system

Abstract

The system according to the embodiment aims to provide real-time customized advice when planning a project. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and an update unit. The reception unit inputs project goals or requirements. The analysis unit analyzes the information input by the reception unit and provides customized advice. The provision unit provides the advice provided by the analysis unit to a user. The update unit updates the advice provided by the provision unit based on user feedback.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the challenge of making it difficult to provide the advice and guidance needed for project planning in real time and in a customized manner.

[0005] The system according to the embodiment aims to provide real-time customized advice when planning a project. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and an update unit. The reception unit inputs project goals or requirements. The analysis unit analyzes the information input by the reception unit and provides customized advice. The provision unit provides the advice provided by the analysis unit to a user. The update unit updates the advice provided by the provision unit based on user feedback. [Effects of the Invention]

[0007] An embodiment of the system can provide real-time, customized advice when developing 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) A project planning support system according to an embodiment of the present invention is an online service that provides advice and guidance necessary for developing a project plan. This project planning support system allows users to input project goals and requirements and receive customized planning advice in real time using a generation AI. This service provides support at each stage of the plan, from formulation to execution, helping users to effectively advance their projects. For example, a user inputs project goals and requirements, such as specific goals and requirements like "new product development project" or "marketing campaign plan." This information is then input into a generation AI. The generation AI then analyzes the input information and provides customized advice for each stage of the project plan. For example, in the early stages of the project, it provides advice on goal setting and resource allocation, and in the intermediate stages, it provides advice on progress management and risk management. Furthermore, in the final stages, it provides advice on deliverable evaluation and next steps. This service helps users effectively advance their project plans. For example, it provides tools and resources for users to check the progress of the project and make necessary adjustments. The generation AI also updates its advice in real time based on user feedback and proposes optimal plans. In this way, an online service is realized that supports users at each stage of project planning and supports effective project progress. This allows the project planning support system to provide support for the user to effectively proceed with the project plan.

[0029] A project planning support system according to an embodiment includes a receiving unit, an analysis unit, a providing unit, and an updating unit. The receiving unit receives input of project goals and requirements from a user. For example, the user can input specific goals and requirements, such as a "new product development project" or a "marketing campaign plan." The receiving unit inputs this information to a generation AI. The analysis unit uses the generation AI to analyze the information input by the receiving unit and provide customized advice for each stage of project planning. For example, in the early stages of a project, the analysis unit provides advice on goal setting and resource allocation. The generation AI can receive a prompt, such as "Please clearly set the project goals," and suggest specific goal setting methods to the user. Furthermore, the analysis unit provides advice on progress management and risk management in the intermediate stages of a project. The generation AI can receive a prompt, such as "Please review the project progress and make necessary adjustments," and suggest progress management methods to the user. Furthermore, in the final stages of a project, the analysis unit provides advice on deliverable evaluation and next steps. For example, the generation AI can receive a prompt such as "Evaluate the project deliverables and plan the next step," and suggest to the user how to evaluate the deliverables and how to plan the next step. The provision unit provides the user with the advice provided by the analysis unit. For example, the provision unit can present specific advice to the user based on information analyzed by the generation AI. The provision unit can provide the advice to the user, for example, through a web application or a mobile application. The update unit updates the advice provided by the provision unit based on user feedback. The generation AI can update the advice in real time based on user feedback and propose an optimal plan. For example, when a user provides feedback on the provided advice, the generation AI can analyze the feedback and update the advice. As a result, the project planning support system according to the embodiment can provide support for the user to effectively advance project planning.

[0030] The reception unit can input project goals or requirements. For example, a user can input specific goals or requirements such as a "new product development project" or a "marketing campaign plan" into the reception unit. The reception unit inputs this information to the generation AI. The generation AI provides customized advice for each stage of the project plan based on the input information. This allows project goals and requirements to be input efficiently. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input information input by a user into the generation AI, which can then analyze the information and provide customized advice.

[0031] The analysis unit can analyze the input information and provide customized advice at each stage of the project plan. The analysis unit uses the generation AI to analyze the information input by the reception unit and provide customized advice at each stage of the project plan. For example, in the early stage of the project, advice on goal setting and resource allocation is provided. The generation AI can receive a prompt, for example, "Please clearly set the project goals," and suggest specific goal setting methods to the user. The analysis unit also provides advice on progress management and risk management at the intermediate stage of the project. The generation AI can receive a prompt, for example, "Please review the project progress and make necessary adjustments," and suggest progress management methods to the user. Furthermore, the analysis unit provides advice on evaluating deliverables and next steps at the final stage of the project. The generation AI can receive a prompt, for example, "Please evaluate the project deliverables and plan the next step," and suggest methods for evaluating deliverables and planning the next step to the user. This allows customized advice to be provided at each stage of the project plan. Some or all of the above-described processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information entered by the user into the generation AI, which can then analyze the information and provide customized advice.

[0032] The providing unit can provide advice regarding goal setting or resource allocation in the early stages of a project. The providing unit uses the generating AI to provide advice regarding goal setting and resource allocation in the early stages of a project. For example, the generating AI can receive a prompt such as "Please clearly set the project goals" and suggest a specific goal setting method to the user. The providing unit also provides advice regarding resource allocation. For example, the generating AI can receive a prompt such as "Please appropriately allocate project resources" and suggest a resource allocation method to the user. This makes it possible to provide advice regarding goal setting and resource allocation in the early stages of a project. Some or all of the above-described processing in the providing unit may be performed using, or without, the generating AI. For example, the providing unit can input information entered by a user into the generating AI, which can then analyze the information and provide customized advice.

[0033] The provision unit can provide advice regarding progress management or risk management during the intermediate stages of a project. The provision unit uses the generation AI to provide advice regarding progress management or risk management during the intermediate stages of a project. For example, the generation AI can receive a prompt such as, "Please review the progress of the project and make any necessary adjustments," and suggest a method of progress management to the user. The provision unit also provides advice regarding risk management. For example, the generation AI can receive a prompt such as, "Please assess the risks of the project and take appropriate measures," and suggest a method of risk management to the user. This allows advice regarding progress management or risk management during the intermediate stages of a project to be provided. Some or all of the above-described processing in the provision unit may be performed using, or without, the generation AI. For example, the provision unit can input information entered by a user into the generation AI, and the generation AI can analyze the information and provide customized advice.

[0034] The providing unit can provide advice regarding the evaluation of a deliverable or the next step in the final stage of a project. The providing unit uses the generating AI to provide advice regarding the evaluation of a deliverable or the next step in the final stage of a project. For example, the generating AI can receive a prompt such as "Please evaluate the project deliverable and plan the next step," and suggest to the user how to evaluate the deliverable or how to plan the next step. This allows advice regarding the evaluation of a deliverable or the next step in the final stage of a project to be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input information entered by a user into the generating AI, and the generating AI can analyze the information and provide customized advice.

[0035] The update unit can instantly update the advice based on user feedback. The update unit uses the generation AI to update the advice in real time based on user feedback. For example, when a user provides feedback in response to the provided advice, the generation AI can analyze the feedback and update the advice. This allows the advice to be updated in real time based on user feedback. Some or all of the above-described processing in the update unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the update unit can input feedback provided by the user to the generation AI, and the generation AI can analyze the feedback and update the advice.

[0036] The project planning support system includes a reception unit that analyzes a user's past project history and selects an optimal input method. The reception unit uses a generation AI to analyze the user's past project history and selects the optimal input method. For example, the reception unit can prioritize and suggest input methods (such as voice or text) that the user has used in the past. It can also predict and suggest specific input patterns based on the user's past project history. Furthermore, it can suggest the optimal input method by referring to input methods that the user has used successfully in past projects. This enables efficient information input by selecting the optimal input method based on the user's past project history. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past project data into the generation AI, which can then analyze the data and suggest the optimal input method.

[0037] The project planning support system includes a reception unit that filters project goals and requirements based on the user's current work situation and areas of interest when the project goals and requirements are input. The reception unit uses a generation AI to filter project goals and requirements based on the user's current work situation and areas of interest when the project goals and requirements are input. For example, the reception unit can analyze the user's current work situation and prioritize displaying relevant project goals and requirements. It can also suggest related project goals and requirements based on the user's areas of interest. Furthermore, it can combine the user's work situation and areas of interest to suggest optimal project goals and requirements. By filtering based on the user's work situation and areas of interest, highly relevant information can be prioritized for input. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input data on the user's work situation and areas of interest to the generation AI, which can then analyze the data and perform filtering.

[0038] The project planning support system includes a reception unit that prioritizes input of highly relevant information based on the user's geographical location information when inputting project goals and requirements. The reception unit prioritizes input of highly relevant information based on the user's geographical location information when inputting project goals and requirements using a generation AI. For example, relevant project goals and requirements can be suggested based on the user's current location. Region-specific information can also be prioritized by taking the user's geographical location information into consideration. Furthermore, optimal project goals and requirements can be suggested based on the user's location information. This allows region-specific information to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information to the generation AI, which can then analyze the information and prioritize input of highly relevant information.

[0039] The project planning support system includes a reception unit that analyzes a user's social media activity and inputs related information when project goals and requirements are input. The reception unit analyzes the user's social media activity and inputs related information when project goals and requirements are input using a generation AI. For example, the reception unit can analyze the user's social media activity and suggest related project goals and requirements. Also, the reception unit can suggest optimal project goals and requirements based on the user's social media interests. Furthermore, the reception unit can input related information with reference to the user's social media activity. This allows highly relevant information to be input by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's social media activity data to the generation AI, which can then analyze the data and input related information.

[0040] The project planning support system includes an analysis unit that adjusts the accuracy of the analysis based on the importance of the project during analysis. The analysis unit adjusts the accuracy of the analysis based on the importance of the project during analysis using the generation AI. For example, for a highly important project, the analysis unit can perform a detailed analysis. On the other hand, for a less important project, the analysis unit can perform a concise analysis. Furthermore, the analysis unit can dynamically adjust the accuracy of the analysis depending on the importance of the project. This enables efficient analysis by adjusting the accuracy of the analysis depending on the importance of the project. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input project importance data to the generation AI, which can analyze the data and adjust the accuracy of the analysis.

[0041] The project planning support system includes an analysis unit that applies an appropriate analysis algorithm depending on the project category during analysis. The analysis unit uses the generation AI to apply the appropriate analysis algorithm depending on the project category during analysis. For example, for a new product development project, the analysis unit can apply a specific analysis algorithm. For a marketing campaign, the analysis unit can apply a different analysis algorithm. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the project category. This enables highly accurate analysis by applying the optimal analysis algorithm depending on the project category. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input project category data into the generation AI, which can analyze the data and apply the appropriate analysis algorithm.

[0042] The project planning support system includes an analysis unit that determines the order of analysis based on the project submission dates during analysis. The analysis unit determines the order of analysis based on the project submission dates during analysis using the generation AI. For example, projects with upcoming submission deadlines can be analyzed preferentially. Projects with distant submission deadlines can also be postponed. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission dates. This enables efficient analysis by determining the analysis priority based on the project submission dates. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input project submission date data into the generation AI, which can analyze the data and determine the analysis order.

[0043] The project planning support system includes an analysis unit that adjusts the order of analysis based on the relevance of projects during analysis. The analysis unit adjusts the order of analysis based on the relevance of projects during analysis using a generation AI. For example, highly relevant projects can be analyzed preferentially. Less relevant projects can be postponed. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of projects. This enables efficient analysis by adjusting the order of analysis based on the relevance of projects. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input project relevance data into the generation AI, which can analyze the data and adjust the order of analysis.

[0044] The project planning support system includes a providing unit that adjusts the accuracy of advice based on the importance of the project when providing advice. The providing unit adjusts the accuracy of advice based on the importance of the project when providing advice using a generation AI. For example, for a project with high importance, the providing unit can provide detailed advice. For a project with low importance, the providing unit can provide concise advice. Furthermore, the providing unit can dynamically adjust the accuracy of the advice depending on the importance of the project. This enables efficient advice by adjusting the accuracy of the advice depending on the importance of the project. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input project importance data to the generation AI, and the generation AI can analyze the data and adjust the accuracy of the advice.

[0045] The project planning support system includes a providing unit that applies an appropriate advice algorithm depending on the project category when providing advice. The providing unit uses a generation AI to apply an appropriate advice algorithm depending on the project category when providing advice. For example, for a new product development project, the providing unit can apply a specific advice algorithm. For a marketing campaign, the providing unit can apply a different advice algorithm. Furthermore, depending on the project category, the providing unit can select the optimal advice algorithm. This enables highly accurate advice by applying the optimal advice algorithm depending on the project category. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input project category data into the generation AI, which can analyze the data and apply an appropriate advice algorithm.

[0046] The project planning support system includes a providing unit that, when providing advice, determines the order of advice based on the project submission dates. The providing unit determines the order of advice based on the project submission dates when providing advice using a generation AI. For example, advice can be given priority to projects with upcoming submission deadlines. Also, projects with distant submission deadlines can be postponed. Furthermore, the providing unit can dynamically adjust the priority of advice based on the submission dates. This enables efficient advice by determining the priority of advice based on the project submission dates. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input project submission date data into the generation AI, and the generation AI can analyze the data to determine the order of advice.

[0047] The project planning support system includes a providing unit that adjusts the order of advice based on the relevance of projects when providing advice. The providing unit adjusts the order of advice based on the relevance of projects when providing advice using a generation AI. For example, advice can be given priority to highly relevant projects. Less relevant projects can be postponed. Furthermore, the providing unit can dynamically adjust the order of advice based on the relevance of projects. This enables efficient advice by adjusting the order of advice based on the relevance of projects. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input project relevance data to the generation AI, and the generation AI can analyze the data and adjust the order of advice.

[0048] The project planning support system includes an update unit that, during an update, refers to past feedback data and appropriately adjusts the update algorithm. The update unit uses a generation AI to appropriately adjust the update algorithm by referring to past feedback data during an update. For example, the update unit can analyze past feedback data and select an optimal update algorithm. The update algorithm can also be dynamically adjusted based on the feedback data. Furthermore, the update algorithm can be optimized by referring to past feedback data. This enables highly accurate updates by optimizing the update algorithm based on past feedback data. Some or all of the above-described processing in the update unit may be performed using or without the generation AI. For example, the update unit can input past feedback data into the generation AI, which can then analyze the data and adjust the update algorithm.

[0049] The project planning support system includes an update unit that, at the time of update, determines the priority of update data based on the time of feedback submission. The update unit, at the time of update, determines the priority of update data based on the time of feedback submission using a generation AI. For example, if feedback has been recently submitted, the update can be performed by prioritizing that data. Also, if the feedback is old, the update can be performed by reducing the weight of that data. Furthermore, the update unit can dynamically adjust the weighting of the update data based on the time of feedback submission. This enables efficient updates by weighting the update data based on the time of feedback submission. Some or all of the above-mentioned processing in the update unit may be performed using or without the generation AI. For example, the update unit can input feedback submission time data to the generation AI, and the generation AI can analyze the data to determine the priority of the update data.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The project planning support system can also include a resource optimization unit that analyzes the user's past project data and proposes optimal resource allocation. For example, it can apply resource allocation patterns that were successful in past projects to the current project. It can also propose resource allocation methods to avoid failures in past projects. Furthermore, it can propose efficient resource utilization methods based on the user's past project data. This makes it possible to optimize resources by utilizing past data, thereby increasing the success rate of projects.

[0052] The project planning support system may also include a task monitoring unit that monitors the user's current task status in real time and provides optimal advice. For example, if the user is busy, the task monitoring unit can provide concise, to-the-point advice. On the other hand, if the user has time, the task monitoring unit can provide detailed advice. Furthermore, the priority of advice can be dynamically adjusted according to the user's task status. This allows optimal advice to be provided according to the user's task status, enabling the project to progress efficiently.

[0053] The project planning support system can also include a management method suggestion unit that suggests the optimal project management method based on the user's past project data. For example, it can apply management methods that have been successful in past projects to the current project. It can also suggest management methods to avoid failures in past projects. Furthermore, it can suggest management methods according to the progress of the project based on the user's past project data. This makes it possible to suggest the optimal management method by utilizing past data, thereby increasing the success rate of the project.

[0054] The project planning support system may also include a partner suggestion unit that suggests optimal project partners based on the user's geographical location information. For example, it may suggest nearby project partners based on the user's current location. It may also suggest partners with specialized knowledge specific to the region, taking into account the user's geographical location information. It may also select optimal project partners based on the user's location information. This makes it possible to suggest optimal partners taking into account the user's geographical location information, thereby increasing the success rate of the project.

[0055] The project planning support system may also include a goal suggestion unit that analyzes a user's social media activity and suggests optimal project goals. For example, the system can analyze a user's social media activity and suggest relevant project goals. Optimal project goals can also be suggested based on the user's social media interests. Furthermore, relevant information can be input based on the user's social media activity. By analyzing the user's social media activity, highly relevant project goals can be suggested, thereby increasing the success rate of the project.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user inputs the project goals and requirements into the reception unit. For example, the user can input specific goals and requirements such as "new product development project" or "marketing campaign plan." The reception unit inputs this information into the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit and provide customized advice for each stage of the project plan. For example, in the early stages of the project, it provides advice on goal setting and resource allocation. In the middle stages of the project, it provides advice on progress management and risk management, and in the final stages, it provides advice on evaluating deliverables and next steps. Step 3: The providing unit provides the advice provided by the analysis unit to the user. For example, the providing unit can present specific advice to the user based on the information analyzed by the generation AI. The providing unit can provide the advice to the user, for example, through a web application or a mobile application. Step 4: The update unit updates the advice provided by the provision unit based on the user's feedback. The generation AI can update the advice in real time based on the user's feedback and propose an optimal plan. For example, when the user provides feedback on the provided advice, the generation AI can analyze the feedback and update the advice.

[0058] (Example 2) A project planning support system according to an embodiment of the present invention is an online service that provides advice and guidance necessary for developing a project plan. This project planning support system allows users to input project goals and requirements and receive customized planning advice in real time using a generation AI. This service provides support at each stage of the plan, from formulation to execution, helping users to effectively advance their projects. For example, a user inputs project goals and requirements, such as specific goals and requirements like "new product development project" or "marketing campaign plan." This information is then input into a generation AI. The generation AI then analyzes the input information and provides customized advice for each stage of the project plan. For example, in the early stages of the project, it provides advice on goal setting and resource allocation, and in the intermediate stages, it provides advice on progress management and risk management. Furthermore, in the final stages, it provides advice on deliverable evaluation and next steps. This service helps users effectively advance their project plans. For example, it provides tools and resources for users to check the progress of the project and make necessary adjustments. The generation AI also updates its advice in real time based on user feedback and proposes optimal plans. In this way, an online service is realized that supports users at each stage of project planning and supports effective project progress. This allows the project planning support system to provide support for the user to effectively proceed with the project plan.

[0059] A project planning support system according to an embodiment includes a receiving unit, an analysis unit, a providing unit, and an updating unit. The receiving unit receives input of project goals and requirements from a user. For example, the user can input specific goals and requirements, such as a "new product development project" or a "marketing campaign plan." The receiving unit inputs this information to a generation AI. The analysis unit uses the generation AI to analyze the information input by the receiving unit and provide customized advice for each stage of project planning. For example, in the early stages of a project, the analysis unit provides advice on goal setting and resource allocation. The generation AI can receive a prompt, such as "Please clearly set the project goals," and suggest specific goal setting methods to the user. Furthermore, the analysis unit provides advice on progress management and risk management in the intermediate stages of a project. The generation AI can receive a prompt, such as "Please review the project progress and make necessary adjustments," and suggest progress management methods to the user. Furthermore, in the final stages of a project, the analysis unit provides advice on deliverable evaluation and next steps. For example, the generation AI can receive a prompt such as "Evaluate the project deliverables and plan the next step," and suggest to the user how to evaluate the deliverables and how to plan the next step. The provision unit provides the user with the advice provided by the analysis unit. For example, the provision unit can present specific advice to the user based on information analyzed by the generation AI. The provision unit can provide the advice to the user, for example, through a web application or a mobile application. The update unit updates the advice provided by the provision unit based on user feedback. The generation AI can update the advice in real time based on user feedback and propose an optimal plan. For example, when a user provides feedback on the provided advice, the generation AI can analyze the feedback and update the advice. As a result, the project planning support system according to the embodiment can provide support for the user to effectively advance project planning.

[0060] The reception unit can input project goals or requirements. For example, a user can input specific goals or requirements such as a "new product development project" or a "marketing campaign plan" into the reception unit. The reception unit inputs this information to the generation AI. The generation AI provides customized advice for each stage of the project plan based on the input information. This allows project goals and requirements to be input efficiently. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input information input by a user into the generation AI, which can then analyze the information and provide customized advice.

[0061] The analysis unit can analyze the input information and provide customized advice at each stage of the project plan. The analysis unit uses the generation AI to analyze the information input by the reception unit and provide customized advice at each stage of the project plan. For example, in the early stage of the project, advice on goal setting and resource allocation is provided. The generation AI can receive a prompt, for example, "Please clearly set the project goals," and suggest specific goal setting methods to the user. The analysis unit also provides advice on progress management and risk management at the intermediate stage of the project. The generation AI can receive a prompt, for example, "Please review the project progress and make necessary adjustments," and suggest progress management methods to the user. Furthermore, the analysis unit provides advice on evaluating deliverables and next steps at the final stage of the project. The generation AI can receive a prompt, for example, "Please evaluate the project deliverables and plan the next step," and suggest methods for evaluating deliverables and planning the next step to the user. This allows customized advice to be provided at each stage of the project plan. Some or all of the above-described processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information entered by the user into the generation AI, which can then analyze the information and provide customized advice.

[0062] The providing unit can provide advice regarding goal setting or resource allocation in the early stages of a project. The providing unit uses the generating AI to provide advice regarding goal setting and resource allocation in the early stages of a project. For example, the generating AI can receive a prompt such as "Please clearly set the project goals" and suggest a specific goal setting method to the user. The providing unit also provides advice regarding resource allocation. For example, the generating AI can receive a prompt such as "Please appropriately allocate project resources" and suggest a resource allocation method to the user. This makes it possible to provide advice regarding goal setting and resource allocation in the early stages of a project. Some or all of the above-described processing in the providing unit may be performed using, or without, the generating AI. For example, the providing unit can input information entered by a user into the generating AI, which can then analyze the information and provide customized advice.

[0063] The provision unit can provide advice regarding progress management or risk management during the intermediate stages of a project. The provision unit uses the generation AI to provide advice regarding progress management or risk management during the intermediate stages of a project. For example, the generation AI can receive a prompt such as, "Please review the progress of the project and make any necessary adjustments," and suggest a method of progress management to the user. The provision unit also provides advice regarding risk management. For example, the generation AI can receive a prompt such as, "Please assess the risks of the project and take appropriate measures," and suggest a method of risk management to the user. This allows advice regarding progress management or risk management during the intermediate stages of a project to be provided. Some or all of the above-described processing in the provision unit may be performed using, or without, the generation AI. For example, the provision unit can input information entered by a user into the generation AI, and the generation AI can analyze the information and provide customized advice.

[0064] The providing unit can provide advice regarding the evaluation of a deliverable or the next step in the final stage of a project. The providing unit uses the generating AI to provide advice regarding the evaluation of a deliverable or the next step in the final stage of a project. For example, the generating AI can receive a prompt such as "Please evaluate the project deliverable and plan the next step," and suggest to the user how to evaluate the deliverable or how to plan the next step. This allows advice regarding the evaluation of a deliverable or the next step in the final stage of a project to be provided. Some or all of the above-described processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input information entered by a user into the generating AI, and the generating AI can analyze the information and provide customized advice.

[0065] The update unit can instantly update the advice based on user feedback. The update unit uses the generation AI to update the advice in real time based on user feedback. For example, when a user provides feedback in response to the provided advice, the generation AI can analyze the feedback and update the advice. This allows the advice to be updated in real time based on user feedback. Some or all of the above-described processing in the update unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the update unit can input feedback provided by the user to the generation AI, and the generation AI can analyze the feedback and update the advice.

[0066] The project planning support system includes a reception unit that estimates a user's emotions and adjusts the input timing of project goals and requirements based on the estimated user emotions. The reception unit estimates the user's emotions using a generation AI and adjusts the input timing of project goals and requirements based on the estimated user emotions. For example, if the user is stressed, the reception unit can delay the input timing to provide a relaxing environment. Also, if the user is relaxed, the reception unit can accelerate the input timing to efficiently collect information. Furthermore, if the user is in a hurry, the reception unit can optimize the input timing to quickly collect information. This allows information to be collected at a more appropriate time by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the input timing.

[0067] The project planning support system includes a reception unit that analyzes a user's past project history and selects an optimal input method. The reception unit uses a generation AI to analyze the user's past project history and selects the optimal input method. For example, the reception unit can prioritize and suggest input methods (such as voice or text) that the user has used in the past. It can also predict and suggest specific input patterns based on the user's past project history. Furthermore, it can suggest the optimal input method by referring to input methods that the user has used successfully in past projects. This enables efficient information input by selecting the optimal input method based on the user's past project history. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past project data into the generation AI, which can then analyze the data and suggest the optimal input method.

[0068] The project planning support system includes a reception unit that filters project goals and requirements based on the user's current work situation and areas of interest when the project goals and requirements are input. The reception unit uses a generation AI to filter project goals and requirements based on the user's current work situation and areas of interest when the project goals and requirements are input. For example, the reception unit can analyze the user's current work situation and prioritize displaying relevant project goals and requirements. It can also suggest related project goals and requirements based on the user's areas of interest. Furthermore, it can combine the user's work situation and areas of interest to suggest optimal project goals and requirements. By filtering based on the user's work situation and areas of interest, highly relevant information can be prioritized for input. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input data on the user's work situation and areas of interest to the generation AI, which can then analyze the data and perform filtering.

[0069] The project planning support system includes a reception unit that estimates a user's emotions and prioritizes the goals and requirements to be input based on the estimated user emotions. The reception unit estimates the user's emotions using a generation AI and prioritizes the goals and requirements to be input based on the estimated user emotions. For example, if the user is stressed, the reception unit can postpone less important goals and requirements. Also, if the user is relaxed, the reception unit can prioritize input of more important goals and requirements. Furthermore, if the user is in a hurry, the reception unit can quickly input the most important goals and requirements. This enables efficient information input by prioritizing goals and requirements according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotional data into the generation AI, which can then analyze the data and determine the priorities of goals and requirements.

[0070] The project planning support system includes a reception unit that prioritizes input of highly relevant information based on the user's geographical location information when inputting project goals and requirements. The reception unit prioritizes input of highly relevant information based on the user's geographical location information when inputting project goals and requirements using a generation AI. For example, relevant project goals and requirements can be suggested based on the user's current location. Region-specific information can also be prioritized by taking the user's geographical location information into consideration. Furthermore, optimal project goals and requirements can be suggested based on the user's location information. This allows region-specific information to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information to the generation AI, which can then analyze the information and prioritize input of highly relevant information.

[0071] The project planning support system includes a reception unit that analyzes a user's social media activity and inputs related information when project goals and requirements are input. The reception unit analyzes the user's social media activity and inputs related information when project goals and requirements are input using a generation AI. For example, the reception unit can analyze the user's social media activity and suggest related project goals and requirements. Also, the reception unit can suggest optimal project goals and requirements based on the user's social media interests. Furthermore, the reception unit can input related information with reference to the user's social media activity. This allows highly relevant information to be input by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's social media activity data to the generation AI, which can then analyze the data and input related information.

[0072] The project planning support system includes an analysis unit that estimates a user's emotions and adjusts the presentation of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions using a generation AI and adjusts the presentation of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can provide concise and concise analysis results. If the user is in a hurry, the analysis unit can provide analysis results in a format that is quickly understandable. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input user emotion data into the generation AI, which can then analyze the data and adjust the presentation of the analysis.

[0073] The project planning support system includes an analysis unit that adjusts the accuracy of the analysis based on the importance of the project during analysis. The analysis unit adjusts the accuracy of the analysis based on the importance of the project during analysis using the generation AI. For example, for a highly important project, the analysis unit can perform a detailed analysis. On the other hand, for a less important project, the analysis unit can perform a concise analysis. Furthermore, the analysis unit can dynamically adjust the accuracy of the analysis depending on the importance of the project. This enables efficient analysis by adjusting the accuracy of the analysis depending on the importance of the project. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input project importance data to the generation AI, which can analyze the data and adjust the accuracy of the analysis.

[0074] The project planning support system includes an analysis unit that applies an appropriate analysis algorithm depending on the project category during analysis. The analysis unit uses the generation AI to apply the appropriate analysis algorithm depending on the project category during analysis. For example, for a new product development project, the analysis unit can apply a specific analysis algorithm. For a marketing campaign, the analysis unit can apply a different analysis algorithm. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the project category. This enables highly accurate analysis by applying the optimal analysis algorithm depending on the project category. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input project category data into the generation AI, which can analyze the data and apply the appropriate analysis algorithm.

[0075] The project planning support system includes an analysis unit that estimates a user's emotions and adjusts the length of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions using a generation AI and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. If the user is stressed, the analysis unit can provide a concise analysis. If the user is in a hurry, the analysis unit can provide an analysis that can be understood in a short time. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without the generation AI. For example, the analysis unit may input user emotion data into the generation AI, which then analyzes the data and adjusts the length of the analysis.

[0076] The project planning support system includes an analysis unit that determines the order of analysis based on the project submission dates during analysis. The analysis unit determines the order of analysis based on the project submission dates during analysis using the generation AI. For example, projects with upcoming submission deadlines can be analyzed preferentially. Projects with distant submission deadlines can also be postponed. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission dates. This enables efficient analysis by determining the analysis priority based on the project submission dates. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input project submission date data into the generation AI, which can analyze the data and determine the analysis order.

[0077] The project planning support system includes an analysis unit that adjusts the order of analysis based on the relevance of projects during analysis. The analysis unit adjusts the order of analysis based on the relevance of projects during analysis using a generation AI. For example, highly relevant projects can be analyzed preferentially. Less relevant projects can be postponed. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of projects. This enables efficient analysis by adjusting the order of analysis based on the relevance of projects. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input project relevance data into the generation AI, which can analyze the data and adjust the order of analysis.

[0078] The project planning support system includes a providing unit that estimates a user's emotions and adjusts the way advice is presented based on the estimated user emotions. The providing unit estimates the user's emotions using a generation AI and adjusts the way advice is presented based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide detailed advice. If the user is stressed, the providing unit can provide concise, to-the-point advice. If the user is in a hurry, the providing unit can provide advice in a format that is quickly understandable. This allows for more appropriate advice to be provided by adjusting the way advice is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the way advice is presented.

[0079] The project planning support system includes a providing unit that adjusts the accuracy of advice based on the importance of the project when providing advice. The providing unit adjusts the accuracy of advice based on the importance of the project when providing advice using a generation AI. For example, for a project with high importance, the providing unit can provide detailed advice. For a project with low importance, the providing unit can provide concise advice. Furthermore, the providing unit can dynamically adjust the accuracy of the advice depending on the importance of the project. This enables efficient advice by adjusting the accuracy of the advice depending on the importance of the project. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input project importance data to the generation AI, and the generation AI can analyze the data and adjust the accuracy of the advice.

[0080] The project planning support system includes a providing unit that applies an appropriate advice algorithm depending on the project category when providing advice. The providing unit uses a generation AI to apply an appropriate advice algorithm depending on the project category when providing advice. For example, for a new product development project, the providing unit can apply a specific advice algorithm. For a marketing campaign, the providing unit can apply a different advice algorithm. Furthermore, depending on the project category, the providing unit can select the optimal advice algorithm. This enables highly accurate advice by applying the optimal advice algorithm depending on the project category. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input project category data into the generation AI, which can analyze the data and apply an appropriate advice algorithm.

[0081] The project planning support system includes a providing unit that estimates a user's emotions and adjusts the length of advice based on the estimated user emotions. The providing unit estimates the user's emotions using a generation AI and adjusts the length of advice based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide detailed advice. If the user is stressed, the providing unit can provide concise advice. If the user is in a hurry, the providing unit can provide advice that can be understood in a short time. This allows for more appropriate advice to be provided by adjusting the length of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input user emotion data into the generation AI, which can then analyze the data and adjust the length of the advice.

[0082] The project planning support system includes a providing unit that, when providing advice, determines the order of advice based on the project submission dates. The providing unit determines the order of advice based on the project submission dates when providing advice using a generation AI. For example, advice can be given priority to projects with upcoming submission deadlines. Also, projects with distant submission deadlines can be postponed. Furthermore, the providing unit can dynamically adjust the priority of advice based on the submission dates. This enables efficient advice by determining the priority of advice based on the project submission dates. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input project submission date data into the generation AI, and the generation AI can analyze the data to determine the order of advice.

[0083] The project planning support system includes a providing unit that adjusts the order of advice based on the relevance of projects when providing advice. The providing unit adjusts the order of advice based on the relevance of projects when providing advice using a generation AI. For example, advice can be given priority to highly relevant projects. Less relevant projects can be postponed. Furthermore, the providing unit can dynamically adjust the order of advice based on the relevance of projects. This enables efficient advice by adjusting the order of advice based on the relevance of projects. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input project relevance data to the generation AI, and the generation AI can analyze the data and adjust the order of advice.

[0084] The project planning support system includes an update unit that estimates a user's emotions and selects update data based on the estimated user emotions. The update unit estimates the user's emotions using a generation AI and selects update data based on the estimated user emotions. For example, if the user is relaxed, the update unit can provide detailed update data. If the user is stressed, the update unit can provide concise update data. If the user is in a hurry, the update unit can provide update data that can be quickly understood. This enables more appropriate updates by selecting update data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the update unit may input user emotion data into the generation AI, which then analyzes the data and selects update data.

[0085] The project planning support system includes an update unit that, during an update, refers to past feedback data and appropriately adjusts the update algorithm. The update unit uses a generation AI to appropriately adjust the update algorithm by referring to past feedback data during an update. For example, the update unit can analyze past feedback data and select an optimal update algorithm. The update algorithm can also be dynamically adjusted based on the feedback data. Furthermore, the update algorithm can be optimized by referring to past feedback data. This enables highly accurate updates by optimizing the update algorithm based on past feedback data. Some or all of the above-described processing in the update unit may be performed using or without the generation AI. For example, the update unit can input past feedback data into the generation AI, which can then analyze the data and adjust the update algorithm.

[0086] The project planning support system includes an update unit that estimates a user's emotions and adjusts the update frequency based on the estimated user emotions. The update unit estimates the user's emotions using a generation AI and adjusts the update frequency based on the estimated user emotions. For example, if the user is relaxed, the update unit can provide frequent update data. If the user is stressed, the update unit can reduce the update frequency and provide only necessary information. If the user is in a hurry, the update unit can prioritize providing important update data. This enables more appropriate updates by adjusting the update frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit may be performed using the generation AI, or may be performed without the generation AI. For example, the update unit can input user emotion data into the generation AI, which can then analyze the data and adjust the update frequency.

[0087] The project planning support system includes an update unit that, at the time of update, determines the priority of update data based on the time of feedback submission. The update unit, at the time of update, determines the priority of update data based on the time of feedback submission using a generation AI. For example, if feedback has been recently submitted, the update can be performed by prioritizing that data. Also, if the feedback is old, the update can be performed by reducing the weight of that data. Furthermore, the update unit can dynamically adjust the weighting of the update data based on the time of feedback submission. This enables efficient updates by weighting the update data based on the time of feedback submission. Some or all of the above-mentioned processing in the update unit may be performed using or without the generation AI. For example, the update unit can input feedback submission time data to the generation AI, and the generation AI can analyze the data to determine the priority of the update data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and update 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 is realized by the control unit 46A of the smart device 14, and allows a user to input project goals and requirements. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI and provides customized advice. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and presents the analysis results to the user. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and updates the advice based on user feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, and update 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 is realized by the control unit 46A of the smart glasses 214, and allows a user to input project goals and requirements. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI and provides customized advice. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and presents the analysis results to the user. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and updates the advice based on user feedback. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and update unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and allows a user to input project goals and requirements. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI and provides customized advice. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and presents the analysis results to the user. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and updates the advice based on user feedback. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and update unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows a user to input project goals and requirements. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI and provides customized advice. The provision unit is realized, for example, by the control unit 46A of the robot 414, and presents the analysis results to the user. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and updates the advice based on user feedback.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The project planning support system may also include an evaluation unit that estimates the user's emotions and evaluates the project progress based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit can carefully evaluate the progress and provide necessary support. If the user is relaxed, the evaluation unit can quickly evaluate the progress and suggest the next step. Furthermore, if the user is in a hurry, the evaluation unit can focus on important progress points. This enables progress evaluation based on the user's emotions and supports the effective progress of the project.

[0090] The project planning support system can also include a resource optimization unit that analyzes the user's past project data and proposes optimal resource allocation. For example, it can apply resource allocation patterns that were successful in past projects to the current project. It can also propose resource allocation methods to avoid failures in past projects. Furthermore, it can propose efficient resource utilization methods based on the user's past project data. This makes it possible to optimize resources by utilizing past data, thereby increasing the success rate of projects.

[0091] The project planning support system may also include a risk management unit that estimates the user's emotions and adjusts risk management methods based on the estimated emotions. For example, if the user is feeling stressed, the risk management unit can analyze risks in detail and propose specific countermeasures. If the user is relaxed, the risk management unit can summarize risks concisely and propose methods for quickly taking countermeasures. Furthermore, if the user is in a hurry, the risk management unit can propose countermeasures focusing on the most important risks. This enables risk management according to the user's emotions, thereby improving the safety of the project.

[0092] The project planning support system may also include a task monitoring unit that monitors the user's current task status in real time and provides optimal advice. For example, if the user is busy, the task monitoring unit can provide concise, to-the-point advice. On the other hand, if the user has time, the task monitoring unit can provide detailed advice. Furthermore, the priority of advice can be dynamically adjusted according to the user's task status. This allows optimal advice to be provided according to the user's task status, enabling the project to progress efficiently.

[0093] The project planning support system may also include a feedback adjustment unit that estimates the user's emotions and adjusts the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback adjustment unit may provide positive feedback preferentially to increase the user's motivation. If the user is relaxed, the feedback adjustment unit may provide detailed feedback to deepen the user's understanding. If the user is in a hurry, the feedback adjustment unit may provide concise and to-the-point feedback. This provides feedback that is appropriate for the user's emotions, supporting the effective progress of the project.

[0094] The project planning support system can also include a management method suggestion unit that suggests the optimal project management method based on the user's past project data. For example, it can apply management methods that have been successful in past projects to the current project. It can also suggest management methods to avoid failures in past projects. Furthermore, it can suggest management methods according to the progress of the project based on the user's past project data. This makes it possible to suggest the optimal management method by utilizing past data, thereby increasing the success rate of the project.

[0095] The project planning support system may also include a priority adjustment unit that estimates the user's emotions and dynamically adjusts project priorities based on the estimated emotions. For example, if the user is feeling stressed, the priority adjustment unit can postpone less important tasks to reduce the user's burden. Also, if the user is relaxed, the priority adjustment unit can prioritize more important tasks. Furthermore, if the user is in a hurry, the priority adjustment unit can quickly process the most important tasks. This makes it possible to adjust priorities according to the user's emotions, supporting the efficient progress of projects.

[0096] The project planning support system may also include a partner suggestion unit that suggests optimal project partners based on the user's geographical location information. For example, it may suggest nearby project partners based on the user's current location. It may also suggest partners with specialized knowledge specific to the region, taking into account the user's geographical location information. It may also select optimal project partners based on the user's location information. This makes it possible to suggest optimal partners taking into account the user's geographical location information, thereby increasing the success rate of the project.

[0097] The project planning support system may also include a goal suggestion unit that analyzes a user's social media activity and suggests optimal project goals. For example, the system can analyze a user's social media activity and suggest relevant project goals. Optimal project goals can also be suggested based on the user's social media interests. Furthermore, relevant information can be input based on the user's social media activity. By analyzing the user's social media activity, highly relevant project goals can be suggested, thereby increasing the success rate of the project.

[0098] The project planning support system may also include a progress monitoring unit that estimates a user's emotions and monitors the progress of the project in real time based on the estimated emotions. For example, if the user is feeling stressed, the progress monitoring unit can monitor the progress in detail and provide necessary support. If the user is relaxed, the progress monitoring unit can quickly monitor the progress and suggest the next step. Furthermore, if the user is in a hurry, the progress monitoring unit can focus on important progress points and monitor them. This enables progress monitoring according to the user's emotions, supporting the effective progress of the project.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The user inputs the project goals and requirements into the reception unit. For example, the user can input specific goals and requirements such as "new product development project" or "marketing campaign plan." The reception unit inputs this information into the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit and provide customized advice for each stage of the project plan. For example, in the early stages of the project, it provides advice on goal setting and resource allocation. In the middle stages of the project, it provides advice on progress management and risk management, and in the final stages, it provides advice on evaluating deliverables and next steps. Step 3: The providing unit provides the advice provided by the analysis unit to the user. For example, the providing unit can present specific advice to the user based on the information analyzed by the generation AI. The providing unit can provide the advice to the user, for example, through a web application or a mobile application. Step 4: The update unit updates the advice provided by the provision unit based on the user's feedback. The generation AI can update the advice in real time based on the user's feedback and propose an optimal plan. For example, when the user provides feedback on the provided advice, the generation AI can analyze the feedback and update the advice.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

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

[0122] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

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

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 section for inputting project goals or requirements; an analysis unit that analyzes the information input by the reception unit and provides customized advice; a providing unit that provides the advice provided by the analysis unit to a user; an update unit that updates the advice provided by the provision unit based on user feedback. A system characterized by:

2. The reception unit Enter your project goals or requirements 2. The system of claim 1.

3. The analysis unit Analyzes input information and provides customized advice at each stage of project planning 2. The system of claim 1.

4. The providing unit Providing advice on goal setting or resource allocation in the early stages of a project 2. The system of claim 1.

5. The providing unit Providing progress or risk management advice during the interim stages of a project 2. The system of claim 1.

6. The providing unit Evaluating deliverables or advising on next steps at the end of a project 2. The system of claim 1.

7. The update unit Instantly update advice based on user feedback 2. The system of claim 1.

8. The reception unit Infer user sentiment and adjust the timing of project goals or requirements input based on the estimated user sentiment 2. The system of claim 1.

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