system

Abstract

The system according to this embodiment aims to analyze voice commands and efficiently execute actions. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and an execution unit. The reception unit receives voice commands. The analysis unit analyzes the voice commands received by the reception unit. The execution unit performs actions based on the results analyzed by the analysis unit.

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

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, operations using voice commands are not performed efficiently enough, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze voice commands and execute actions efficiently. [[ID=4,0]]

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and an execution unit. The reception unit receives voice commands. The analysis unit analyzes the voice commands received by the reception unit. The execution unit executes an action based on the result analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can analyze voice commands and efficiently execute actions. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (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 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) An AI personal assistant system according to an embodiment of the present invention is a system that utilizes speech recognition technology to enable users to quickly manage schedules, process emails, add tasks, arrange meetings, and more using voice commands. A major advantage of this system is that it can be operated hands-free. The AI ​​personal assistant system operates when a user issues a voice command. For example, the user might issue a command such as, "Schedule tomorrow's meeting for 3pm." This voice command is input to the AI ​​personal assistant. Next, the AI ​​personal assistant analyzes the voice command. The AI ​​analyzes the voice command using natural language processing technology and understands the user's intent. For example, in response to the command, "Schedule tomorrow's meeting for 3pm," the AI ​​opens a schedule management app and sets the meeting for the specified time. Furthermore, the AI ​​personal assistant learns the user's work patterns. This allows it to understand the commands and schedule patterns that the user frequently uses, enabling optimal scheduling and email filtering. For example, if a user schedules a regular meeting every Monday, the AI ​​automatically learns this pattern and suggests the regular meeting for subsequent Mondays. This system allows users to instantly manage their schedules, check and reply to emails, manage tasks, arrange meetings, and set reminders without using their hands. This enables users to manage multiple tasks simultaneously and respond quickly even during meetings or while out of the office. For example, if a user issues a voice command such as "Email me the materials for the next meeting" while out, the AI ​​personal assistant analyzes the command and sends the specified materials via email. Similarly, if a user issues a voice command such as "Add today's tasks," the AI ​​opens a task management app and adds the specified tasks. In this way, the AI ​​personal assistant enables users to perform tasks quickly using voice commands, significantly improving work efficiency and productivity. As a result, the AI ​​personal assistant system allows users to perform their tasks quickly and efficiently.

[0029] The AI ​​personal assistant system according to this embodiment comprises a reception unit, an analysis unit, and an execution unit. The reception unit receives voice commands uttered by the user. For example, the reception unit can receive a voice command such as "Schedule tomorrow's meeting for 3pm." The reception unit can convert voice commands into text data using speech recognition technology. For example, the reception unit analyzes voice commands using a speech recognition algorithm and converts them into text data. The analysis unit analyzes the voice commands received by the reception unit. The analysis unit analyzes voice commands using natural language processing technology and understands the user's intent. For example, the analysis unit analyzes the voice command "Schedule tomorrow's meeting for 3pm" and understands the user's intent to schedule a meeting. The analysis unit analyzes the content of the voice command and determines the appropriate action. For example, the analysis unit opens a schedule management app and sets the meeting at the specified time. The execution unit executes actions based on the results analyzed by the analysis unit. The execution unit can perform actions such as schedule management, email processing, task addition, and meeting arrangement. For example, the execution unit can open a schedule management app and set up a meeting at a specified time. It can also open an email app and send a specified email. Furthermore, it can open a task management app and add a specified task. In this way, the AI ​​personal assistant system according to the embodiment allows the user to perform their tasks quickly by receiving, analyzing, and executing user voice commands.

[0030] The reception unit receives voice commands from users. For example, the reception unit can receive a voice command such as, "Schedule tomorrow's meeting for 3 PM." The reception unit can convert voice commands into text data using speech recognition technology. Specifically, the reception unit uses a high-precision speech recognition algorithm to analyze the user's voice in real time and convert the voice signal into text data. This speech recognition algorithm incorporates noise cancellation technology, which removes ambient noise and allows for clear recognition of the user's voice. Furthermore, the speech recognition algorithm has the ability to learn differences in the user's pronunciation and accent, improving recognition accuracy with each use. After converting the voice command into text data, the reception unit sends that data to the analysis unit. This allows the reception unit to accurately receive the user's voice commands and smoothly pass them on to the next processing step.

[0031] The analysis unit analyzes voice commands received by the reception unit. The analysis unit uses natural language processing (NLP) technology to analyze voice commands and understand the user's intent. Specifically, the analysis unit analyzes the text data of the voice commands using a natural language processing (NLP) algorithm to understand the grammatical structure and meaning. This NLP algorithm is pre-trained with a large amount of data to accurately grasp the user's intent while considering the context. For example, when analyzing the voice command "Schedule tomorrow's meeting for 3pm," the analysis unit extracts the keywords "tomorrow," "meeting," "3pm," and "schedule," and understands the relationships between these keywords. After understanding the user's intent, the analysis unit determines the appropriate action. For example, it might decide to open a scheduling app and schedule a meeting at the specified time. The analysis unit can also suggest more personalized actions by considering the user's past command history and individual settings. This allows the analysis unit to accurately understand the user's voice commands and determine the appropriate action.

[0032] The execution unit performs actions based on the results analyzed by the analysis unit. The execution unit can perform actions such as schedule management, email processing, task addition, and meeting arrangements. Specifically, the execution unit opens a schedule management app and sets a meeting at a specified time, following instructions received from the analysis unit. The execution unit uses the schedule management app's API to input and save meeting details. The execution unit can also open an email app and send a specified email. For example, if a user instructs "Send a meeting invitation email," the execution unit uses the email app's API to create and send a meeting invitation email to the specified recipient. Furthermore, the execution unit can open a task management app and add a specified task. For example, if a user instructs "Add a task to a new project," the execution unit uses the task management app's API to create a new task and add it to the project. The execution unit is familiar with the APIs and interfaces of each application and can operate them seamlessly to perform these actions quickly and accurately. As a result, the execution unit can quickly perform various actions based on the user's voice commands, significantly improving the user's work efficiency.

[0033] The learning unit can learn the user's work patterns. For example, if a user schedules a regular meeting every Monday, the learning unit can learn this pattern and suggest the meeting for subsequent Mondays. The learning unit can use AI to learn the user's work patterns. For example, the learning unit can input the user's schedule data into the AI, which can then analyze the data to learn the work patterns. This allows the learning unit to optimize scheduling and email filtering by learning the user's work patterns.

[0034] The filtering unit can filter emails. For example, the filtering unit can filter out spam emails and prioritize the processing of important emails. The filtering unit can use AI to filter emails. For instance, the filtering unit can input email data into the AI, which then analyzes the data and filters out spam emails. This allows the filtering unit to prioritize the processing of important emails by filtering them.

[0035] The reception unit can receive voice commands from users. For example, the reception unit can receive a voice command from a user such as, "Schedule tomorrow's meeting for 3 PM." The reception unit can convert voice commands into text data using speech recognition technology. For example, the reception unit analyzes voice commands using a speech recognition algorithm and converts them into text data. This allows the reception unit to operate hands-free by receiving voice commands from users.

[0036] The analysis unit can analyze voice commands and understand the user's intent. For example, the analysis unit can analyze a voice command such as "Schedule tomorrow's meeting for 3pm" and understand the user's intention to schedule a meeting. The analysis unit analyzes the content of the voice command and determines the appropriate action. For example, the analysis unit opens a scheduling app and schedules the meeting at the specified time. The analysis unit can use AI to analyze voice commands. For example, the analysis unit can input a voice command into AI, which will analyze its content and understand the user's intent. This allows the analysis unit to analyze voice commands, understand the user's intent, and then perform the appropriate action.

[0037] The execution unit can perform tasks such as schedule management, email processing, task addition, and meeting arrangements based on the analysis results. For example, the execution unit can open a schedule management app and set a meeting at a specified time. It can also open an email app and send a specified email. Furthermore, it can open a task management app and add a specified task. The execution unit can use AI to perform actions based on the analysis results. For example, the execution unit can input the analysis results into AI, which can then perform actions based on those results. This allows the execution unit to streamline the user's work by performing actions based on the analysis results.

[0038] The reception unit can analyze the user's past voice command history and select the optimal reception method. For example, the reception unit can prioritize receiving voice commands that the user has frequently used in the past. Furthermore, the reception unit can predict commands to be used during specific time periods based on the user's past voice command history and adjust the reception method accordingly. In addition, the reception unit can analyze the user's past voice command history and select the optimal voice recognition algorithm. Thus, the reception unit can select the optimal reception method by analyzing the user's past voice command history.

[0039] The reception system can filter voice commands based on the user's current situation and areas of interest. For example, if the user is in a meeting, the reception system can only receive voice commands related to the meeting. Similarly, if the user is out of the office, the reception system can prioritize receiving voice commands related to their location. Furthermore, the reception system can filter and receive relevant voice commands based on the user's areas of interest. This allows the reception system to prioritize receiving highly relevant commands by filtering them based on the user's current situation and areas of interest.

[0040] The reception unit can prioritize receiving voice commands that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit can prioritize receiving voice commands related to that location. Furthermore, if the user is on the move, the reception unit can prioritize receiving voice commands related to their destination. In addition, the reception unit can filter and receive highly relevant voice commands based on the user's current location. This allows the reception unit to prioritize receiving highly relevant voice commands by considering the user's geographical location.

[0041] The reception unit can analyze the user's social media activity when receiving a voice command and receive relevant commands. For example, the reception unit can prioritize receiving relevant voice commands based on what the user has mentioned on social media. Furthermore, the reception unit can analyze the user's current interests from their social media activity and receive relevant voice commands. In addition, the reception unit can filter and receive relevant voice commands based on the content of the user's social media posts. This allows the reception unit to prioritize receiving relevant voice commands by analyzing the user's social media activity.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice command during analysis. For example, the analysis unit can perform a detailed analysis for voice commands of high importance. Conversely, the analysis unit can perform a simplified analysis for voice commands of low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the voice command. This enables efficient analysis by allowing the analysis unit to adjust the level of detail according to the importance of the voice command.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the voice command during analysis. For example, the analysis unit can apply a schedule analysis algorithm to voice commands related to schedule management. It can also apply an email analysis algorithm to voice commands related to email processing. Furthermore, it can apply a task analysis algorithm to voice commands related to task addition. This allows the analysis unit to perform more accurate analysis by applying the appropriate analysis algorithm according to the category of the voice command.

[0044] The analysis unit can determine the priority of analysis based on the timing of voice command submissions during the analysis process. For example, the analysis unit can prioritize the analysis of voice commands submitted earlier. Conversely, it can postpone the analysis of voice commands submitted later. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission timing. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the timing of voice command submissions.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the voice commands during analysis. For example, the analysis unit can prioritize the analysis of highly relevant voice commands. Furthermore, it can postpone the analysis of less relevant voice commands. In addition, the analysis unit can dynamically adjust the order of analysis according to the relevance of the voice commands. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the voice commands.

[0046] The execution unit can analyze the user's past behavior history during execution to select the optimal execution method. For example, the execution unit can prioritize execution methods that the user has frequently used in the past. Furthermore, the execution unit can select the optimal execution method for a specific time period based on the user's past behavior history. In addition, the execution unit can analyze the user's past behavior history to select the optimal execution algorithm. Thus, the execution unit can select the optimal execution method by analyzing the user's past behavior history.

[0047] The execution unit can customize the execution methods based on the user's current situation during execution. For example, if the user is in a meeting, the execution unit can customize the execution methods related to the meeting. Similarly, if the user is out of the office, the execution unit can customize the execution methods related to their location. Furthermore, the execution unit can customize the optimal execution method based on the user's current situation. This allows the execution unit to perform more appropriate execution by customizing the execution methods based on the user's current situation.

[0048] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location. For example, if the user is in a specific location, the execution unit can prioritize selecting an execution method related to that location. Furthermore, if the user is on the move, the execution unit can prioritize selecting an execution method related to their destination. In addition, the execution unit can select the optimal execution method based on the user's current location. Thus, the execution unit can select the optimal execution method by considering the user's geographical location.

[0049] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can propose relevant means of execution based on what the user has mentioned on social media. Furthermore, the execution unit can analyze the user's current interests from their social media activity and propose relevant means of execution. In addition, the execution unit can propose relevant means of execution based on the content of the user's social media posts. Thus, the execution unit can propose relevant means of execution by analyzing the user's social media activity.

[0050] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. Furthermore, the learning unit can optimize algorithms to improve learning accuracy based on past learning data. In addition, the learning unit can select algorithms to improve learning efficiency by referring to past learning data. Thus, the learning unit can optimize its learning algorithm by referring to past learning data.

[0051] The learning unit can weight the training data based on the timing of voice command submissions during training. For example, it can give higher weight to voice commands submitted earlier and lower weight to voice commands submitted later. Furthermore, the learning unit can dynamically adjust the weighting of the training data according to the submission timing. This enables efficient training by weighting the training data based on the timing of voice command submissions.

[0052] The filtering unit can optimize the filtering algorithm by referring to past filtering data during the filtering process. For example, the filtering unit can analyze past filtering data and select the optimal filtering algorithm. Furthermore, the filtering unit can optimize the algorithm to improve filtering accuracy based on past filtering data. In addition, the filtering unit can select an algorithm to improve filtering efficiency by referring to past filtering data. Thus, the filtering unit can optimize the filtering algorithm by referring to past filtering data.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The analysis unit can refer to the user's past behavior history when analyzing the user's voice commands. For example, it can prioritize the analysis of voice commands that the user has frequently used in the past. Furthermore, the analysis unit can predict commands used during specific time periods based on the user's past behavior history and adjust the analysis method accordingly. In addition, the analysis unit can analyze the user's past behavior history and select the optimal analysis algorithm. This allows the analysis unit to perform efficient analysis by referring to the user's past behavior history.

[0055] The reception unit can prioritize receiving highly relevant commands by considering the user's geographical location when receiving user voice commands. For example, if the user is in a specific location, it can prioritize receiving voice commands related to that location. Also, if the user is on the move, it can prioritize receiving voice commands related to their destination. Furthermore, the reception unit can filter and receive highly relevant voice commands based on the user's current location. In this way, the reception unit can prioritize receiving highly relevant voice commands by considering the user's geographical location.

[0056] The analysis unit can adjust the level of detail of voice commands based on their importance. For example, it can perform detailed analysis on high-importance voice commands and simplified analysis on low-importance voice commands. Furthermore, the analysis unit can dynamically adjust the level of detail according to the importance of the voice commands. This allows the analysis unit to perform efficient analysis by adjusting the level of detail according to the importance of the voice commands.

[0057] The execution unit can analyze the user's past behavior history during execution to select the optimal execution method. For example, it can prioritize selecting execution methods that the user has frequently used in the past. Furthermore, the execution unit can select the optimal execution method for a specific time period based on the user's past behavior history. In addition, the execution unit can analyze the user's past behavior history to select the optimal execution algorithm. Thus, the execution unit can select the optimal execution method by analyzing the user's past behavior history.

[0058] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, it can analyze past learning data and select the optimal learning algorithm. Furthermore, the learning unit can optimize algorithms to improve learning accuracy based on past learning data. In addition, the learning unit can select algorithms to improve learning efficiency by referring to past learning data. Thus, the learning unit can optimize its learning algorithm by referring to past learning data.

[0059] The filtering unit can optimize its filtering algorithm by referring to past filtering data during the filtering process. For example, it can analyze past filtering data and select the optimal filtering algorithm. Furthermore, the filtering unit can optimize algorithms to improve filtering accuracy based on past filtering data. In addition, the filtering unit can select algorithms to improve filtering efficiency by referring to past filtering data. Thus, the filtering unit can optimize its filtering algorithm by referring to past filtering data.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives voice commands from the user. For example, the reception desk can receive a voice command from the user such as, "Schedule tomorrow's meeting for 3pm." The reception desk can use speech recognition technology to convert the voice command into text data. Step 2: The analysis unit analyzes the voice command received by the reception unit. The analysis unit uses natural language processing technology to analyze the voice command and understand the user's intent. For example, the analysis unit analyzes the voice command, "Schedule tomorrow's meeting for 3pm," and understands the user's intention to schedule a meeting. The analysis unit analyzes the content of the voice command and determines the appropriate action. Step 3: The execution unit performs actions based on the results analyzed by the analysis unit. The execution unit can perform actions such as schedule management, email processing, task addition, and meeting arrangements. For example, the execution unit can open a schedule management app and set up a meeting at a specified time. It can also open an email app and send a specified email. Furthermore, it can open a task management app and add a specified task.

[0062] (Example of form 2) An AI personal assistant system according to an embodiment of the present invention is a system that utilizes speech recognition technology to enable users to quickly manage schedules, process emails, add tasks, arrange meetings, and more using voice commands. A major advantage of this system is that it can be operated hands-free. The AI ​​personal assistant system operates when a user issues a voice command. For example, the user might issue a command such as, "Schedule tomorrow's meeting for 3pm." This voice command is input to the AI ​​personal assistant. Next, the AI ​​personal assistant analyzes the voice command. The AI ​​analyzes the voice command using natural language processing technology and understands the user's intent. For example, in response to the command, "Schedule tomorrow's meeting for 3pm," the AI ​​opens a schedule management app and sets the meeting for the specified time. Furthermore, the AI ​​personal assistant learns the user's work patterns. This allows it to understand the commands and schedule patterns that the user frequently uses, enabling optimal scheduling and email filtering. For example, if a user schedules a regular meeting every Monday, the AI ​​automatically learns this pattern and suggests the regular meeting for subsequent Mondays. This system allows users to instantly manage their schedules, check and reply to emails, manage tasks, arrange meetings, and set reminders without using their hands. This enables users to manage multiple tasks simultaneously and respond quickly even during meetings or while out of the office. For example, if a user issues a voice command such as "Email me the materials for the next meeting" while out, the AI ​​personal assistant analyzes the command and sends the specified materials via email. Similarly, if a user issues a voice command such as "Add today's tasks," the AI ​​opens a task management app and adds the specified tasks. In this way, the AI ​​personal assistant enables users to perform tasks quickly using voice commands, significantly improving work efficiency and productivity. As a result, the AI ​​personal assistant system allows users to perform their tasks quickly and efficiently.

[0063] The AI ​​personal assistant system according to this embodiment comprises a reception unit, an analysis unit, and an execution unit. The reception unit receives voice commands uttered by the user. For example, the reception unit can receive a voice command such as "Schedule tomorrow's meeting for 3pm." The reception unit can convert voice commands into text data using speech recognition technology. For example, the reception unit analyzes voice commands using a speech recognition algorithm and converts them into text data. The analysis unit analyzes the voice commands received by the reception unit. The analysis unit analyzes voice commands using natural language processing technology and understands the user's intent. For example, the analysis unit analyzes the voice command "Schedule tomorrow's meeting for 3pm" and understands the user's intent to schedule a meeting. The analysis unit analyzes the content of the voice command and determines the appropriate action. For example, the analysis unit opens a schedule management app and sets the meeting at the specified time. The execution unit executes actions based on the results analyzed by the analysis unit. The execution unit can perform actions such as schedule management, email processing, task addition, and meeting arrangement. For example, the execution unit can open a schedule management app and set up a meeting at a specified time. It can also open an email app and send a specified email. Furthermore, it can open a task management app and add a specified task. In this way, the AI ​​personal assistant system according to the embodiment allows the user to perform their tasks quickly by receiving, analyzing, and executing user voice commands.

[0064] The reception unit receives voice commands from users. For example, the reception unit can receive a voice command such as, "Schedule tomorrow's meeting for 3 PM." The reception unit can convert voice commands into text data using speech recognition technology. Specifically, the reception unit uses a high-precision speech recognition algorithm to analyze the user's voice in real time and convert the voice signal into text data. This speech recognition algorithm incorporates noise cancellation technology, which removes ambient noise and allows for clear recognition of the user's voice. Furthermore, the speech recognition algorithm has the ability to learn differences in the user's pronunciation and accent, improving recognition accuracy with each use. After converting the voice command into text data, the reception unit sends that data to the analysis unit. This allows the reception unit to accurately receive the user's voice commands and smoothly pass them on to the next processing step.

[0065] The analysis unit analyzes voice commands received by the reception unit. The analysis unit uses natural language processing (NLP) technology to analyze voice commands and understand the user's intent. Specifically, the analysis unit analyzes the text data of the voice commands using a natural language processing (NLP) algorithm to understand the grammatical structure and meaning. This NLP algorithm is pre-trained with a large amount of data to accurately grasp the user's intent while considering the context. For example, when analyzing the voice command "Schedule tomorrow's meeting for 3pm," the analysis unit extracts the keywords "tomorrow," "meeting," "3pm," and "schedule," and understands the relationships between these keywords. After understanding the user's intent, the analysis unit determines the appropriate action. For example, it might decide to open a scheduling app and schedule a meeting at the specified time. The analysis unit can also suggest more personalized actions by considering the user's past command history and individual settings. This allows the analysis unit to accurately understand the user's voice commands and determine the appropriate action.

[0066] The execution unit performs actions based on the results analyzed by the analysis unit. The execution unit can perform actions such as schedule management, email processing, task addition, and meeting arrangements. Specifically, the execution unit opens a schedule management app and sets a meeting at a specified time, following instructions received from the analysis unit. The execution unit uses the schedule management app's API to input and save meeting details. The execution unit can also open an email app and send a specified email. For example, if a user instructs "Send a meeting invitation email," the execution unit uses the email app's API to create and send a meeting invitation email to the specified recipient. Furthermore, the execution unit can open a task management app and add a specified task. For example, if a user instructs "Add a task to a new project," the execution unit uses the task management app's API to create a new task and add it to the project. The execution unit is familiar with the APIs and interfaces of each application and can operate them seamlessly to perform these actions quickly and accurately. As a result, the execution unit can quickly perform various actions based on the user's voice commands, significantly improving the user's work efficiency.

[0067] The learning unit can learn the user's work patterns. For example, if a user schedules a regular meeting every Monday, the learning unit can learn this pattern and suggest the meeting for subsequent Mondays. The learning unit can use AI to learn the user's work patterns. For example, the learning unit can input the user's schedule data into the AI, which can then analyze the data to learn the work patterns. This allows the learning unit to optimize scheduling and email filtering by learning the user's work patterns.

[0068] The filtering unit can filter emails. For example, the filtering unit can filter out spam emails and prioritize the processing of important emails. The filtering unit can use AI to filter emails. For instance, the filtering unit can input email data into the AI, which then analyzes the data and filters out spam emails. This allows the filtering unit to prioritize the processing of important emails by filtering them.

[0069] The reception unit can receive voice commands from users. For example, the reception unit can receive a voice command from a user such as, "Schedule tomorrow's meeting for 3 PM." The reception unit can convert voice commands into text data using speech recognition technology. For example, the reception unit analyzes voice commands using a speech recognition algorithm and converts them into text data. This allows the reception unit to operate hands-free by receiving voice commands from users.

[0070] The analysis unit can analyze voice commands and understand the user's intent. For example, the analysis unit can analyze a voice command such as "Schedule tomorrow's meeting for 3pm" and understand the user's intention to schedule a meeting. The analysis unit analyzes the content of the voice command and determines the appropriate action. For example, the analysis unit opens a scheduling app and schedules the meeting at the specified time. The analysis unit can use AI to analyze voice commands. For example, the analysis unit can input a voice command into AI, which will analyze its content and understand the user's intent. This allows the analysis unit to analyze voice commands, understand the user's intent, and then perform the appropriate action.

[0071] The execution unit can perform tasks such as schedule management, email processing, task addition, and meeting arrangements based on the analysis results. For example, the execution unit can open a schedule management app and set a meeting at a specified time. It can also open an email app and send a specified email. Furthermore, it can open a task management app and add a specified task. The execution unit can use AI to perform actions based on the analysis results. For example, the execution unit can input the analysis results into AI, which can then perform actions based on those results. This allows the execution unit to streamline the user's work by performing actions based on the analysis results.

[0072] The reception unit can estimate the user's emotions and adjust the timing of voice command reception based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the timing of voice command reception and wait until the user calms down. Conversely, if the user is relaxed, the reception unit can speed up the timing of voice command reception and respond quickly. Furthermore, if the user is in a hurry, the reception unit can make the timing of voice command reception instantaneous and start processing quickly. 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. This allows the reception unit to receive voice commands at a more appropriate time by adjusting the timing of voice command reception according to the user's emotions.

[0073] The reception unit can analyze the user's past voice command history and select the optimal reception method. For example, the reception unit can prioritize receiving voice commands that the user has frequently used in the past. Furthermore, the reception unit can predict commands to be used during specific time periods based on the user's past voice command history and adjust the reception method accordingly. In addition, the reception unit can analyze the user's past voice command history and select the optimal voice recognition algorithm. Thus, the reception unit can select the optimal reception method by analyzing the user's past voice command history.

[0074] The reception system can filter voice commands based on the user's current situation and areas of interest. For example, if the user is in a meeting, the reception system can only receive voice commands related to the meeting. Similarly, if the user is out of the office, the reception system can prioritize receiving voice commands related to their location. Furthermore, the reception system can filter and receive relevant voice commands based on the user's areas of interest. This allows the reception system to prioritize receiving highly relevant commands by filtering them based on the user's current situation and areas of interest.

[0075] The reception system can estimate the user's emotions and determine the priority of voice commands to receive based on the estimated emotions. For example, if the user is stressed, the reception system can prioritize receiving high-priority voice commands. If the user is relaxed, the reception system can receive all voice commands equally. Furthermore, if the user is in a hurry, the reception system can prioritize receiving voice commands that require a quick response. 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. This allows the reception system to prioritize important commands by determining the priority of voice commands according to the user's emotions.

[0076] The reception unit can prioritize receiving voice commands that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit can prioritize receiving voice commands related to that location. Furthermore, if the user is on the move, the reception unit can prioritize receiving voice commands related to their destination. In addition, the reception unit can filter and receive highly relevant voice commands based on the user's current location. This allows the reception unit to prioritize receiving highly relevant voice commands by considering the user's geographical location.

[0077] The reception unit can analyze the user's social media activity when receiving a voice command and receive relevant commands. For example, the reception unit can prioritize receiving relevant voice commands based on what the user has mentioned on social media. Furthermore, the reception unit can analyze the user's current interests from their social media activity and receive relevant voice commands. In addition, the reception unit can filter and receive relevant voice commands based on the content of the user's social media posts. This allows the reception unit to prioritize receiving relevant voice commands by analyzing the user's social media activity.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simple and easy-to-understand presentation. If the user is relaxed, the analysis unit can use a presentation that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can use a concise presentation that gets straight to the point. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the analysis unit to provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice command during analysis. For example, the analysis unit can perform a detailed analysis for voice commands of high importance. Conversely, the analysis unit can perform a simplified analysis for voice commands of low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the voice command. This enables efficient analysis by allowing the analysis unit to adjust the level of detail according to the importance of the voice command.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the voice command during analysis. For example, the analysis unit can apply a schedule analysis algorithm to voice commands related to schedule management. It can also apply an email analysis algorithm to voice commands related to email processing. Furthermore, it can apply a task analysis algorithm to voice commands related to task addition. This allows the analysis unit to perform more accurate analysis by applying the appropriate analysis algorithm according to the category of the voice command.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. 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. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions.

[0082] The analysis unit can determine the priority of analysis based on the timing of voice command submissions during the analysis process. For example, the analysis unit can prioritize the analysis of voice commands submitted earlier. Conversely, it can postpone the analysis of voice commands submitted later. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission timing. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the timing of voice command submissions.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the voice commands during analysis. For example, the analysis unit can prioritize the analysis of highly relevant voice commands. Furthermore, it can postpone the analysis of less relevant voice commands. In addition, the analysis unit can dynamically adjust the order of analysis according to the relevance of the voice commands. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the voice commands.

[0084] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated emotions. For example, if the user is stressed, the execution unit can select a simple and quick execution method. If the user is relaxed, the execution unit can select an execution method that includes detailed steps. Furthermore, if the user is in a hurry, the execution unit can select a method that can be executed in the shortest time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the execution unit to perform more appropriate actions by adjusting the execution method according to the user's emotions.

[0085] The execution unit can analyze the user's past behavior history during execution to select the optimal execution method. For example, the execution unit can prioritize execution methods that the user has frequently used in the past. Furthermore, the execution unit can select the optimal execution method for a specific time period based on the user's past behavior history. In addition, the execution unit can analyze the user's past behavior history to select the optimal execution algorithm. Thus, the execution unit can select the optimal execution method by analyzing the user's past behavior history.

[0086] The execution unit can customize the execution methods based on the user's current situation during execution. For example, if the user is in a meeting, the execution unit can customize the execution methods related to the meeting. Similarly, if the user is out of the office, the execution unit can customize the execution methods related to their location. Furthermore, the execution unit can customize the optimal execution method based on the user's current situation. This allows the execution unit to perform more appropriate execution by customizing the execution methods based on the user's current situation.

[0087] The execution unit can estimate the user's emotions and determine the priority of executions based on the estimated emotions. For example, if the user is stressed, the execution unit can prioritize high-priority executions. If the user is relaxed, the execution unit can distribute all executions evenly. Furthermore, if the user is in a hurry, the execution unit can prioritize executions that require a quick response. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the execution unit to prioritize important executions by determining the priority of executions according to the user's emotions.

[0088] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location. For example, if the user is in a specific location, the execution unit can prioritize selecting an execution method related to that location. Furthermore, if the user is on the move, the execution unit can prioritize selecting an execution method related to their destination. In addition, the execution unit can select the optimal execution method based on the user's current location. Thus, the execution unit can select the optimal execution method by considering the user's geographical location.

[0089] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can propose relevant means of execution based on what the user has mentioned on social media. Furthermore, the execution unit can analyze the user's current interests from their social media activity and propose relevant means of execution. In addition, the execution unit can propose relevant means of execution based on the content of the user's social media posts. Thus, the execution unit can propose relevant means of execution by analyzing the user's social media activity.

[0090] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit can select training data that helps reduce stress. If the user is relaxed, the learning unit can select training data that helps maintain that relaxed state. Furthermore, if the user is in a hurry, the learning unit can select data that allows for rapid learning. 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. This allows the learning unit to select training data according to the user's emotions, enabling more appropriate learning.

[0091] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. Furthermore, the learning unit can optimize algorithms to improve learning accuracy based on past learning data. In addition, the learning unit can select algorithms to improve learning efficiency by referring to past learning data. Thus, the learning unit can optimize its learning algorithm by referring to past learning data.

[0092] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency to alleviate the burden. Conversely, if the user is relaxed, the learning unit can increase the learning frequency to improve efficiency. Furthermore, if the user is in a hurry, the learning unit can adjust the learning frequency to accelerate learning. 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. This allows the learning unit to adjust the learning frequency according to the user's emotions, enabling more appropriate learning.

[0093] The learning unit can weight the training data based on the timing of voice command submissions during training. For example, it can give higher weight to voice commands submitted earlier and lower weight to voice commands submitted later. Furthermore, the learning unit can dynamically adjust the weighting of the training data according to the submission timing. This enables efficient training by weighting the training data based on the timing of voice command submissions.

[0094] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated emotions. For example, if the user is stressed, the filtering unit can filter only high-priority emails. If the user is relaxed, the filtering unit can filter all emails equally. Furthermore, if the user is in a hurry, the filtering unit can prioritize filtering emails that require immediate attention. 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. This allows the filtering unit to adjust the filtering criteria according to the user's emotions, enabling more appropriate filtering.

[0095] The filtering unit can optimize the filtering algorithm by referring to past filtering data during the filtering process. For example, the filtering unit can analyze past filtering data and select the optimal filtering algorithm. Furthermore, the filtering unit can optimize the algorithm to improve filtering accuracy based on past filtering data. In addition, the filtering unit can select an algorithm to improve filtering efficiency by referring to past filtering data. Thus, the filtering unit can optimize the filtering algorithm by referring to past filtering data.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] When receiving a user's voice command, the reception system can analyze the user's voice tone and speed to estimate the user's emotions. For example, if a user is in a hurry, their voice tone is often high and their speed is fast. The reception system can detect these vocal characteristics and estimate that the user is in a hurry. Conversely, if a user is relaxed, their voice tone is often low and their speed is slow. The reception system can detect these vocal characteristics and estimate that the user is relaxed. Furthermore, if a user is stressed, their voice tone is often unstable and their speed fluctuates. The reception system can detect these vocal characteristics and estimate that the user is stressed. As a result, the reception system can adjust how it receives voice commands according to the user's emotions.

[0098] The analysis unit can refer to the user's past behavior history when analyzing the user's voice commands. For example, it can prioritize the analysis of voice commands that the user has frequently used in the past. Furthermore, the analysis unit can predict commands used during specific time periods based on the user's past behavior history and adjust the analysis method accordingly. In addition, the analysis unit can analyze the user's past behavior history and select the optimal analysis algorithm. This allows the analysis unit to perform efficient analysis by referring to the user's past behavior history.

[0099] The execution unit can estimate the user's emotions and customize the execution method based on those emotions. For example, if the user is stressed, the execution unit can select a simple and quick execution method. If the user is relaxed, the execution unit can select an execution method that includes detailed instructions. Furthermore, if the user is in a hurry, the execution unit can select a method that can be completed in the shortest time. In this way, the execution unit can perform more appropriate actions by customizing the execution method according to the user's emotions.

[0100] The learning unit can estimate the user's emotions while learning the user's work patterns and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency to alleviate the burden. Conversely, if the user is relaxed, the learning unit can increase the learning frequency to improve efficiency. Furthermore, if the user is in a hurry, the learning unit can adjust the learning frequency to accelerate the learning process. In this way, the learning unit can enable more appropriate learning by adjusting the learning frequency according to the user's emotions.

[0101] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on those emotions. For example, if the user is stressed, the filtering unit can filter only high-priority emails. If the user is relaxed, the filtering unit can filter all emails equally. Furthermore, if the user is in a hurry, the filtering unit can prioritize filtering emails that require immediate attention. In this way, the filtering unit can perform more appropriate filtering by adjusting the filtering criteria according to the user's emotions.

[0102] The reception unit can prioritize receiving highly relevant commands by considering the user's geographical location when receiving user voice commands. For example, if the user is in a specific location, it can prioritize receiving voice commands related to that location. Also, if the user is on the move, it can prioritize receiving voice commands related to their destination. Furthermore, the reception unit can filter and receive highly relevant voice commands based on the user's current location. In this way, the reception unit can prioritize receiving highly relevant voice commands by considering the user's geographical location.

[0103] The analysis unit can adjust the level of detail of voice commands based on their importance. For example, it can perform detailed analysis on high-importance voice commands and simplified analysis on low-importance voice commands. Furthermore, the analysis unit can dynamically adjust the level of detail according to the importance of the voice commands. This allows the analysis unit to perform efficient analysis by adjusting the level of detail according to the importance of the voice commands.

[0104] The execution unit can analyze the user's past behavior history during execution to select the optimal execution method. For example, it can prioritize selecting execution methods that the user has frequently used in the past. Furthermore, the execution unit can select the optimal execution method for a specific time period based on the user's past behavior history. In addition, the execution unit can analyze the user's past behavior history to select the optimal execution algorithm. Thus, the execution unit can select the optimal execution method by analyzing the user's past behavior history.

[0105] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, it can analyze past learning data and select the optimal learning algorithm. Furthermore, the learning unit can optimize algorithms to improve learning accuracy based on past learning data. In addition, the learning unit can select algorithms to improve learning efficiency by referring to past learning data. Thus, the learning unit can optimize its learning algorithm by referring to past learning data.

[0106] The filtering unit can optimize its filtering algorithm by referring to past filtering data during the filtering process. For example, it can analyze past filtering data and select the optimal filtering algorithm. Furthermore, the filtering unit can optimize algorithms to improve filtering accuracy based on past filtering data. In addition, the filtering unit can select algorithms to improve filtering efficiency by referring to past filtering data. Thus, the filtering unit can optimize its filtering algorithm by referring to past filtering data.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives voice commands from the user. For example, the reception desk can receive a voice command from the user such as, "Schedule tomorrow's meeting for 3pm." The reception desk can use speech recognition technology to convert the voice command into text data. Step 2: The analysis unit analyzes the voice command received by the reception unit. The analysis unit uses natural language processing technology to analyze the voice command and understand the user's intent. For example, the analysis unit analyzes the voice command, "Schedule tomorrow's meeting for 3pm," and understands the user's intention to schedule a meeting. The analysis unit analyzes the content of the voice command and determines the appropriate action. Step 3: The execution unit performs actions based on the results analyzed by the analysis unit. The execution unit can perform actions such as schedule management, email processing, task addition, and meeting arrangements. For example, the execution unit can open a schedule management app and set up a meeting at a specified time. It can also open an email app and send a specified email. Furthermore, it can open a task management app and add a specified task.

[0109] 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.

[0110] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, execution unit, learning unit, and filtering 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 microphone 38B and control unit 46A of the smart device 14 and receives the user's voice command. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the voice command to understand the user's intent. The execution unit is implemented by the control unit 46A of the smart device 14 and performs an action based on the analysis result. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's work patterns. The filtering unit is implemented by the specific processing unit 290 of the data processing unit 12 and filters emails. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, execution unit, learning unit, and filtering unit, is implemented 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 and control unit 46A of the smart glasses 214 and receives the user's voice command. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the voice command to understand the user's intent. The execution unit is implemented by the control unit 46A of the smart glasses 214 and performs an action based on the analysis result. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's work patterns. The filtering unit is implemented by the specific processing unit 290 of the data processing unit 12 and filters emails. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, execution unit, learning unit, and filtering unit, is implemented in 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 and control unit 46A of the headset terminal 314 and receives the user's voice command. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the voice command to understand the user's intent. The execution unit is implemented by the control unit 46A of the headset terminal 314 and performs an action based on the analysis result. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's work patterns. The filtering unit is implemented by the specific processing unit 290 of the data processing unit 12 and filters emails. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, execution unit, learning unit, and filtering unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the robot 414 and receives voice commands from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the voice commands to understand the user's intent. The execution unit is implemented by the control unit 46A of the robot 414 and performs actions based on the analysis results. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's work patterns. The filtering unit is implemented by the specific processing unit 290 of the data processing unit 12 and filters emails. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception area that accepts voice commands, An analysis unit that analyzes voice commands received by the reception unit, The system includes an execution unit that performs an action based on the results of the analysis performed by the analysis unit. A system characterized by the following features. (Note 2) It includes a learning unit that learns the user's work patterns. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a filtering unit for filtering emails. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Accepts user voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze voice commands to understand user intent. The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, Based on the analysis results, schedule management, email processing, task addition, and meeting arrangements are performed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of voice command acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past voice command history and selects the optimal response method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving voice commands, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice commands to accept based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving voice commands, the system prioritizes accepting commands that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a voice command is received, the system analyzes the user's social media activity and accepts relevant commands. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the voice command. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the voice command. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the voice commands were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 19) The execution unit is, It estimates the user's emotions and adjusts the execution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The execution unit is, During execution, the system analyzes the user's past behavior history to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The execution unit is, At runtime, the execution method is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The execution unit is, It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The execution unit is, During execution, the system selects the optimal execution method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The execution unit is, During execution, the system analyzes the user's social media activity and suggests implementation methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned learning unit, During training, the training data is weighted based on when voice commands were submitted. The system described in Appendix 2, characterized by the features described herein. (Note 29) The filtering unit is It estimates the user's sentiment and adjusts the filtering criteria based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 30) The filtering unit is During filtering, the filtering algorithm is optimized by referring to past filtering data. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0181] 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 area that accepts voice commands, An analysis unit that analyzes voice commands received by the reception unit, The system includes an execution unit that performs an action based on the results of the analysis performed by the analysis unit. A system characterized by the following features.

2. It includes a learning unit that learns the user's work patterns. The system according to feature 1.

3. It includes a filtering unit for filtering emails. The system according to feature 1.

4. The aforementioned reception unit is Accepts user voice commands. The system according to feature 1.

5. The aforementioned analysis unit, Analyze voice commands to understand user intent. The system according to feature 1.

6. The execution unit is, Based on the analysis results, schedule management, email processing, task addition, and meeting arrangements are performed. The system according to feature 1.

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

8. The aforementioned reception unit is The system analyzes the user's past voice command history and selects the optimal response method. The system according to feature 1.

9. The aforementioned reception unit is When receiving voice commands, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

Images