JP7834817B2 - system - Google Patents

Description

The technology disclosed herein relates to a system.

Patent Document 1 discloses a persona chatbot control method performed by at least one processor, comprising the steps of: receiving a user utterance; adding the user utterance to a prompt containing instructions related to a description of the chatbot's character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Japanese Patent Publication No. 2022-180282

Traditional technologies required programming skills and significant effort to create programs for business process automation, which hindered their widespread adoption.

The system according to this embodiment aims to generate application creation script code for business automation without requiring programming skills.

The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of business content from the user. The generation unit analyzes the business content received by the reception unit and generates corresponding application creation script code. The provision unit provides the application creation script code generated by the generation unit to the user.

The system according to this embodiment can generate application creation script code for business automation even without programming skills.

This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. This shows an emotion map where multiple emotions are mapped. This shows an emotion map where multiple emotions are mapped.

An example of an embodiment of the system relating to the technology of this disclosure will be described below with reference to the attached drawings.

First, let's explain the terminology used in the following explanation.

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

In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

In the following embodiments, the coded communication interface (I/F) is an interface including a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi®, or Bluetooth®.

In the following embodiments, "A and/or B" is synonymous with "at least one of A and B." That is, "A and/or B" means that A alone, B alone, or a combination of A and B may be used. Furthermore, the same concept applies when expressing three or more things linked by "and/or" in this specification.

[First Embodiment]
Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

As shown in Figure 1, the data processing system 10 comprises a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 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).

The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to the bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A receives user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting it in a user-perceptible form (e.g., audio and/or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

The communication interface 44 is connected to the network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via the network 54.

Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. The storage 32 stores a specific processing program 56. The specific processing program 56 is an example of a "program" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores the specific processing program 60. The specific processing program 60 is used in conjunction 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 program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has data generation model 58 and emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by the user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

(Example of form 1)
The business automation system according to an embodiment of the present invention is a system that reduces the development time of business automation programs by generating application creation script code (e.g., GAS (Google® Apps Script) or AppleScript, etc.) using a generation AI. In this business automation system, the user inputs the business content they want to automate in natural language, the generation AI analyzes the input and generates corresponding application creation script code, and the generated application creation script code is provided to the user in an easy-to-use format. For example, if the user inputs specific business content such as "open a specific spreadsheet and update the data at 9am every day," this input is sent to the generation AI. The generation AI understands the natural language input and generates corresponding application creation script code. For example, in response to the input "open a specific spreadsheet and update the data at 9am every day," the generation AI generates application creation script code to execute that task. The generated application creation script code is automatically inserted into the user's application creation script editor or sent via email. In this way, the user can use the generated code as is. This mechanism allows business automation programs to be created even without programming skills, promoting the widespread adoption of business automation. For example, program creation that previously took several hours can now be completed in just a few minutes. Furthermore, since the generative AI generates optimal code based on user input, the quality of business automation programs is also improved. For example, the generative AI can generate code that incorporates the latest application creation script features and best practices. This allows users to easily create high-quality automation programs. As a result, business automation systems can reduce the development effort of business automation programs and promote the widespread adoption of business automation by having users input their business details in natural language and providing application creation script code generated by the generative AI.

The business automation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of business content from the user. This input includes, but is not limited to, accounting, sales, and project management. The reception unit can analyze the user's input using, for example, natural language processing technology. The generation unit uses a generation AI to analyze the business content received by the reception unit and generate corresponding application creation script code. The generation AI uses, for example, deep learning and natural language processing technology to analyze the user's input and generate optimal application creation script code. For example, in response to the generation AI input "open a specific spreadsheet and update the data at 9 AM every day," the generation unit generates application creation script code to execute that task. The provision unit provides the application creation script code generated by the generation unit to the user. The provision unit can, for example, automatically insert the generated application creation script code into the user's application creation script editor. The provision unit can also send the generated application creation script code to the user via email. This allows the user to easily utilize the generated application creation script code. For example, the service provider automatically inserts the generated application creation script code into the user's application creation script editor, making it ready for immediate use. The service provider also sends the generated application creation script code via email, allowing the user to access and use the code from the received email. This allows the business automation system to reduce development time for business automation programs and promote the widespread adoption of business automation by having the user input their business details in natural language and generating application creation script code using AI.

The reception desk receives input from users regarding their work tasks. This input may include, but is not limited to, accounting, sales, and project management. The reception desk can analyze user input using, for example, natural language processing technology. Specifically, it tokenizes the text entered by the user and analyzes the meaning of each token to accurately understand the work task. For example, if a user enters "Create a sales report and send it by email at the end of each month," the reception desk extracts keywords such as "end of each month," "sales report," "create," and "send by email," and analyzes the meaning of each. Furthermore, the reception desk can understand the user's input based on context and complete ambiguous expressions or omitted parts. For example, in response to input such as "Prepare for next week's meeting," it can infer the specific meeting date and preparation details and confirm them with the user. The reception desk can also learn from the user's past input history and work patterns to improve the accuracy of the input. This allows the reception desk to accurately and quickly receive user work tasks and smoothly hand them over to the next processing step.

The generation unit uses generation AI to analyze the business content received by the reception unit and generate corresponding application creation script code. The generation AI analyzes user input using techniques such as deep learning and natural language processing to generate optimal application creation script code. Specifically, the generation AI analyzes user input and identifies a series of operations and procedures corresponding to the business content. For example, in response to input such as "Open a specific spreadsheet and update the data at 9 AM every day," the generation AI analyzes the spreadsheet identification, data update method, and execution timing, and generates application creation script code based on this. The generation AI can also refer to a database of past business content and generated code to suggest the optimal code for similar business content. Furthermore, the generation AI evaluates the quality of the generated code and makes corrections and optimizations as needed. For example, it can evaluate the execution efficiency and appropriateness of error handling of the code and automatically reflect improvements. This allows the generation unit to quickly and efficiently generate application creation script code optimized for the user's business content, maximizing the effectiveness of business automation.

The provider unit provides the user with the application creation script code generated by the generation unit. The provider unit can, for example, automatically insert the generated application creation script code into the user's application creation script editor. The provider unit can also send the generated application creation script code to the user via email. Specifically, the provider unit directly inserts the generated application creation script code into the user's application creation script editor, allowing the user to execute the code immediately. This enables the user to begin automating business processes without any extra effort. Furthermore, the provider unit can send the generated application creation script code via email, allowing the user to copy and use the code from the received email. In addition, the provider unit can automatically generate and provide the user with documentation including instructions and precautions for using the generated application creation script code. For example, it can generate detailed documentation including explanations of each part of the code, configuration methods, and troubleshooting guides, helping users correctly understand and effectively utilize the code. This allows the provider unit to quickly and reliably deliver the generated application creation script code to the user, enabling a smooth implementation of business process automation.

The generation unit can analyze natural language business content using generation AI and generate corresponding application creation script code. For example, the generation unit uses generation AI to analyze natural language business content entered by the user. The generation AI, for example, uses deep learning and natural language processing technologies to analyze the user's input and generate the optimal application creation script code. For example, if the user enters the business content "Open a specific spreadsheet and update the data at 9 AM every day," the generation AI can generate application creation script code to execute that business. The generation AI, for example, uses natural language processing technologies to analyze the user's input and generate the corresponding application creation script code. The generation AI, for example, can use deep learning to analyze the user's input and generate the optimal application creation script code. Therefore, the generation unit can analyze natural language business content and generate corresponding application creation script code by using generation AI.

The provider can automatically insert the generated application creation script code into the user's application creation script editor. For example, the provider can automatically insert the generated application creation script code into the user's application creation script editor. Specific methods of automatic insertion include, but are not limited to, the use of APIs or automatic insertion functions that do not require user intervention. For example, the provider can automatically insert the generated application creation script code into the user's application creation script editor, making it ready for immediate use by the user. The provider can, for example, use an API to automatically insert the generated application creation script code into the user's application creation script editor. Furthermore, the provider has a function to automatically insert the generated application creation script code without requiring user intervention. This allows the provider to easily make the generated application creation script code available to users by automatically inserting it into the application creation script editor.

The service provider can send the generated application creation script code to the user via email. For example, the service provider can send the generated application creation script code to the user via email. Specific methods of sending via email include, but are not limited to, using the SMTP protocol and setting the sending timing. For example, the service provider can send the generated application creation script code via email, making the code available to the user from the email they receive. The service provider can, for example, use the SMTP protocol to send the generated application creation script code via email. Furthermore, the service provider has the functionality to set the sending timing and send the generated application creation script code via email at the appropriate time. This allows the service provider to make the generated application creation script code easily available to users by sending it via email.

The generation unit can generate code that incorporates the latest application development script features and recommended practices. For example, it can use generation AI to generate code that incorporates the latest application development script features and best practices. These features include, but are not limited to, new APIs and added functionalities. The generation unit can also generate code that incorporates recommended practices (best practices). For example, it can generate optimal application development script code based on industry standards and best practices. This allows the generation unit to improve the quality of business automation programs by generating code that incorporates the latest application development script features and best practices.

The reception desk can analyze the user's past work content input history and suggest appropriate input methods. For example, the reception desk can analyze the user's past work content input history and suggest the optimal input method. The reception desk can also use AI to analyze the user's past work content input history. For example, the reception desk can automatically display work content that the user has frequently entered in the past as suggestions. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. In addition, the reception desk can predict and suggest work content to be used during specific time periods based on the user's past input history. As a result, by analyzing past input history, the reception desk can suggest the optimal input method to the user and improve input efficiency.

The reception system can filter input content based on the user's current projects and areas of interest when they enter their work details. For example, the reception system can filter input content based on the user's current projects and areas of interest. The reception system can also use AI to analyze the user's current projects and areas of interest and filter the input content accordingly. For example, the reception system can prioritize displaying work content related to the user's current ongoing projects. Furthermore, the reception system can suggest relevant work content based on the user's areas of interest. In addition, the reception system can filter input content based on areas the user has shown interest in in the past. This allows the reception system to prioritize input of highly relevant work content by filtering it based on the user's current projects and areas of interest.

The reception desk can prioritize the input of highly relevant tasks by considering the user's geographical location when inputting task details. For example, the reception desk can prioritize the input of highly relevant tasks by considering the user's geographical location. The reception desk can, for example, use AI to analyze the user's geographical location and prioritize the input of highly relevant tasks. For instance, if the user is in a specific region, the reception desk will prioritize displaying tasks related to that region. Furthermore, if the user is on a business trip, the reception desk can prioritize the input of tasks related to their business trip destination. Additionally, if the user is at home, the reception desk can prioritize the input of tasks to be performed at home. In this way, the reception desk can prioritize the input of highly relevant tasks by considering geographical location.

The reception desk can analyze a user's social media activity when they input their work details and suggest relevant work. For example, the reception desk can analyze a user's social media activity and suggest relevant work. For example, it can use AI to analyze a user's social media activity and suggest relevant work. For instance, the reception desk can automatically suggest work that the user has mentioned on social media. Furthermore, the reception desk can predict and suggest work that the user is interested in based on their social media activity. In addition, the reception desk can suggest work related to accounts the user follows on social media. This allows the reception desk to analyze social media activity, suggest relevant work, and improve input efficiency.

The generation unit can adjust the level of detail of the application script code based on the importance of the business process. For example, the generation unit can use generation AI to evaluate the importance of the business process and generate the optimal application script code. For instance, for critical business processes, the generation unit can generate application script code that includes detailed error handling. For general business processes, the generation unit can generate application script code that includes only basic functionality. Furthermore, for simple business processes, the generation unit can generate executable application script code with minimal code. This allows the generation unit to generate application script code at an appropriate level by adjusting the level of detail based on the importance of the business process.

The generation unit can apply different generation algorithms depending on the category of business content when generating application creation script code. For example, the generation unit can use generation AI to analyze the business content category and apply the optimal generation algorithm. For instance, for data processing tasks, the generation unit generates application creation script code that performs efficient data manipulation. Furthermore, for notification tasks, the generation unit can generate application creation script code that includes appropriate notification methods. Additionally, for schedule management tasks, the generation unit can generate application creation script code that includes calendar operations. This allows the generation unit to generate optimal application creation script code by applying different generation algorithms depending on the category of business content.

The generation unit can determine the priority of application creation script code based on the submission timing of the work content when generating the code. For example, the generation unit can determine the priority of the code based on the submission timing of the work content when generating the application creation script code. For example, the generation unit can use generation AI to analyze the submission timing of the work content and generate the optimal application creation script code. For example, the generation unit will generate application creation script code with the highest priority for urgent work content. Furthermore, the generation unit can generate application creation script code based on the schedule for regular work content. In addition, the generation unit can generate application creation script code systematically for long-term work content. This allows the generation unit to prioritize code based on the submission timing of the work content, enabling responses tailored to urgency.

The generation unit can adjust the order of application creation script code based on the relevance of business processes when generating the application creation script code. For example, the generation unit can analyze the relevance of business processes using generation AI and generate the application creation script code in the optimal order. For instance, the generation unit prioritizes generating application creation script code for highly relevant business processes. It can also postpone generating application creation script code for less relevant business processes. Furthermore, the generation unit can generate application creation script code in the optimal order based on the relevance of business processes. This allows the generation unit to efficiently generate code by adjusting the order of code based on the relevance of business processes.

The service provider can select the optimal delivery method when providing application creation script code by referring to the user's past code usage history. For example, the service provider can select the optimal delivery method by referring to the user's past code usage history when providing application creation script code. The service provider can also, for example, use AI to analyze the user's past code usage history and select the optimal delivery method. For example, the service provider can prioritize delivery methods the user has used in the past (email, editor insertion, etc.). Furthermore, the service provider can suggest the optimal delivery method based on the user's past usage history. In addition, the service provider can provide application creation script code based on the delivery methods the user has preferred to use in the past. This allows the service provider to select the optimal delivery method for the user by referring to past usage history, thereby improving convenience.

The service provider can customize the application creation script code provided based on the user's current project status. For example, the service provider can customize the provided application creation script code based on the user's current project status. For example, the service provider can use AI to analyze the user's current project status and provide the optimal application creation script code. For instance, the service provider can prioritize providing application creation script code related to the user's current project. Furthermore, the service provider can suggest the optimal application creation script code based on the user's project status. In addition, the service provider can customize the provided application creation script code to match the user's current project. This allows the service provider to provide the optimal application creation script code for the user by customizing the provided content based on the current project status.

The service provider can select the optimal delivery method when providing application creation script code, taking into account the user's geographical location. For example, the service provider can select the optimal delivery method by considering the user's geographical location when providing application creation script code. For example, the service provider can use AI to analyze the user's geographical location and select the optimal delivery method. For instance, if the user is in a specific region, the service provider can provide application creation script code related to that region. Furthermore, if the user is on a business trip, the service provider can provide application creation script code related to the destination. Additionally, if the user is at home, the service provider can provide application creation script code related to tasks performed at home. This allows the service provider to select the optimal delivery method for the user by considering their geographical location.

The service provider can analyze the user's social media activity and propose content when providing application creation script code. For example, the service provider can analyze the user's social media activity and propose content when providing application creation script code. For example, the service provider can use AI to analyze the user's social media activity and provide application creation script code based on relevant business content. For example, the service provider can provide application creation script code related to business content mentioned by the user on social media. Furthermore, the service provider can propose application creation script code related to business content of interest based on the user's social media activity. In addition, the service provider can provide application creation script code related to accounts the user follows on social media. This allows the service provider to provide application creation script code based on relevant business content by analyzing social media activity.

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

The reception desk can analyze a user's past work input history and suggest appropriate input methods. For example, it can automatically display suggestions for work content that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used previously. Furthermore, it can predict and suggest work content to be used during specific time periods based on the user's past input history. This allows the reception desk to analyze past input history, suggest the optimal input method for the user, and improve input efficiency.

The generation unit can adjust the level of detail of application script code based on the importance of the business process. For example, the generation unit can use generation AI to evaluate the importance of the business process and generate the optimal application script code. For critical business processes, the generation unit generates application script code that includes detailed error handling. For general business processes, the generation unit can generate application script code that includes only basic functions. Furthermore, for simple business processes, the generation unit can generate application script code that is executable with minimal code. This allows the generation unit to generate application script code at an appropriate level by adjusting the level of detail based on the importance of the business process.

The reception system can filter input content based on the user's current projects and areas of interest when they enter their work details. For example, the reception system can use AI to analyze the user's current projects and areas of interest and filter the input content accordingly. The reception system will prioritize displaying work details related to the user's current projects. Furthermore, the reception system can suggest relevant work details based on the user's areas of interest. In addition, the reception system can filter input content based on areas the user has previously shown interest in. This allows the reception system to prioritize input of highly relevant work details by filtering input content based on the user's current projects and areas of interest.

The service provider can select the optimal delivery method when providing application creation script code by referring to the user's past code usage history. For example, the service provider can use AI to analyze the user's past code usage history and select the optimal delivery method. The service provider will prioritize delivery methods the user has used in the past (email, editor insertion, etc.). Furthermore, the service provider can suggest the optimal delivery method based on the user's past usage history. In addition, the service provider can provide application creation script code based on the delivery method the user has preferred in the past. This allows the service provider to select the most suitable delivery method for the user by referring to past usage history, thereby improving convenience.

The generation unit can apply different generation algorithms depending on the category of the business content when generating application creation script code. For example, the generation unit can use generation AI to analyze the category of the business content and apply the optimal generation algorithm. For data processing tasks, the generation unit generates application creation script code that performs efficient data manipulation. Furthermore, for notification tasks, the generation unit can generate application creation script code that includes appropriate notification methods. Additionally, for schedule management tasks, the generation unit can generate application creation script code that includes calendar operations. In this way, the generation unit can generate optimal application creation script code by applying different generation algorithms depending on the category of the business content.

The reception desk can prioritize the input of highly relevant tasks by considering the user's geographical location when entering task details. For example, the reception desk can use AI to analyze the user's geographical location and prioritize the input of highly relevant tasks. If the user is in a specific region, the reception desk will prioritize displaying tasks related to that region. Furthermore, if the user is on a business trip, the reception desk can prioritize the input of tasks related to their destination. Additionally, if the user is at home, the reception desk can prioritize the input of tasks to be performed at home. In this way, the reception desk can prioritize the input of highly relevant tasks by considering geographical location.

The following briefly describes the processing flow for example 1.

Step 1: The reception desk receives input from users regarding their work. This input may include, but is not limited to, accounting, sales, and project management. The reception desk can analyze user input using, for example, natural language processing technology.
Step 2: The generation unit uses a generation AI to analyze the business content received by the reception unit and generate the corresponding application creation script code. The generation AI uses, for example, deep learning and natural language processing technology to analyze the user's input and generate the optimal application creation script code. For example, if the generation AI receives input such as "Open a specific spreadsheet and update the data at 9am every day," the generation unit will generate the application creation script code to execute that task.
Step 3: The provider unit provides the user with the application creation script code generated by the generator unit. The provider unit can, for example, automatically insert the generated application creation script code into the user's application creation script editor. The provider unit can also send the generated application creation script code to the user via email. This allows the user to easily use the generated application creation script code.

(Example of form 2)
The business automation system according to an embodiment of the present invention is a system that reduces the development time of business automation programs by generating application creation script code (e.g., GAS (Google Apps Script) or AppleScript) using a generation AI. In this business automation system, the user inputs the business content they want to automate in natural language, the generation AI analyzes the input and generates corresponding application creation script code, and the generated application creation script code is provided to the user in an easy-to-use format. For example, if the user inputs specific business content such as "open a specific spreadsheet and update the data at 9am every day," this input is sent to the generation AI. The generation AI understands the natural language input and generates corresponding application creation script code. For example, in response to the input "open a specific spreadsheet and update the data at 9am every day," the generation AI generates application creation script code to execute that task. The generated application creation script code is automatically inserted into the user's application creation script editor or sent via email. In this way, the user can use the generated code as is. This mechanism allows business automation programs to be created even without programming skills, promoting the widespread adoption of business automation. For example, program creation that previously took several hours can now be completed in just a few minutes. Furthermore, since the generative AI generates optimal code based on user input, the quality of business automation programs is also improved. For example, the generative AI can generate code that incorporates the latest application creation script features and best practices. This allows users to easily create high-quality automation programs. As a result, business automation systems can reduce the development effort of business automation programs and promote the widespread adoption of business automation by having users input their business details in natural language and providing application creation script code generated by the generative AI.

The business automation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of business content from the user. This input includes, but is not limited to, accounting, sales, and project management. The reception unit can analyze the user's input using, for example, natural language processing technology. The generation unit uses a generation AI to analyze the business content received by the reception unit and generate corresponding application creation script code. The generation AI uses, for example, deep learning and natural language processing technology to analyze the user's input and generate optimal application creation script code. For example, in response to the generation AI input "open a specific spreadsheet and update the data at 9 AM every day," the generation unit generates application creation script code to execute that task. The provision unit provides the application creation script code generated by the generation unit to the user. The provision unit can, for example, automatically insert the generated application creation script code into the user's application creation script editor. The provision unit can also send the generated application creation script code to the user via email. This allows the user to easily utilize the generated application creation script code. For example, the service provider automatically inserts the generated application creation script code into the user's application creation script editor, making it ready for immediate use. The service provider also sends the generated application creation script code via email, allowing the user to access and use the code from the received email. This allows the business automation system to reduce development time for business automation programs and promote the widespread adoption of business automation by having the user input their business details in natural language and generating application creation script code using AI.

The reception desk receives input from users regarding their work tasks. This input may include, but is not limited to, accounting, sales, and project management. The reception desk can analyze user input using, for example, natural language processing technology. Specifically, it tokenizes the text entered by the user and analyzes the meaning of each token to accurately understand the work task. For example, if a user enters "Create a sales report and send it by email at the end of each month," the reception desk extracts keywords such as "end of each month," "sales report," "create," and "send by email," and analyzes the meaning of each. Furthermore, the reception desk can understand the user's input based on context and complete ambiguous expressions or omitted parts. For example, in response to input such as "Prepare for next week's meeting," it can infer the specific meeting date and preparation details and confirm them with the user. The reception desk can also learn from the user's past input history and work patterns to improve the accuracy of the input. This allows the reception desk to accurately and quickly receive user work tasks and smoothly hand them over to the next processing step.

The generation unit uses generation AI to analyze the business content received by the reception unit and generate corresponding application creation script code. The generation AI analyzes user input using techniques such as deep learning and natural language processing to generate optimal application creation script code. Specifically, the generation AI analyzes user input and identifies a series of operations and procedures corresponding to the business content. For example, in response to input such as "Open a specific spreadsheet and update the data at 9 AM every day," the generation AI analyzes the spreadsheet identification, data update method, and execution timing, and generates application creation script code based on this. The generation AI can also refer to a database of past business content and generated code to suggest the optimal code for similar business content. Furthermore, the generation AI evaluates the quality of the generated code and makes corrections and optimizations as needed. For example, it can evaluate the execution efficiency and appropriateness of error handling of the code and automatically reflect improvements. This allows the generation unit to quickly and efficiently generate application creation script code optimized for the user's business content, maximizing the effectiveness of business automation.

The provider unit provides the user with the application creation script code generated by the generation unit. For example, the provider unit can automatically insert the generated application creation script code into the user's application creation script editor. The provider unit can also send the generated application creation script code to the user via email. Specifically, the provider unit directly inserts the generated application creation script code into the user's application creation script editor, allowing the user to execute the code immediately. This enables the user to begin automating business processes without any extra effort. Furthermore, the provider unit can send the generated application creation script code via email, allowing the user to copy and use the code from the received email. In addition, the provider unit can automatically generate and provide the user with documentation including instructions and precautions for using the generated application creation script code. For example, it can generate detailed documentation including explanations of each part of the code, configuration methods, and troubleshooting guides, helping users correctly understand and effectively utilize the code. This allows the provider unit to quickly and reliably deliver the generated application creation script code to the user, enabling a smooth implementation of business process automation.

The generation unit can analyze natural language business content using generation AI and generate corresponding application creation script code. For example, the generation unit uses generation AI to analyze natural language business content entered by the user. The generation AI, for example, uses deep learning and natural language processing technologies to analyze the user's input and generate the optimal application creation script code. For example, if the user enters the business content "Open a specific spreadsheet and update the data at 9 AM every day," the generation AI can generate application creation script code to execute that business. The generation AI, for example, uses natural language processing technologies to analyze the user's input and generate the corresponding application creation script code. The generation AI, for example, can use deep learning to analyze the user's input and generate the optimal application creation script code. Therefore, the generation unit can analyze natural language business content and generate corresponding application creation script code by using generation AI.

The provider can automatically insert the generated application creation script code into the user's application creation script editor. For example, the provider can automatically insert the generated application creation script code into the user's application creation script editor. Specific methods of automatic insertion include, but are not limited to, the use of APIs or automatic insertion functions that do not require user intervention. For example, the provider can automatically insert the generated application creation script code into the user's application creation script editor, making it ready for immediate use by the user. The provider can, for example, use an API to automatically insert the generated application creation script code into the user's application creation script editor. Furthermore, the provider has a function to automatically insert the generated application creation script code without requiring user intervention. This allows the provider to easily make the generated application creation script code available to users by automatically inserting it into the application creation script editor.

The service provider can send the generated application creation script code to the user via email. For example, the service provider can send the generated application creation script code to the user via email. Specific methods of sending via email include, but are not limited to, using the SMTP protocol and setting the sending timing. For example, the service provider can send the generated application creation script code via email, making the code available to the user from the email they receive. The service provider can, for example, use the SMTP protocol to send the generated application creation script code via email. Furthermore, the service provider has the functionality to set the sending timing and send the generated application creation script code via email at the appropriate time. This allows the service provider to make the generated application creation script code easily available to users by sending it via email.

The generation unit can generate code that incorporates the latest application development script features and recommended practices. For example, it can use generation AI to generate code that incorporates the latest application development script features and best practices. These features include, but are not limited to, new APIs and added functionalities. The generation unit can also generate code that incorporates recommended practices (best practices). For example, it can generate optimal application development script code based on industry standards and best practices. This allows the generation unit to improve the quality of business automation programs by generating code that incorporates the latest application development script features and best practices.

The reception desk can estimate the user's emotions and adjust the input interface for the task based on those emotions. For example, the reception desk can estimate the user's emotions and adjust the input interface based on those emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick task entry. In this way, the reception desk reduces user stress and enables efficient input by adjusting the input interface according to the user's emotions.

The reception desk can analyze the user's past work content input history and suggest appropriate input methods. For example, the reception desk can analyze the user's past work content input history and suggest the optimal input method. The reception desk can also use AI to analyze the user's past work content input history. For example, the reception desk can automatically display work content that the user has frequently entered in the past as suggestions. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. In addition, the reception desk can predict and suggest work content to be used during specific time periods based on the user's past input history. As a result, by analyzing past input history, the reception desk can suggest the optimal input method to the user and improve input efficiency.

The reception system can filter input content based on the user's current projects and areas of interest when they enter their work details. For example, the reception system can filter input content based on the user's current projects and areas of interest. The reception system can also use AI to analyze the user's current projects and areas of interest and filter the input content accordingly. For example, the reception system can prioritize displaying work content related to the user's current ongoing projects. Furthermore, the reception system can suggest relevant work content based on the user's areas of interest. In addition, the reception system can filter input content based on areas the user has shown interest in in the past. This allows the reception system to prioritize input of highly relevant work content by filtering it based on the user's current projects and areas of interest.

The reception desk can estimate the user's emotions and prioritize input content based on those emotions. For example, the reception desk can estimate the user's emotions and prioritize input content based on those emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the reception desk may prioritize inputting important tasks. If the user is relaxed, the reception desk may also prioritize inputting detailed tasks. Furthermore, if the user is in a hurry, the reception desk may prioritize inputting tasks that can be completed quickly. This allows the reception desk to prioritize important tasks by determining input content according to the user's emotions.

The reception desk can prioritize the input of highly relevant tasks by considering the user's geographical location when inputting task details. For example, the reception desk can prioritize the input of highly relevant tasks by considering the user's geographical location. The reception desk can, for example, use AI to analyze the user's geographical location and prioritize the input of highly relevant tasks. For instance, if the user is in a specific region, the reception desk will prioritize displaying tasks related to that region. Furthermore, if the user is on a business trip, the reception desk can prioritize the input of tasks related to their business trip destination. Additionally, if the user is at home, the reception desk can prioritize the input of tasks to be performed at home. In this way, the reception desk can prioritize the input of highly relevant tasks by considering geographical location.

The reception desk can analyze a user's social media activity when they input their work details and suggest relevant work. For example, the reception desk can analyze a user's social media activity and suggest relevant work. For example, it can use AI to analyze a user's social media activity and suggest relevant work. For instance, the reception desk can automatically suggest work that the user has mentioned on social media. Furthermore, the reception desk can predict and suggest work that the user is interested in based on their social media activity. In addition, the reception desk can suggest work related to accounts the user follows on social media. This allows the reception desk to analyze social media activity, suggest relevant work, and improve input efficiency.

The generation unit can estimate the user's emotions and adjust the expression of the generated application creation script code based on those emotions. For example, the generation unit can estimate the user's emotions and adjust the expression of the generated application creation script code based on those emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, if the user is relaxed, the generation unit can generate application creation script code with detailed comments. If the user is in a hurry, the generation unit can also generate concise and executable application creation script code. Furthermore, if the user is stressed, the generation unit can generate simple and easy-to-understand application creation script code. In this way, the generation unit can provide code that is easy for the user to understand by adjusting the expression of the application creation script code according to the user's emotions.

The generation unit can adjust the level of detail of the application script code based on the importance of the business process. For example, the generation unit can use generation AI to evaluate the importance of the business process and generate the optimal application script code. For instance, for critical business processes, the generation unit can generate application script code that includes detailed error handling. For general business processes, the generation unit can generate application script code that includes only basic functionality. Furthermore, for simple business processes, the generation unit can generate executable application script code with minimal code. This allows the generation unit to generate application script code at an appropriate level by adjusting the level of detail based on the importance of the business process.

The generation unit can apply different generation algorithms depending on the category of business content when generating application creation script code. For example, the generation unit can analyze the category of business content using generation AI and apply the optimal generation algorithm. For instance, for data processing tasks, the generation unit generates application creation script code that performs efficient data manipulation. Furthermore, for notification tasks, the generation unit can generate application creation script code that includes appropriate notification methods. Additionally, for schedule management tasks, the generation unit can generate application creation script code that includes calendar operations. This allows the generation unit to generate optimal application creation script code by applying different generation algorithms depending on the category of business content.

The generation unit can estimate the user's emotions and adjust the length of the application creation script code it generates based on those emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the application creation script code it generates based on those emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, if the user is in a hurry, the generation unit can generate short, concise application creation script code. If the user is relaxed, the generation unit can also generate longer application creation script code with detailed explanations. Furthermore, if the user is stressed, the generation unit can generate simple, easy-to-understand application creation script code. This allows the generation unit to provide the user with optimal code by adjusting the length of the application creation script code according to the user's emotions.

The generation unit can determine the priority of application creation script code based on the submission timing of the work content when generating the code. For example, the generation unit can determine the priority of the code based on the submission timing of the work content when generating the application creation script code. For example, the generation unit can use generation AI to analyze the submission timing of the work content and generate the optimal application creation script code. For example, the generation unit will generate application creation script code with the highest priority for urgent work content. Furthermore, the generation unit can generate application creation script code based on the schedule for regular work content. In addition, the generation unit can generate application creation script code systematically for long-term work content. This allows the generation unit to prioritize code based on the submission timing of the work content, enabling responses tailored to urgency.

The generation unit can adjust the order of application creation script code based on the relevance of business processes when generating the application creation script code. For example, the generation unit can analyze the relevance of business processes using generation AI and generate the application creation script code in the optimal order. For instance, the generation unit prioritizes generating application creation script code for highly relevant business processes. It can also postpone generating application creation script code for less relevant business processes. Furthermore, the generation unit can generate application creation script code in the optimal order based on the relevance of business processes. This allows the generation unit to efficiently generate code by adjusting the order of code based on the relevance of business processes.

The service provider can estimate the user's emotions and adjust the method of providing the application creation script code based on the estimated emotions. For example, the service provider can estimate the user's emotions and adjust the method of providing the application creation script code based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is relaxed, the service provider can provide the application creation script code with detailed explanations. If the user is in a hurry, the service provider can also provide the application creation script code with concise explanations. Furthermore, if the user is stressed, the service provider can provide the application creation script code in a simple and easy-to-understand format. This allows the service provider to provide the application creation script code in the most optimal way for the user by adjusting the method of provision according to the user's emotions.

The service provider can select the optimal delivery method when providing application creation script code by referring to the user's past code usage history. For example, the service provider can select the optimal delivery method by referring to the user's past code usage history when providing application creation script code. The service provider can also, for example, use AI to analyze the user's past code usage history and select the optimal delivery method. For example, the service provider can prioritize delivery methods the user has used in the past (email, editor insertion, etc.). Furthermore, the service provider can suggest the optimal delivery method based on the user's past usage history. In addition, the service provider can provide application creation script code based on the delivery methods the user has preferred to use in the past. This allows the service provider to select the optimal delivery method for the user by referring to past usage history, thereby improving convenience.

The service provider can customize the application creation script code provided based on the user's current project status. For example, the service provider can customize the provided application creation script code based on the user's current project status. For example, the service provider can use AI to analyze the user's current project status and provide the optimal application creation script code. For instance, the service provider can prioritize providing application creation script code related to the user's current project. Furthermore, the service provider can suggest the optimal application creation script code based on the user's project status. In addition, the service provider can customize the provided application creation script code to match the user's current project. This allows the service provider to provide the optimal application creation script code for the user by customizing the provided content based on the current project status.

The service provider can estimate the user's emotions and determine the priority of providing application creation script code based on those emotions. For example, the service provider can estimate the user's emotions and determine the priority of providing application creation script code based on those emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is in a hurry, the service provider will provide the application creation script code with the highest priority. If the user is relaxed, the service provider may also provide the application creation script code with detailed explanations. Furthermore, if the user is stressed, the service provider may provide the application creation script code in a simple and easy-to-understand format. This allows the service provider to provide application creation script code at the optimal time for the user by determining the priority of provision according to the user's emotions.

The service provider can select the optimal delivery method when providing application creation script code, taking into account the user's geographical location. For example, the service provider can select the optimal delivery method by considering the user's geographical location when providing application creation script code. For example, the service provider can use AI to analyze the user's geographical location and select the optimal delivery method. For instance, if the user is in a specific region, the service provider can provide application creation script code related to that region. Furthermore, if the user is on a business trip, the service provider can provide application creation script code related to the destination. Additionally, if the user is at home, the service provider can provide application creation script code related to tasks performed at home. This allows the service provider to select the optimal delivery method for the user by considering their geographical location.

The service provider can analyze the user's social media activity and propose content when providing application creation script code. For example, the service provider can analyze the user's social media activity and propose content when providing application creation script code. For example, the service provider can use AI to analyze the user's social media activity and provide application creation script code based on relevant business content. For example, the service provider can provide application creation script code related to business content mentioned by the user on social media. Furthermore, the service provider can propose application creation script code related to business content of interest based on the user's social media activity. In addition, the service provider can provide application creation script code related to accounts the user follows on social media. This allows the service provider to provide application creation script code based on relevant business content by analyzing social media activity.

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

The reception desk can analyze a user's past work input history and suggest appropriate input methods. For example, it can automatically display suggestions for work content that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used previously. Furthermore, it can predict and suggest work content to be used during specific time periods based on the user's past input history. This allows the reception desk to analyze past input history, suggest the optimal input method for the user, and improve input efficiency.

The generation unit can adjust the level of detail of application script code based on the importance of the business process. For example, the generation unit can use generation AI to evaluate the importance of the business process and generate the optimal application script code. For critical business processes, the generation unit generates application script code that includes detailed error handling. For general business processes, the generation unit can generate application script code that includes only basic functions. Furthermore, for simple business processes, the generation unit can generate application script code that is executable with minimal code. This allows the generation unit to generate application script code at an appropriate level by adjusting the level of detail based on the importance of the business process.

The service provider can estimate the user's emotions and adjust how the application creation script code is delivered based on those emotions. For example, if the user is relaxed, the service provider can provide the application creation script code with detailed explanations. If the user is in a hurry, the service provider can provide the application creation script code with concise explanations. Furthermore, if the user is stressed, the service provider can provide the application creation script code in a simple and easy-to-understand format. This allows the service provider to deliver the application creation script code in the most optimal way for the user by adjusting the delivery method according to their emotions.

The reception system can filter input content based on the user's current projects and areas of interest when they enter their work details. For example, the reception system can use AI to analyze the user's current projects and areas of interest and filter the input content accordingly. The reception system will prioritize displaying work details related to the user's current projects. Furthermore, the reception system can suggest relevant work details based on the user's areas of interest. In addition, the reception system can filter input content based on areas the user has previously shown interest in. This allows the reception system to prioritize input of highly relevant work details by filtering input content based on the user's current projects and areas of interest.

The generation unit can estimate the user's emotions and adjust the way it expresses the application creation script code based on those emotions. For example, if the user is relaxed, the generation unit can generate application creation script code with detailed comments. If the user is in a hurry, it can also generate concise and executable application creation script code. Furthermore, if the user is stressed, it can generate simple and easy-to-understand application creation script code. In this way, the generation unit can provide user-friendly code by adjusting the expression of the application creation script code according to the user's emotions.

The service provider can select the optimal delivery method when providing application creation script code by referring to the user's past code usage history. For example, the service provider can use AI to analyze the user's past code usage history and select the optimal delivery method. The service provider will prioritize delivery methods the user has used in the past (email, editor insertion, etc.). Furthermore, the service provider can suggest the optimal delivery method based on the user's past usage history. In addition, the service provider can provide application creation script code based on the delivery method the user has preferred in the past. This allows the service provider to select the most suitable delivery method for the user by referring to past usage history, thereby improving convenience.

The reception desk can estimate the user's emotions and adjust the input interface for their tasks based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick task completion. In this way, the reception desk reduces user stress and enables efficient input by adjusting the input interface according to the user's emotions.

The generation unit can apply different generation algorithms depending on the category of the business content when generating application creation script code. For example, the generation unit can use generation AI to analyze the category of the business content and apply the optimal generation algorithm. For data processing tasks, the generation unit generates application creation script code that performs efficient data manipulation. Furthermore, for notification tasks, the generation unit can generate application creation script code that includes appropriate notification methods. Additionally, for schedule management tasks, the generation unit can generate application creation script code that includes calendar operations. In this way, the generation unit can generate optimal application creation script code by applying different generation algorithms depending on the category of the business content.

The service provider can estimate the user's emotions and determine the priority of providing application creation script code based on those emotions. For example, if the user is in a hurry, the service provider will provide the application creation script code with the highest priority. If the user is relaxed, the service provider may provide the application creation script code with detailed explanations. Furthermore, if the user is stressed, the service provider may provide the application creation script code in a simple and easy-to-understand format. This allows the service provider to provide application creation script code at the optimal time for the user by determining the priority of provision according to the user's emotions.

The reception desk can prioritize the input of highly relevant tasks by considering the user's geographical location when entering task details. For example, the reception desk can use AI to analyze the user's geographical location and prioritize the input of highly relevant tasks. If the user is in a specific region, the reception desk will prioritize displaying tasks related to that region. Furthermore, if the user is on a business trip, the reception desk can prioritize the input of tasks related to their destination. Additionally, if the user is at home, the reception desk can prioritize the input of tasks to be performed at home. In this way, the reception desk can prioritize the input of highly relevant tasks by considering geographical location.

The following briefly describes the processing flow for example 2.

Step 1: The reception desk receives input from users regarding their work. This input may include, but is not limited to, accounting, sales, and project management. The reception desk can analyze user input using, for example, natural language processing technology.
Step 2: The generation unit uses a generation AI to analyze the business content received by the reception unit and generate the corresponding application creation script code. The generation AI uses, for example, deep learning and natural language processing technology to analyze the user's input and generate the optimal application creation script code. For example, if the generation AI receives input such as "Open a specific spreadsheet and update the data at 9am every day," the generation unit will generate the application creation script code to execute that task.
Step 3: The provider unit provides the user with the application creation script code generated by the generator unit. The provider unit can, for example, automatically insert the generated application creation script code into the user's application creation script editor. The provider unit can also send the generated application creation script code to the user via email. This allows the user to easily use the generated application creation script code.

The specific processing unit 290 transmits the result of the specific processing to the smart device 14. On 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 user input regarding the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. On the data processing device 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is a generative AI such as ChatGPT® (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a finely tuned model that outputs inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or Naive Bayes, and can perform various processes, but is not limited to these examples. The AI may also be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI in part or in whole, but is not limited to these examples. Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

Furthermore, the processing performed 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 it may also be executed by both the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Also, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or external devices, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or external devices.

Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input of business content from the user. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates application creation script code using generation AI. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated application creation script code to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[Second Embodiment]
Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

As shown in Figure 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.

The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and/or LAN.

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

The microphone 238 receives user voice commands and other information. The microphone 238 captures the user's voice, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

The communication interface 44 is connected to the network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via the network 54. The exchange of various types of information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

The processor 28 reads a specific processing program 56 from the storage 32 and executes the read program on the RAM 30. The specific processing is achieved by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores the specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes it on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have data generation model 58 and emotion identification model 59, similar to those of the smart glasses 214, and can perform processing similar to that of the specific processing unit 290 using these models.

Furthermore, other devices besides the data processing device 12 may also possess the data generation model 58. For example, a server device may possess the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device possessing the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (e.g., a mobile phone, robot, home appliance, etc.).

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 user input regarding the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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 prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a finely tuned model that outputs inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or Naive Bayes, and can perform various processes, but is not limited to these examples. The AI may also be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI in part or in whole, but is not limited to these examples. Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

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, or it may be executed by both 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 necessary for processing from the smart glasses 214 or external devices, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or external devices.

Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input of business content from the user. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates application creation script code using generation AI. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated application creation script code to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

As shown in Figure 5, the data processing system 310 comprises a data processing device 12 and a headset-type terminal 314. An example of the data processing device 12 is a server.

The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and/or LAN.

The headset terminal 314 comprises a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to the bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

The microphone 238 receives user voice signals and instructions. The microphone 238 captures the user's voice, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

The communication interface 44 is connected to the network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via the network 54. The exchange of various types of information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

The processor 28 reads a specific processing program 56 from the storage 32 and executes the read program on the RAM 30. The specific processing is achieved by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read program 60 on the RAM 48. The specific processing is achieved by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has data generation model 58 and emotion identification model 59, similar to those of the data generation model 58 and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

Furthermore, other devices besides the data processing device 12 may also possess the data generation model 58. For example, a server device may possess the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device possessing the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (e.g., a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input regarding the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

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 prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a finely tuned model that outputs inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or Naive Bayes, and can perform various processes, but is not limited to these examples. The AI may also be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI in part or in whole, but is not limited to these examples. Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

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 terminal 314, or it may be executed by both the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input of business content from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates application creation script code using generation AI. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated application creation script code to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and/or LAN.

The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to the bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

The microphone 238 receives user voice commands and other information. The microphone 238 captures the user's voice, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

The communication interface 44 is connected to the network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via the network 54. The exchange of various types of information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

The processor 28 reads a specific processing program 56 from the storage 32 and executes the read program on the RAM 30. The specific processing is achieved by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

In robot 414, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read program 60 on the RAM 48. The specific processing is achieved by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. Furthermore, robot 414 has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

Furthermore, other devices besides the data processing device 12 may also possess the data generation model 58. For example, a server device may possess the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device possessing the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (e.g., a mobile phone, robot, home appliance, etc.).

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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input regarding the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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 prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a finely tuned model that outputs inference results from prompts that do not contain instructions. In this case, the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or Naive Bayes, and can perform various processes, but is not limited to these examples. The AI may also be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI in part or in whole, but is not limited to these examples. Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

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 both 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 necessary for processing from the robot 414 or external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the following: for example, the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input of business content from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates application creation script code using generation AI. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated application creation script code to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

Furthermore, the emotion identification model 59, acting 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 a specific mapping, namely an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Furthermore, the emotions of "pleasure" are located on the upper side of the concentric circles, and emotions of "displeasure" are located on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calmer impression.

The inner part of the Emotion Map 400 represents inner thoughts, while the outer part represents actions. Therefore, the further you go from the outer edge of the Emotion Map 400, the more visible (expressed through actions) your emotions become.

Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort; when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort; when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a Brain Physiological Signal Analysis System for Speech Emotion Recognition and Emotion, Tokushima University, Doctoral Dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map displays emotions belonging to the "response" region, where sensation is dominant. The right half of the emotion map displays emotions belonging to the "situation" region, where situational awareness is dominant.

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

The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassurance," "peace of mind," and "reassurance" have similar emotion values.

In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, involving multiple computers, including computer 22.

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

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.

Furthermore, it is not necessary to store the entirety 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, nor is it necessary to store the entirety of the specific processing program 56 in the storage device 32. It is acceptable to store only a portion of the specific processing program 56.

The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

The hardware resource performing a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource performing the specific process may consist of a single processor.

Examples of configurations using a single processor include, firstly, a configuration where one or more CPUs and software are combined to form a single processor, which functions as a hardware resource for executing specific processing. Secondly, a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources for executing specific processing, on a single IC chip, as exemplified by SoC (System-on-a-chip). Thus, specific processing is realized using one or more of the above-mentioned types of processors as hardware resources.

Furthermore, the hardware structure of these various processors can, more specifically, utilize electrical circuits combining circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be removed, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

Furthermore, although the above examples were described separately as the first to fourth embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset-type terminal 314, and robot 414 are just examples; they may be combined, or other devices may be used. Furthermore, although the above examples were described separately as Embodiment 1 and Embodiment 2, these may be combined.

The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technology. For example, the above descriptions of the structure, function, operation, and effects are examples of the structure, function, operation, and effects of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technology presented. Furthermore, to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other information that do not require special explanation to enable the implementation of the technology have been omitted from the descriptions and illustrations presented above.

All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

(Note 1)
A reception desk that receives input of work details from users,
A generation unit analyzes the business content received by the reception unit and generates corresponding application creation script code,
A system characterized by comprising: a provisioning unit that provides the user with the application creation script code generated by the generation unit.
(Note 2)
The generating unit is
The system described in Appendix 1, characterized by using a generative AI to analyze business content in natural language and generate corresponding application creation script code.
(Note 3)
The aforementioned supply unit is,
The system described in Appendix 1, characterized by automatically inserting the generated application creation script code into the user's application creation script editor.
(Note 4)
The aforementioned supply unit is,
The system described in Appendix 1, characterized by sending the generated application creation script code to the user via email.
(Note 5)
The generating unit is
The system described in Appendix 1 is characterized by generating code that incorporates the latest application creation script features and recommended practices.
(Note 6)
The aforementioned reception unit is
The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the input interface for work content based on the estimated user emotions.
(Note 7)
The aforementioned reception unit is
The system described in Appendix 1, characterized by analyzing the user's past work content input history and proposing an appropriate input method.
(Note 8)
The aforementioned reception unit is
The system described in Appendix 1, characterized in that when entering work details, the input content is filtered based on the user's current projects and areas of interest.
(Note 9)
The aforementioned reception unit is
The system described in Appendix 1, characterized by estimating the user's emotions and determining the priority of input content based on the estimated user emotions.
(Note 10)
The aforementioned reception unit is
The system described in Appendix 1, characterized in that, when inputting work details, it prioritizes inputting highly relevant work details by taking into account the user's geographical location information.
(Note 11)
The aforementioned reception unit is
The system described in Appendix 1, characterized in that it analyzes the user's social media activity when they input their work details and suggests relevant work content.
(Note 12)
The generating unit is
The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the expression method of the application creation script code generated based on the estimated user emotions.
(Note 13)
The generating unit is
The system described in Appendix 1, characterized in that, when generating application creation script code, the level of detail of the code is adjusted based on the importance of the business content.
(Note 14)
The generating unit is
The system described in Appendix 1, characterized in that it applies different generation algorithms depending on the category of business content when generating application creation script code.
(Note 15)
The generating unit is
The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the length of the application creation script code generated based on the estimated user emotions.
(Note 16)
The generating unit is
The system described in Appendix 1, characterized in that when generating application creation script code, the priority of the code is determined based on the submission timing of the work details.
(Note 17)
The generating unit is
The system described in Appendix 1, characterized in that, when generating application creation script code, the order of the code is adjusted based on the relevance of the business content.
(Note 18)
The aforementioned supply unit is,
The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the method of providing application creation script code based on the estimated user emotions.
(Note 19)
The aforementioned supply unit is,
The system described in Appendix 1, characterized in that when providing application creation script code, it selects the optimal method of provision by referring to the user's past code usage history.
(Note 20)
The aforementioned supply unit is,
The system described in Appendix 1, characterized in that when providing application creation script code, the provided content is customized based on the user's current project status.
(Note 21)
The aforementioned supply unit is,
The system described in Appendix 1, characterized by estimating the user's emotions and determining the priority of providing application creation script code based on the estimated user emotions.
(Note 22)
The aforementioned supply unit is,
The system described in Appendix 1, characterized in that when providing application creation script code, it selects the optimal delivery method considering the user's geographical location information.
(Note 23)
The aforementioned supply unit is,
The system described in Appendix 1, characterized in that it analyzes the user's social media activity and proposes content to be provided when providing application creation script code.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset terminal 414 Robot

Claims (8)

A reception desk that receives input of work details from users,
A generation unit analyzes the business content received by the reception unit using generation AI and generates corresponding application creation script code.
The system includes a providing unit that provides the user with the application creation script code generated by the generation unit,
The generation unit estimates the user's emotions and, based on the estimated emotions, adjusts the way it expresses the generated application creation script code so that it generates detailed commented application creation script code when the user is relaxed, generates concise and executable application creation script code when the user is in a hurry, and generates simple and easy-to-understand application creation script code when the user is stressed.
A system characterized by the following features. The aforementioned generating AI is
It consists of a Large-Scale Language Model (LLM).
The system according to feature 1. The aforementioned supply unit is,
The system according to claim 1, characterized in that it automatically inserts the application creation script code generated by the generation unit into the user's application creation script editor. The aforementioned supply unit is,
The system according to claim 1, characterized in that it sends the application creation script code generated by the generation unit to the user via email. The generating unit is
The system according to claim 1, characterized by generating code that incorporates the latest application creation script features and recommended practices. The aforementioned reception unit is
The system according to claim 1, characterized in that it estimates the user's emotions and, based on the estimated user emotions, adjusts the input interface for work content to provide a simple interface when the user is stressed, provide detailed input options when the user is relaxed, and prioritize voice input when the user is in a hurry . The aforementioned reception unit is
The system according to claim 1, characterized in that it analyzes the user's past work content input history and proposes an appropriate input method. The aforementioned reception unit is
The system according to claim 1, characterized in that when entering work details, the input content is filtered based on the user's current projects and areas of interest.