System

Abstract

To provide a system that performs optimal assignment by taking into account the progress of developers and the difficulty of tasks.SOLUTION: The system according to an embodiment includes a collection unit, an acquisition unit, and an assignment unit. The collection unit collects task information indicating the progress of each developer's tasks and the difficulty of the tasks. The acquisition unit acquires information on new tasks. The assignment unit determines a developer to be assigned to a new task based on the task information collected by the collection unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to make optimal assignments that took into account the progress of developers and the difficulty of the tasks, making efficient project management difficult.

[0005] The system according to the embodiment aims to make optimal assignments taking into consideration the progress of the developer and the degree of difficulty of the work. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an acquisition unit, and an assignment unit. The collection unit collects task information indicating the progress of tasks by each developer and the difficulty of the tasks. The acquisition unit acquires information on new tasks. The assignment unit determines a developer to be assigned to the new task based on the task information collected by the collection unit. Effect of the Invention

[0007] The system according to the embodiment can make optimal assignments taking into consideration the progress of the developer and the difficulty of the work. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A programmer assignment system according to an embodiment of the present invention is a system that proposes the assignment of an optimal programmer, taking into consideration the progress of the programmer and the difficulty of the work. This programmer assignment system proposes the assignment of an optimal programmer, taking into consideration the progress of the programmer and the difficulty of the work. In addition, the system also has a function of integrating opinions between different developers and promoting a common understanding. For example, the programmer assignment system includes a collection unit that collects work information indicating the progress of the work by each developer and the difficulty of the work. This collection unit collects work information from a project management tool, and also collects productivity metrics of each developer to reflect them in the progress level. Next, the system includes an acquisition unit that acquires information on new work. This acquisition unit acquires new work information within a specific project. The system includes an assignment unit that determines a developer to be assigned to the new work based on the work information collected by the collection unit. This assignment unit uses a machine learning algorithm to analyze past performance data of each developer and assigns an optimal developer. In addition, the system refers to the past work history of each developer and preferentially assigns developers who have experience in performing similar work. In addition, the system monitors the health of each developer, and adjusts the assignment as necessary. It also has an integration section that integrates the opinions of different developers. This integration section detects conflicts of opinion between developers and proposes solutions. It also performs sentiment analysis that arises during the opinion integration process to improve the quality of communication between developers. This system realizes optimal assignment of programmers and promotes the integration of opinions between different developers, which is expected to improve project efficiency and quality. This enables the programmer assignment system to propose optimal programmer assignment taking into account the progress of each programmer and the difficulty of the work. It can also integrate opinions between different developers to promote common understanding.

[0029] The programmer assignment system according to the embodiment includes a collection unit, an acquisition unit, and an assignment unit. The collection unit collects task information indicating the degree of progress of tasks by each developer and the difficulty of the tasks. For example, the collection unit can collect task information from a project management tool. Project management tools include, but are not limited to, JIRA (registered trademark), Trello (registered trademark), Asana (registered trademark), and the like. The collection unit can also collect productivity metrics of each developer and reflect the progress degree. Productivity metrics include, but are not limited to, the number of lines of code, the number of bug fixes, the number of reviews, and the like. The acquisition unit acquires information on new tasks. For example, the acquisition unit can acquire new task information within a specific project. The assignment unit determines a developer to be assigned to a new task based on the task information collected by the collection unit. The assignment unit uses a machine learning algorithm to analyze past performance data of each developer and assign an optimal developer. Machine learning algorithms include, but are not limited to, linear regression, random forest, neural network, and the like. The assignment unit can also refer to the past work history of each developer and preferentially assign developers who have experience in similar work. The work history includes, for example, past projects, records of completed tasks, etc., but is not limited to such examples. The assignment unit can also monitor the health status of each developer and adjust the assignment as necessary. The health status includes, for example, regular health checkups, self-reports, etc., but is not limited to such examples. In this way, the programmer assignment system according to the embodiment can propose the most suitable programmer assignment, taking into account the progress of the programmer and the difficulty of the work.

[0030] The collection unit collects work information indicating the degree of progress of work by each developer and the difficulty of the work. Specifically, the collection unit can collect work information from a project management tool. Project management tools include, but are not limited to, for example, JIRA, Trello, Asana, etc. These tools are widely used to manage information such as task progress, deadlines, and responsible parties. The collection unit obtains data from these tools through APIs and grasps the work progress of each developer in real time. In addition, the collection unit can collect productivity metrics of each developer and reflect them in the degree of progress. Productivity metrics include, but are not limited to, for example, the number of lines of code, the number of bug fixes, the number of reviews, etc. The number of lines of code indicates how much code a developer has written, and the number of bug fixes indicates how many bugs a developer has fixed. The number of reviews indicates how much code a developer has reviewed. These metrics are important indicators for evaluating the productivity of developers. The collection unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the acquisition and assignment departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection department to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The acquisition unit acquires new work information. Specifically, the acquisition unit can acquire new work information within a specific project. For example, when a task for adding a new function or fixing a bug occurs, the acquisition unit automatically acquires this information from the project management tool. The acquisition unit can receive a real-time notification when a new task is added by using the API of the project management tool. This allows the acquisition unit to always grasp the latest work information and respond quickly. Furthermore, the acquisition unit also acquires information such as project priority and deadline, and provides it as reference information for the assignment unit to select the most suitable developer. For example, if a specific task is urgent, the acquisition unit can notify the assignment unit of the information and encourage a quick response. In addition, the acquisition unit can monitor the progress of the project and the utilization status of resources, and update new work information as necessary. This allows the acquisition unit to respond flexibly according to the progress of the project and improve the efficiency of the entire system.

[0032] The assignment unit determines a developer to be assigned to a new task based on the task information collected by the collection unit. Specifically, the assignment unit uses a machine learning algorithm to analyze past performance data of each developer and assigns the most suitable developer. Machine learning algorithms include, but are not limited to, linear regression, random forest, neural network, and the like. Linear regression is used to predict future performance based on the performance data of a developer. Random forest is used to combine multiple decision trees to make more accurate predictions. Neural networks are used to learn complex patterns and make advanced predictions. The assignment unit can use these algorithms to analyze the past work history of each developer and preferentially assign developers who have experience performing similar tasks. The work history includes, but is not limited to, past projects, records of completed tasks, and the like. Furthermore, the assignment unit can monitor the health status of each developer and adjust the assignment as necessary. The health status includes, but is not limited to, regular health checkups, self-reports, and the like. This allows the assignment unit to take into account the health status of the developer and make the most suitable assignment within a reasonable range. In addition, the Assignment Department can select the most suitable developer based on the developer's skill set and expertise, enabling the Assignment Department to realize optimal resource allocation for the success of the project and improve the efficiency and quality of the entire system.

[0033] The programmer assignment system includes an integration unit that integrates opinions between different developers. The integration unit integrates opinions between different developers. For example, the integration unit can detect a conflict of opinions between developers and propose a solution. Methods for detecting a conflict of opinions include, for example, an analysis of a discussion log, a questionnaire survey, and the like, but are not limited to such examples. The integration unit can also perform sentiment analysis that occurs in the process of integrating opinions to improve the quality of communication between developers. Sentiment analysis includes, for example, text mining, sentiment recognition algorithms, and the like, but are not limited to such examples. This makes it possible to integrate opinions between different developers and promote common understanding. A part or all of the above-mentioned processing in the integration unit may be performed, for example, using AI, or may be performed without using AI. For example, the integration unit can input a discussion log to a generation AI and cause the generation AI to execute a process of detecting a conflict of opinions.

[0034] The collection unit can collect work information from a project management tool. Project management tools include, but are not limited to, JIRA, Trello, Asana, and the like. For example, the collection unit can acquire the progress status of each developer's task from JIRA. The collection unit can also acquire the progress status of each developer's task from Trello. Furthermore, the collection unit can also acquire the completion status of each developer's task from Asana. In this way, by collecting work information from the project management tool, the progress degree of each developer can be accurately grasped. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired from the project management tool to the generation AI and cause the generation AI to evaluate the progress degree.

[0035] The collection unit can collect the productivity metrics of each developer and reflect them in the degree of progress. Productivity metrics include, for example, the number of lines of code, the number of bug fixes, the number of reviews, and the like, but are not limited to such examples. For example, the collection unit can collect the number of lines of code of each developer and reflect them in the degree of progress. The collection unit can also collect the number of bug fixes of each developer and reflect them in the degree of progress. Furthermore, the collection unit can collect the number of reviews of each developer and reflect them in the degree of progress. In this way, by collecting the productivity metrics of each developer, the degree of progress can be more accurately evaluated. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the productivity metrics of each developer to the generation AI and cause the generation AI to execute an evaluation of the degree of progress.

[0036] The assignment unit can use a machine learning algorithm to analyze the past performance data of each developer and assign an appropriate developer to a new task. Machine learning algorithms include, but are not limited to, linear regression, random forest, neural network, and the like. For example, the assignment unit can use linear regression to analyze the performance data of each developer and assign the optimal developer. The assignment unit can also use random forest to analyze the performance data of each developer and assign the optimal developer. Furthermore, the assignment unit can also use neural network to analyze the performance data of each developer and assign the optimal developer. As a result, the accuracy of assigning the optimal developer is improved by using the machine learning algorithm. A part or all of the above-mentioned processing in the assignment unit is performed using a generation AI. For example, the assignment unit can input the performance data of each developer to the generation AI and cause the generation AI to execute the assignment of the optimal developer.

[0037] The assignment unit can refer to the past work history of each developer and assign a developer who has experience in similar work to a new work with priority. The work history includes, for example, past projects, records of completed tasks, etc., but is not limited to such examples. For example, the assignment unit can refer to the history of past projects and assign a developer who has experience in similar work with priority. The assignment unit can also refer to the records of completed tasks and assign a developer who has experience in similar work with priority. In this way, by referring to the past work history, a developer who has experience in similar work can be assigned with priority. A part or all of the above-mentioned processing in the assignment unit may be performed, for example, using AI, or may be performed without using AI. For example, the assignment unit can input the past work history to the generation AI and cause the generation AI to identify a developer who has experience in similar work.

[0038] The assignment unit can monitor the health condition of each developer and adjust the assignment as necessary. The health condition includes, for example, regular health checkups, self-reports, etc., but is not limited to such examples. For example, the assignment unit can refer to the results of regular health checkups and assign developers in good health conditions preferentially. The assignment unit can also refer to the self-reported health condition and assign developers in good health conditions preferentially. In this way, appropriate assignment can be made by monitoring the health condition of each developer. Part or all of the above-mentioned processing in the assignment unit may be performed, for example, using AI, or may be performed without using AI. For example, the assignment unit can input the results of a health checkup to the generation AI and cause the generation AI to evaluate the health condition.

[0039] The collection unit can analyze the past work performance of each developer and select the optimal collection method. The optimal collection method includes, for example, the type of data, the collection frequency, the collection means, etc., but is not limited to such examples. For example, the collection unit can analyze the past work performance of each developer and select a detailed collection method if the performance is high. The collection unit can also analyze the past work performance of each developer and select a simple collection method if the performance is low. Furthermore, the collection unit can analyze the past work performance of each developer and select a flexible collection method if the performance fluctuates greatly. In this way, the optimal collection method can be selected by analyzing the past work performance. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past work performance data to the generation AI and cause the generation AI to select the optimal collection method.

[0040] When collecting work information, the collection unit can perform filtering based on the current project and field of interest of each developer. Filtering includes, for example, keyword matching, rule-based filtering, and the like, but is not limited to such examples. For example, the collection unit can grasp the current project of each developer and collect only related work information. In addition, the collection unit can grasp the field of interest of each developer and collect only related work information. Furthermore, the collection unit can grasp the current project and field of interest of each developer and collect only work information related to both. In this way, highly relevant work information can be collected by filtering based on the current project and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input project information and field of interest data to the generation AI and cause the generation AI to perform filtering.

[0041] When collecting work information, the collection unit can preferentially collect highly relevant information by considering the geographical location information of each developer. The acquisition of geographical location information includes, for example, GPS data, IP addresses, etc., but is not limited to such examples. For example, the collection unit can grasp the geographical location information of each developer and preferentially collect work information related to nearby projects. In addition, the collection unit can grasp the geographical location information of each developer and preferentially collect work information of projects in which developers in the same region are involved. Furthermore, the collection unit can grasp the geographical location information of each developer and preferentially collect work information related to problems specific to the region. In this way, by considering the geographical location information, highly relevant work information can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0042] The collection unit can analyze the social media activity of each developer when collecting the work information and collect related information. The analysis of social media activity includes, for example, the content of posts, the number of likes, the number of comments, and the like, but is not limited to such examples. For example, the collection unit can analyze the social media activity of each developer and collect work information based on related technology trends. The collection unit can also analyze the social media activity of each developer and collect work information related to technologies in which the developer is interested. Furthermore, the collection unit can analyze the social media activity of each developer and collect work information related to projects in which followers and friends are involved. In this way, the related work information can be collected by analyzing the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media data to the generation AI and cause the generation AI to collect related information.

[0043] The acquisition unit can analyze the past work history of each developer and select the optimal acquisition method. The optimal acquisition method includes, for example, the type of data, the acquisition frequency, the acquisition means, etc., but is not limited to such examples. For example, the acquisition unit can analyze the past work history of each developer and select a detailed acquisition method if the developer has experience performing similar work. In addition, the acquisition unit can also analyze the past work history of each developer and select a simple acquisition method if the developer has no experience performing the work. Furthermore, the acquisition unit can analyze the past work history of each developer and select an acquisition method according to the difficulty of the work. In this way, the optimal acquisition method can be selected by analyzing the past work history. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input past work history data to the generation AI and cause the generation AI to select the optimal acquisition method.

[0044] When acquiring new work information, the acquisition unit can perform filtering based on the current life situation and field of interest of each developer. Acquisition of the life situation includes, for example, questionnaire surveys, self-reports, etc., but is not limited to such examples. For example, the acquisition unit can grasp the current life situation of each developer, and filter out work information of low importance when the developer is busy. The acquisition unit can also grasp the field of interest of each developer and acquire only related work information. Furthermore, the acquisition unit can grasp the current life situation and field of interest of each developer, and acquire only work information related to both. In this way, by filtering based on the current life situation and field of interest, highly related work information can be acquired. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the life situation data and field of interest data to the generation AI and cause the generation AI to execute filtering.

[0045] When acquiring new work information, the acquisition unit can preferentially acquire highly relevant information by considering the geographical location information of each developer. The acquisition of geographical location information includes, but is not limited to, GPS data, IP addresses, and the like. For example, the acquisition unit can grasp the geographical location information of each developer and preferentially acquire work information related to nearby projects. The acquisition unit can also grasp the geographical location information of each developer and preferentially acquire work information of projects in which developers in the same region are involved. Furthermore, the acquisition unit can grasp the geographical location information of each developer and preferentially acquire work information related to problems specific to the region. In this way, by considering the geographical location information, highly relevant work information can be preferentially acquired. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information to the generation AI and cause the generation AI to execute acquisition of highly relevant information.

[0046] The acquisition unit can analyze the social media activity of each developer when acquiring new work information and acquire related information. The analysis of social media activity includes, for example, the content of posts, the number of likes, the number of comments, and the like, but is not limited to such examples. For example, the acquisition unit can analyze the social media activity of each developer and acquire work information based on related technology trends. The acquisition unit can also analyze the social media activity of each developer and acquire work information related to technologies in which the developer is interested. Furthermore, the acquisition unit can analyze the social media activity of each developer and acquire work information related to projects in which followers and friends are involved. In this way, related work information can be acquired by analyzing social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input social media data to the generation AI and cause the generation AI to execute acquisition of related information.

[0047] The assignment unit can adjust the level of detail of the assignment based on the importance of the work at the time of assignment. The evaluation of the importance of the work includes, for example, the priority of the project, business impact, etc., but is not limited to such examples. For example, the assignment unit can provide detailed assignment information when the importance of the work is high. In addition, the assignment unit can also provide simple assignment information when the importance of the work is low. Furthermore, the assignment unit can flexibly adjust the level of detail of the assignment information according to the importance of the work. As a result, by adjusting the level of detail based on the importance of the work, more appropriate assignment can be performed. A part or all of the above-mentioned processing in the assignment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assignment unit can input the importance data of the work to the generation AI and cause the generation AI to adjust the level of detail of the assignment information.

[0048] The assignment unit can apply different assignment algorithms depending on the category of the work at the time of assignment. The classification of the work category includes, for example, development, testing, design, etc., but is not limited to such examples. For example, when the work category is development, the assignment unit can apply an assignment algorithm specialized for development. Furthermore, when the work category is testing, the assignment unit can also apply an assignment algorithm specialized for testing. Furthermore, when the work category is document creation, the assignment unit can also apply an assignment algorithm specialized for document creation. This makes it possible to apply an optimal assignment algorithm according to the category of the work. A part or all of the above-mentioned processing in the assignment unit may be performed, for example, using AI, or may be performed without using AI. For example, the assignment unit can input the work category data to the generation AI and cause the generation AI to apply the assignment algorithm.

[0049] The assignment unit can determine the priority of assignment based on the submission time of the work at the time of assignment. The determination of the submission time includes, but is not limited to, for example, a project deadline, a milestone, and the like. For example, the assignment unit can prioritize assignment when the submission time of the work is near. In addition, the assignment unit can postpone the assignment when the submission time of the work is far away. Furthermore, the assignment unit can flexibly adjust the priority of assignment according to the submission time. As a result, more appropriate assignment can be performed by determining the priority based on the submission time of the work. Part or all of the above-mentioned processing in the assignment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assignment unit can input the submission time data to the generation AI and cause the generation AI to execute the determination of the priority of assignment.

[0050] The assignment unit can adjust the order of assignment based on the relevance of the tasks at the time of assignment. The evaluation of the relevance of the tasks includes, for example, dependency, common resources, and the like, but is not limited to such examples. For example, the assignment unit can assign tasks with high relevance preferentially. The assignment unit can also assign tasks with low relevance later. Furthermore, the assignment unit can flexibly adjust the order of assignment according to the relevance of the tasks. As a result, more appropriate assignment can be performed by adjusting the order based on the relevance of the tasks. A part or all of the above-mentioned processing in the assignment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assignment unit can input task relevance data to the generation AI and cause the generation AI to adjust the order of assignment.

[0051] The integration unit can select an optimal integration method by referring to the past opinion history of each developer when integrating opinions. Reference to the opinion history includes, for example, past discussion logs, feedback records, etc., but is not limited to such examples. For example, the integration unit can select a quick integration method by referring to the past opinion history of each developer when there is a lot of agreement in opinions. In addition, the integration unit can select a careful integration method by referring to the past opinion history of each developer when there is a lot of conflict in opinions. Furthermore, the integration unit can select a flexible integration method by referring to the past opinion history of each developer when there is a large fluctuation in opinions. In this way, the optimal opinion integration method can be selected by referring to the past opinion history. Part or all of the above-mentioned processing in the integration unit may be performed, for example, using AI, or may be performed without using AI. For example, the integration unit can input the opinion history data to the generation AI and cause the generation AI to select the optimal integration method.

[0052] The integration unit can customize the means of integration based on the current life situation of each developer at the time of opinion integration. The acquisition of the life situation includes, for example, a questionnaire survey, a self-report, and the like, but is not limited to such examples. For example, the integration unit can grasp the current life situation of each developer and provide a simple integration means if the developer is busy. The integration unit can also grasp the current life situation of each developer and provide a detailed integration means if the developer has time. Furthermore, the integration unit can grasp the current life situation of each developer and provide a flexible integration means according to the situation. As a result, by customizing the means of integration based on the current life situation, more appropriate opinion integration can be performed. Part or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input the life situation data to the generation AI and cause the generation AI to execute customization of the integration means.

[0053] The integration unit can select the optimal integration method by considering the geographical location information of each developer when integrating opinions. Examples of the acquisition of geographical location information include, but are not limited to, GPS data, IP addresses, and the like. For example, the integration unit can grasp the geographical location information of each developer and prioritize opinion integration between developers in the vicinity. In addition, the integration unit can grasp the geographical location information of each developer and prioritize opinion integration between developers in the same region. In addition, the integration unit can grasp the geographical location information of each developer and prioritize opinion integration related to issues specific to the region. In this way, the optimal opinion integration method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the integration unit may be performed, for example, using AI, or may be performed without using AI. For example, the integration unit can input geographical location information to the generation AI and cause the generation AI to select the optimal integration method.

[0054] The integration unit can analyze the social media activity of each developer at the time of opinion integration and propose a means of integration. The analysis of social media activity includes, for example, the content of posts, the number of likes, the number of comments, and the like, but is not limited to such examples. For example, the integration unit can analyze the social media activity of each developer and propose an integration means based on related technology trends. The integration unit can also analyze the social media activity of each developer and propose an integration means related to a technology in which the developer is interested. Furthermore, the integration unit can analyze the social media activity of each developer and propose an integration means related to a project in which followers or friends are involved. In this way, by analyzing social media activity, a related means of opinion integration can be proposed. A part or all of the above-mentioned processing in the integration unit may be performed, for example, using AI, or may be performed without using AI. For example, the integration unit can input social media data to the generation AI and cause the generation AI to execute the proposal of the integration means.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The collection unit can monitor the work environment of each developer and adjust the collection method of work information according to changes in the environment. For example, when a developer is working remotely, the collection unit can monitor the network status and reduce the collection frequency if the network is unstable. In addition, when a developer is working in an office, the collection unit can monitor the noise level in the office and change the collection method if the noise level is high. Furthermore, the collection unit can monitor the temperature and humidity of the developer's work environment and adjust the collection frequency if the environment is not comfortable. This makes it possible to provide a flexible collection method according to the work environment.

[0057] The integration department can incorporate the opinions of third-party experts to resolve conflicts of opinion between developers. For example, the integration department can collect the opinions of experts who are familiar with a specific technical field and use them as a reference for resolving conflicts of opinion between developers. The integration department can also collect the opinions of project managers and leaders and use them as a guideline for resolving conflicts of opinion. Furthermore, the integration department can collect the opinions of external consultants and provide them with advice for resolving conflicts of opinion. In this way, by incorporating the opinions of third parties, more objective and fair opinion integration is possible.

[0058] The collection unit can analyze the work style of each developer and determine the optimal collection timing. For example, the collection unit can analyze the working hours of each developer and collect work information during times when they are most concentrated. The collection unit can also analyze each developer's break time and collect work information during breaks. Furthermore, the collection unit can analyze the work pace of each developer and increase the collection frequency when work is progressing smoothly. This makes it possible to provide the optimal collection timing according to each developer's work style.

[0059] The collection unit can analyze the skill set of each developer and prioritize collecting work information according to the skills. For example, the collection unit can analyze the programming language skills of each developer and prioritize collecting work information related to the language in which the developer is good at. The collection unit can also analyze the database management skills of each developer and prioritize collecting database-related work information. Furthermore, the collection unit can analyze the test automation skills of each developer and prioritize collecting work information related to test automation. This makes it possible to provide optimal work information according to the skill set of each developer.

[0060] The assignment department can estimate each developer's motivation to learn and preferentially assign work using new technologies to developers with a high motivation. For example, the assignment department can analyze each developer's usage of an online learning platform and assign projects using new technologies to developers with a high motivation to learn. The assignment department can also analyze each developer's posting status to technical blogs and forums and assign work using new technologies to developers who actively disseminate information. Furthermore, the assignment department can analyze each developer's participation status in an in-house training program and assign work using new technologies to developers who actively participate. This makes it possible to assign appropriate work to developers with a high motivation to learn.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects work information that indicates the progress and difficulty of each developer's work. For example, the collection unit can collect work information from project management tools (JIRA, Trello, Asana, etc.). It can also collect productivity metrics (number of lines of code, number of bug fixes, number of reviews, etc.) for each developer and reflect them in the progress. Step 2: The acquisition unit acquires information on new work. For example, the acquisition unit can acquire information on new work within a specific project. Step 3: The assignment department determines which developer to assign to new work based on the work information collected by the collection department. The assignment department uses machine learning algorithms (linear regression, random forest, neural network, etc.) to analyze each developer's past performance data and assign the most suitable developer. It can also refer to each developer's past work history to preferentially assign developers who have experience performing similar work. In addition, it can monitor the health of each developer and adjust assignments as necessary.

[0063] (Example 2) A programmer assignment system according to an embodiment of the present invention is a system that proposes the assignment of an optimal programmer, taking into consideration the progress of the programmer and the difficulty of the work. This programmer assignment system proposes the assignment of an optimal programmer, taking into consideration the progress of the programmer and the difficulty of the work. In addition, the system also has a function of integrating opinions between different developers and promoting a common understanding. For example, the programmer assignment system includes a collection unit that collects work information indicating the progress of the work by each developer and the difficulty of the work. This collection unit collects work information from a project management tool, and also collects productivity metrics of each developer to reflect them in the progress level. Next, the system includes an acquisition unit that acquires information on new work. This acquisition unit acquires new work information within a specific project. The system includes an assignment unit that determines a developer to be assigned to the new work based on the work information collected by the collection unit. This assignment unit uses a machine learning algorithm to analyze past performance data of each developer and assigns an optimal developer. In addition, the system refers to the past work history of each developer and preferentially assigns developers who have experience in performing similar work. In addition, the system monitors the health of each developer, and adjusts the assignment as necessary. It also has an integration section that integrates the opinions of different developers. This integration section detects conflicts of opinion between developers and proposes solutions. It also performs sentiment analysis that arises during the opinion integration process to improve the quality of communication between developers. This system realizes optimal assignment of programmers and promotes the integration of opinions between different developers, which is expected to improve project efficiency and quality. This enables the programmer assignment system to propose optimal programmer assignment taking into account the progress of each programmer and the difficulty of the work. It can also integrate opinions between different developers to promote common understanding.

[0064] The programmer assignment system according to the embodiment includes a collection unit, an acquisition unit, and an assignment unit. The collection unit collects work information indicating the degree of progress of work by each developer and the difficulty of the work. For example, the collection unit can collect the work information from a project management tool. Project management tools include, but are not limited to, JIRA, Trello, Asana, and the like. The collection unit can also collect productivity metrics of each developer and reflect them in the degree of progress. Productivity metrics include, but are not limited to, the number of lines of code, the number of bug fixes, the number of reviews, and the like. The acquisition unit acquires information on new work. For example, the acquisition unit can acquire new work information within a specific project. The assignment unit determines a developer to be assigned to the new work based on the work information collected by the collection unit. The assignment unit uses a machine learning algorithm to analyze past performance data of each developer and assign an optimal developer. Machine learning algorithms include, but are not limited to, linear regression, random forest, neural network, and the like. The assignment unit can also refer to the past work history of each developer and preferentially assign developers who have experience in similar work. The work history includes, for example, past projects, records of completed tasks, etc., but is not limited to such examples. The assignment unit can also monitor the health status of each developer and adjust the assignment as necessary. The health status includes, for example, regular health checkups, self-reports, etc., but is not limited to such examples. In this way, the programmer assignment system according to the embodiment can propose the most suitable programmer assignment, taking into account the progress of the programmer and the difficulty of the work.

[0065] The collection unit collects work information indicating the degree of progress of work by each developer and the difficulty of the work. Specifically, the collection unit can collect work information from a project management tool. Project management tools include, but are not limited to, for example, JIRA, Trello, Asana, etc. These tools are widely used to manage information such as task progress, deadlines, and responsible parties. The collection unit obtains data from these tools through APIs and grasps the work progress of each developer in real time. In addition, the collection unit can collect productivity metrics of each developer and reflect them in the degree of progress. Productivity metrics include, but are not limited to, for example, the number of lines of code, the number of bug fixes, the number of reviews, etc. The number of lines of code indicates how much code a developer has written, and the number of bug fixes indicates how many bugs a developer has fixed. The number of reviews indicates how much code a developer has reviewed. These metrics are important indicators for evaluating the productivity of developers. The collection unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the acquisition and assignment departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection department to collect data efficiently and effectively, improving the performance of the entire system.

[0066] The acquisition unit acquires new work information. Specifically, the acquisition unit can acquire new work information within a specific project. For example, when a task for adding a new function or fixing a bug occurs, the acquisition unit automatically acquires this information from the project management tool. The acquisition unit can receive a real-time notification when a new task is added by using the API of the project management tool. This allows the acquisition unit to always grasp the latest work information and respond quickly. Furthermore, the acquisition unit also acquires information such as project priority and deadline, and provides it as reference information for the assignment unit to select the most suitable developer. For example, if a specific task is urgent, the acquisition unit can notify the assignment unit of the information and encourage a quick response. In addition, the acquisition unit can monitor the progress of the project and the utilization status of resources, and update new work information as necessary. This allows the acquisition unit to respond flexibly according to the progress of the project and improve the efficiency of the entire system.

[0067] The assignment unit determines a developer to be assigned to a new task based on the task information collected by the collection unit. Specifically, the assignment unit uses a machine learning algorithm to analyze past performance data of each developer and assigns the most suitable developer. Machine learning algorithms include, but are not limited to, linear regression, random forest, neural network, and the like. Linear regression is used to predict future performance based on the performance data of a developer. Random forest is used to combine multiple decision trees to make more accurate predictions. Neural networks are used to learn complex patterns and make advanced predictions. The assignment unit can use these algorithms to analyze the past work history of each developer and preferentially assign developers who have experience performing similar tasks. The work history includes, but is not limited to, past projects, records of completed tasks, and the like. Furthermore, the assignment unit can monitor the health status of each developer and adjust the assignment as necessary. The health status includes, but is not limited to, regular health checkups, self-reports, and the like. This allows the assignment unit to take into account the health status of the developer and make the most suitable assignment within a reasonable range. In addition, the Assignment Department can select the most suitable developer based on the developer's skill set and expertise, enabling the Assignment Department to realize optimal resource allocation for the success of the project and improve the efficiency and quality of the entire system.

[0068] The programmer assignment system includes an integration unit that integrates opinions between different developers. The integration unit integrates opinions between different developers. For example, the integration unit can detect a conflict of opinions between developers and propose a solution. Methods for detecting a conflict of opinions include, for example, an analysis of a discussion log, a questionnaire survey, and the like, but are not limited to such examples. The integration unit can also perform sentiment analysis that occurs in the process of integrating opinions to improve the quality of communication between developers. Sentiment analysis includes, for example, text mining, sentiment recognition algorithms, and the like, but are not limited to such examples. This makes it possible to integrate opinions between different developers and promote common understanding. A part or all of the above-mentioned processing in the integration unit may be performed, for example, using AI, or may be performed without using AI. For example, the integration unit can input a discussion log to a generation AI and cause the generation AI to execute a process of detecting a conflict of opinions.

[0069] The collection unit can collect work information from a project management tool. Project management tools include, but are not limited to, JIRA, Trello, Asana, and the like. For example, the collection unit can acquire the progress status of each developer's task from JIRA. The collection unit can also acquire the progress status of each developer's task from Trello. Furthermore, the collection unit can also acquire the completion status of each developer's task from Asana. In this way, by collecting work information from the project management tool, the progress degree of each developer can be accurately grasped. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired from the project management tool to the generation AI and cause the generation AI to evaluate the progress degree.

[0070] The collection unit can collect the productivity metrics of each developer and reflect them in the degree of progress. Productivity metrics include, for example, the number of lines of code, the number of bug fixes, the number of reviews, and the like, but are not limited to such examples. For example, the collection unit can collect the number of lines of code of each developer and reflect them in the degree of progress. The collection unit can also collect the number of bug fixes of each developer and reflect them in the degree of progress. Furthermore, the collection unit can collect the number of reviews of each developer and reflect them in the degree of progress. In this way, by collecting the productivity metrics of each developer, the degree of progress can be more accurately evaluated. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the productivity metrics of each developer to the generation AI and cause the generation AI to execute an evaluation of the degree of progress.

[0071] The assignment unit can use a machine learning algorithm to analyze the past performance data of each developer and assign an appropriate developer to a new task. Machine learning algorithms include, but are not limited to, linear regression, random forest, neural network, and the like. For example, the assignment unit can use linear regression to analyze the performance data of each developer and assign the optimal developer. The assignment unit can also use random forest to analyze the performance data of each developer and assign the optimal developer. Furthermore, the assignment unit can also use neural network to analyze the performance data of each developer and assign the optimal developer. As a result, the accuracy of assigning the optimal developer is improved by using the machine learning algorithm. A part or all of the above-mentioned processing in the assignment unit is performed using a generation AI. For example, the assignment unit can input the performance data of each developer to the generation AI and cause the generation AI to execute the assignment of the optimal developer.

[0072] The assignment unit can refer to the past work history of each developer and assign a developer who has experience in similar work to a new work with priority. The work history includes, for example, past projects, records of completed tasks, etc., but is not limited to such examples. For example, the assignment unit can refer to the history of past projects and assign a developer who has experience in similar work with priority. The assignment unit can also refer to the records of completed tasks and assign a developer who has experience in similar work with priority. In this way, by referring to the past work history, a developer who has experience in similar work can be assigned with priority. A part or all of the above-mentioned processing in the assignment unit may be performed, for example, using AI, or may be performed without using AI. For example, the assignment unit can input the past work history to the generation AI and cause the generation AI to identify a developer who has experience in similar work.

[0073] The assignment unit can monitor the health condition of each developer and adjust the assignment as necessary. The health condition includes, for example, regular health checkups, self-reports, etc., but is not limited to such examples. For example, the assignment unit can refer to the results of regular health checkups and assign developers in good health conditions preferentially. The assignment unit can also refer to the self-reported health condition and assign developers in good health conditions preferentially. In this way, appropriate assignment can be made by monitoring the health condition of each developer. Part or all of the above-mentioned processing in the assignment unit may be performed, for example, using AI, or may be performed without using AI. For example, the assignment unit can input the results of a health checkup to the generation AI and cause the generation AI to evaluate the health condition.

[0074] The integration unit can detect a conflict of opinion between developers and propose a solution. Methods for detecting a conflict of opinion include, for example, analysis of a discussion log, a questionnaire survey, and the like, but are not limited to such examples. For example, the integration unit can analyze a discussion log to detect a conflict of opinion. The integration unit can also conduct a questionnaire survey to detect a conflict of opinion. Furthermore, the integration unit can use an emotion estimation algorithm to identify the cause of the conflict of opinion and propose a solution. This makes it possible to improve the quality of communication between developers by detecting a conflict of opinion and proposing a solution. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the integration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the integration unit can input a discussion log to the generation AI and cause the generation AI to execute detection of a conflict of opinion.

[0075] The integration unit performs emotion analysis occurring in the process of opinion integration, and can improve the quality of communication between developers. The emotion analysis includes, for example, text mining, emotion recognition algorithms, and the like, but is not limited to such examples. For example, the integration unit can use text mining to analyze emotions occurring in the process of opinion integration. The integration unit can also use an emotion recognition algorithm to analyze emotions occurring in the process of opinion integration. Furthermore, the integration unit can also use an emotion estimation algorithm to analyze emotions occurring in the process of opinion integration, and improve the quality of communication between developers. Thereby, by performing emotion analysis, the quality of communication between developers can be improved. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the integration unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the integration unit can input text data to the generation AI and cause the generation AI to execute emotion analysis.

[0076] The collection unit can estimate the emotion of each developer and adjust the collection frequency of the work information based on the estimated emotion. The method of estimating emotion includes, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to such examples. For example, the collection unit can estimate the emotion of each developer using facial expression recognition technology, and reduce the collection frequency when the developer is highly stressed and increase the collection frequency when the developer is relaxed. The collection unit can also estimate the emotion of each developer using voice analysis technology, and reduce the collection frequency when the emotion fluctuates greatly and increase the collection frequency when the emotion is stable. Furthermore, the collection unit can estimate the emotion of each developer using text analysis technology, and reduce the collection frequency when the emotion is negative and increase the collection frequency when the emotion is positive. In this way, the collection frequency can be adjusted based on the emotion of each developer, so that the work information can be collected at a more appropriate time. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input facial expression data to a generation AI and have the generation AI perform emotion estimation.

[0077] The collection unit can analyze the past work performance of each developer and select the optimal collection method. The optimal collection method includes, for example, the type of data, the collection frequency, the collection means, etc., but is not limited to such examples. For example, the collection unit can analyze the past work performance of each developer and select a detailed collection method if the performance is high. The collection unit can also analyze the past work performance of each developer and select a simple collection method if the performance is low. Furthermore, the collection unit can analyze the past work performance of each developer and select a flexible collection method if the performance fluctuates greatly. In this way, the optimal collection method can be selected by analyzing the past work performance. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past work performance data to the generation AI and cause the generation AI to select the optimal collection method.

[0078] When collecting work information, the collection unit can perform filtering based on the current project and field of interest of each developer. Filtering includes, for example, keyword matching, rule-based filtering, and the like, but is not limited to such examples. For example, the collection unit can grasp the current project of each developer and collect only related work information. In addition, the collection unit can grasp the field of interest of each developer and collect only related work information. Furthermore, the collection unit can grasp the current project and field of interest of each developer and collect only work information related to both. In this way, highly relevant work information can be collected by filtering based on the current project and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input project information and field of interest data to the generation AI and cause the generation AI to perform filtering.

[0079] The collection unit can estimate the emotion of each developer and determine the priority of the work information to be collected based on the estimated emotion. The determination of the priority includes, for example, importance, urgency, dependency, etc., but is not limited to such examples. For example, the collection unit can estimate the emotion of each developer using facial expression recognition technology, and when stress is high, preferentially collect work information of low importance. The collection unit can also estimate the emotion of each developer using voice analysis technology, and when the developer is relaxed, preferentially collect work information of high importance. Furthermore, the collection unit can estimate the emotion of each developer using text analysis technology, and when the emotion fluctuates greatly, preferentially collect balanced work information. In this way, by determining the priority based on the emotion of each developer, more appropriate work information can be collected. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input facial expression data to a generation AI and have the generation AI perform emotion estimation.

[0080] When collecting work information, the collection unit can preferentially collect highly relevant information by considering the geographical location information of each developer. The acquisition of geographical location information includes, for example, GPS data, IP addresses, etc., but is not limited to such examples. For example, the collection unit can grasp the geographical location information of each developer and preferentially collect work information related to nearby projects. In addition, the collection unit can grasp the geographical location information of each developer and preferentially collect work information of projects in which developers in the same region are involved. Furthermore, the collection unit can grasp the geographical location information of each developer and preferentially collect work information related to problems specific to the region. In this way, by considering the geographical location information, highly relevant work information can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0081] The collection unit can analyze the social media activity of each developer when collecting the work information and collect related information. The analysis of social media activity includes, for example, the content of posts, the number of likes, the number of comments, and the like, but is not limited to such examples. For example, the collection unit can analyze the social media activity of each developer and collect work information based on related technology trends. The collection unit can also analyze the social media activity of each developer and collect work information related to technologies in which the developer is interested. Furthermore, the collection unit can analyze the social media activity of each developer and collect work information related to projects in which followers and friends are involved. In this way, the related work information can be collected by analyzing the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media data to the generation AI and cause the generation AI to collect related information.

[0082] The acquisition unit can estimate the emotion of each developer and adjust the timing of acquiring new work information based on the estimated emotion. The adjustment of the acquisition timing includes, for example, the progress of the work, the schedule of the developer, etc., but is not limited to such examples. For example, the acquisition unit can estimate the emotion of each developer using facial expression recognition technology, and delay the acquisition of new work information when stress is high. The acquisition unit can also estimate the emotion of each developer using voice analysis technology, and accelerate the acquisition of new work information when the developer is relaxed. Furthermore, the acquisition unit can estimate the emotion of each developer using text analysis technology, and flexibly adjust the acquisition of new work information when the emotion fluctuates greatly. As a result, by adjusting the acquisition timing based on the emotion of each developer, new work information can be acquired at a more appropriate timing. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0083] The acquisition unit can analyze the past work history of each developer and select the optimal acquisition method. The optimal acquisition method includes, for example, the type of data, the acquisition frequency, the acquisition means, etc., but is not limited to such examples. For example, the acquisition unit can analyze the past work history of each developer and select a detailed acquisition method if the developer has experience performing similar work. In addition, the acquisition unit can also analyze the past work history of each developer and select a simple acquisition method if the developer has no experience performing the work. Furthermore, the acquisition unit can analyze the past work history of each developer and select an acquisition method according to the difficulty of the work. In this way, the optimal acquisition method can be selected by analyzing the past work history. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input past work history data to the generation AI and cause the generation AI to select the optimal acquisition method.

[0084] When acquiring new work information, the acquisition unit can perform filtering based on the current life situation and field of interest of each developer. Acquisition of the life situation includes, for example, questionnaire surveys, self-reports, etc., but is not limited to such examples. For example, the acquisition unit can grasp the current life situation of each developer, and filter out work information of low importance when the developer is busy. The acquisition unit can also grasp the field of interest of each developer and acquire only related work information. Furthermore, the acquisition unit can grasp the current life situation and field of interest of each developer, and acquire only work information related to both. In this way, by filtering based on the current life situation and field of interest, highly related work information can be acquired. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the life situation data and field of interest data to the generation AI and cause the generation AI to execute filtering.

[0085] The acquisition unit can estimate the emotion of each developer and determine the priority of the work information to be acquired based on the estimated emotion. The determination of the priority includes, for example, importance, urgency, dependency, etc., but is not limited to such examples. For example, the acquisition unit can estimate the emotion of each developer using facial expression recognition technology, and when stress is high, preferentially acquire work information of low importance. In addition, the acquisition unit can estimate the emotion of each developer using voice analysis technology, and when the developer is relaxed, preferentially acquire work information of high importance. Furthermore, the acquisition unit can estimate the emotion of each developer using text analysis technology, and when the emotion fluctuates greatly, preferentially acquire balanced work information. In this way, by determining the priority based on the emotion of each developer, more appropriate work information can be acquired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0086] When acquiring new work information, the acquisition unit can preferentially acquire highly relevant information by considering the geographical location information of each developer. The acquisition of geographical location information includes, but is not limited to, GPS data, IP addresses, and the like. For example, the acquisition unit can grasp the geographical location information of each developer and preferentially acquire work information related to nearby projects. The acquisition unit can also grasp the geographical location information of each developer and preferentially acquire work information of projects in which developers in the same region are involved. Furthermore, the acquisition unit can grasp the geographical location information of each developer and preferentially acquire work information related to problems specific to the region. In this way, by considering the geographical location information, highly relevant work information can be preferentially acquired. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information to the generation AI and cause the generation AI to execute acquisition of highly relevant information.

[0087] The acquisition unit can analyze the social media activity of each developer when acquiring new work information and acquire related information. The analysis of social media activity includes, for example, the content of posts, the number of likes, the number of comments, and the like, but is not limited to such examples. For example, the acquisition unit can analyze the social media activity of each developer and acquire work information based on related technology trends. The acquisition unit can also analyze the social media activity of each developer and acquire work information related to technologies in which the developer is interested. Furthermore, the acquisition unit can analyze the social media activity of each developer and acquire work information related to projects in which followers and friends are involved. In this way, related work information can be acquired by analyzing social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input social media data to the generation AI and cause the generation AI to execute acquisition of related information.

[0088] The assignment unit can estimate the emotion of each developer and adjust the expression method of the assignment based on the estimated emotion. The adjustment of the expression method includes, for example, the wording, the format of the message, and the like, but is not limited to such examples. For example, the assignment unit can estimate the emotion of each developer using facial expression recognition technology, and use a simple expression method when the developer is highly stressed. The assignment unit can also estimate the emotion of each developer using voice analysis technology, and use a detailed expression method when the developer is relaxed. Furthermore, the assignment unit can estimate the emotion of each developer using text analysis technology, and use a flexible expression method when the emotion fluctuates greatly. This allows more appropriate assignment by adjusting the expression method based on the emotion of each developer. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the assignment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the assignment unit can input facial expression data to a generation AI and have the generation AI perform emotion estimation.

[0089] The assignment unit can adjust the level of detail of the assignment based on the importance of the work at the time of assignment. The evaluation of the importance of the work includes, for example, the priority of the project, business impact, etc., but is not limited to such examples. For example, the assignment unit can provide detailed assignment information when the importance of the work is high. In addition, the assignment unit can also provide simple assignment information when the importance of the work is low. Furthermore, the assignment unit can flexibly adjust the level of detail of the assignment information according to the importance of the work. As a result, by adjusting the level of detail based on the importance of the work, more appropriate assignment can be performed. A part or all of the above-mentioned processing in the assignment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assignment unit can input the importance data of the work to the generation AI and cause the generation AI to adjust the level of detail of the assignment information.

[0090] The assignment unit can apply different assignment algorithms depending on the category of the work at the time of assignment. The classification of the work category includes, for example, development, testing, design, etc., but is not limited to such examples. For example, when the work category is development, the assignment unit can apply an assignment algorithm specialized for development. Furthermore, when the work category is testing, the assignment unit can also apply an assignment algorithm specialized for testing. Furthermore, when the work category is document creation, the assignment unit can also apply an assignment algorithm specialized for document creation. This makes it possible to apply an optimal assignment algorithm according to the category of the work. A part or all of the above-mentioned processing in the assignment unit may be performed, for example, using AI, or may be performed without using AI. For example, the assignment unit can input the work category data to the generation AI and cause the generation AI to apply the assignment algorithm.

[0091] The assignment unit can determine the priority of assignment based on the submission time of the work at the time of assignment. The determination of the submission time includes, but is not limited to, for example, a project deadline, a milestone, and the like. For example, the assignment unit can prioritize assignment when the submission time of the work is near. In addition, the assignment unit can postpone the assignment when the submission time of the work is far away. Furthermore, the assignment unit can flexibly adjust the priority of assignment according to the submission time. As a result, more appropriate assignment can be performed by determining the priority based on the submission time of the work. Part or all of the above-mentioned processing in the assignment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assignment unit can input the submission time data to the generation AI and cause the generation AI to execute the determination of the priority of assignment.

[0092] The assignment unit can adjust the order of assignment based on the relevance of the tasks at the time of assignment. The evaluation of the relevance of the tasks includes, for example, dependency, common resources, and the like, but is not limited to such examples. For example, the assignment unit can assign tasks with high relevance preferentially. The assignment unit can also assign tasks with low relevance later. Furthermore, the assignment unit can flexibly adjust the order of assignment according to the relevance of the tasks. As a result, more appropriate assignment can be performed by adjusting the order based on the relevance of the tasks. A part or all of the above-mentioned processing in the assignment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assignment unit can input task relevance data to the generation AI and cause the generation AI to adjust the order of assignment.

[0093] The integration unit can estimate the emotion of each developer and adjust the opinion integration method based on the estimated emotion. The adjustment of the opinion integration method includes, for example, a voting system, a consensus process, and the like, but is not limited to such examples. For example, the integration unit can estimate the emotion of each developer using facial expression recognition technology, and use a simple opinion integration method when stress is high. The integration unit can also estimate the emotion of each developer using voice analysis technology, and use a detailed opinion integration method when the developer is relaxed. Furthermore, the integration unit can estimate the emotion of each developer using text analysis technology, and use a flexible opinion integration method when the emotion fluctuates greatly. This allows more appropriate opinion integration by adjusting the opinion integration method based on the emotion of each developer. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the integration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the integration unit can input facial expression data to a generation AI and have the generation AI perform emotion estimation.

[0094] The integration unit can select an optimal integration method by referring to the past opinion history of each developer when integrating opinions. Reference to the opinion history includes, for example, past discussion logs, feedback records, etc., but is not limited to such examples. For example, the integration unit can select a quick integration method by referring to the past opinion history of each developer when there is a lot of agreement in opinions. In addition, the integration unit can select a careful integration method by referring to the past opinion history of each developer when there is a lot of conflict in opinions. Furthermore, the integration unit can select a flexible integration method by referring to the past opinion history of each developer when there is a large fluctuation in opinions. In this way, the optimal opinion integration method can be selected by referring to the past opinion history. Part or all of the above-mentioned processing in the integration unit may be performed, for example, using AI, or may be performed without using AI. For example, the integration unit can input the opinion history data to the generation AI and cause the generation AI to select the optimal integration method.

[0095] The integration unit can customize the means of integration based on the current life situation of each developer at the time of opinion integration. The acquisition of the life situation includes, for example, a questionnaire survey, a self-report, and the like, but is not limited to such examples. For example, the integration unit can grasp the current life situation of each developer and provide a simple integration means if the developer is busy. The integration unit can also grasp the current life situation of each developer and provide a detailed integration means if the developer has time. Furthermore, the integration unit can grasp the current life situation of each developer and provide a flexible integration means according to the situation. As a result, by customizing the means of integration based on the current life situation, more appropriate opinion integration can be performed. Part or all of the above-mentioned processing in the integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the integration unit can input the life situation data to the generation AI and cause the generation AI to execute customization of the integration means.

[0096] The integration unit can estimate the emotion of each developer and determine the priority of opinion integration based on the estimated emotion. The determination of the priority includes, for example, importance, urgency, dependency, etc., but is not limited to such examples. For example, the integration unit can estimate the emotion of each developer using facial expression recognition technology, and when stress is high, can prioritize and integrate opinions with low importance. In addition, the integration unit can estimate the emotion of each developer using voice analysis technology, and when the developer is relaxed, can prioritize and integrate opinions with high importance. Furthermore, the integration unit can estimate the emotion of each developer using text analysis technology, and when the emotion fluctuates greatly, can prioritize and integrate balanced opinions. In this way, by determining the priority based on the emotion of each developer, more appropriate opinion integration can be performed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the integration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the integration unit can input facial expression data to a generation AI and have the generation AI perform emotion estimation.

[0097] The integration unit can select the optimal integration method by considering the geographical location information of each developer when integrating opinions. Examples of the acquisition of geographical location information include, but are not limited to, GPS data, IP addresses, and the like. For example, the integration unit can grasp the geographical location information of each developer and prioritize opinion integration between developers in the vicinity. In addition, the integration unit can grasp the geographical location information of each developer and prioritize opinion integration between developers in the same region. In addition, the integration unit can grasp the geographical location information of each developer and prioritize opinion integration related to issues specific to the region. In this way, the optimal opinion integration method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the integration unit may be performed, for example, using AI, or may be performed without using AI. For example, the integration unit can input geographical location information to the generation AI and cause the generation AI to select the optimal integration method.

[0098] The integration unit can analyze the social media activity of each developer at the time of opinion integration and propose a means of integration. The analysis of social media activity includes, for example, the content of posts, the number of likes, the number of comments, and the like, but is not limited to such examples. For example, the integration unit can analyze the social media activity of each developer and propose an integration means based on related technology trends. The integration unit can also analyze the social media activity of each developer and propose an integration means related to a technology in which the developer is interested. Furthermore, the integration unit can analyze the social media activity of each developer and propose an integration means related to a project in which followers or friends are involved. In this way, by analyzing social media activity, a related means of opinion integration can be proposed. A part or all of the above-mentioned processing in the integration unit may be performed, for example, using AI, or may be performed without using AI. For example, the integration unit can input social media data to the generation AI and cause the generation AI to execute the proposal of the integration means.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The collection unit can monitor the work environment of each developer and adjust the collection method of work information according to changes in the environment. For example, when a developer is working remotely, the collection unit can monitor the network status and reduce the collection frequency if the network is unstable. In addition, when a developer is working in an office, the collection unit can monitor the noise level in the office and change the collection method if the noise level is high. Furthermore, the collection unit can monitor the temperature and humidity of the developer's work environment and adjust the collection frequency if the environment is not comfortable. This makes it possible to provide a flexible collection method according to the work environment.

[0101] The integration department can incorporate the opinions of third-party experts to resolve conflicts of opinion between developers. For example, the integration department can collect the opinions of experts who are familiar with a specific technical field and use them as a reference for resolving conflicts of opinion between developers. The integration department can also collect the opinions of project managers and leaders and use them as a guideline for resolving conflicts of opinion. Furthermore, the integration department can collect the opinions of external consultants and provide them with advice for resolving conflicts of opinion. In this way, by incorporating the opinions of third parties, more objective and fair opinion integration is possible.

[0102] The collection unit can analyze the work style of each developer and determine the optimal collection timing. For example, the collection unit can analyze the working hours of each developer and collect work information during times when they are most concentrated. The collection unit can also analyze each developer's break time and collect work information during breaks. Furthermore, the collection unit can analyze the work pace of each developer and increase the collection frequency when work is progressing smoothly. This makes it possible to provide the optimal collection timing according to each developer's work style.

[0103] The collection unit can analyze the skill set of each developer and prioritize collecting work information according to the skills. For example, the collection unit can analyze the programming language skills of each developer and prioritize collecting work information related to the language in which the developer is good at. The collection unit can also analyze the database management skills of each developer and prioritize collecting database-related work information. Furthermore, the collection unit can analyze the test automation skills of each developer and prioritize collecting work information related to test automation. This makes it possible to provide optimal work information according to the skill set of each developer.

[0104] The assignment department can estimate each developer's motivation to learn and preferentially assign work using new technologies to developers with a high motivation. For example, the assignment department can analyze each developer's usage of an online learning platform and assign projects using new technologies to developers with a high motivation to learn. The assignment department can also analyze each developer's posting status to technical blogs and forums and assign work using new technologies to developers who actively disseminate information. Furthermore, the assignment department can analyze each developer's participation status in an in-house training program and assign work using new technologies to developers who actively participate. This makes it possible to assign appropriate work to developers with a high motivation to learn.

[0105] The assignment unit can estimate the emotion of each developer and adjust the timing of assignment based on the estimated emotion. For example, the assignment unit can estimate the emotion of each developer using facial expression recognition technology and delay the assignment if the developer is highly stressed. The assignment unit can also estimate the emotion of each developer using voice analysis technology and expedite the assignment if the developer is relaxed. Furthermore, the assignment unit can estimate the emotion of each developer using text analysis technology and flexibly adjust the timing of assignment if the developer's emotions fluctuate greatly. This makes it possible to provide optimal assignment timing based on the emotion of each developer.

[0106] The assignment unit can estimate the emotion of each developer and customize the assignment method based on the estimated emotion. For example, the assignment unit can estimate the emotion of each developer using facial expression recognition technology and use a simple assignment method when the developer is highly stressed. The assignment unit can also estimate the emotion of each developer using voice analysis technology and use a detailed assignment method when the developer is relaxed. Furthermore, the assignment unit can estimate the emotion of each developer using text analysis technology and use a flexible assignment method when the emotion fluctuates greatly. This makes it possible to provide an optimal assignment method based on the emotion of each developer.

[0107] The integration unit can estimate the emotion of each developer and adjust the opinion integration process based on the estimated emotion. For example, the integration unit can estimate the emotion of each developer using facial expression recognition technology and simplify the opinion integration process when the developer is highly stressed. The integration unit can also estimate the emotion of each developer using voice analysis technology and use a detailed opinion integration process when the developer is relaxed. Furthermore, the integration unit can estimate the emotion of each developer using text analysis technology and use a flexible opinion integration process when the emotion fluctuates greatly. This makes it possible to provide an optimal opinion integration process based on the emotion of each developer.

[0108] The integration unit can estimate the emotion of each developer and determine the priority of opinion integration based on the estimated emotion. For example, the integration unit can estimate the emotion of each developer using facial expression recognition technology, and prioritize integration of less important opinions when the developer is highly stressed. The integration unit can also estimate the emotion of each developer using voice analysis technology, and prioritize integration of more important opinions when the developer is relaxed. Furthermore, the integration unit can estimate the emotion of each developer using text analysis technology, and prioritize integration of more balanced opinions when the developer's emotions fluctuate greatly. This allows for more appropriate opinion integration by determining the priority based on the emotion of each developer.

[0109] The integration unit can estimate the emotion of each developer and adjust the opinion integration method based on the estimated emotion. For example, the integration unit can estimate the emotion of each developer using facial expression recognition technology, and use a simple opinion integration method when the developer is highly stressed. The integration unit can also estimate the emotion of each developer using voice analysis technology, and use a detailed opinion integration method when the developer is relaxed. Furthermore, the integration unit can estimate the emotion of each developer using text analysis technology, and use a flexible opinion integration method when the emotion fluctuates greatly. This makes it possible to provide an optimal opinion integration method based on the emotion of each developer.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The collection unit collects work information that indicates the progress and difficulty of each developer's work. For example, the collection unit can collect work information from project management tools (JIRA, Trello, Asana, etc.). It can also collect productivity metrics (number of lines of code, number of bug fixes, number of reviews, etc.) for each developer and reflect them in the progress. Step 2: The acquisition unit acquires information on new work. For example, the acquisition unit can acquire information on new work within a specific project. Step 3: The assignment department determines which developer to assign to new work based on the work information collected by the collection department. The assignment department uses machine learning algorithms (linear regression, random forest, neural network, etc.) to analyze each developer's past performance data and assign the most suitable developer. It can also refer to each developer's past work history to preferentially assign developers who have experience performing similar work. In addition, it can monitor the health of each developer and adjust assignments as necessary.

[0112] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0113] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0114] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] For example, the collection unit is realized by either the smart device 14 or the data processing device 12. For example, the collection unit collects work information from a project management tool by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing device 12. The acquisition unit acquires new work information by, for example, the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. The assignment unit assigns an optimal developer using a machine learning algorithm by, for example, the specific processing unit 290 of the data processing device 12. The integration unit integrates opinions between developers and performs sentiment analysis by, for example, the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0123] Fig. 4 shows an example of 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.

[0124] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] For example, the collection unit is realized by either the smart glasses 214 or the data processing device 12. For example, the collection unit collects work information from a project management tool by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The acquisition unit acquires new work information by, for example, the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The assignment unit assigns an optimal developer using a machine learning algorithm by, for example, the specific processing unit 290 of the data processing device 12. The integration unit integrates opinions between developers and performs sentiment analysis by, for example, the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The 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.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0143] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] 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 voice indicating a user input for 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.

[0145] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] For example, the collection unit is realized by either the headset type terminal 314 or the data processing device 12. For example, the collection unit collects work information from a project management tool by the processor 46 of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The acquisition unit acquires new work information by, for example, the control unit 46A of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The assignment unit assigns an optimal developer using a machine learning algorithm by, for example, the specific processing unit 290 of the data processing device 12. The integration unit integrates opinions between developers and performs sentiment analysis by, for example, the control unit 46A of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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, the RAM 30, and the 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.

[0151] 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.

[0152] 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 the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0154] 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 performed in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0156] 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.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0160] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] 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 a voice indicating a user input for 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.

[0162] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0164] For example, the collection unit is realized by either the robot 414 or the data processing device 12. For example, the collection unit collects work information from a project management tool by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing device 12. The acquisition unit acquires new work information by, for example, the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12. The assignment unit assigns an optimal developer using a machine learning algorithm by, for example, the specific processing unit 290 of the data processing device 12. The integration unit integrates opinions between developers and performs sentiment analysis by, for example, the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0166] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0168] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0170] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0172] 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.

[0173] 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 Universal Serial Bus (USB) 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.

[0174] In addition, 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 upon request from the data processing device 12.

[0175] 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.

[0176] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0177] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0178] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0181] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0183] (Appendix 1) a collection unit that collects work information indicating the degree of progress of work by each developer and the degree of difficulty of the work; An acquisition unit that acquires new work information; An assignment unit that determines a developer to be assigned to a new task based on the task information collected by the collection unit. A system characterized by: (Appendix 2) Have an integration department that integrates opinions between different developers 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collect the above work information from the project management tool 2. The system of claim 1, (Appendix 4) The collecting unit includes: Collect productivity metrics for each developer and reflect their progress 2. The system of claim 1, (Appendix 5) The assignment unit is Use machine learning algorithms to analyze each developer's past performance data and assign the right developers to new work 2. The system of claim 1, (Appendix 6) The assignment unit is By referring to the past work history of each developer, developers who have experience with similar work are preferentially assigned to the new work. 2. The system of claim 1, (Appendix 7) The assignment unit is Monitor the health of each developer and adjust assignments as needed 2. The system of claim 1, (Appendix 8) The integration unit includes: Detect conflicts between developers and suggest solutions 3. The system of claim 2, (Appendix 9) The integration unit includes: Improve the quality of communication between developers by analyzing the sentiment that arises during the opinion integration process. 3. The system of claim 2, (Appendix 10) The collecting unit includes: Estimate the sentiment of each developer and adjust the frequency of collecting work information based on the estimated sentiment. 2. The system of claim 1, (Appendix 11) The collecting unit includes: Analyze each developer's past work performance and select the optimal collection method 2. The system of claim 1, (Appendix 12) The collecting unit includes: As work information is collected, it is filtered based on each developer's current projects and areas of interest. 2. The system of claim 1, (Appendix 13) The collecting unit includes: Estimate the sentiment of each developer and prioritize the work information to be collected based on the estimated sentiment. 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting work information, the geographic location of each developer is taken into consideration to prioritize collection of relevant information. 2. The system of claim 1, (Appendix 15) The collecting unit includes: When collecting work information, analyze each developer's social media activity and collect relevant information. 2. The system of claim 1, (Appendix 16) The acquisition unit is Estimate the emotions of each developer and adjust the timing of acquiring new work information based on the estimated emotions. 2. The system of claim 1, (Appendix 17) The acquisition unit is Analyze each developer's past work history and select the optimal acquisition method 2. The system of claim 1, (Appendix 18) The acquisition unit is Filter new work information based on each developer's current life situation and interests 2. The system of claim 1, (Appendix 19) The acquisition unit is Estimate the sentiment of each developer and prioritize the work information to be acquired based on the estimated sentiment. 2. The system of claim 1, (Appendix 20) The acquisition unit is When acquiring new work information, the geographic location of each developer is taken into account to prioritize the most relevant information. 2. The system of claim 1, (Appendix 21) The acquisition unit is When new work information is acquired, the social media activity of each developer is analyzed to obtain relevant information. 2. The system of claim 1, (Appendix 22) The assignment unit is Infer the sentiment of each developer and adjust the wording of the assignment based on the inferred sentiment 2. The system of claim 1, (Appendix 23) The assignment unit is When assigning, adjust the assignment detail based on the importance of the work 2. The system of claim 1, (Appendix 24) The assignment unit is During assignment, apply different assignment algorithms depending on the category of work 2. The system of claim 1, (Appendix 25) The assignment unit is During assignment, prioritize assignments based on when work is submitted 2. The system of claim 1, (Appendix 26) The assignment unit is During assignment, adjust the order of assignments based on work relevance 2. The system of claim 1, (Appendix 27) The integration unit includes: Estimate the sentiment of each developer and adjust the opinion integration method based on the estimated sentiment. 3. The system of claim 2, (Appendix 28) The integration unit includes: When integrating opinions, the optimal integration method is selected by referring to the past opinion history of each developer. 3. The system of claim 2, (Appendix 29) The integration unit includes: During integration, customize the integration method based on each developer's current life situation. 3. The system of claim 2, (Appendix 30) The integration unit includes: Estimate the sentiment of each developer and prioritize opinion integration based on the estimated sentiment. 3. The system of claim 2, (Appendix 31) The integration unit includes: When merging opinions, the optimal merger method is selected taking into account the geographic location information of each developer. 3. The system of claim 2, (Appendix 32) The integration unit includes: When merging ideas, we analyze each developer's social media activity to suggest ways to merge them. 3. The system of claim 2, [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects work information indicating the degree of progress of work by each developer and the degree of difficulty of the work; An acquisition unit that acquires new work information; and an assignment unit that determines a developer to be assigned to a new task based on the task information collected by the collection unit. A system characterized by:

2. Have an integration department that integrates opinions between different developers 2. The system of claim 1 .

3. The collecting unit includes: Collect the above work information from the project management tool 2. The system of claim 1 .

4. The collecting unit includes: Collect productivity metrics for each developer and reflect their progress 2. The system of claim 1 .

5. The assignment unit is Use machine learning algorithms to analyze each developer's past performance data and assign the right developers to new work 2. The system of claim 1 .

6. The assignment unit is By referring to the past work history of each developer, developers who have experience with similar work are preferentially assigned to the new work.

2. The system of claim 1 .

7. The assignment unit is Monitor the health of each developer and adjust assignments as needed 2. The system of claim 1 .

8. The integration unit includes: Detect conflicts between developers and suggest solutions 3. The system of claim 2.

9. The integration unit includes: Improve the quality of communication between developers by analyzing the sentiment that arises during the opinion integration process.

3. The system of claim 2.

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