system

Abstract

The system according to the embodiment aims to unify the internal application flow and process application contents efficiently. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a determination unit, and an allocation unit. The reception unit receives application content. The analysis unit analyzes the application content received by the reception unit. The determination unit determines an assignee based on the content analyzed by the analysis unit. The allocation unit allocates the application to the assignee determined by the determination unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the internal application process was fragmented, making it difficult to remember and understand.

[0005] The system according to the embodiment aims to unify the internal application flow and process application contents efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a determination unit, and an allocation unit. The reception unit receives application content. The analysis unit analyzes the application content received by the reception unit. The determination unit determines an assignee based on the content analyzed by the analysis unit. The allocation unit allocates the application to the assignee determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment unifies the internal application flow and can process application contents efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An application management system according to an embodiment of the present invention unifies the application destinations from sales, uses AI to interpret the application content, and assigns the application to the appropriate assignment. This application management system streamlines the application flow and reduces the burden on sales representatives by accepting and analyzing the application content from sales representatives, determining the appropriate assignment, and assigning the application. For example, when a sales representative requests data support for a new project, the AI ​​analyzes the request and automatically assigns it to the data support team. Similarly, when another sales representative requests use of the CSS assignment tool, the AI ​​analyzes the request and automatically assigns it to the CSS assignment tool team. In this way, automatic assignment to the appropriate assignment based on the application content simplifies the application flow. This system eliminates the need for sales representatives to remember the application destinations; simply entering the application content automatically assigns the application to the appropriate assignment. Furthermore, because the AI ​​analyzes the application content, errors and omissions in the application content are reduced, improving the efficiency of the application flow. This allows the application management system to efficiently accept, analyze, interpret, and assign application content from sales representatives.

[0029] The request management system according to the embodiment includes a reception unit, an analysis unit, a determination unit, and an allocation unit. The reception unit receives application content from sales. The application content from sales includes, but is not limited to, sales reports, customer feedback, and requests for data support for new projects. The reception unit can receive the application content, for example, through an online form. The reception unit can also receive the application content via email or a dedicated application system. The analysis unit analyzes the application content received by the reception unit. The analysis unit can analyze the application content using, for example, text analysis technology. The analysis unit can extract keywords and phrases from the application content and classify the application content based on the extracted keywords and phrases. The analysis unit can also analyze the application content using a machine learning algorithm. For example, the analysis unit can learn from past application data and build a model for appropriately analyzing new application content. The determination unit determines an assignment destination based on the content analyzed by the analysis unit. The determination unit can determine an assignment destination based on the type and importance of the application content. The determination unit can also determine the assignee using a machine learning algorithm. For example, the determination unit learns past assignment data and builds a model for determining the optimal assignee for new request content. The allocation unit allocates the request to the assignee determined by the determination unit. The allocation unit can allocate requests based on priority, for example. The allocation unit can allocate requests to an appropriate assignee based on the urgency or importance of the request content. The allocation unit can also allocate requests randomly. For example, the allocation unit can randomly allocate requests to evenly distribute the load on the assignees. This allows the request management system according to the embodiment to efficiently accept, analyze, determine, and allocate request content from sales.

[0030] The reception unit can accept application details from sales. Examples of application details from sales include, but are not limited to, sales reports, customer feedback, and requests for data support for new projects. The reception unit can accept application details, for example, through an online form. The reception unit can also accept application details via email or a dedicated application system. For example, when the reception unit accepts application details through an online form, the user enters necessary information into the form and clicks a send button, which sends the application details to the reception unit. When the reception unit accepts application details through email, the user sends the application details to a specified email address, which causes the reception unit to accept the application details. When the reception unit accepts application details through a dedicated application system, the user logs in to the system, enters and sends the application details, which causes the reception unit to accept the application details. This allows applications from sales to be accepted efficiently.

[0031] The analysis unit can analyze the application content received by the reception unit. The analysis unit analyzes the application content using, for example, text analysis technology. The analysis unit can extract keywords and phrases from the application content and classify the application content based on the keywords and phrases. The analysis unit can also analyze the application content using a machine learning algorithm. For example, the analysis unit learns from past application data and builds a model for appropriately analyzing new application content. The analysis unit can analyze the application content using, for example, natural language processing technology. Natural language processing technology is a technology for analyzing text data and understanding meaning and context. The analysis unit can analyze the meaning of the application content using natural language processing technology and extract information for determining an appropriate assignment destination. When analyzing the application content using a machine learning algorithm, the analysis unit learns from past application data and builds a model for analyzing new application content. For example, the analysis unit uses past application data to learn the characteristics of the application content and builds a model for analyzing new application content. This enables the application content to be analyzed efficiently.

[0032] The discrimination unit can determine the assignment destination based on the content analyzed by the analysis unit. The discrimination unit, for example, determines the assignment destination using a rule-based discrimination algorithm. The discrimination unit can determine the appropriate assignment destination based on the type and importance of the request content. The discrimination unit can also determine the assignment destination using a machine learning algorithm. For example, the discrimination unit learns past assignment data and builds a model for determining the optimal assignment destination for new request content. For example, when determining the assignment destination based on the type of request content, the discrimination unit can assign requests related to data support to the data support team and requests related to CSS assignment tool to the CSS assignment tool team. When determining the assignment destination based on the importance of the request content, the discrimination unit can assign highly important requests to an assignment destination that will be processed with priority. This allows the assignment destination to be determined efficiently.

[0033] The allocation unit can allocate the request to the assignee determined by the determination unit. The allocation unit can allocate the request based on, for example, priority. The allocation unit can allocate the request to an appropriate assignee based on the urgency or importance of the request content. The allocation unit can also allocate the request randomly. For example, the allocation unit can allocate the request randomly in order to even out the load on the assignees. For example, when allocating requests based on the urgency of the request content, the allocation unit can allocate highly urgent request content to an assignee that will be processed with priority. When allocating requests based on the importance of the request content, the allocation unit can allocate highly important request content to an assignee that will be processed with priority. This allows the requests to be allocated efficiently.

[0034] Furthermore, the application management system includes a reception unit that analyzes past application history and selects the optimal reception method. The reception unit can, for example, prioritize the selection of application methods that the user has frequently used in the past. The reception unit can also suggest the most efficient reception method based on the user's past application history. The reception unit can also analyze the user's past application history and suggest the optimal reception time slot. This allows the optimal reception method to be selected based on the past application history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past application history data into a generation AI and have the generation AI select the optimal reception method.

[0035] Furthermore, the application management system includes a reception unit that filters application content based on the user's current project and areas of interest when receiving the application content. The reception unit can, for example, prioritize application content related to a project currently in progress by the user. The reception unit can also filter related application content based on the user's areas of interest. The reception unit can also receive appropriate application content according to the progress of the user's current project. This allows the application content to be filtered based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI perform filtering.

[0036] Furthermore, the application management system includes a reception unit that, when receiving application content, prioritizes receiving highly relevant applications based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving application content related to that area. Furthermore, the reception unit can prioritize receiving application content related to locations close to the user's current location. Furthermore, the reception unit can suggest optimal application content based on the user's geographical location information. This allows highly relevant applications to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into a generation AI and cause the generation AI to prioritize receiving highly relevant applications.

[0037] Furthermore, the application management system includes a reception unit that analyzes the user's social media activity when receiving the application content and receives related applications. The reception unit can, for example, prioritize receiving related application content based on the user's social media activity content. The reception unit can also analyze the user's social media interests and suggest appropriate application content. The reception unit can also receive optimal application content based on the user's social media activity history. This makes it possible to receive related applications based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to receive related applications.

[0038] Furthermore, the application management system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the application content during analysis. The analysis unit can, for example, perform a detailed analysis of application content with high importance. The analysis unit can also perform a simplified analysis of application content with low importance. The analysis unit can also set an appropriate level of detail of the analysis depending on the importance of the application content. This makes it possible to adjust the level of detail of the analysis depending on the importance of the application content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the application content to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0039] Furthermore, the application management system includes an analysis unit that applies different analysis algorithms depending on the category of the application content during analysis. For example, the analysis unit can apply a data analysis algorithm to application content related to data support. Furthermore, the analysis unit can apply a tool analysis algorithm to application content related to a CSS assignment tool. Furthermore, the analysis unit can select an optimal analysis algorithm depending on the category of the application content. This makes it possible to apply the optimal analysis algorithm depending on the category of the application content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the application content into a generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0040] Furthermore, the application management system includes an analysis unit that determines the priority of analysis based on the submission time of the application content during analysis. The analysis unit can, for example, prioritize analysis of application content submitted earlier. The analysis unit can also postpone application content submitted later. Furthermore, the analysis unit can set appropriate analysis priorities based on the submission time. This makes it possible to determine the priority of analysis based on the submission time of the application content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data of the application content to the generation AI and have the generation AI determine the analysis priority.

[0041] Furthermore, the application management system includes an analysis unit that adjusts the order of analysis based on the relevance of the application contents during analysis. The analysis unit can, for example, prioritize analysis of highly relevant application contents. The analysis unit can also postpone analysis of less relevant application contents. Furthermore, the analysis unit can set an appropriate analysis order based on the relevance of the application contents. This makes it possible to adjust the analysis order based on the relevance of the application contents. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the application contents to a generation AI and have the generation AI adjust the analysis order.

[0042] Furthermore, the application management system includes a discrimination unit that improves discrimination accuracy by taking into account interrelationships between application contents during discrimination. The discrimination unit can, for example, analyze interrelationships between application contents and prioritize discrimination of highly related contents. The discrimination unit can also improve discrimination accuracy by taking into account interrelationships between application contents. Furthermore, the discrimination unit can set optimal discrimination criteria based on interrelationships between application contents. This makes it possible to improve discrimination accuracy by taking into account interrelationships between application contents. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input interrelationship data between application contents into the generation AI and cause the generation AI to improve discrimination accuracy.

[0043] Furthermore, the application management system includes a discrimination unit that makes discrimination taking into account the applicant's attribute information. The discrimination unit can set appropriate discrimination criteria based on, for example, the applicant's job title or department. The discrimination unit can also improve the accuracy of discrimination by taking into account the applicant's past application history. The discrimination unit can also set optimal discrimination criteria based on the applicant's attribute information. This allows discrimination to be made based on the applicant's attribute information. Some or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input the applicant's attribute information data into a generation AI and have the generation AI perform discrimination.

[0044] Furthermore, the application management system includes a discrimination unit that performs discrimination taking into account the geographical distribution of application content. The discrimination unit can, for example, analyze the geographical distribution of application content and prioritize discrimination of highly relevant content. The discrimination unit can also improve the accuracy of discrimination by taking into account the geographical distribution of application content. Furthermore, the discrimination unit can set optimal discrimination criteria based on the geographical distribution of application content. This allows discrimination to be performed based on the geographical distribution of application content. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input geographical distribution data of application content to a generation AI and have the generation AI perform discrimination.

[0045] Furthermore, the application management system includes a discrimination unit that, during discrimination, improves discrimination accuracy by referring to literature related to the application content. The discrimination unit can, for example, improve discrimination accuracy by referring to literature related to the application content. The discrimination unit can also set optimal discrimination criteria based on literature related to the application content. The discrimination unit can also analyze literature related to the application content to improve discrimination accuracy. This makes it possible to improve discrimination accuracy based on literature related to the application content. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input literature data related to the application content into a generation AI and have the generation AI perform discrimination.

[0046] Furthermore, the request management system includes an allocation unit that improves allocation accuracy by taking into account interrelationships between request contents during allocation. The allocation unit can, for example, analyze interrelationships between request contents and prioritize allocation of highly related contents. The allocation unit can also improve allocation accuracy by taking into account interrelationships between request contents. Furthermore, the allocation unit can set optimal allocation criteria based on interrelationships between request contents. This makes it possible to improve allocation accuracy by taking into account interrelationships between request contents. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input interrelationship data between request contents into a generation AI and have the generation AI perform allocation.

[0047] Furthermore, the application management system includes an allocation unit that performs allocation taking into consideration the applicant's attribute information. The allocation unit can set appropriate allocation criteria based on, for example, the applicant's job title or department. The allocation unit can also improve the accuracy of allocation by taking into consideration the applicant's past application history. Furthermore, the allocation unit can set optimal allocation criteria based on the applicant's attribute information. This allows allocation to be performed based on the applicant's attribute information. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input the applicant's attribute information data into a generation AI and have the generation AI perform the allocation.

[0048] Furthermore, the application management system includes an allocation unit that performs allocation taking into account the geographic distribution of application content. The allocation unit can, for example, analyze the geographic distribution of application content and prioritize allocation of highly relevant content. The allocation unit can also improve the accuracy of allocation by taking into account the geographic distribution of application content. Furthermore, the allocation unit can set optimal allocation criteria based on the geographic distribution of application content. This allows allocation to be performed based on the geographic distribution of application content. Some or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input geographic distribution data of application content to a generation AI and have the generation AI perform allocation.

[0049] Furthermore, the application management system includes an allocation unit that, during allocation, improves the accuracy of allocation by referring to literature related to the application content. The allocation unit can, for example, improve the accuracy of allocation by referring to literature related to the application content. The allocation unit can also set optimal allocation criteria based on the literature related to the application content. The allocation unit can also analyze the literature related to the application content and improve the accuracy of allocation. This allows the accuracy of allocation to be improved based on the literature related to the application content. Some or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input literature data related to the application content into a generation AI and have the generation AI perform the allocation.

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

[0051] The application management system can further include a priority adjustment unit that automatically adjusts the priority of application contents based on the user's past application history. For example, it can be set so that applications that have been applied for frequently in the past are processed with priority. It can also learn specific patterns from past application history and automatically set a high priority if a similar pattern occurs again. Furthermore, if it is determined based on past application history that a specific application content is more important than other application contents, it can process that application content with priority. This makes it possible to efficiently adjust the priority of application contents by utilizing past application history.

[0052] The application management system can further include a workload adjustment unit that adjusts the acceptance of application content in consideration of the user's current workload. For example, if the user is busy, the acceptance of application content can be temporarily delayed. Alternatively, if the user has relatively free time, the application content can be accepted immediately. Furthermore, the timing of accepting application content can be adjusted according to the user's workload. This allows the acceptance of application content to be efficiently adjusted according to the user's workload.

[0053] The application management system can further include a geographic information analysis unit that selects the most appropriate assignment destination based on the user's geographic location information. For example, if the user is in a specific area, an assignment destination related to that area can be selected preferentially. Also, an assignment destination related to a location close to the user's current location can be selected. Furthermore, the most appropriate assignment destination can be suggested based on the user's geographic location information. This allows the most appropriate assignment destination to be selected based on the user's geographic location information.

[0054] The application management system may further include a social media analysis unit that analyzes the user's social media activity and prioritizes the acceptance of related application content. For example, it may be possible to prioritize the acceptance of related application content based on the user's social media activity. It may also be possible to analyze the user's social media interests and suggest appropriate application content. Furthermore, it may be possible to accept the most appropriate application content based on the user's social media activity history. This makes it possible to accept related application content based on the user's social media activity.

[0055] The application management system can further include a schedule adjustment unit that adjusts the acceptance of application content in consideration of the user's work schedule. For example, if the user is in a meeting, the acceptance of application content can be temporarily delayed. Also, application content can be accepted preferentially during time periods when the user is free. Furthermore, the timing of accepting application content can be adjusted according to the user's work schedule. This allows the acceptance of application content to be efficiently adjusted according to the user's work schedule.

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

[0057] Step 1: The reception department accepts applications from sales. Applications from sales include sales reports, customer feedback, and requests for data support for new projects. The reception department can accept applications through an online form, email, or a dedicated application system. Step 2: The analysis unit analyzes the application content received by the reception unit. The analysis unit analyzes the application content using text analysis technology and machine learning algorithms, extracts keywords and phrases, and classifies the application content. Step 3: The discrimination unit determines the appropriate assignment destination based on the content analyzed by the analysis unit. The discrimination unit uses a rule-based discrimination algorithm or a machine learning algorithm to determine the appropriate assignment destination based on the type and importance of the application content. Step 4: The allocation unit allocates the request to the assignee determined by the determination unit. The allocation unit can allocate the request to the appropriate assignee based on the priority, urgency, and importance of the request content. It can also allocate requests randomly to even out the load on the assignees.

[0058] (Example 2) An application management system according to an embodiment of the present invention unifies the application destinations from sales, uses AI to interpret the application content, and assigns the application to the appropriate assignment. This application management system streamlines the application flow and reduces the burden on sales representatives by accepting and analyzing the application content from sales representatives, determining the appropriate assignment, and assigning the application. For example, when a sales representative requests data support for a new project, the AI ​​analyzes the request and automatically assigns it to the data support team. Similarly, when another sales representative requests use of the CSS assignment tool, the AI ​​analyzes the request and automatically assigns it to the CSS assignment tool team. In this way, automatic assignment to the appropriate assignment based on the application content simplifies the application flow. This system eliminates the need for sales representatives to remember the application destinations; simply entering the application content automatically assigns the application to the appropriate assignment. Furthermore, because the AI ​​analyzes the application content, errors and omissions in the application content are reduced, improving the efficiency of the application flow. This allows the application management system to efficiently accept, analyze, interpret, and assign application content from sales representatives.

[0059] The request management system according to the embodiment includes a reception unit, an analysis unit, a determination unit, and an allocation unit. The reception unit receives application content from sales. The application content from sales includes, but is not limited to, sales reports, customer feedback, and requests for data support for new projects. The reception unit can receive the application content, for example, through an online form. The reception unit can also receive the application content via email or a dedicated application system. The analysis unit analyzes the application content received by the reception unit. The analysis unit can analyze the application content using, for example, text analysis technology. The analysis unit can extract keywords and phrases from the application content and classify the application content based on the extracted keywords and phrases. The analysis unit can also analyze the application content using a machine learning algorithm. For example, the analysis unit can learn from past application data and build a model for appropriately analyzing new application content. The determination unit determines an assignment destination based on the content analyzed by the analysis unit. The determination unit can determine an assignment destination based on the type and importance of the application content. The determination unit can also determine the assignee using a machine learning algorithm. For example, the determination unit learns past assignment data and builds a model for determining the optimal assignee for new request content. The allocation unit allocates the request to the assignee determined by the determination unit. The allocation unit can allocate requests based on priority, for example. The allocation unit can allocate requests to an appropriate assignee based on the urgency or importance of the request content. The allocation unit can also allocate requests randomly. For example, the allocation unit can randomly allocate requests to evenly distribute the load on the assignees. This allows the request management system according to the embodiment to efficiently accept, analyze, determine, and allocate request content from sales.

[0060] The reception unit can accept application details from sales. Examples of application details from sales include, but are not limited to, sales reports, customer feedback, and requests for data support for new projects. The reception unit can accept application details, for example, through an online form. The reception unit can also accept application details via email or a dedicated application system. For example, when the reception unit accepts application details through an online form, the user enters necessary information into the form and clicks a send button, which sends the application details to the reception unit. When the reception unit accepts application details through email, the user sends the application details to a specified email address, which causes the reception unit to accept the application details. When the reception unit accepts application details through a dedicated application system, the user logs in to the system, enters and sends the application details, which causes the reception unit to accept the application details. This allows applications from sales to be accepted efficiently.

[0061] The analysis unit can analyze the application content received by the reception unit. The analysis unit analyzes the application content using, for example, text analysis technology. The analysis unit can extract keywords and phrases from the application content and classify the application content based on the keywords and phrases. The analysis unit can also analyze the application content using a machine learning algorithm. For example, the analysis unit learns from past application data and builds a model for appropriately analyzing new application content. The analysis unit can analyze the application content using, for example, natural language processing technology. Natural language processing technology is a technology for analyzing text data and understanding meaning and context. The analysis unit can analyze the meaning of the application content using natural language processing technology and extract information for determining an appropriate assignment destination. When analyzing the application content using a machine learning algorithm, the analysis unit learns from past application data and builds a model for analyzing new application content. For example, the analysis unit uses past application data to learn the characteristics of the application content and builds a model for analyzing new application content. This enables the application content to be analyzed efficiently.

[0062] The discrimination unit can determine the assignment destination based on the content analyzed by the analysis unit. The discrimination unit, for example, determines the assignment destination using a rule-based discrimination algorithm. The discrimination unit can determine the appropriate assignment destination based on the type and importance of the request content. The discrimination unit can also determine the assignment destination using a machine learning algorithm. For example, the discrimination unit learns past assignment data and builds a model for determining the optimal assignment destination for new request content. For example, when determining the assignment destination based on the type of request content, the discrimination unit can assign requests related to data support to the data support team and requests related to CSS assignment tool to the CSS assignment tool team. When determining the assignment destination based on the importance of the request content, the discrimination unit can assign highly important requests to an assignment destination that will be processed with priority. This allows the assignment destination to be determined efficiently.

[0063] The allocation unit can allocate the request to the assignee determined by the determination unit. The allocation unit can allocate the request based on, for example, priority. The allocation unit can allocate the request to an appropriate assignee based on the urgency or importance of the request content. The allocation unit can also allocate the request randomly. For example, the allocation unit can allocate the request randomly in order to even out the load on the assignees. For example, when allocating requests based on the urgency of the request content, the allocation unit can allocate highly urgent request content to an assignee that will be processed with priority. When allocating requests based on the importance of the request content, the allocation unit can allocate highly important request content to an assignee that will be processed with priority. This allows the requests to be allocated efficiently.

[0064] The application management system further includes a reception unit that estimates a user's emotions and adjusts the timing of receiving the application content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of receiving the application content and wait until the user is relaxed. Furthermore, if the user is in a hurry, the reception unit can immediately receive the application content. Furthermore, if the user is relaxed, the reception unit can receive the application content at a normal reception timing. This allows the timing of receiving the application content to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0065] Furthermore, the application management system includes a reception unit that analyzes past application history and selects the optimal reception method. The reception unit can, for example, prioritize the selection of application methods that the user has frequently used in the past. The reception unit can also suggest the most efficient reception method based on the user's past application history. The reception unit can also analyze the user's past application history and suggest the optimal reception time slot. This allows the optimal reception method to be selected based on the past application history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past application history data into a generation AI and have the generation AI select the optimal reception method.

[0066] Furthermore, the application management system includes a reception unit that filters application content based on the user's current project and areas of interest when receiving the application content. The reception unit can, for example, prioritize application content related to a project currently in progress by the user. The reception unit can also filter related application content based on the user's areas of interest. The reception unit can also receive appropriate application content according to the progress of the user's current project. This allows the application content to be filtered based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI perform filtering.

[0067] The request management system further includes a reception unit that estimates a user's emotions and determines the priority of the requests to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize requests of high importance. Furthermore, when the user is relaxed, the reception unit can prioritize requests of high urgency. This allows the priority of requests to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0068] Furthermore, the application management system includes a reception unit that, when receiving application content, prioritizes receiving highly relevant applications based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving application content related to that area. Furthermore, the reception unit can prioritize receiving application content related to locations close to the user's current location. Furthermore, the reception unit can suggest optimal application content based on the user's geographical location information. This allows highly relevant applications to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into a generation AI and cause the generation AI to prioritize receiving highly relevant applications.

[0069] Furthermore, the application management system includes a reception unit that analyzes the user's social media activity when receiving the application content and receives related applications. The reception unit can, for example, prioritize receiving related application content based on the user's social media activity content. The reception unit can also analyze the user's social media interests and suggest appropriate application content. The reception unit can also receive optimal application content based on the user's social media activity history. This makes it possible to receive related applications based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to receive related applications.

[0070] The application management system further includes an analysis unit that estimates a user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. This allows the presentation method of the analysis to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0071] Furthermore, the application management system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the application content during analysis. The analysis unit can, for example, perform a detailed analysis of application content with high importance. The analysis unit can also perform a simplified analysis of application content with low importance. The analysis unit can also set an appropriate level of detail of the analysis depending on the importance of the application content. This makes it possible to adjust the level of detail of the analysis depending on the importance of the application content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the application content to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0072] Furthermore, the application management system includes an analysis unit that applies different analysis algorithms depending on the category of the application content during analysis. For example, the analysis unit can apply a data analysis algorithm to application content related to data support. Furthermore, the analysis unit can apply a tool analysis algorithm to application content related to a CSS assignment tool. Furthermore, the analysis unit can select an optimal analysis algorithm depending on the category of the application content. This makes it possible to apply the optimal analysis algorithm depending on the category of the application content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the application content into a generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0073] The application management system further includes an analysis unit that estimates a user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI adjust the length of the analysis.

[0074] Furthermore, the application management system includes an analysis unit that determines the priority of analysis based on the submission time of the application content during analysis. The analysis unit can, for example, prioritize analysis of application content submitted earlier. The analysis unit can also postpone application content submitted later. Furthermore, the analysis unit can set appropriate analysis priorities based on the submission time. This makes it possible to determine the priority of analysis based on the submission time of the application content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data of the application content to the generation AI and have the generation AI determine the analysis priority.

[0075] Furthermore, the application management system includes an analysis unit that adjusts the order of analysis based on the relevance of the application contents during analysis. The analysis unit can, for example, prioritize analysis of highly relevant application contents. The analysis unit can also postpone analysis of less relevant application contents. Furthermore, the analysis unit can set an appropriate analysis order based on the relevance of the application contents. This makes it possible to adjust the analysis order based on the relevance of the application contents. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the application contents to a generation AI and have the generation AI adjust the analysis order.

[0076] The application management system further includes a discrimination unit that estimates a user's emotions and adjusts discrimination criteria based on the estimated user emotions. For example, if the user is nervous, the discrimination unit can provide simple, highly visible discrimination criteria. Furthermore, if the user is relaxed, the discrimination unit can provide detailed discrimination criteria. Furthermore, if the user is in a hurry, the discrimination unit can provide discrimination criteria that focus on the key points. This allows the discrimination criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the discrimination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the discrimination unit may input the user's facial expression data into the generation AI and cause the generation AI to adjust the discrimination criteria.

[0077] Furthermore, the application management system includes a discrimination unit that improves discrimination accuracy by taking into account interrelationships between application contents during discrimination. The discrimination unit can, for example, analyze interrelationships between application contents and prioritize discrimination of highly related contents. The discrimination unit can also improve discrimination accuracy by taking into account interrelationships between application contents. Furthermore, the discrimination unit can set optimal discrimination criteria based on interrelationships between application contents. This makes it possible to improve discrimination accuracy by taking into account interrelationships between application contents. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input interrelationship data between application contents into the generation AI and cause the generation AI to improve discrimination accuracy.

[0078] Furthermore, the application management system includes a discrimination unit that makes discrimination taking into account the applicant's attribute information. The discrimination unit can set appropriate discrimination criteria based on, for example, the applicant's job title or department. The discrimination unit can also improve the accuracy of discrimination by taking into account the applicant's past application history. The discrimination unit can also set optimal discrimination criteria based on the applicant's attribute information. This allows discrimination to be made based on the applicant's attribute information. Some or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input the applicant's attribute information data into a generation AI and have the generation AI perform discrimination.

[0079] The application management system further includes a discrimination unit that estimates a user's emotions and adjusts the order in which the discrimination results are displayed based on the estimated user emotions. For example, if the user is nervous, the discrimination unit can display important results first. Furthermore, if the user is relaxed, the discrimination unit can sequentially display detailed results. Furthermore, if the user is in a hurry, the discrimination unit can display results that highlight the main points first. This allows the order in which the discrimination results are displayed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the discrimination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the discrimination unit may input the user's facial expression data to the generation AI and cause the generation AI to adjust the display order of the discrimination results.

[0080] Furthermore, the application management system includes a discrimination unit that performs discrimination taking into account the geographical distribution of application content. The discrimination unit can, for example, analyze the geographical distribution of application content and prioritize discrimination of highly relevant content. The discrimination unit can also improve the accuracy of discrimination by taking into account the geographical distribution of application content. Furthermore, the discrimination unit can set optimal discrimination criteria based on the geographical distribution of application content. This allows discrimination to be performed based on the geographical distribution of application content. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input geographical distribution data of application content to a generation AI and have the generation AI perform discrimination.

[0081] Furthermore, the application management system includes a discrimination unit that, during discrimination, improves discrimination accuracy by referring to literature related to the application content. The discrimination unit can, for example, improve discrimination accuracy by referring to literature related to the application content. The discrimination unit can also set optimal discrimination criteria based on literature related to the application content. The discrimination unit can also analyze literature related to the application content to improve discrimination accuracy. This makes it possible to improve discrimination accuracy based on literature related to the application content. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input literature data related to the application content into a generation AI and have the generation AI perform discrimination.

[0082] The request management system further includes an allocation unit that estimates a user's emotions and adjusts the allocation method based on the estimated user emotions. For example, if the user is nervous, the allocation unit can provide a simple, highly visible allocation method. Furthermore, if the user is relaxed, the allocation unit can provide a detailed allocation method. Furthermore, if the user is in a hurry, the allocation unit can provide a concise allocation method. This allows the allocation method to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the allocation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the allocation unit may input the user's facial expression data into the generation AI and cause the generation AI to adjust the allocation method.

[0083] Furthermore, the request management system includes an allocation unit that improves allocation accuracy by taking into account interrelationships between request contents during allocation. The allocation unit can, for example, analyze interrelationships between request contents and prioritize allocation of highly related contents. The allocation unit can also improve allocation accuracy by taking into account interrelationships between request contents. Furthermore, the allocation unit can set optimal allocation criteria based on interrelationships between request contents. This makes it possible to improve allocation accuracy by taking into account interrelationships between request contents. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input interrelationship data between request contents into a generation AI and have the generation AI perform allocation.

[0084] Furthermore, the application management system includes an allocation unit that performs allocation taking into consideration the applicant's attribute information. The allocation unit can set appropriate allocation criteria based on, for example, the applicant's job title or department. The allocation unit can also improve the accuracy of allocation by taking into consideration the applicant's past application history. Furthermore, the allocation unit can set optimal allocation criteria based on the applicant's attribute information. This allows allocation to be performed based on the applicant's attribute information. Some or all of the above-described processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input the applicant's attribute information data into a generation AI and have the generation AI perform the allocation.

[0085] The request management system further includes an allocation unit that estimates a user's emotions and determines allocation priorities based on the estimated user emotions. For example, if the user is nervous, the allocation unit can prioritize allocation of requests with high importance. Furthermore, if the user is relaxed, the allocation unit can prioritize allocation of requests with normal priority. Furthermore, if the user is in a hurry, the allocation unit can prioritize allocation of requests with high urgency. This allows allocation priorities to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the allocation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the allocation unit may input the user's facial expression data into the generation AI and have the generation AI determine the allocation priorities.

[0086] Furthermore, the application management system includes an allocation unit that performs allocation taking into account the geographic distribution of application content. The allocation unit can, for example, analyze the geographic distribution of application content and prioritize allocation of highly relevant content. The allocation unit can also improve the accuracy of allocation by taking into account the geographic distribution of application content. Furthermore, the allocation unit can set optimal allocation criteria based on the geographic distribution of application content. This allows allocation to be performed based on the geographic distribution of application content. Some or all of the above-mentioned processing in the allocation unit may be performed using, for example, AI, or may be performed without using AI. For example, the allocation unit can input geographic distribution data of application content to a generation AI and have the generation AI perform allocation.

[0087] Furthermore, the application management system includes an allocation unit that, during allocation, improves the accuracy of allocation by referring to literature related to the application content. The allocation unit can, for example, improve the accuracy of allocation by referring to literature related to the application content. The allocation unit can also set optimal allocation criteria based on the literature related to the application content. The allocation unit can also analyze the literature related to the application content and improve the accuracy of allocation. This allows the accuracy of allocation to be improved based on the literature related to the application content. Some or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input literature data related to the application content into a generation AI and have the generation AI perform the allocation. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, determination unit, and allocation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives application content via an online form or email. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the application content using text analysis technology or a machine learning algorithm. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the assignment destination using a rule-based determination algorithm or a machine learning algorithm. The allocation unit is realized, for example, by the control unit 46A of the smart device 14 and allocates applications based on priority and urgency. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, discrimination unit, and allocation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives application content via an online form or email. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the application content using text analysis technology or a machine learning algorithm. The discrimination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the assignment destination using a rule-based discrimination algorithm or a machine learning algorithm. The allocation unit is realized, for example, by the control unit 46A of the smart glasses 214 and allocates applications based on priority and urgency. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, discrimination unit, and allocation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives application content via an online form or email. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the application content using text analysis technology or a machine learning algorithm. The discrimination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the assignment destination using a rule-based discrimination algorithm or a machine learning algorithm. The allocation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and allocates applications based on priority and urgency. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, discrimination unit, and allocation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives application content via an online form or email. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the application content using text analysis technology or a machine learning algorithm. The discrimination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the assignment destination using a rule-based discrimination algorithm or a machine learning algorithm. The allocation unit is realized, for example, by the control unit 46A of the robot 414 and allocates applications based on priority and urgency.

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

[0089] The application management system can further include a priority adjustment unit that automatically adjusts the priority of application contents based on the user's past application history. For example, it can be set so that applications that have been applied for frequently in the past are processed with priority. It can also learn specific patterns from past application history and automatically set a high priority if a similar pattern occurs again. Furthermore, if it is determined based on past application history that a specific application content is more important than other application contents, it can process that application content with priority. This makes it possible to efficiently adjust the priority of application contents by utilizing past application history.

[0090] The application management system can further include a workload adjustment unit that adjusts the acceptance of application content in consideration of the user's current workload. For example, if the user is busy, the acceptance of application content can be temporarily delayed. Alternatively, if the user has relatively free time, the application content can be accepted immediately. Furthermore, the timing of accepting application content can be adjusted according to the user's workload. This allows the acceptance of application content to be efficiently adjusted according to the user's workload.

[0091] The application management system may further include an analysis unit that estimates the user's emotions and adjusts the analysis method for the application content based on the estimated user emotions. For example, if the user is feeling stressed, a concise summary of the analysis results may be provided. If the user is relaxed, a detailed analysis result may be provided. If the user is in a hurry, a summary of the analysis results may be provided. This allows the analysis method to be adjusted according to the user's emotions.

[0092] The application management system can further include a determination unit that estimates the user's emotions and adjusts the selection criteria for assignees based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible assignment can be provided. If the user is relaxed, a detailed assignment can be provided. If the user is in a hurry, a brief assignment can be provided. This makes it possible to adjust the selection criteria for assignees according to the user's emotions.

[0093] The request management system may further include an allocation unit that estimates the user's emotions and adjusts the allocation method of request contents based on the estimated user emotions. For example, if the user is feeling stressed, it is possible to allocate request contents with high importance with priority. If the user is relaxed, it is possible to allocate request contents with normal priority. If the user is in a hurry, it is possible to allocate request contents with high urgency with priority. In this way, it is possible to adjust the allocation method of request contents according to the user's emotions.

[0094] The application management system can further include a geographic information analysis unit that selects the most appropriate assignment destination based on the user's geographic location information. For example, if the user is in a specific area, an assignment destination related to that area can be selected preferentially. Also, an assignment destination related to a location close to the user's current location can be selected. Furthermore, the most appropriate assignment destination can be suggested based on the user's geographic location information. This allows the most appropriate assignment destination to be selected based on the user's geographic location information.

[0095] The application management system may further include a social media analysis unit that analyzes the user's social media activity and prioritizes the acceptance of related application content. For example, it may be possible to prioritize the acceptance of related application content based on the user's social media activity. It may also be possible to analyze the user's social media interests and suggest appropriate application content. Furthermore, it may be possible to accept the most appropriate application content based on the user's social media activity history. This makes it possible to accept related application content based on the user's social media activity.

[0096] The application management system may further include a feedback unit that estimates the user's emotions and adjusts the feedback method for the application content based on the estimated user emotions. For example, if the user is feeling stressed, concise and positive feedback may be provided. If the user is relaxed, detailed feedback may be provided. If the user is in a hurry, feedback that focuses on the main points may be provided. In this way, the feedback method may be adjusted according to the user's emotions.

[0097] The application management system can further include a schedule adjustment unit that adjusts the acceptance of application content in consideration of the user's work schedule. For example, if the user is in a meeting, the acceptance of application content can be temporarily delayed. Also, application content can be accepted preferentially during time periods when the user is free. Furthermore, the timing of accepting application content can be adjusted according to the user's work schedule. This allows the acceptance of application content to be efficiently adjusted according to the user's work schedule.

[0098] The application management system may further include a notification unit that estimates the user's emotions and adjusts the notification method of the application contents based on the estimated user emotions. For example, if the user is feeling stressed, the notification may be made less frequent. If the user is relaxed, the application contents may be notified using a normal notification method. Furthermore, if the user is in a hurry, the notification may be made immediately. In this way, the notification method of the application contents may be adjusted according to the user's emotions.

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

[0100] Step 1: The reception department accepts applications from sales. Applications from sales include sales reports, customer feedback, and requests for data support for new projects. The reception department can accept applications through an online form, email, or a dedicated application system. Step 2: The analysis unit analyzes the application content received by the reception unit. The analysis unit analyzes the application content using text analysis technology and machine learning algorithms, extracts keywords and phrases, and classifies the application content. Step 3: The discrimination unit determines the appropriate assignment destination based on the content analyzed by the analysis unit. The discrimination unit uses a rule-based discrimination algorithm or a machine learning algorithm to determine the appropriate assignment destination based on the type and importance of the application content. Step 4: The allocation unit allocates the request to the assignee determined by the determination unit. The allocation unit can allocate the request to the appropriate assignee based on the priority, urgency, and importance of the request content. It can also allocate requests randomly to even out the load on the assignees.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

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

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

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives application details; an analysis unit that analyzes the application content received by the reception unit; a determination unit that determines an assignment destination based on the content analyzed by the analysis unit; an allocation unit that allocates the application to the assignee determined by the determination unit. A system characterized by:

2. The reception unit Accepting applications from sales 2. The system of claim 1.

3. The analysis unit Analyzing the application content received by the reception unit 2. The system of claim 1.

4. The determination unit The assignee is determined based on the content analyzed by the analysis unit.

2. The system of claim 1.

5. The allocation unit The application is assigned to the assignee determined by the determination unit.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of accepting application content based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze past application history and select the most appropriate reception method 2. The system of claim 1.

8. The reception unit Filtering applications based on users' current projects and interests 2. The system of claim 1.

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