identification method. For example, it may identify tasks based on the waterfall method. The task identification unit also allows the generation AI to refer to different project management methods and propose the optimal task identification method. For example, it may propose a method that combines agile and waterfall. In this way, the optimal task identification method can be proposed by referring to different project management methods.

[0063] The task identification unit allows the generation AI to incorporate the opinions of external experts and consultants, thereby generating a more accurate task list. For example, the task identification unit allows the generation AI to incorporate the opinions of external experts and consultants, thereby generating a more accurate task list. For example, it identifies tasks based on the opinions of experts. Furthermore, when identifying tasks, the task identification unit allows the generation AI to incorporate the opinions of external experts and consultants, thereby generating a more accurate task list. For example, it reflects the advice of consultants. Furthermore, the task identification unit allows the generation AI to incorporate the opinions of external experts and consultants, thereby generating a more accurate task list. For example, it adjusts tasks based on the knowledge of experts. In this way, by incorporating the opinions of external experts and consultants, a more accurate task list can be generated.

[0064] The task identification unit can use the emotion estimation function to analyze the emotion of the user when entering a task in real time and suggest tasks that elicit positive emotions. The task identification unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering a task in real time. For example, it analyzes the user's facial expressions and voice and displays an emotion score. The task identification unit also analyzes the emotion of the user when entering a task in real time and suggests tasks that elicit positive emotions. For example, it displays a message that elicits positive emotions. The task identification unit also uses the emotion estimation function to analyze the emotion of the user when entering a task in real time and suggests tasks that elicit positive emotions. For example, it adjusts tasks according to the emotion score. In this way, the user's emotions are analyzed in real time and tasks that elicit positive emotions are suggested, thereby improving motivation for the project.

[0065] The application task organizing unit allows the generation AI to learn from past internal application data and propose the optimal application procedure or timing. For example, the application task organizing unit allows the generation AI to learn from past internal application data and propose the optimal application procedure and timing. For example, it determines the application procedure based on past success stories. The application task organizing unit also allows the generation AI to learn from past internal application data and propose the optimal application procedure and timing. For example, it sets the application timing based on past data. The application task organizing unit also allows the generation AI to learn from past internal application data and propose the optimal application procedure and timing. For example, it adjusts the application procedure based on the progress of past applications. This improves the efficiency of the application process by proposing the optimal application procedure and timing based on past application data.

[0066] The application task organizing unit uses a generation AI to automatically analyze the dependencies of each application and propose an efficient application schedule. For example, the application task organizing unit uses a generation AI to automatically analyze the dependencies of internal applications and generate an efficient application schedule. For example, it creates a schedule taking into account the dependencies between applications. Furthermore, when organizing tasks for internal applications, the application task organizing unit uses a generation AI to automatically analyze the dependencies of each application and generate an efficient application schedule. For example, it determines the order of applications based on the dependencies. Furthermore, the application task organizing unit uses a generation AI to automatically analyze the dependencies of internal applications and generate an efficient application schedule. For example, it sets the application period taking into account the dependencies. In this way, the application process is smoothed by analyzing the dependencies of applications and generating an efficient application schedule.

[0067] The application task organizing unit can use the emotion estimation function to propose application procedures or support to reduce the stress or burden the user feels regarding the application tasks. The application task organizing unit, for example, uses the emotion estimation function to propose application procedures or support to reduce the stress or burden the user feels regarding the application tasks. For example, it divides application tasks that cause high stress. The application task organizing unit also proposes application procedures or support to reduce the stress or burden the user feels regarding the application tasks. For example, it rearranges application tasks that cause high burdens. The application task organizing unit also uses the emotion estimation function to propose application procedures or support to reduce the stress or burden the user feels regarding the application tasks. For example, it combines application tasks that cause high stress with other tasks. In this way, by proposing application procedures or support to reduce the user's stress and burden, the application process progresses smoothly.

[0068] The application task organizing unit allows the generation AI to refer to application procedures from different companies or industries and propose the optimal application procedure. For example, the application task organizing unit allows the generation AI to refer to application procedures from different companies or industries and propose the optimal application procedure. For example, it sets application procedures based on success stories from other companies. The application task organizing unit also allows the generation AI to refer to application procedures from different companies or industries and propose the optimal application procedure. For example, it incorporates application procedures from other industries. The application task organizing unit also allows the generation AI to refer to application procedures from different companies or industries and propose the optimal application procedure. For example, it refers to application procedures from different industries. In this way, it is possible to propose the optimal application procedure by referring to application procedures from different companies or industries.

[0069] The application task organizing unit allows the generation AI to incorporate the opinions of external legal experts or consultants, thereby generating more accurate application tasks. For example, the application task organizing unit allows the generation AI to incorporate the opinions of external legal experts or consultants, thereby generating more accurate application tasks. For example, it identifies application tasks based on the opinions of experts. Furthermore, when organizing tasks for internal applications, the application task organizing unit allows the generation AI to incorporate the opinions of external legal experts or consultants, thereby generating more accurate application tasks. For example, it reflects the advice of consultants. Furthermore, the application task organizing unit allows the generation AI to incorporate the opinions of external legal experts or consultants, thereby generating more accurate application tasks. For example, it adjusts application tasks based on the knowledge of experts. In this way, by incorporating the opinions of external legal experts or consultants, more accurate application tasks can be generated.

[0070] The request task organizing unit can use the emotion estimation function to analyze the emotion of the user when entering the request task in real time and propose an request procedure that elicits positive emotions. The request task organizing unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering the request task in real time. For example, it analyzes the user's facial expressions and voice and displays an emotion score. The request task organizing unit also analyzes the emotion of the user when entering the request task in real time and proposes an request procedure that elicits positive emotions. For example, it displays a message that elicits positive emotions. The request task organizing unit also uses the emotion estimation function to analyze the emotion of the user when entering the request task in real time and proposes an request procedure that elicits positive emotions. For example, it adjusts the request procedure according to the emotion score. In this way, by analyzing the user's emotion in real time and proposing an request procedure that elicits positive emotions, motivation for the request process is improved.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The goal input unit can refer to data on similar past projects based on the goal and completion date entered by the user and suggest the optimal completion date. For example, it can adjust the completion date based on the average duration of past projects. The goal input unit also refers to past successes and failures and suggests the optimal completion date if the completion date is not realistic. For example, it can reset the completion date depending on the scale and complexity of the project. This improves the success rate of projects by suggesting realistic completion dates based on past data.

[0073] When the goal input section inputs the goal and achievement date, the generation AI can automatically analyze the project's risk factors and propose risk mitigation measures. For example, it can identify risk factors based on past data and propose risk mitigation measures. It can also list specific countermeasures for risk factors and present them to the user. This allows the system to identify project risk factors in advance and propose risk mitigation measures, thereby improving the project's success rate.

[0074] The goal input unit uses the emotion estimation function to analyze the user's emotions regarding the goal entered and propose goal settings that will elicit positive emotions. For example, it can analyze the user's facial expressions and voice to calculate an emotion score. If the emotion score is low, it can reset the goal. This adjusts the goal setting based on the user's emotions, thereby improving project motivation.

[0075] The goal input unit can provide an interface that allows a user to input a goal and a completion date more intuitively using voice input or gesture input. For example, the user can input the goal and the completion date by voice. Alternatively, the user can input the goal and the completion date by gesture using gesture input. This allows the user to intuitively input the goal and the completion date by using voice input or gesture input.

[0076] The goal input unit can refer to goal setting examples from different industries or fields and provide the user with new goal setting ideas. For example, goals can be set based on successful examples from other industries. It can also incorporate goal setting methods from other fields. In this way, by referring to examples from different industries or fields, the user can be provided with new goal setting ideas.

[0077] The goal input unit uses an emotion estimation function to analyze the user's emotions regarding the goal entered in real time, thereby increasing the motivation to achieve the goal. For example, it can analyze the user's facial expressions and voice and display an emotion score. It can also display messages that elicit positive emotions. This provides real-time feedback based on the user's emotions, increasing the motivation to achieve the goal.

[0078] The task identification part uses the generation AI to learn from past project data and propose the optimal task order or duration. For example, it can determine the task order based on past success stories. It can also set task durations based on past data. This improves project efficiency by proposing the optimal task order and duration based on past project data.

[0079] The task identification part uses a generation AI to automatically analyze the dependencies between tasks and propose an efficient task schedule. For example, it can create a schedule taking into account the dependencies between tasks. It can also determine the order of tasks based on the dependencies. This allows the system to analyze task dependencies and generate an efficient task schedule, helping to smooth the progress of the project.

[0080] The task identification unit uses an emotion estimation function to analyze the emotions of users when they enter tasks in real time and can suggest tasks that elicit positive emotions. For example, it can analyze the user's facial expressions and voice and display an emotion score. It can also display messages that elicit positive emotions. This allows the unit to analyze the user's emotions in real time and suggest tasks that elicit positive emotions, thereby improving project motivation.

[0081] The application task organization unit uses a generation AI to learn from the company's past application data and propose optimal application procedures or timing. For example, it can determine application procedures based on past success stories. It can also set application timing based on past data. This improves the efficiency of the application process by proposing optimal application procedures and timing based on past application data.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: In the goal input section, the user inputs the goal and the date of achievement. For example, the user inputs a specific goal and date, such as "launching a new product to market" and "December 31, 2024." Step 2: The task identification section identifies tasks to be done by period based on the goal and achievement date entered by the goal input section. For example, the generation AI will list tasks such as "market research," "product development," "formulation of marketing plan," and "securing sales channels" for each period. The generation AI will identify tasks based on prompts that include instructions on what the user wants the generation AI to do. Step 3: The Request Task Organizer organizes the internal requests required to achieve the goal, including company rules. For example, it lists specific request tasks such as "requesting the legal department to review the contract," "creating and submitting a request form," and "obtaining budget approval." The Generator AI has learned company rules and procedures in advance and suggests the necessary request tasks at the appropriate time.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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 goal input section where a user inputs a goal and a goal achievement date; a task identification unit that identifies tasks to be done for each period based on the target and achievement date of the goal input by the goal input unit; An application task organizing unit that organizes internal applications required to achieve the goal, including internal rules, is provided. A system characterized by:

2. The goal input unit When the goal and the achievement date are entered, the generation AI automatically analyzes the project's risk factors and proposes risk mitigation measures.

2. The system of claim 1.

3. The goal input unit Provide a more intuitive interface for entering goals and achievement dates using voice or gesture input.

2. The system of claim 1.

4. The task identification unit Generative AI learns from past project data and suggests the optimal order or duration of the tasks.

2. The system of claim 1.

5. The task identification unit Generative AI incorporates the opinions of external experts or consultants to generate a more accurate task list 2. The system of claim 1.

6. The application task organizing unit Generative AI learns from past application data within the company and suggests optimal application procedures or timing 2. The system of claim 1.

7. The application task organizing unit Propose application procedures or support to reduce the stress or burden users feel about the application task.

2. The system of claim 1.

8. The goal input unit Analyzes the emotions users have about their goals and suggests goal settings that will elicit positive emotions.

2. The system of claim 1.

Images