System

Abstract

An object of a system according to an embodiment is to automatically generate a project plan efficiently.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives an input from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a project plan based on the information analyzed by the analysis unit. The providing unit provides the project plan generated by the generating unit.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 techniques have had the problem that creating a project plan is time-consuming and labor-intensive, making it difficult to do so efficiently.

[0005] The system according to the embodiment aims to efficiently and automatically generate a project plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives input from a user. The analyzing unit analyzes the information received by the receiving unit. The generating unit generates a project plan based on the information analyzed by the analyzing unit. The providing unit provides the project plan generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently and automatically generate a project plan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automatic project plan generation system according to an embodiment of the present invention uses a generation AI to automatically generate a project plan. In this system, a user selects one of the following services from a menu on their PC: "Data," "Voice," or "Mobile." The user then inputs the service scale (number of lines, number of terminals), duration, number of phases, configuration (new, modified, or mixed), and project elements (implementation, operation, billing, design, etc.) in the form of questions. The generation AI then automatically generates a project plan (scope, milestones, WBS, etc.). The plan is output in a format that takes into account the lead time of each service provided by the communications service provider. For example, if a user selects the "Data" service and inputs the number of lines as 100, the duration as 6 months, the number of phases as 3, the configuration as "New," and the project elements as "Implementation" and "Operation," the generation AI generates a plan based on this information. This allows the user to obtain a project plan based on a realistic schedule. This allows the automatic project plan generation system to streamline project plan creation, allowing users to create plans quickly and accurately. For example, even for large-scale projects, the generation AI automatically generates a plan, making it possible to obtain a detailed plan without much effort. In addition, the plan is more reliable because it takes into account the lead times of each service provided by the communications service provider.

[0029] A project plan automatic generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input from a user. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit receives, for example, a user selecting one of the services "data," "voice," or "mobile" from a menu on a PC, and inputting the service scale (number of lines, number of terminals), duration, number of phases, configuration (new, modified, mixed), and project elements (implementation, operation, billing, design, etc.) in the form of a question. The analysis unit analyzes the information received by the reception unit. The analysis is performed, for example, based on a data analysis method or algorithm, but is not limited to, examples. For example, the analysis unit calculates an appropriate lead time by referring to a lead time database for each service provider. The generation unit generates a project plan based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate a project plan including a scope, milestones, WBS, etc. For example, the generation AI automatically generates each element of the project plan based on the information entered by the user. The providing unit provides the project plan generated by the generating unit. The provision may be in the form of, for example, an email, a download link, a printed document, or the like, but is not limited to these examples. The providing unit can quickly provide the generated plan to the user. In this way, the project plan automatic generation system according to the embodiment can automatically generate and provide a project plan based on user input.

[0030] The reception unit can display the scale, period, number of phases, configuration, and project elements of the service in question format and accept input from the user. Examples of service scales include, but are not limited to, small, medium, and large. The reception unit, for example, displays the scale of the service in question format and accepts input from the user. Examples of durations include, but are not limited to, days, weeks, months, and years. The reception unit, for example, displays the period in question format and accepts input from the user. Examples of the number of phases include, but are not limited to, the number of stages in the project. The reception unit, for example, displays the number of phases in question format and accepts input from the user. Examples of configurations include, but are not limited to, team configuration and resource configuration. The reception unit, for example, displays the configuration in question format and accepts input from the user. Examples of project elements include, but are not limited to, tasks, resources, and risks. The reception unit, for example, displays the project elements in question format and accepts input from the user. This allows the user to efficiently input necessary information.

[0031] The analysis unit can analyze the information received by the reception unit and calculate the lead time by referring to the lead time database of each service provider. Examples of lead times include, but are not limited to, average lead time and longest lead time. For example, the analysis unit can analyze the information received by the reception unit and calculate an appropriate lead time by referring to the lead time database of each service provider. For example, the analysis unit can analyze the information received by the reception unit using a data analysis method. The analysis unit can also calculate the lead time based on the type of algorithm. This allows a realistic lead time to be reflected in the project plan.

[0032] The generation unit can generate a project plan including a scope, milestones, and WBS based on the information analyzed by the analysis unit. The scope includes, for example, project objectives, goals, and constraints, but is not limited to these examples. The generation unit generates a project plan including the scope based on, for example, the information analyzed by the analysis unit. The milestones include, for example, important turning points and goals to be achieved, but are not limited to these examples. The generation unit generates a project plan including the milestones based on, for example, the information analyzed by the analysis unit. The WBS includes, for example, a task hierarchy and a work breakdown structure, but is not limited to these examples. The generation unit generates a project plan including the WBS based on, for example, the information analyzed by the analysis unit. This allows automatic generation of a detailed project plan.

[0033] The providing unit can provide the project plan generated by the generating unit to the user. Examples of the providing method include, but are not limited to, email, a download link, and a printed copy. For example, the providing unit can provide the project plan generated by the generating unit to the user by email. The providing unit can also provide the project plan generated by the generating unit to the user via a download link. Furthermore, the providing unit can also provide the project plan generated by the generating unit to the user as a printed copy. This allows the user to quickly receive the generated plan.

[0034] The reception unit can analyze the user's past input history and select a question format. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past input history. This makes it possible to provide an efficient question format based on the user's past input history.

[0035] The reception unit can customize the question format according to the user's level of expertise. For example, if the user is a beginner, the reception unit prioritizes basic questions and avoids technical terms. If the user is an intermediate user, the reception unit can add detailed questions and use technical terms moderately. Furthermore, if the user is an advanced user, the reception unit can include many technical questions and collect detailed information. This makes it possible to provide a question format according to the user's level of expertise.

[0036] The reception unit can select the optimal question format depending on the user's input method. For example, when the user uses voice input, the reception unit prioritizes short questions to improve the accuracy of voice recognition. When the user uses text input, the reception unit can also display detailed questions to increase the degree of freedom in input. Furthermore, when the user uses image input, the reception unit can perform image analysis and automatically generate related questions. This makes it possible to provide a question format that suits the user's input method.

[0037] The reception unit can include region-specific project elements in the question based on the user's geographic location information. For example, if the user is in a specific region, the reception unit can include region-specific regulations and requirements in the question. In addition, if the user is in a different region, the reception unit can display questions that take into account the characteristics of each region. Furthermore, if the user is traveling, the reception unit can automatically update the optimal questions based on the user's current location. This allows the user to be provided with questions that take into account region-specific project elements.

[0038] The reception unit can analyze the user's social media activity and present related questions. For example, the reception unit can automatically generate related questions based on information shared by the user on social media. The reception unit can also analyze the user's social media activity history and suggest questions that the user may be interested in. Furthermore, the reception unit can present related questions by referring to the activity of the user's friends on social media. This makes it possible to provide questions based on the user's social media activity.

[0039] The reception unit can customize the question format by reflecting the user's past feedback. For example, the reception unit can improve the content and format of the question based on the feedback provided by the user in the past. The reception unit can also analyze the user's feedback and suggest the optimal question format. Furthermore, the reception unit can also adjust the order and content of the questions by reflecting the user's feedback. This makes it possible to provide a question format based on the user's past feedback.

[0040] During analysis, the analysis unit can optimize the analysis algorithm by referring to past project data. For example, the analysis unit selects an optimal analysis algorithm based on past project data. The analysis unit can also analyze past project data to improve the accuracy of the analysis algorithm. Furthermore, the analysis unit can also increase the reliability of the analysis results by referring to past project data. This makes it possible to provide an optimal analysis algorithm based on past project data.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can provide basic analysis results and avoid technical terms. If the user is an intermediate user, the analysis unit can provide detailed analysis results and use technical terms moderately. Furthermore, if the user is an advanced user, the analysis unit can provide specialized analysis results and include detailed information. This makes it possible to provide analysis results according to the user's level of expertise.

[0042] During analysis, the analysis unit can customize the analysis method based on the user's current project status. For example, if the user's project is in the early stages, the analysis unit uses a basic analysis method. If the user's project is in the middle stages, the analysis unit can also use a detailed analysis method. Furthermore, if the user's project is in the final stages, the analysis unit can also use the most detailed analysis method. This makes it possible to provide an analysis method that suits the user's project status.

[0043] During analysis, the analysis unit can take into account the user's geographic location information and reflect region-specific factors in the analysis results. For example, if the user is in a specific region, the analysis unit can reflect region-specific regulations and requirements in the analysis results. In addition, if the user is in a different region, the analysis unit can provide analysis results that take into account the characteristics of each region. Furthermore, if the user is moving, the analysis unit can automatically update the analysis results based on the user's current location. This allows the analysis results to be provided that take into account region-specific factors.

[0044] During the analysis, the analysis unit can analyze the user's social media activities and provide related analysis results. For example, the analysis unit can automatically generate related analysis results based on information shared by the user on social media. The analysis unit can also analyze the user's social media activity history and provide analysis results that may be of interest. Furthermore, the analysis unit can provide related analysis results by referring to the activities of the user's friends on social media. This makes it possible to provide analysis results based on the user's social media activities.

[0045] During analysis, the analysis unit can customize the analysis method by reflecting the user's past feedback. For example, the analysis unit can improve the analysis method based on feedback provided by the user in the past. The analysis unit can also analyze the user's feedback and propose an optimal analysis method. Furthermore, the analysis unit can also adjust the analysis method by reflecting the user's feedback. This makes it possible to provide an analysis method based on the user's past feedback.

[0046] The generation unit can adjust the level of detail of the plan based on the importance of the project when generating the plan. For example, the generation unit generates a detailed plan for a project of high importance. The generation unit can also generate a basic plan for a project of low importance. Furthermore, the generation unit can also generate a plan with an appropriate level of detail for a project of medium importance. This makes it possible to provide a plan with a level of detail that corresponds to the importance of the project.

[0047] The generation unit can apply different generation algorithms depending on the project category during generation. For example, in the case of a data project, the generation unit applies a data-specific generation algorithm. In addition, in the case of an audio project, the generation unit can also apply an audio-specific generation algorithm. Furthermore, in the case of a mobile project, the generation unit can also apply a mobile-specific generation algorithm. This makes it possible to provide the optimal generation algorithm depending on the project category.

[0048] The generation unit can improve the accuracy of generation by referring to the user's past plans during generation. The generation unit, for example, selects an optimal generation algorithm based on plans created by the user in the past. The generation unit can also analyze the user's past plans and improve the accuracy of the generation algorithm. Furthermore, the generation unit can also increase the reliability of the generation results by referring to the user's past plans. This makes it possible to provide a highly accurate plan based on the user's past plans.

[0049] The generation unit can determine the priority of the plan based on the submission date of the project at the time of generation. For example, the generation unit generates the plan with priority for a project with an upcoming submission deadline. The generation unit can also postpone the generation of the plan for a project with a distant submission deadline. Furthermore, the generation unit can generate the plan with a moderate priority for a project with a medium submission deadline. This makes it possible to provide the priority of the plan according to the submission date of the project.

[0050] The generation unit can adjust the order of the plan documents based on the relevance of the projects during generation. For example, the generation unit can generate detailed plan documents with priority for highly relevant projects. The generation unit can also generate basic plan documents later for projects with low relevance. Furthermore, the generation unit can generate plan documents in an appropriate order for projects with medium relevance. This makes it possible to provide an order of plan documents according to the relevance of the projects.

[0051] The generation unit can adjust the use of technical terms in the plan according to the user's level of expertise during generation. For example, if the user is a beginner, the generation unit can avoid technical terms and use basic terms. Also, if the user is an intermediate user, the generation unit can use technical terms moderately. Furthermore, if the user is an advanced user, the generation unit can generate a detailed plan using a lot of technical terms. This makes it possible to provide technical terms in the plan according to the user's level of expertise.

[0052] At the time of provision, the provision unit can select the optimal provision method by referring to the user's past plan provision history. For example, the provision unit preferentially suggests a provision method that the user has used favorably in the past. The provision unit can also analyze the user's past provision history and select the optimal provision method. Furthermore, the provision unit can also suggest the optimal provision method for a specific time period based on the user's past provision history. This makes it possible to provide the optimal provision method based on the user's past provision history.

[0053] The providing unit can customize the content to be provided based on the user's current project status at the time of provision. For example, if the user's project is in the early stages, the providing unit can provide basic information. If the user's project is in the middle stages, the providing unit can also provide detailed information. Furthermore, if the user's project is in the final stages, the providing unit can also provide the most detailed information. This makes it possible to provide content according to the user's project status.

[0054] The providing unit can improve the providing method by reflecting the user's feedback when providing the information. The providing unit can improve the providing method, for example, based on feedback provided by the user in the past. The providing unit can also analyze the user's feedback and propose an optimal providing method. Furthermore, the providing unit can adjust the providing method by reflecting the user's feedback. This makes it possible to provide a providing method based on the user's feedback.

[0055] The providing unit can select the optimal delivery method by taking into consideration the user's geographical location information when providing the information. For example, if the user is in a specific area, the providing unit can suggest a delivery method specific to that area. Furthermore, if the user is in a different area, the providing unit can also provide a delivery method that takes into consideration the characteristics of each area. Furthermore, if the user is moving, the providing unit can automatically update the optimal delivery method based on the user's current location. This makes it possible to provide the optimal delivery method based on the user's geographical location information.

[0056] The providing unit can analyze the user's social media activity and suggest related provision methods at the time of provision. For example, the providing unit automatically generates related provision methods based on information shared by the user on social media. The providing unit can also analyze the user's social media activity history and suggest provision methods that the user may be interested in. Furthermore, the providing unit can present related provision methods with reference to the activities of the user's friends on social media. This makes it possible to provide provision methods based on the user's social media activity.

[0057] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit can improve the delivery method based on, for example, feedback provided by the user in the past. The providing unit can also analyze the user's feedback and propose an optimal delivery method. Furthermore, the providing unit can also adjust the delivery method by reflecting the user's feedback. This makes it possible to provide a delivery method based on the user's past feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can automatically extract risk factors for the project based on the user's input and perform a risk assessment. For example, the reception unit analyzes information entered by the user, such as the project's scale, duration, and number of phases, and compares it with a past database to identify risk factors. The reception unit can also present the results of the risk assessment to the user and propose risk avoidance measures. Furthermore, the reception unit can add risk management items to the project plan based on the results of the risk assessment. This allows the user to understand project risks in advance and take appropriate measures.

[0060] The analysis unit can predict the success probability of a project based on the user's input. For example, the analysis unit analyzes information such as the project size, duration, and number of phases entered by the user, and calculates the success probability by comparing it with past successful projects. The analysis unit can also present improvements and recommendations for projects with a low success probability. Furthermore, the analysis unit can add success strategies to the project plan based on the predicted success probability. This allows the user to take specific actions to increase the success probability of the project.

[0061] The reception unit can automatically generate a project cost estimate based on the user's input. For example, the reception unit analyzes information entered by the user, such as the project's scale, duration, and number of phases, and compares it with a past database to calculate a cost estimate. The reception unit can also present the cost estimate results to the user and provide budget management advice. Furthermore, the reception unit can add budget management items to the project plan based on the cost estimate results. This allows the user to understand the project costs in advance and perform appropriate budget management.

[0062] The generation unit can optimize resource allocation for a project based on user input. For example, the generation unit analyzes information such as the project size, duration, and number of phases entered by the user, and compares it with a past database to calculate the optimal resource allocation. The generation unit can also present the results of resource allocation to the user and provide resource management advice. Furthermore, the generation unit can add resource management items to the project plan based on the results of resource allocation. This allows the user to efficiently allocate project resources and perform appropriate resource management.

[0063] The reception unit can optimize the project schedule based on the user's input. For example, the reception unit analyzes information such as the project scale, duration, and number of phases entered by the user, and compares it with a past database to calculate an optimal schedule. The reception unit can also present the schedule results to the user and provide schedule management advice. Furthermore, the reception unit can add schedule management items to the project plan based on the schedule results. This allows the user to efficiently manage the project schedule and perform appropriate schedule management.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit accepts input from the user. User input includes, for example, text input, voice input, and image input. The reception unit accepts input in the form of questions, in which the user selects one of the services—"data," "voice," or "mobile"—from a menu on the PC, and enters the service scale (number of lines, number of terminals), period, number of phases, configuration (new, changed, mixed), and project elements (implementation, operation, billing, design, etc.). Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed, for example, based on a data analysis method or a type of algorithm. The analysis unit calculates an appropriate lead time by referring to the lead time database of each service provider. Step 3: The generation unit generates a project plan based on the information analyzed by the analysis unit. The generation unit uses the generation AI to generate a project plan including the scope, milestones, WBS, etc. The generation AI automatically generates each element of the project plan based on the information entered by the user. Step 4: The providing unit provides the project plan generated by the generating unit. The provision is performed in the form of, for example, email, a download link, printed material, etc. The providing unit can quickly provide the generated plan to the user.

[0066] (Example 2) The automatic project plan generation system according to an embodiment of the present invention uses a generation AI to automatically generate a project plan. In this system, a user selects one of the following services from a menu on their PC: "Data," "Voice," or "Mobile." The user then inputs the service scale (number of lines, number of terminals), duration, number of phases, configuration (new, modified, or mixed), and project elements (implementation, operation, billing, design, etc.) in the form of questions. The generation AI then automatically generates a project plan (scope, milestones, WBS, etc.). The plan is output in a format that takes into account the lead time of each service provided by the communications service provider. For example, if a user selects the "Data" service and inputs the number of lines as 100, the duration as 6 months, the number of phases as 3, the configuration as "New," and the project elements as "Implementation" and "Operation," the generation AI generates a plan based on this information. This allows the user to obtain a project plan based on a realistic schedule. This allows the automatic project plan generation system to streamline project plan creation, allowing users to create plans quickly and accurately. For example, even for large-scale projects, the generation AI automatically generates a plan, making it possible to obtain a detailed plan without much effort. In addition, the plan is more reliable because it takes into account the lead times of each service provided by the communications service provider.

[0067] A project plan automatic generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input from a user. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit receives, for example, a user selecting one of the services "data," "voice," or "mobile" from a menu on a PC, and inputting the service scale (number of lines, number of terminals), duration, number of phases, configuration (new, modified, mixed), and project elements (implementation, operation, billing, design, etc.) in the form of a question. The analysis unit analyzes the information received by the reception unit. The analysis is performed, for example, based on a data analysis method or algorithm, but is not limited to, examples. For example, the analysis unit calculates an appropriate lead time by referring to a lead time database for each service provider. The generation unit generates a project plan based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate a project plan including a scope, milestones, WBS, etc. For example, the generation AI automatically generates each element of the project plan based on the information entered by the user. The providing unit provides the project plan generated by the generating unit. The provision may be in the form of, for example, an email, a download link, a printed document, or the like, but is not limited to these examples. The providing unit can quickly provide the generated plan to the user. In this way, the project plan automatic generation system according to the embodiment can automatically generate and provide a project plan based on user input.

[0068] The reception unit can display the scale, period, number of phases, configuration, and project elements of the service in question format and accept input from the user. Examples of service scales include, but are not limited to, small, medium, and large. The reception unit, for example, displays the scale of the service in question format and accepts input from the user. Examples of durations include, but are not limited to, days, weeks, months, and years. The reception unit, for example, displays the period in question format and accepts input from the user. Examples of the number of phases include, but are not limited to, the number of stages in the project. The reception unit, for example, displays the number of phases in question format and accepts input from the user. Examples of configurations include, but are not limited to, team configuration and resource configuration. The reception unit, for example, displays the configuration in question format and accepts input from the user. Examples of project elements include, but are not limited to, tasks, resources, and risks. The reception unit, for example, displays the project elements in question format and accepts input from the user. This allows the user to efficiently input necessary information.

[0069] The analysis unit can analyze the information received by the reception unit and calculate the lead time by referring to the lead time database of each service provider. Examples of lead times include, but are not limited to, average lead time and longest lead time. For example, the analysis unit can analyze the information received by the reception unit and calculate an appropriate lead time by referring to the lead time database of each service provider. For example, the analysis unit can analyze the information received by the reception unit using a data analysis method. The analysis unit can also calculate the lead time based on the type of algorithm. This allows a realistic lead time to be reflected in the project plan.

[0070] The generation unit can generate a project plan including a scope, milestones, and WBS based on the information analyzed by the analysis unit. The scope includes, for example, project objectives, goals, and constraints, but is not limited to these examples. The generation unit generates a project plan including the scope based on, for example, the information analyzed by the analysis unit. The milestones include, for example, important turning points and goals to be achieved, but are not limited to these examples. The generation unit generates a project plan including the milestones based on, for example, the information analyzed by the analysis unit. The WBS includes, for example, a task hierarchy and a work breakdown structure, but is not limited to these examples. The generation unit generates a project plan including the WBS based on, for example, the information analyzed by the analysis unit. This allows automatic generation of a detailed project plan.

[0071] The providing unit can provide the project plan generated by the generating unit to the user. Examples of the providing method include, but are not limited to, email, a download link, and a printed copy. For example, the providing unit can provide the project plan generated by the generating unit to the user by email. The providing unit can also provide the project plan generated by the generating unit to the user via a download link. Furthermore, the providing unit can also provide the project plan generated by the generating unit to the user as a printed copy. This allows the user to quickly receive the generated plan.

[0072] The reception unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can reduce the number of questions and change the question format to a simpler one. Furthermore, if the user is relaxed, the reception unit can add detailed questions to collect more specific information. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying important questions to enable the user to complete input quickly. This allows the user to be provided with a question format that matches their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The reception unit can analyze the user's past input history and select a question format. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past input history. This makes it possible to provide an efficient question format based on the user's past input history.

[0074] The reception unit can customize the question format according to the user's level of expertise. For example, if the user is a beginner, the reception unit prioritizes basic questions and avoids technical terms. If the user is an intermediate user, the reception unit can add detailed questions and use technical terms moderately. Furthermore, if the user is an advanced user, the reception unit can include many technical questions and collect detailed information. This makes it possible to provide a question format according to the user's level of expertise.

[0075] The reception unit can select the optimal question format depending on the user's input method. For example, when the user uses voice input, the reception unit prioritizes short questions to improve the accuracy of voice recognition. When the user uses text input, the reception unit can also display detailed questions to increase the degree of freedom in input. Furthermore, when the user uses image input, the reception unit can perform image analysis and automatically generate related questions. This makes it possible to provide a question format that suits the user's input method.

[0076] The reception unit can estimate the user's emotions and adjust the difficulty of questions based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize easy questions and make them easier to answer. Also, if the user is relaxed, the reception unit can add more difficult questions and collect more detailed information. Furthermore, if the user is in a hurry, the reception unit can prioritize important questions and allow the user to complete the input quickly. This makes it possible to provide questions with a level of difficulty that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The reception unit can include region-specific project elements in the question based on the user's geographic location information. For example, if the user is in a specific region, the reception unit can include region-specific regulations and requirements in the question. In addition, if the user is in a different region, the reception unit can display questions that take into account the characteristics of each region. Furthermore, if the user is traveling, the reception unit can automatically update the optimal questions based on the user's current location. This allows the user to be provided with questions that take into account region-specific project elements.

[0078] The reception unit can analyze the user's social media activity and present related questions. For example, the reception unit can automatically generate related questions based on information shared by the user on social media. The reception unit can also analyze the user's social media activity history and suggest questions that the user may be interested in. Furthermore, the reception unit can present related questions by referring to the activity of the user's friends on social media. This makes it possible to provide questions based on the user's social media activity.

[0079] The reception unit can customize the question format by reflecting the user's past feedback. For example, the reception unit can improve the content and format of the question based on the feedback provided by the user in the past. The reception unit can also analyze the user's feedback and suggest the optimal question format. Furthermore, the reception unit can also adjust the order and content of the questions by reflecting the user's feedback. This makes it possible to provide a question format based on the user's past feedback.

[0080] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize important analyses and provide results quickly. The analysis unit can also perform detailed analyses and provide comprehensive results when the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can prioritize the most important analyses and provide results quickly. This makes it possible to provide analysis priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] During analysis, the analysis unit can optimize the analysis algorithm by referring to past project data. For example, the analysis unit selects an optimal analysis algorithm based on past project data. The analysis unit can also analyze past project data to improve the accuracy of the analysis algorithm. Furthermore, the analysis unit can also increase the reliability of the analysis results by referring to past project data. This makes it possible to provide an optimal analysis algorithm based on past project data.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can provide basic analysis results and avoid technical terms. If the user is an intermediate user, the analysis unit can provide detailed analysis results and use technical terms moderately. Furthermore, if the user is an advanced user, the analysis unit can provide specialized analysis results and include detailed information. This makes it possible to provide analysis results according to the user's level of expertise.

[0083] During analysis, the analysis unit can customize the analysis method based on the user's current project status. For example, if the user's project is in the early stages, the analysis unit uses a basic analysis method. If the user's project is in the middle stages, the analysis unit can also use a detailed analysis method. Furthermore, if the user's project is in the final stages, the analysis unit can also use the most detailed analysis method. This makes it possible to provide an analysis method that suits the user's project status.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to provide a display method of the analysis results that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] During analysis, the analysis unit can take into account the user's geographic location information and reflect region-specific factors in the analysis results. For example, if the user is in a specific region, the analysis unit can reflect region-specific regulations and requirements in the analysis results. In addition, if the user is in a different region, the analysis unit can provide analysis results that take into account the characteristics of each region. Furthermore, if the user is moving, the analysis unit can automatically update the analysis results based on the user's current location. This allows the analysis results to be provided that take into account region-specific factors.

[0086] During the analysis, the analysis unit can analyze the user's social media activities and provide related analysis results. For example, the analysis unit can automatically generate related analysis results based on information shared by the user on social media. The analysis unit can also analyze the user's social media activity history and provide analysis results that may be of interest. Furthermore, the analysis unit can provide related analysis results by referring to the activities of the user's friends on social media. This makes it possible to provide analysis results based on the user's social media activities.

[0087] During analysis, the analysis unit can customize the analysis method by reflecting the user's past feedback. For example, the analysis unit can improve the analysis method based on feedback provided by the user in the past. The analysis unit can also analyze the user's feedback and propose an optimal analysis method. Furthermore, the analysis unit can also adjust the analysis method by reflecting the user's feedback. This makes it possible to provide an analysis method based on the user's past feedback.

[0088] The generation unit can estimate the user's emotions and adjust the expression method of the generated plan based on the estimated user's emotions. For example, if the user is nervous, the generation unit generates a simple, highly visible plan. If the user is relaxed, the generation unit can also generate a plan that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can also generate a plan that focuses on the main points. This makes it possible to provide a method of expressing the plan according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The generation unit can adjust the level of detail of the plan based on the importance of the project when generating the plan. For example, the generation unit generates a detailed plan for a project of high importance. The generation unit can also generate a basic plan for a project of low importance. Furthermore, the generation unit can also generate a plan with an appropriate level of detail for a project of medium importance. This makes it possible to provide a plan with a level of detail that corresponds to the importance of the project.

[0090] The generation unit can apply different generation algorithms depending on the project category during generation. For example, in the case of a data project, the generation unit applies a data-specific generation algorithm. In addition, in the case of an audio project, the generation unit can also apply an audio-specific generation algorithm. Furthermore, in the case of a mobile project, the generation unit can also apply a mobile-specific generation algorithm. This makes it possible to provide the optimal generation algorithm depending on the project category.

[0091] The generation unit can improve the accuracy of generation by referring to the user's past plans during generation. The generation unit, for example, selects an optimal generation algorithm based on plans created by the user in the past. The generation unit can also analyze the user's past plans and improve the accuracy of the generation algorithm. Furthermore, the generation unit can also increase the reliability of the generation results by referring to the user's past plans. This makes it possible to provide a highly accurate plan based on the user's past plans.

[0092] The generation unit can estimate the user's emotions and adjust the length of the plan based on the estimated user's emotions. For example, if the user is nervous, the generation unit can generate a short plan that is concise. If the user is relaxed, the generation unit can also generate a longer plan that includes detailed explanations. Furthermore, if the user is in a hurry, the generation unit can also generate a plan that summarizes the main points so that it can be understood quickly. This makes it possible to provide a plan of a length that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The generation unit can determine the priority of the plan based on the submission date of the project at the time of generation. For example, the generation unit generates the plan with priority for a project with an upcoming submission deadline. The generation unit can also postpone the generation of the plan for a project with a distant submission deadline. Furthermore, the generation unit can generate the plan with a moderate priority for a project with a medium submission deadline. This makes it possible to provide the priority of the plan according to the submission date of the project.

[0094] The generation unit can adjust the order of the plan documents based on the relevance of the projects during generation. For example, the generation unit can generate detailed plan documents with priority for highly relevant projects. The generation unit can also generate basic plan documents later for projects with low relevance. Furthermore, the generation unit can generate plan documents in an appropriate order for projects with medium relevance. This makes it possible to provide an order of plan documents according to the relevance of the projects.

[0095] The generation unit can adjust the use of technical terms in the plan according to the user's level of expertise during generation. For example, if the user is a beginner, the generation unit can avoid technical terms and use basic terms. Also, if the user is an intermediate user, the generation unit can use technical terms moderately. Furthermore, if the user is an advanced user, the generation unit can generate a detailed plan using a lot of technical terms. This makes it possible to provide technical terms in the plan according to the user's level of expertise.

[0096] The providing unit can estimate the user's emotions and adjust the method of providing the plan based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide the plan in a simple, highly visible format. Furthermore, if the user is relaxed, the providing unit can also provide the plan in a format including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide the plan in a format that focuses on the main points. This makes it possible to provide a method of providing a plan that suits the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] At the time of provision, the provision unit can select the optimal provision method by referring to the user's past plan provision history. For example, the provision unit preferentially suggests a provision method that the user has used favorably in the past. The provision unit can also analyze the user's past provision history and select the optimal provision method. Furthermore, the provision unit can also suggest the optimal provision method for a specific time period based on the user's past provision history. This makes it possible to provide the optimal provision method based on the user's past provision history.

[0098] The providing unit can customize the content to be provided based on the user's current project status at the time of provision. For example, if the user's project is in the early stages, the providing unit can provide basic information. If the user's project is in the middle stages, the providing unit can also provide detailed information. Furthermore, if the user's project is in the final stages, the providing unit can also provide the most detailed information. This makes it possible to provide content according to the user's project status.

[0099] The providing unit can improve the providing method by reflecting the user's feedback when providing the information. The providing unit can improve the providing method, for example, based on feedback provided by the user in the past. The providing unit can also analyze the user's feedback and propose an optimal providing method. Furthermore, the providing unit can adjust the providing method by reflecting the user's feedback. This makes it possible to provide a providing method based on the user's feedback.

[0100] The providing unit can estimate the user's emotions and adjust the timing of providing the plan based on the estimated user's emotions. For example, if the user is nervous, the providing unit can quickly provide the plan. Furthermore, if the user is relaxed, the providing unit can also provide a plan that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can quickly provide a plan that covers the main points. This makes it possible to provide the timing of providing the plan according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The providing unit can select the optimal delivery method by taking into consideration the user's geographical location information when providing the information. For example, if the user is in a specific area, the providing unit can suggest a delivery method specific to that area. Furthermore, if the user is in a different area, the providing unit can also provide a delivery method that takes into consideration the characteristics of each area. Furthermore, if the user is moving, the providing unit can automatically update the optimal delivery method based on the user's current location. This makes it possible to provide the optimal delivery method based on the user's geographical location information.

[0102] The providing unit can analyze the user's social media activity and suggest related provision methods at the time of provision. For example, the providing unit automatically generates related provision methods based on information shared by the user on social media. The providing unit can also analyze the user's social media activity history and suggest provision methods that the user may be interested in. Furthermore, the providing unit can present related provision methods with reference to the activities of the user's friends on social media. This makes it possible to provide provision methods based on the user's social media activity.

[0103] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit can improve the delivery method based on, for example, feedback provided by the user in the past. The providing unit can also analyze the user's feedback and propose an optimal delivery method. Furthermore, the providing unit can also adjust the delivery method by reflecting the user's feedback. This makes it possible to provide a delivery method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive input from a user via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information received from the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a project plan based on the analyzed information. The provision unit provides the generated project plan via, for example, the output device 40 of the smart device 14 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive input from a user via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information received from the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a project plan based on the analyzed information. The provision unit provides the generated project plan via, for example, the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can receive input from a user via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information received from the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a project plan based on the analyzed information. The provision unit provides the generated project plan via, for example, the display 343 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive input from a user via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information received from the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a project plan based on the analyzed information. The provision unit provides the generated project plan via, for example, the speaker 240 of the robot 414 or the communication I / F 26 of the data processing device 12.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can automatically extract risk factors for the project based on the user's input and perform a risk assessment. For example, the reception unit analyzes information entered by the user, such as the project's scale, duration, and number of phases, and compares it with a past database to identify risk factors. The reception unit can also present the results of the risk assessment to the user and propose risk avoidance measures. Furthermore, the reception unit can add risk management items to the project plan based on the results of the risk assessment. This allows the user to understand project risks in advance and take appropriate measures.

[0106] The analysis unit can predict the success probability of a project based on the user's input. For example, the analysis unit analyzes information such as the project size, duration, and number of phases entered by the user, and calculates the success probability by comparing it with past successful projects. The analysis unit can also present improvements and recommendations for projects with a low success probability. Furthermore, the analysis unit can add success strategies to the project plan based on the predicted success probability. This allows the user to take specific actions to increase the success probability of the project.

[0107] The generation unit can estimate the user's emotions and customize the design of the project plan based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can adopt a simple, highly visible design. If the user is relaxed, the generation unit can also adopt a design that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can also adopt a design that emphasizes the main points. This makes it possible to provide a project plan with an optimal design according to the user's emotions.

[0108] The providing unit can estimate the user's emotions and adjust the method of providing the plan based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide the plan in a simple, highly visible format. If the user is relaxed, the providing unit can also provide the plan in a format including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide the plan in a format that focuses on the main points. This makes it possible to provide a method of providing a plan that suits the user's emotions.

[0109] The reception unit can automatically generate a project cost estimate based on the user's input. For example, the reception unit analyzes information entered by the user, such as the project's scale, duration, and number of phases, and compares it with a past database to calculate a cost estimate. The reception unit can also present the cost estimate results to the user and provide budget management advice. Furthermore, the reception unit can add budget management items to the project plan based on the cost estimate results. This allows the user to understand the project costs in advance and perform appropriate budget management.

[0110] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can prioritize important analyses and provide results quickly. Alternatively, if the user is relaxed, the analysis unit can perform detailed analyses and provide comprehensive results. Furthermore, if the user is in a hurry, the analysis unit can prioritize the most important analyses and provide results quickly. This makes it possible to provide analysis priorities according to the user's emotions.

[0111] The generation unit can optimize resource allocation for a project based on user input. For example, the generation unit analyzes information such as the project size, duration, and number of phases entered by the user, and compares it with a past database to calculate the optimal resource allocation. The generation unit can also present the results of resource allocation to the user and provide resource management advice. Furthermore, the generation unit can add resource management items to the project plan based on the results of resource allocation. This allows the user to efficiently allocate project resources and perform appropriate resource management.

[0112] The providing unit can estimate the user's emotions and adjust the timing of providing the plan based on the estimated user's emotions. For example, the providing unit can quickly provide a plan when the user is nervous. Furthermore, the providing unit can also provide a plan that includes detailed information when the user is relaxed. Furthermore, the providing unit can quickly provide a plan that covers the main points when the user is in a hurry. This makes it possible to provide the timing of providing a plan according to the user's emotions.

[0113] The reception unit can optimize the project schedule based on the user's input. For example, the reception unit analyzes information such as the project scale, duration, and number of phases entered by the user, and compares it with a past database to calculate an optimal schedule. The reception unit can also present the schedule results to the user and provide schedule management advice. Furthermore, the reception unit can add schedule management items to the project plan based on the schedule results. This allows the user to efficiently manage the project schedule and perform appropriate schedule management.

[0114] The generation unit can estimate the user's emotions and adjust the length of the plan based on the estimated user's emotions. For example, if the user is nervous, the generation unit can generate a short plan that is concise. Alternatively, if the user is relaxed, the generation unit can generate a longer plan that includes detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a plan that summarizes the main points so that it can be quickly understood. This allows the length of the plan to be adjusted according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit accepts input from the user. User input includes, for example, text input, voice input, and image input. The reception unit accepts input in the form of questions, in which the user selects one of the services—"data," "voice," or "mobile"—from a menu on the PC, and enters the service scale (number of lines, number of terminals), period, number of phases, configuration (new, changed, mixed), and project elements (implementation, operation, billing, design, etc.). Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed, for example, based on a data analysis method or a type of algorithm. The analysis unit calculates an appropriate lead time by referring to the lead time database of each service provider. Step 3: The generation unit generates a project plan based on the information analyzed by the analysis unit. The generation unit uses the generation AI to generate a project plan including the scope, milestones, WBS, etc. The generation AI automatically generates each element of the project plan based on the information entered by the user. Step 4: The providing unit provides the project plan generated by the generating unit. The provision is performed in the form of, for example, email, a download link, printed material, etc. The providing unit can quickly provide the generated plan to the user.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

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

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

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

[0140] The 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.

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

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

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

[0144] Fig. 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.

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

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

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0151] 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.

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

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

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

[0156] The 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.

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

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates a project plan based on the information analyzed by the analysis unit; a providing unit that provides the project plan generated by the generating unit; Equipped with A system characterized by:

2. The reception unit Display the service scale, duration, number of phases, structure, and project elements in the form of questions and accept input from the user.

2. The system of claim 1.

3. The analysis unit The information received by the reception unit is analyzed, and the lead time is calculated by referring to the lead time database of each service provider.

2. The system of claim 1.

4. The generation unit Based on the information analyzed by the analysis department, a project plan including scope, milestones, and WBS is generated.

2. The system of claim 1.

5. The providing unit The project plan generated by the generating unit is provided to a user.

2. The system of claim 1.

6. The reception unit Infer user sentiment and adjust the order and content of questions based on the estimated sentiment 2. The system of claim 1.

7. The reception unit Analyze the user's past input history and select the question format 2. The system of claim 1.

8. The reception unit Customize the question format to suit the user's level of expertise 2. The system of claim 1.

Images