system

Abstract

The system according to this embodiment aims to automate PC tasks and improve work efficiency, even for users without programming knowledge. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an execution unit, and a sharing unit. The reception unit receives user operations as input. The generation unit analyzes the operations input by the reception unit and encodes them. The execution unit executes the code generated by the generation unit. The sharing unit shares the code generated by the execution unit with the entire system.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

[0007] The system according to this embodiment allows even users without programming knowledge to automate PC tasks and improve work efficiency. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] 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. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] 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. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] 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. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] 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. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] 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).

[0012] 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.

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

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

[0015] 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 it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 2%. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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 small digital camera equipped with an optical system such as 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.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" 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.

[0025] 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.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a 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 specific processing 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 a data generation model 58 and an 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.

[0027] 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 a 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.

[0028] (Example of form 1) The PC work efficiency system according to an embodiment of the present invention is a system that uses a generation AI to code user operations and share them throughout the company. In this PC work efficiency system, when a user inputs a prompt or operation, the generation AI reproduces and codes that operation. This allows even people without programming knowledge to automate repetitive tasks. Furthermore, once an automated task is shared throughout the company, it is expected that all employees will reduce unproductive time. For example, the user inputs a prompt or operation. For example, it records mouse and keyboard movements, like the macro function in Excel. This information is input into the generation AI. Next, the generation AI analyzes the input information and generates code to reproduce the operation. The generation AI understands the user's operation and expresses it as a program. For example, it records the operation of entering data into a specific cell in Excel and generates code to reproduce that operation. The generated code can be repeatedly executed by the user. For example, by automating the task of entering the same data every day, the user's work time can be significantly reduced. Furthermore, once an automated task is shared throughout the company, other employees can automate the same task, reducing unproductive time throughout the company. For example, by automating the creation of a specific report and sharing the code with all employees, everyone can perform the same task efficiently. This system allows even those without programming knowledge to easily automate PC tasks, leading to increased efficiency throughout the company. In this way, the PC work efficiency system efficiently codes user operations, making them executable and shareable.

[0029] The PC work efficiency system according to the embodiment comprises a reception unit, a generation unit, an execution unit, and a sharing unit. The reception unit receives user operations as input. User operations include, but are not limited to, clicks, taps, and voice commands. The reception unit can, for example, accept text input or voice input. The reception unit can also record user operations and input that information into the generation AI. For example, the reception unit records mouse and keyboard movements, such as Excel macros, and inputs that information into the generation AI. The generation unit uses the generation AI to analyze the operations input by the reception unit and encode them. The generation AI uses, for example, a natural language processing model such as GPT-4 (registered trademark) or Gemini to understand user operations and express them as a program. The generation unit can, for example, record an operation to input data into a specific cell in Excel and generate code to reproduce that operation. The generation unit can also use the generation AI to analyze user operations and generate optimal code. For example, the generation unit analyzes user operations and generates code that efficiently reproduces those operations. The execution unit executes the code generated by the generation unit. The execution unit can execute generated code, such as Python® code or JavaScript® code. By executing the generated code, the execution unit automates user operations. For example, the execution unit can significantly reduce user time by automating the task of entering the same data every day. The execution unit can also manage the execution environment and execution procedures when executing the generated code. For example, the execution unit sets up the environment for executing the generated code and manages the execution procedures. The sharing unit shares the generated code throughout the company. The sharing unit can share the generated code throughout the company using sharing platforms such as Google® Drive or SharePoint. By sharing the generated code throughout the company, the sharing unit enables other employees to automate the same tasks. For example, the sharing unit can automate the creation of a specific report and share that code with all employees, allowing everyone to perform the same task efficiently.Furthermore, the shared section can manage access permissions using a shared platform. For example, the shared section can set access permissions for the generated code, ensuring that only specific employees can access it. This allows the PC work efficiency system according to the embodiment to efficiently code, execute, and share user operations.

[0030] The reception unit can receive specific prompts or operations. These specific prompts or operations include, but are not limited to, text input or voice input. For example, the user can input instructions via text. The reception unit can also receive instructions via voice. For instance, the reception unit can use speech recognition technology to convert the user's voice instructions into text and input that text into a generating AI. This allows the system to accept operations when the user inputs prompts or operations.

[0031] The generation unit can analyze and code the input operations using a generative AI. The generative AI includes, but is not limited to, natural language processing models such as GPT-4 and Gemini. The generation unit uses the generative AI to analyze user operations and express them as a program. For example, the generation unit can record the operation of entering data into a specific cell in Excel and generate code to reproduce that operation. Furthermore, the generation unit can use the generative AI to analyze user operations and generate optimal code. For example, the generation unit can analyze user operations and generate code that efficiently reproduces those operations. Thus, by using the generative AI, the analysis and coding of operations are performed automatically.

[0032] The execution unit can execute the generated code. The generated code includes, but is not limited to, Python code, JavaScript code, etc. By executing the generated code, the execution unit automates user operations. For example, the execution unit can significantly reduce user time by automating the task of entering the same data every day. Furthermore, the execution unit can manage the execution environment and execution procedures when executing the generated code. For example, the execution unit sets up the environment for executing the generated code and manages the execution procedures. This automates user operations by executing the generated code.

[0033] The shared department can share the generated code company-wide. This company-wide sharing can include, but is not limited to, the entire company or specific teams. By sharing the generated code company-wide, the shared department can enable other employees to automate the same tasks. For example, the shared department can automate the creation of a specific report and share that code with all employees, allowing everyone to perform the same task efficiently. The shared department can also manage access permissions using a shared platform. For example, the shared department can set access permissions for the generated code, ensuring only specific employees can access it. This allows all employees to automate the same tasks by sharing the generated code company-wide.

[0034] The shared department can manage access permissions using a shared platform. This includes, but is not limited to, Google Drive and SharePoint. The shared department uses the shared platform to manage access permissions for generated code. For example, the shared department can set access permissions for generated code, ensuring that only specific employees can access it. Furthermore, the shared department can ensure security by managing access permissions. For example, by setting access permissions, the shared department can prevent the leakage of confidential information. Thus, using a shared platform enables the management of access permissions.

[0035] The reception desk can analyze the user's past operation history and select the optimal input method. For example, the reception desk can prioritize suggesting operation methods that the user has frequently used in the past. Furthermore, the reception desk can select the most efficient input method based on the user's past operation history. In addition, the reception desk can analyze the user's past operation history, learn operation patterns, and suggest the optimal input method. This allows the system to suggest the optimal input method by analyzing past operation history.

[0036] The reception desk can filter the input of operations based on the user's current projects and areas of interest. For example, it can display only operations related to the project the user is currently working on. It can also prioritize the display of relevant operations based on the user's areas of interest. Furthermore, it can suggest the most suitable operations, taking into account the user's current projects and areas of interest. This allows for the prioritization of highly relevant operations by filtering them based on the current project and areas of interest.

[0037] The reception desk can prioritize inputting operations that are highly relevant to the user's geographical location when inputting operations. For example, if the user is in a specific location, the reception desk will prioritize inputting operations related to that location. The reception desk can also suggest the most suitable operations based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can prioritize inputting operations related to their current location. In this way, by considering geographical location, the reception desk can prioritize inputting operations that are highly relevant.

[0038] The reception desk can analyze the user's social media activity and input relevant actions when an action is being performed. For example, the reception desk can analyze the user's social media activity and suggest relevant actions. It can also input the optimal action based on information the user has shared on social media. Furthermore, the reception desk can prioritize inputting relevant actions by considering the user's social media activity. This allows for the priority input of relevant actions by analyzing social media activity.

[0039] The generation unit can adjust the level of detail of the code based on the importance of the operation during code generation. For example, it can generate detailed code for high-importance operations. It can also generate concise code for low-importance operations. Furthermore, the generation unit can dynamically adjust the level of detail of the code according to the importance of the operation. This allows for the generation of more appropriate code by adjusting the level of detail according to the importance of the operation.

[0040] The generation unit can apply different generation algorithms depending on the category of the operation during code generation. For example, the generation unit can apply the optimal algorithm to generate code for data input operations. It can also apply the optimal algorithm to generate code for file operations. Furthermore, the generation unit can apply the optimal algorithm to generate code for network operations. By applying the optimal generation algorithm according to the category of the operation, more appropriate code is generated.

[0041] The generation unit can determine the priority of code based on the timing of operation execution during code generation. For example, it can prioritize generating code for operations that will be executed sooner. It can also postpone code generation for operations that will be executed later. Furthermore, the generation unit can dynamically adjust the code priority according to the timing of operation execution. This allows for the generation of more appropriate code by determining the code priority according to the timing of operation execution.

[0042] The generation unit can adjust the order of code based on the relevance of operations during code generation. For example, the generation unit prioritizes coding operations with high relevance. It can also postpone coding operations with low relevance. Furthermore, the generation unit can dynamically adjust the order of code according to the relevance of operations. This allows for the generation of more appropriate code by adjusting the order of code according to the relevance of operations.

[0043] The execution unit can analyze the user's past execution history and select the optimal execution method when executing code. For example, the execution unit can suggest the optimal execution method based on the methods the user has used in the past. It can also select the most efficient execution method from the user's past execution history. Furthermore, the execution unit can analyze the user's past execution history and suggest an execution method with fewer errors. In this way, by analyzing past execution history, it can suggest the optimal execution method.

[0044] The execution unit can customize the execution method based on the user's current work situation when executing code. For example, if the user is busy, the execution unit can provide a concise execution procedure. Conversely, if the user is relaxed, the execution unit can provide a detailed execution procedure. Furthermore, the execution unit can suggest the optimal execution method considering the user's current work situation. This allows for more appropriate execution by customizing the execution method according to the current work situation.

[0045] The execution unit can select the optimal execution method when executing code, taking into account the user's geographical location. For example, if the user is in a specific location, the execution unit will suggest the most suitable execution method for that location. Furthermore, the execution unit can select the optimal execution method based on the user's geographical location. Additionally, if the user is on the move, the execution unit can suggest the most suitable execution method for their current location. This allows for the selection of the optimal execution method by considering geographical location information.

[0046] The execution unit can analyze the user's social media activity and propose an execution method during code execution. For example, the execution unit can analyze the user's social media activity and propose the optimal execution method. Furthermore, the execution unit can propose the optimal execution method based on information shared by the user on social media. In addition, the execution unit can propose the optimal execution method considering the user's social media activity. Thus, by analyzing social media activity, the optimal execution method can be proposed.

[0047] The sharing function can select the optimal sharing method by referring to the user's past sharing history when sharing. For example, the sharing function can suggest the optimal sharing method based on the methods the user has used to share in the past. It can also select the most efficient sharing method from the user's past sharing history. Furthermore, the sharing function can analyze the user's past sharing history and suggest a sharing method with fewer errors. In this way, it can suggest the optimal sharing method by referring to past sharing history.

[0048] The sharing function can customize the sharing method based on the user's current project during the sharing process. For example, it can suggest sharing methods relevant to the project the user is currently working on. It can also select the most suitable sharing method considering the user's current project. Furthermore, it can customize the sharing method based on the user's current project. This allows for more appropriate sharing by customizing the sharing method based on the current project.

[0049] The sharing function can select the optimal sharing method by considering the user's geographical location information during sharing. For example, if the user is in a specific location, the sharing function will suggest the most suitable sharing method for that location. Furthermore, the sharing function can select the optimal sharing method based on the user's geographical location information. Additionally, if the user is on the move, the sharing function can suggest the most suitable sharing method for their current location. This allows for the selection of the optimal sharing method by considering geographical location information.

[0050] The sharing function can analyze the user's social media activity and suggest sharing methods when sharing. For example, the sharing function can analyze the user's social media activity and suggest the optimal sharing method. Furthermore, the sharing function can suggest the optimal sharing method based on the information the user has shared on social media. In addition, the sharing function can suggest the optimal sharing method considering the user's social media activity. This allows for the suggestion of the optimal sharing method by analyzing social media activity.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can analyze the user's past operation history when inputting user actions and suggest the optimal input method. For example, it can prioritize suggesting operation methods that the user has frequently used in the past. The reception desk can also select the most efficient input method based on the user's past operation history. Furthermore, the reception desk can analyze the user's past operation history, learn operation patterns, and suggest the optimal input method. This allows the system to suggest the optimal input method by analyzing past operation history.

[0053] The generation unit can adjust the level of detail of the code based on the importance of the operation during code generation. For example, it can generate detailed code for high-importance operations. It can also generate concise code for low-importance operations. Furthermore, the generation unit can dynamically adjust the level of detail of the code according to the importance of the operation. This allows for the generation of more appropriate code by adjusting the level of detail according to the importance of the operation.

[0054] The execution unit can analyze the user's past execution history to select the optimal execution method during code execution. For example, it can suggest the optimal execution method based on the methods the user has used in the past. The execution unit can also select the most efficient execution method from the user's past execution history. Furthermore, it can analyze the user's past execution history and suggest an execution method with fewer errors. In this way, by analyzing past execution history, it can suggest the optimal execution method.

[0055] The sharing function can select the optimal sharing method by referring to the user's past sharing history when sharing. For example, it can suggest the optimal sharing method based on the methods the user has used in the past. The sharing function can also select the most efficient sharing method from the user's past sharing history. Furthermore, the sharing function can analyze the user's past sharing history and suggest a sharing method with fewer errors. In this way, by referring to past sharing history, it can suggest the optimal sharing method.

[0056] The reception desk can filter the input of operations based on the user's current projects and areas of interest. For example, it can display only operations related to the project the user is currently working on. The reception desk can also prioritize the display of operations relevant to the user's areas of interest. Furthermore, the reception desk can suggest the most suitable operations, taking into account the user's current projects and areas of interest. This allows for the prioritization of highly relevant operations by filtering them based on the current project and areas of interest.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception unit inputs user actions. User actions include clicks, taps, and voice commands. The reception unit can accept text and voice input, record user actions, and input that information into the generating AI. For example, like the macro function in Excel, it can record mouse and keyboard movements and input that information into the generating AI. Step 2: The generation unit uses a generation AI to analyze and code the operations entered by the reception unit. The generation AI uses natural language processing models such as GPT-4 and Gemini to understand user operations and express them as a program. The generation unit records the operation of entering data into a specific cell and generates code to reproduce that operation. The generation unit can also analyze user operations and generate the optimal code. Step 3: The execution unit executes the code generated by the generation unit. The execution unit can execute the generated code, such as Python code or JavaScript code. By executing the generated code, the execution unit automates user operations. For example, automating the task of entering the same data every day can significantly reduce the user's working time. The execution unit can also manage the execution environment and execution procedure when executing the generated code. Step 4: The sharing department shares the generated code company-wide. The sharing department can share the generated code company-wide using a sharing platform such as Google Drive or SharePoint. This allows other employees to automate the same tasks. For example, by automating the creation of a specific report and sharing the code with all employees, everyone can perform the same task efficiently. The sharing department can also manage access permissions using the sharing platform.

[0059] (Example of form 2) The PC work efficiency system according to an embodiment of the present invention is a system that uses a generation AI to code user operations and share them throughout the company. In this PC work efficiency system, when a user inputs a prompt or operation, the generation AI reproduces and codes that operation. This allows even people without programming knowledge to automate repetitive tasks. Furthermore, once an automated task is shared throughout the company, it is expected that all employees will reduce unproductive time. For example, the user inputs a prompt or operation. For example, it records mouse and keyboard movements, like the macro function in Excel. This information is input into the generation AI. Next, the generation AI analyzes the input information and generates code to reproduce the operation. The generation AI understands the user's operation and expresses it as a program. For example, it records the operation of entering data into a specific cell in Excel and generates code to reproduce that operation. The generated code can be repeatedly executed by the user. For example, by automating the task of entering the same data every day, the user's work time can be significantly reduced. Furthermore, once an automated task is shared throughout the company, other employees can automate the same task, reducing unproductive time throughout the company. For example, by automating the creation of a specific report and sharing the code with all employees, everyone can perform the same task efficiently. This system allows even those without programming knowledge to easily automate PC tasks, leading to increased efficiency throughout the company. In this way, the PC work efficiency system efficiently codes user operations, making them executable and shareable.

[0060] The PC work efficiency system according to the embodiment comprises a reception unit, a generation unit, an execution unit, and a sharing unit. The reception unit receives user operations as input. User operations include, but are not limited to, clicks, taps, and voice commands. The reception unit can, for example, accept text input or voice input. The reception unit can also record user operations and input that information into the generation AI. For example, the reception unit records mouse and keyboard movements, such as Excel macros, and inputs that information into the generation AI. The generation unit uses the generation AI to analyze the operations input by the reception unit and encode them. The generation AI uses, for example, a natural language processing model such as GPT-4 or Gemini to understand user operations and express them as a program. The generation unit can, for example, record an operation to input data into a specific cell in Excel and generate code to reproduce that operation. The generation unit can also use the generation AI to analyze user operations and generate optimal code. For example, the generation unit analyzes user operations and generates code that efficiently reproduces those operations. The execution unit executes the code generated by the generation unit. The execution unit can execute generated code, such as Python code or JavaScript code. By executing the generated code, the execution unit automates user operations. For example, the execution unit can significantly reduce user time by automating the task of entering the same data every day. The execution unit can also manage the execution environment and execution procedures when executing the generated code. For example, the execution unit sets up the environment for executing the generated code and manages the execution procedures. The sharing unit shares the generated code throughout the company. The sharing unit can share the generated code throughout the company using a sharing platform such as Google Drive or SharePoint. By sharing the generated code throughout the company, the sharing unit enables other employees to automate the same tasks. For example, the sharing unit can automate the creation of a specific report and share that code with all employees, allowing everyone to perform the same task efficiently. The sharing unit can also manage access permissions using a sharing platform.For example, the shared section sets access permissions for the generated code, ensuring that only specific employees can access it. This allows the PC work efficiency system according to the embodiment to efficiently code, execute, and share user operations.

[0061] The reception unit can receive specific prompts or operations. These specific prompts or operations include, but are not limited to, text input or voice input. For example, the user can input instructions via text. The reception unit can also receive instructions via voice. For instance, the reception unit can use speech recognition technology to convert the user's voice instructions into text and input that text into a generating AI. This allows the system to accept operations when the user inputs prompts or operations.

[0062] The generation unit can analyze and code the input operations using a generative AI. The generative AI includes, but is not limited to, natural language processing models such as GPT-4 and Gemini. The generation unit uses the generative AI to analyze user operations and express them as a program. For example, the generation unit can record the operation of entering data into a specific cell in Excel and generate code to reproduce that operation. Furthermore, the generation unit can use the generative AI to analyze user operations and generate optimal code. For example, the generation unit can analyze user operations and generate code that efficiently reproduces those operations. Thus, by using the generative AI, the analysis and coding of operations are performed automatically.

[0063] The execution unit can execute the generated code. The generated code includes, but is not limited to, Python code, JavaScript code, etc. By executing the generated code, the execution unit automates user operations. For example, the execution unit can significantly reduce user time by automating the task of entering the same data every day. Furthermore, the execution unit can manage the execution environment and execution procedures when executing the generated code. For example, the execution unit sets up the environment for executing the generated code and manages the execution procedures. This automates user operations by executing the generated code.

[0064] The shared department can share the generated code company-wide. This company-wide sharing can include, but is not limited to, the entire company or specific teams. By sharing the generated code company-wide, the shared department can enable other employees to automate the same tasks. For example, the shared department can automate the creation of a specific report and share that code with all employees, allowing everyone to perform the same task efficiently. The shared department can also manage access permissions using a shared platform. For example, the shared department can set access permissions for the generated code, ensuring only specific employees can access it. This allows all employees to automate the same tasks by sharing the generated code company-wide.

[0065] The shared department can manage access permissions using a shared platform. This includes, but is not limited to, Google Drive and SharePoint. The shared department uses the shared platform to manage access permissions for generated code. For example, the shared department can set access permissions for generated code, ensuring that only specific employees can access it. Furthermore, the shared department can ensure security by managing access permissions. For example, by setting access permissions, the shared department can prevent the leakage of confidential information. Thus, using a shared platform enables the management of access permissions.

[0066] The reception desk can estimate the user's emotions and adjust the timing of inputs based on those emotions. For example, if the user is stressed, the reception desk can delay the timing of inputs to help the user relax. Conversely, if the user is relaxed, the reception desk can speed up the timing of inputs to allow for more efficient work. Furthermore, if the user is in a hurry, the reception desk can optimize the timing of inputs to allow for quick completion of the task. By adjusting the timing of inputs according to the user's emotions, more appropriate operations become possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The reception desk can analyze the user's past operation history and select the optimal input method. For example, the reception desk can prioritize suggesting operation methods that the user has frequently used in the past. Furthermore, the reception desk can select the most efficient input method based on the user's past operation history. In addition, the reception desk can analyze the user's past operation history, learn operation patterns, and suggest the optimal input method. This allows the system to suggest the optimal input method by analyzing past operation history.

[0068] The reception desk can filter the input of operations based on the user's current projects and areas of interest. For example, it can display only operations related to the project the user is currently working on. It can also prioritize the display of relevant operations based on the user's areas of interest. Furthermore, it can suggest the most suitable operations, taking into account the user's current projects and areas of interest. This allows for the prioritization of highly relevant operations by filtering them based on the current project and areas of interest.

[0069] The reception desk can estimate the user's emotions and determine the priority of input operations based on the estimated emotions. For example, if the user is stressed, the reception desk may postpone less important operations. Conversely, if the user is relaxed, the reception desk may prioritize highly important operations. Furthermore, if the user is in a hurry, the reception desk may prioritize the most important operations. This allows for more appropriate operations by prioritizing operations according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The reception desk can prioritize inputting operations that are highly relevant to the user's geographical location when inputting operations. For example, if the user is in a specific location, the reception desk will prioritize inputting operations related to that location. The reception desk can also suggest the most suitable operations based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can prioritize inputting operations related to their current location. In this way, by considering geographical location, the reception desk can prioritize inputting operations that are highly relevant.

[0071] The reception desk can analyze the user's social media activity and input relevant actions when an action is being performed. For example, the reception desk can analyze the user's social media activity and suggest relevant actions. It can also input the optimal action based on information the user has shared on social media. Furthermore, the reception desk can prioritize inputting relevant actions by considering the user's social media activity. This allows for the priority input of relevant actions by analyzing social media activity.

[0072] The generation unit can estimate the user's emotions and adjust the code generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate detailed code. It can also generate concise code if the user is in a hurry. Furthermore, if the user is stressed, the generation unit can generate code with fewer errors. By adjusting the code generation method according to the user's emotions, more appropriate code is generated. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The generation unit can adjust the level of detail of the code based on the importance of the operation during code generation. For example, it can generate detailed code for high-importance operations. It can also generate concise code for low-importance operations. Furthermore, the generation unit can dynamically adjust the level of detail of the code according to the importance of the operation. This allows for the generation of more appropriate code by adjusting the level of detail according to the importance of the operation.

[0074] The generation unit can apply different generation algorithms depending on the category of the operation during code generation. For example, the generation unit can apply the optimal algorithm to generate code for data input operations. It can also apply the optimal algorithm to generate code for file operations. Furthermore, the generation unit can apply the optimal algorithm to generate code for network operations. By applying the optimal generation algorithm according to the category of the operation, more appropriate code is generated.

[0075] The generation unit can estimate the user's emotions and adjust the code length based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate short, concise code. If the user is relaxed, the generation unit can also generate longer code with more detailed explanations. Furthermore, if the user is stressed, the generation unit can generate short code with fewer errors. By adjusting the code length according to the user's emotions, more appropriate code can be generated. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The generation unit can determine the priority of code based on the timing of operation execution during code generation. For example, it can prioritize generating code for operations that will be executed sooner. It can also postpone code generation for operations that will be executed later. Furthermore, the generation unit can dynamically adjust the code priority according to the timing of operation execution. This allows for the generation of more appropriate code by determining the code priority according to the timing of operation execution.

[0077] The generation unit can adjust the order of code based on the relevance of operations during code generation. For example, the generation unit prioritizes coding operations with high relevance. It can also postpone coding operations with low relevance. Furthermore, the generation unit can dynamically adjust the order of code according to the relevance of operations. This allows for the generation of more appropriate code by adjusting the order of code according to the relevance of operations.

[0078] The execution unit can estimate the user's emotions and adjust how the code is executed based on those emotions. For example, if the user is relaxed, the execution unit can display detailed execution steps. If the user is in a hurry, it can display concise execution steps. Furthermore, if the user is stressed, it can display execution steps with fewer errors. This allows for more appropriate execution by adjusting how the code is executed according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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.

[0079] The execution unit can analyze the user's past execution history and select the optimal execution method when executing code. For example, the execution unit can suggest the optimal execution method based on the methods the user has used in the past. It can also select the most efficient execution method from the user's past execution history. Furthermore, the execution unit can analyze the user's past execution history and suggest an execution method with fewer errors. In this way, by analyzing past execution history, it can suggest the optimal execution method.

[0080] The execution unit can customize the execution method based on the user's current work situation when executing code. For example, if the user is busy, the execution unit can provide a concise execution procedure. Conversely, if the user is relaxed, the execution unit can provide a detailed execution procedure. Furthermore, the execution unit can suggest the optimal execution method considering the user's current work situation. This allows for more appropriate execution by customizing the execution method according to the current work situation.

[0081] The execution unit can estimate the user's emotions and determine the execution priority of code based on the estimated emotions. For example, if the user is in a hurry, the execution unit will prioritize executing the most important code. If the user is relaxed, the execution unit can also execute less important code. Furthermore, if the user is stressed, the execution unit can prioritize executing code with fewer errors. This allows for more appropriate execution by determining execution priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The execution unit can select the optimal execution method when executing code, taking into account the user's geographical location. For example, if the user is in a specific location, the execution unit will suggest the most suitable execution method for that location. Furthermore, the execution unit can select the optimal execution method based on the user's geographical location. Additionally, if the user is on the move, the execution unit can suggest the most suitable execution method for their current location. This allows for the selection of the optimal execution method by considering geographical location information.

[0083] The execution unit can analyze the user's social media activity and propose an execution method during code execution. For example, the execution unit can analyze the user's social media activity and propose the optimal execution method. Furthermore, the execution unit can propose the optimal execution method based on information shared by the user on social media. In addition, the execution unit can propose the optimal execution method considering the user's social media activity. Thus, by analyzing social media activity, the optimal execution method can be proposed.

[0084] The sharing function can estimate the user's emotions and adjust the sharing method based on those emotions. For example, if the user is relaxed, the sharing function can provide detailed sharing instructions. If the user is in a hurry, it can provide concise sharing instructions. Furthermore, if the user is stressed, it can provide sharing instructions with fewer errors. This allows for more appropriate sharing by adjusting the sharing method according to the user's 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.

[0085] The sharing function can select the optimal sharing method by referring to the user's past sharing history when sharing. For example, the sharing function can suggest the optimal sharing method based on the methods the user has used to share in the past. It can also select the most efficient sharing method from the user's past sharing history. Furthermore, the sharing function can analyze the user's past sharing history and suggest a sharing method with fewer errors. In this way, it can suggest the optimal sharing method by referring to past sharing history.

[0086] The sharing function can customize the sharing method based on the user's current project during the sharing process. For example, it can suggest sharing methods relevant to the project the user is currently working on. It can also select the most suitable sharing method considering the user's current project. Furthermore, it can customize the sharing method based on the user's current project. This allows for more appropriate sharing by customizing the sharing method based on the current project.

[0087] The sharing function can estimate the user's emotions and determine the priority of sharing based on those emotions. For example, if the user is in a hurry, the sharing function will prioritize the most important sharing. If the user is relaxed, the sharing function can also prioritize less important sharing. Furthermore, if the user is stressed, the sharing function can prioritize sharing with fewer errors. This allows for more appropriate sharing by determining the priority of sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The sharing function can select the optimal sharing method by considering the user's geographical location information during sharing. For example, if the user is in a specific location, the sharing function will suggest the most suitable sharing method for that location. Furthermore, the sharing function can select the optimal sharing method based on the user's geographical location information. Additionally, if the user is on the move, the sharing function can suggest the most suitable sharing method for their current location. This allows for the selection of the optimal sharing method by considering geographical location information.

[0089] The sharing function can analyze the user's social media activity and suggest sharing methods when sharing. For example, the sharing function can analyze the user's social media activity and suggest the optimal sharing method. Furthermore, the sharing function can suggest the optimal sharing method based on the information the user has shared on social media. In addition, the sharing function can suggest the optimal sharing method considering the user's social media activity. This allows for the suggestion of the optimal sharing method by analyzing social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and sharing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which records user operations and inputs that information into the generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes and codes user operations. The execution unit executes the code generated by the specific processing unit 290 of the data processing unit 12. The sharing unit shares the generated code throughout the company via the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and sharing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which records the user's operations and inputs that information into the generation AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes and codes the user's operations. The execution unit executes, for example, the code generated by the specific processing unit 290 of the data processing unit 12. The sharing unit shares the generated code throughout the company, for example, via the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and sharing unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, which records user operations and inputs that information to the generation AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes and codes user operations. The execution unit executes, for example, the code generated by the specific processing unit 290 of the data processing unit 12. The sharing unit shares the generated code throughout the company, for example, via the communication I / F 44 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and sharing unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which records user operations and inputs that information into the generation AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes and codes user operations. The execution unit executes, for example, the code generated by the specific processing unit 290 of the data processing unit 12. The sharing unit shares the generated code throughout the company, for example, via the communication I / F 44 of the robot 414.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The reception desk can analyze the user's past operation history when inputting user actions and suggest the optimal input method. For example, it can prioritize suggesting operation methods that the user has frequently used in the past. The reception desk can also select the most efficient input method based on the user's past operation history. Furthermore, the reception desk can analyze the user's past operation history, learn operation patterns, and suggest the optimal input method. This allows the system to suggest the optimal input method by analyzing past operation history.

[0092] The generation unit can adjust the level of detail of the code based on the importance of the operation during code generation. For example, it can generate detailed code for high-importance operations. It can also generate concise code for low-importance operations. Furthermore, the generation unit can dynamically adjust the level of detail of the code according to the importance of the operation. This allows for the generation of more appropriate code by adjusting the level of detail according to the importance of the operation.

[0093] The execution unit can analyze the user's past execution history to select the optimal execution method during code execution. For example, it can suggest the optimal execution method based on the methods the user has used in the past. The execution unit can also select the most efficient execution method from the user's past execution history. Furthermore, it can analyze the user's past execution history and suggest an execution method with fewer errors. In this way, by analyzing past execution history, it can suggest the optimal execution method.

[0094] The sharing function can select the optimal sharing method by referring to the user's past sharing history when sharing. For example, it can suggest the optimal sharing method based on the methods the user has used in the past. The sharing function can also select the most efficient sharing method from the user's past sharing history. Furthermore, the sharing function can analyze the user's past sharing history and suggest a sharing method with fewer errors. In this way, by referring to past sharing history, it can suggest the optimal sharing method.

[0095] The reception desk can filter the input of operations based on the user's current projects and areas of interest. For example, it can display only operations related to the project the user is currently working on. The reception desk can also prioritize the display of operations relevant to the user's areas of interest. Furthermore, the reception desk can suggest the most suitable operations, taking into account the user's current projects and areas of interest. This allows for the prioritization of highly relevant operations by filtering them based on the current project and areas of interest.

[0096] The reception desk can estimate the user's emotions and adjust the timing of inputs based on those emotions. For example, if the user is stressed, the timing of inputs can be delayed to allow the user to relax. Conversely, if the user is relaxed, the reception desk can speed up the input timing to allow for more efficient work. Furthermore, if the user is in a hurry, the reception desk can optimize the input timing to allow for quick completion of the task. In this way, adjusting the timing of inputs according to the user's emotions enables more appropriate operation.

[0097] The generation unit can estimate the user's emotions and adjust the code generation method based on the estimated emotions. For example, if the user is relaxed, it will generate detailed code. The generation unit can also generate concise code if the user is in a hurry. Furthermore, if the user is stressed, it can generate code with fewer errors. By adjusting the code generation method according to the user's emotions, more appropriate code is generated.

[0098] The execution unit can estimate the user's emotions and adjust how the code is executed based on those emotions. For example, if the user is relaxed, it can display detailed execution steps. If the user is in a hurry, the execution unit can display concise execution steps. Furthermore, if the user is stressed, the execution unit can display execution steps that minimize errors. By adjusting how the code is executed according to the user's emotions, more appropriate execution becomes possible.

[0099] The sharing function can estimate the user's emotions and adjust the sharing method based on those emotions. For example, if the user is relaxed, it can provide detailed sharing instructions. If the user is in a hurry, it can provide concise sharing instructions. Furthermore, if the user is stressed, it can provide sharing instructions with fewer errors. By adjusting the sharing method according to the user's emotions, more appropriate sharing becomes possible.

[0100] The reception desk can estimate the user's emotions and determine the priority of input operations based on those emotions. For example, if the user is stressed, it will postpone less important operations. Conversely, if the user is relaxed, the reception desk can prioritize highly important operations. Furthermore, if the user is in a hurry, the reception desk can prioritize the most important operations. By prioritizing operations according to the user's emotions, a more appropriate user experience can be achieved.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception unit inputs user actions. User actions include clicks, taps, and voice commands. The reception unit can accept text and voice input, record user actions, and input that information into the generating AI. For example, like the macro function in Excel, it can record mouse and keyboard movements and input that information into the generating AI. Step 2: The generation unit uses a generation AI to analyze and code the operations entered by the reception unit. The generation AI uses natural language processing models such as GPT-4 and Gemini to understand user operations and express them as a program. The generation unit records the operation of entering data into a specific cell and generates code to reproduce that operation. The generation unit can also analyze user operations and generate the optimal code. Step 3: The execution unit executes the code generated by the generation unit. The execution unit can execute the generated code, such as Python code or JavaScript code. By executing the generated code, the execution unit automates user operations. For example, automating the task of entering the same data every day can significantly reduce the user's working time. The execution unit can also manage the execution environment and execution procedure when executing the generated code. Step 4: The sharing department shares the generated code company-wide. The sharing department can share the generated code company-wide using a sharing platform such as Google Drive or SharePoint. This allows other employees to automate the same tasks. For example, by automating the creation of a specific report and sharing the code with all employees, everyone can perform the same task efficiently. The sharing department can also manage access permissions using the sharing platform.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for 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. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. 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 (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. 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 fine-tuned model that outputs inference results from prompts that do not contain instructions, in which 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out 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 carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises 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.

[0110] 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.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0112] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] 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.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an 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.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] 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 for 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.

[0120] The data generation model 58 is a so-called generative AI. An example of a 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 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 such as audio data, text data, and image data. 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 fine-tuned model that outputs inference results from prompts that do not contain instructions, in which 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 (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises 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.

[0126] The headset terminal 314 includes 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 a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0128] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] 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.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] 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.

[0133] 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 specific program 60 on the RAM 48. The specific processing is realized 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 a data generation model 58 and an 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.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] 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 for 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.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 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 such as audio data, text data, and image data. 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 fine-tuned model that outputs inference results from prompts that do not contain instructions, in which 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 (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, 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.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises 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.

[0142] 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 a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0144] 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 area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] 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.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] 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.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also 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.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] 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 for 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.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 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 such as audio data, text data, and image data. 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 fine-tuned model that outputs inference results from prompts that do not contain instructions, in which 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 (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] 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, which is 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.

[0157] Figure 9 shows the 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. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, 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.

[0158] 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 calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] 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, and 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, and 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 system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] 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 "reassured," "calm," and "confident" have similar emotion values.

[0163] 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, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given 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 specific processing according to the specific processing program 56.

[0165] 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.

[0166] 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, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] 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.

[0168] The hardware resource that performs 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 that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine 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 deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect 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 technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] 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 to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where the user inputs their actions, A generation unit analyzes and codes the operations input by the reception unit, An execution unit that executes the code generated by the generation unit, The system comprises a sharing unit that shares the code generated by the execution unit with the entire system. A system characterized by the following features.

2. The aforementioned reception unit is Enter a specific prompt or action. The system according to feature 1.

3. The generating unit is The AI ​​generates the input operations and codes them. The system according to feature 1.

4. The execution unit is, Execute the generated code. The system according to feature 1.

5. The aforementioned shared portion is, Share the generated code with everyone. The system according to feature 1.

6. The aforementioned shared portion is, Manage access permissions using a shared platform The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputs based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past operation history and select the optimal input method. The system according to feature 1.

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