System

Abstract

An object of the system according to the embodiment is to generate an appropriate instruction sentence that maximizes the output of a generative AI from a general instruction sentence.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives a general instruction sentence. The generation unit analyzes the general instruction sentence received by the reception unit and generates an appropriate instruction sentence. The providing unit re-inputs the appropriate instruction statement generated by the generating unit to the generated AI to obtain an appropriate output.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating appropriate instructions to effectively utilize generative AI required specialized expertise, making it difficult for general users.

[0005] The system according to the embodiment aims to generate appropriate instructions from mediocre instructions that maximize the output of the generation AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives a mediocre instruction sentence. The generating unit analyzes the mediocre instruction sentence received by the receiving unit and generates an appropriate instruction sentence. The providing unit re-inputs the appropriate instruction sentence generated by the generating unit into the generation AI to obtain an appropriate output. [Effects of the Invention]

[0007] The system according to the embodiment can generate appropriate instructions that maximize the output of the generation AI from mediocre instructions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An optimal prompt translation system according to an embodiment of the present invention is a system that accepts mediocre instructions, analyzes them with a generation AI, generates optimal instructions, and inputs them again into the generation AI to obtain optimal output. The optimal prompt translation system accepts mediocre instructions, analyzes them with a generation AI, and generates optimal instructions, thereby maximizing the output of the generation AI. For example, in the optimal prompt translation system, a user inputs mediocre instructions to the generation AI. For example, the instruction "Please write a report" is input. This instruction is input into the generation AI. Next, the optimal prompt translation system uses the generation AI to analyze the input mediocre instructions and generate optimal instructions. The generation AI understands the intent of the input instruction and generates optimal instructions to maximize the output of the generation AI. For example, in response to the instruction "Please write a report," the optimal instruction is generated: "Please write a detailed report based on a specific analysis." The generated optimal instruction is then input again into the generation AI to obtain optimal output. For example, the generation AI writes a detailed report based on a specific analysis. This allows the optimal prompt translation system to enable anyone to master generative AI and maximize its output. This allows the optimal prompt translation system to provide support for mastering generative AI and maximize its output. This will expand the use of generative AI in a variety of fields, such as creating business reports, writing academic papers, and generating creative content.

[0029] An optimal prompt translation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives a trivial instruction sentence input by a user. Trivial instructions include, but are not limited to, general expressions and vague instructions. The receiving unit receives, for example, an instruction sentence such as "Please write a report." The receiving unit can also support various input methods, such as voice input and text input. The generating unit uses a generation AI to analyze the trivial instruction sentence received by the receiving unit and generate an optimal instruction sentence. The generation AI can analyze the instruction sentence using, for example, a text generation AI (e.g., LLM) and generate the optimal instruction sentence. The generating unit can also use the generation AI to understand the intent of the instruction sentence and generate an optimal instruction sentence to maximize the output of the generation AI. For example, in response to the instruction sentence "Please write a report," the generation AI generates an optimal instruction sentence such as "Please write a detailed report based on a specific analysis." The providing unit re-inputs the optimal instruction sentence generated by the generation unit into the generation AI to obtain an optimal output. The providing unit, for example, re-inputs instructions so that the generation AI creates a detailed report based on a specific analysis. The providing unit can also provide the user with the optimal output generated by the generation AI. For example, the providing unit displays the report created by the generation AI to the user. In this way, the optimal prompt translation system according to the embodiment can convert mediocre instructions into optimal instructions and maximize the output of the generation AI.

[0030] The generation unit can understand the intent of the instruction sentence input by the generation AI and generate an appropriate instruction sentence. The generation unit, for example, uses the generation AI to understand the intent of the input instruction sentence. For example, the generation unit uses natural language processing technology to analyze the context of the instruction sentence and understand its intent. The generation unit can also use the generation AI to develop an algorithm for understanding the intent of the instruction sentence. For example, the generation unit uses context analysis technology to understand the intent of the instruction sentence. Furthermore, the generation unit uses the generation AI to understand the intent and generate an optimal instruction sentence. For example, in response to the instruction sentence "Create a report," the generation unit generates the optimal instruction sentence, "Create a detailed report based on a specific analysis." In this way, the generation AI can understand the intent of the instruction sentence and generate the optimal instruction sentence, thereby maximizing the output of the generation AI.

[0031] The provision unit can maximize the output of the generation AI based on the appropriate instruction sentence re-input to the generation AI. For example, the provision unit re-inputs the optimal instruction sentence generated by the generation unit to the generation AI. For example, the provision unit re-inputs the instruction sentence "Create a detailed report based on a specific analysis" to the generation AI. The provision unit can also provide the optimal output generated by the generation AI to a user. For example, the provision unit displays the report created by the generation AI to the user. Furthermore, the provision unit can develop an algorithm for maximizing the output of the generation AI. For example, the provision unit builds a feedback loop to improve the accuracy of the output of the generation AI. This makes it possible to provide the user with optimal results by maximizing the output of the generation AI.

[0032] The reception unit can input a mediocre instruction sentence input by a user to the generation AI. For example, the reception unit inputs a mediocre instruction sentence input by a user to the generation AI. For example, the reception unit inputs the instruction sentence "write a report" to the generation AI. The reception unit can also support various input methods, such as voice input and text input. Furthermore, the reception unit can adjust the timing of receiving the instruction sentence input to the generation AI. For example, the reception unit estimates the user's emotions and adjusts the timing of receiving the instruction sentence based on the estimated emotions. In this way, the generation AI's processing can be started by inputting the instruction sentence input by the user to the generation AI.

[0033] The generation unit can generate specific instructions based on a specific analysis using the generation AI. The generation unit, for example, uses the generation AI to generate specific instructions based on the specific analysis. For example, the generation unit uses data analysis technology to generate instructions based on the specific analysis. The generation unit can also use the generation AI to generate instructions using pattern recognition technology. For example, the generation unit uses the generation AI to generate instructions based on a specific data pattern. The generation unit can also use the generation AI to generate detailed instructions based on the specific analysis. For example, the generation unit generates the instruction "Create a detailed report based on the specific analysis." In this way, by generating detailed instructions based on the specific analysis, the accuracy of the output of the generation AI can be improved.

[0034] The providing unit can provide the user with an appropriate output generated by the generation AI. For example, the providing unit provides the user with an appropriate output generated by the generation AI. For example, the providing unit displays a report created by the generation AI to the user. The providing unit can also select an optimal display method for providing the output generated by the generation AI to the user. For example, the providing unit estimates the user's emotions and adjusts the display method of the output based on the estimated emotions. Furthermore, the providing unit can establish a feedback loop for providing the user with the output generated by the generation AI. For example, the providing unit improves the display method of the output based on user feedback. This allows the user to use the results of the generation AI by providing the user with the optimal output generated by the generation AI.

[0035] The reception unit can analyze the user's past instruction input history and select an appropriate reception method. The reception unit, for example, analyzes the user's past instruction input history and selects the optimal reception method. For example, the reception unit preferentially receives instruction formats that the user has frequently used in the past. The reception unit can also predict instruction pattern to be used in a specific time period from the user's past input history and adjust the reception method. Furthermore, the reception unit can analyze the content of instruction statements input by the user in the past and automatically complete similar instruction statements. For example, the reception unit automatically generates related instruction statements based on the content of instruction statements input by the user in the past. In this way, the optimal reception method can be provided to the user by analyzing the past instruction input history.

[0036] The reception unit can filter the instruction sentences based on the user's current project or field of interest when receiving the instruction sentences. For example, the reception unit can preferentially receive only instruction sentences related to the user's current project or field of interest when receiving the instruction sentences. For example, the reception unit can preferentially receive only instruction sentences related to the user's current project or field of interest. The reception unit can also filter and receive highly relevant instruction sentences based on the user's field of interest. Furthermore, the reception unit can customize the reception of instruction sentences based on topics in which the user has shown interest in the past. For example, the reception unit preferentially receives instruction sentences related to topics in which the user has shown interest in the past. In this way, by filtering instruction sentences based on the user's current project or field of interest, highly relevant instruction sentences can be preferentially received.

[0037] The reception unit can select an appropriate reception means according to the user's input method when receiving an instruction. For example, the reception unit selects an appropriate reception means according to the user's input method when receiving an instruction. For example, when the user uses voice input, the reception unit receives the instruction using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also receive the instruction using text analysis technology. Furthermore, when the user uses image input, the reception unit can also receive the instruction using image recognition technology. For example, when the user uploads an image, the reception unit analyzes the image and recognizes it as an instruction. In this way, by selecting the optimal reception means according to the user's input method, it is possible to improve the accuracy of receiving instruction messages.

[0038] The reception unit can, when receiving a command, preferentially receive relevant command statements by taking into account the user's geographical location information. For example, when receiving a command, the reception unit preferentially receives relevant command statements by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially receives command statements related to that area. Furthermore, when the user is traveling, the reception unit can preferentially receive command statements related to the user's travel destination. Furthermore, when the user is at home, the reception unit can preferentially receive command statements related to the user's home. For example, the reception unit filters relevant command statements based on the user's geographical location information. In this way, highly relevant command statements can be preferentially received by taking into account the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity when receiving the instruction and receive related instruction messages. For example, the reception unit analyzes the user's social media activity when receiving the instruction and receives related instruction messages. For example, the reception unit can preferentially receive related instruction messages based on information shared by the user on social media. The reception unit can also analyze the user's social media activity and filter and receive related instruction messages. Furthermore, the reception unit can also receive related instruction messages by referring to the activity of the user's friends on social media. For example, the reception unit preferentially receives related instruction messages based on information shared by the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant instruction messages.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an instruction. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving an instruction. For example, the reception unit adjusts the reception method for the instruction based on feedback provided by the user in the past. The reception unit can also preferentially adopt a specific reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's feedback and suggest an optimal reception method. For example, the reception unit improves the reception method for the instruction based on the user's evaluation comments and improvement requests. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0041] The generation unit can adjust the level of detail of the generated directive based on the importance of the directive when generating the directive. For example, the generation unit adjusts the level of detail of the generated directive based on the importance of the directive when generating the directive. For example, the generation unit generates a directive including a detailed explanation for a directive with high importance. The generation unit can also generate a concise directive for a directive with low importance. Furthermore, the generation unit can adjust the length and content of the generated directive based on the importance of the directive. For example, the generation unit evaluates the importance of the directive based on the urgency and scope of the task, and adjusts the level of detail of the directive accordingly. In this way, by adjusting the level of detail of the generated directive based on the importance of the directive, it is possible to generate a directive including a detailed explanation for an important directive.

[0042] The generation unit can apply different generation algorithms depending on the category of the directive when generating the directive. For example, the generation unit applies different generation algorithms depending on the category of the directive when generating the directive. For example, the generation unit applies a business generation algorithm to a business-related directive. The generation unit can also apply an academic generation algorithm to a directive related to an academic paper. Furthermore, the generation unit can apply a creative generation algorithm to a directive related to creative content. For example, the generation unit selects an optimal generation algorithm depending on the category of the directive and generates the directive. In this way, by applying different generation algorithms depending on the category of the directive, more appropriate directives can be generated.

[0043] The generation unit can improve the accuracy of generation when generating a command sentence by referring to the user's past generation results. For example, the generation unit can improve the accuracy of generation when generating a command sentence by referring to the user's past generation results. For example, the generation unit can analyze the results of command sentences generated by the user in the past and improve the accuracy of generation. The generation unit can also extract specific patterns from the user's past generation results and reflect them in the generation algorithm. Furthermore, the generation unit can generate an optimal command sentence based on the user's past generation results. For example, the generation unit can generate a similar command sentence by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0044] The generation unit can determine the priority of generation when generating an instruction statement based on the submission time of the instruction statement. For example, the generation unit determines the priority of generation when generating an instruction statement based on the submission time of the instruction statement. For example, the generation unit generates instruction statements with an approaching deadline with priority. The generation unit can also postpone the generation of instruction statements with a more distant submission time. Furthermore, the generation unit can dynamically adjust the priority of generation according to the submission time. For example, the generation unit determines the priority of instruction statements based on the submission date and time and the degree of urgency. In this way, by determining the priority of generation based on the submission time of the instruction statement, instruction statements with a higher urgency can be generated with priority.

[0045] The generation unit can adjust the order of generation based on the relevance of the directives when generating the directives. For example, the generation unit adjusts the order of generation based on the relevance of the directives when generating the directives. For example, the generation unit generates directives with high relevance with priority. The generation unit can also generate directives with low relevance at a later date. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance of the directives. For example, the generation unit evaluates the relevance of the directives based on the degree of content similarity or related topics, and adjusts the order of generation accordingly. In this way, by adjusting the order of generation based on the relevance of the directives, highly relevant directives can be generated with priority.

[0046] The generation unit can adjust the use of technical terminology when generating an instruction sentence according to the user's level of expertise. For example, the generation unit adjusts the use of technical terminology when generating an instruction sentence according to the user's level of expertise. For example, if the user is an expert, the generation unit generates an instruction sentence that uses a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can generate a concise instruction sentence that avoids technical terminology. Furthermore, the generation unit can adjust the content of the instruction sentence to be generated according to the user's level of expertise. For example, the generation unit evaluates the user's level of expertise based on the user's occupation or past experience, and adjusts the content of the instruction sentence accordingly. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to generate an instruction sentence that is easier to understand.

[0047] The providing unit can select an appropriate display method by referring to the user's past output results when providing output. For example, the providing unit selects an appropriate display method by referring to the user's past output results when providing output. For example, the providing unit provides an optimal display method based on display methods that the user has preferred in the past. The providing unit can also preferentially adopt a specific display method from the user's past output results. Furthermore, the providing unit can analyze the user's past output results and suggest an optimal display method. For example, the providing unit adjusts the display method based on the user's output result history and evaluation results. In this way, the optimal display method can be provided by referring to the user's past output results.

[0048] The providing unit can customize the display content according to the user's current task when providing output. For example, the providing unit customizes the display content according to the user's current task when providing output. For example, the providing unit prioritizes displaying information related to a task that the user is currently performing. The providing unit can also filter and display highly relevant information based on the user's current task. Furthermore, the providing unit can customize the display content based on tasks in which the user has previously shown interest. For example, the providing unit prioritizes displaying information related to tasks in which the user has previously shown interest. In this way, highly relevant information can be provided by customizing the display content according to the user's current task.

[0049] The providing unit can improve the providing method by reflecting user feedback when providing output. For example, the providing unit improves the providing method by reflecting user feedback when providing output. For example, the providing unit adjusts the output display method based on feedback provided by the user. The providing unit can also preferentially adopt a specific display method based on user feedback. Furthermore, the providing unit can analyze user feedback and suggest an optimal display method. For example, the providing unit improves the output display method based on user evaluation comments and improvement requests. In this way, the providing method can be improved by reflecting user feedback, and more appropriate output can be provided.

[0050] The providing unit can select an appropriate output method by taking into consideration the user's geographical location information when providing output. For example, the providing unit selects an appropriate output method by taking into consideration the user's geographical location information when providing output. For example, when the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, when the user is traveling, the providing unit can prioritize providing information related to the travel destination. Furthermore, when the user is at home, the providing unit can prioritize providing information related to the user's home. For example, the providing unit filters relevant information based on the user's geographical location information. In this way, highly relevant output can be provided by taking into consideration the user's geographical location information.

[0051] The providing unit may provide relevant output by analyzing the user's social media activity when providing output. For example, the providing unit may provide relevant output by analyzing the user's social media activity when providing output. For example, the providing unit may provide relevant output preferentially based on information shared by the user on social media. The providing unit may also analyze the user's social media activity and filter and provide relevant output. Furthermore, the providing unit may provide relevant output by referring to the activity of the user's friends on social media. For example, the providing unit may provide relevant output preferentially based on information shared by the user's friends on social media. In this way, highly relevant output can be provided by analyzing the user's social media activity.

[0052] The providing unit can customize the output providing method by reflecting the user's past feedback when providing output. For example, the providing unit customizes the output providing method by reflecting the user's past feedback when providing output. For example, the providing unit adjusts the output providing method based on feedback provided by the user in the past. The providing unit can also preferentially adopt a specific providing method based on the user's past feedback. Furthermore, the providing unit can analyze the user's feedback and propose an optimal providing method. For example, the providing unit improves the output providing method based on the user's evaluation comments and improvement requests. In this way, the optimal providing method can be provided by reflecting the user's past feedback.

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

[0054] The reception unit can analyze the user's input in real time and provide appropriate feedback even while the user is in the middle of input. For example, if the user is inputting "create a report," the reception unit can infer the user's intention and provide feedback such as "Please specify a specific topic." The reception unit can also point out errors or ambiguities in the input before the user completes the input and prompt the user to correct them. Furthermore, the reception unit can analyze the user's input speed and pattern and provide input assistance. For example, if the user is taking a long time to input, the reception unit can provide an auto-completion function to smoothly proceed with input. In this way, by analyzing the user's input in real time and providing appropriate feedback, it is possible to support the user in inputting instructions more accurately and efficiently.

[0055] The generation unit can learn the user's past command sentence generation history and customize the command sentence based on the user's preferences and tendencies. For example, the generation unit can analyze the expressions and styles that the user has used in the past and generate the command sentence based on the results. The generation unit can also generate optimal command sentences by taking into account the success rate and evaluation of the command sentences that the user has generated in the past. Furthermore, the generation unit can extract specific patterns and trends from the user's past command sentence generation history and generate command sentences based on the patterns and trends. For example, the generation unit can prioritize keywords and phrases that the user has frequently used in the past. In this way, by learning the user's past command sentence generation history, customized command sentences based on the user's preferences and tendencies can be generated.

[0056] The providing unit can optimize the output generated by the generation AI for the user's device and environment before providing it. For example, if the user is using a smartphone, the providing unit adjusts the output to fit the smartphone's screen size and resolution. In addition, if the user is using a voice assistant, the providing unit can also optimize the voice output. Furthermore, if the user is using a specific application or platform, the providing unit can provide the output in a format optimal for that environment. For example, if the user is using presentation software, the providing unit provides the output in slide format. In this way, by optimizing the output generated by the generation AI for the user's device and environment before providing it, the user can use the output more effectively.

[0057] The reception unit supports multiple languages ​​for user input and can accept instructions in different languages. For example, the reception unit translates instructions entered by the user in English into Japanese and inputs the instructions to the generation AI. The reception unit can also translate instructions entered by the user in French into English and input the instructions to the generation AI. Furthermore, the reception unit can automatically detect instructions entered by the user in different languages ​​and translate them into the appropriate language. For example, the reception unit automatically detects instructions entered by the user in Spanish and translates them into English. This allows users to enter instructions in different languages, expanding the scope of use of the generation AI.

[0058] The reception unit can analyze the user's input content and automatically complete related information based on the input content. For example, if the user inputs "create a report," the reception unit automatically searches for and completes related data and materials. Also, if the user inputs "create minutes of a meeting," the reception unit can automatically search for and complete minutes of past meetings and related documents. Furthermore, if the user inputs "create a presentation," the reception unit can automatically search for and complete related templates and slides. This automatically completes related information based on the user's input content, thereby supporting more efficient input of instructions.

[0059] The providing unit can customize and provide the output generated by the generation AI based on the user's past behavioral history. For example, the providing unit adjusts the output based on the format and style that the user has used in the past. The providing unit can also provide optimal output by taking into account the user's evaluation of outputs that he or she has generated in the past. Furthermore, the providing unit can extract specific patterns and trends from the user's past behavioral history and provide output based on them. For example, the providing unit prioritizes keywords and phrases that the user has used frequently in the past. This allows the output to be customized based on the user's past behavioral history, thereby providing more appropriate output.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives a common instruction sentence entered by the user. Common instructions include general expressions and vague instructions. For example, the reception unit receives the instruction sentence "Write a report." The reception unit also supports various input methods, such as voice input and text input. Step 2: The generation unit uses a generation AI to analyze the mediocre instruction received by the reception unit and generate an optimal instruction. The generation AI analyzes the instruction using, for example, a text generation AI (e.g., LLM) and generates an optimal instruction. The generation unit also understands the intent of the instruction and generates an optimal instruction to maximize the output of the generation AI. For example, in response to the instruction "Write a report," the optimal instruction is generated: "Write a detailed report based on a specific analysis." Step 3: The providing unit re-inputs the optimal instruction sentence generated by the generating unit into the generating AI to obtain an optimal output. For example, the providing unit re-inputs the instruction sentence so that the generating AI creates a detailed report based on a specific analysis. The providing unit also provides the optimal output generated by the generating AI to a user. For example, the providing unit displays the report created by the generating AI to the user.

[0062] (Example 2) An optimal prompt translation system according to an embodiment of the present invention is a system that accepts mediocre instructions, analyzes them with a generation AI, generates optimal instructions, and inputs them again into the generation AI to obtain optimal output. The optimal prompt translation system accepts mediocre instructions, analyzes them with a generation AI, and generates optimal instructions, thereby maximizing the output of the generation AI. For example, in the optimal prompt translation system, a user inputs mediocre instructions to the generation AI. For example, the instruction "Please write a report" is input. This instruction is input into the generation AI. Next, the optimal prompt translation system uses the generation AI to analyze the input mediocre instructions and generate optimal instructions. The generation AI understands the intent of the input instruction and generates optimal instructions to maximize the output of the generation AI. For example, in response to the instruction "Please write a report," the optimal instruction is generated: "Please write a detailed report based on a specific analysis." The generated optimal instruction is then input again into the generation AI to obtain optimal output. For example, the generation AI writes a detailed report based on a specific analysis. This allows the optimal prompt translation system to enable anyone to master generative AI and maximize its output. This allows the optimal prompt translation system to provide support for mastering generative AI and maximize its output. This will expand the use of generative AI in a variety of fields, such as creating business reports, writing academic papers, and generating creative content.

[0063] An optimal prompt translation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives a trivial instruction sentence input by a user. Trivial instructions include, but are not limited to, general expressions and vague instructions. The receiving unit receives, for example, an instruction sentence such as "Please write a report." The receiving unit can also support various input methods, such as voice input and text input. The generating unit uses a generation AI to analyze the trivial instruction sentence received by the receiving unit and generate an optimal instruction sentence. The generation AI can analyze the instruction sentence using, for example, a text generation AI (e.g., LLM) and generate the optimal instruction sentence. The generating unit can also use the generation AI to understand the intent of the instruction sentence and generate an optimal instruction sentence to maximize the output of the generation AI. For example, in response to the instruction sentence "Please write a report," the generation AI generates an optimal instruction sentence such as "Please write a detailed report based on a specific analysis." The providing unit re-inputs the optimal instruction sentence generated by the generation unit into the generation AI to obtain an optimal output. The providing unit, for example, re-inputs instructions so that the generation AI creates a detailed report based on a specific analysis. The providing unit can also provide the user with the optimal output generated by the generation AI. For example, the providing unit displays the report created by the generation AI to the user. In this way, the optimal prompt translation system according to the embodiment can convert mediocre instructions into optimal instructions and maximize the output of the generation AI.

[0064] The generation unit can understand the intent of the instruction sentence input by the generation AI and generate an appropriate instruction sentence. The generation unit, for example, uses the generation AI to understand the intent of the input instruction sentence. For example, the generation unit uses natural language processing technology to analyze the context of the instruction sentence and understand its intent. The generation unit can also use the generation AI to develop an algorithm for understanding the intent of the instruction sentence. For example, the generation unit uses context analysis technology to understand the intent of the instruction sentence. Furthermore, the generation unit uses the generation AI to understand the intent and generate an optimal instruction sentence. For example, in response to the instruction sentence "Create a report," the generation unit generates the optimal instruction sentence, "Create a detailed report based on a specific analysis." In this way, the generation AI can understand the intent of the instruction sentence and generate the optimal instruction sentence, thereby maximizing the output of the generation AI.

[0065] The provision unit can maximize the output of the generation AI based on the appropriate instruction sentence re-input to the generation AI. For example, the provision unit re-inputs the optimal instruction sentence generated by the generation unit to the generation AI. For example, the provision unit re-inputs the instruction sentence "Create a detailed report based on a specific analysis" to the generation AI. The provision unit can also provide the optimal output generated by the generation AI to a user. For example, the provision unit displays the report created by the generation AI to the user. Furthermore, the provision unit can develop an algorithm for maximizing the output of the generation AI. For example, the provision unit builds a feedback loop to improve the accuracy of the output of the generation AI. This makes it possible to provide the user with optimal results by maximizing the output of the generation AI.

[0066] The reception unit can input a mediocre instruction sentence input by a user to the generation AI. For example, the reception unit inputs a mediocre instruction sentence input by a user to the generation AI. For example, the reception unit inputs the instruction sentence "write a report" to the generation AI. The reception unit can also support various input methods, such as voice input and text input. Furthermore, the reception unit can adjust the timing of receiving the instruction sentence input to the generation AI. For example, the reception unit estimates the user's emotions and adjusts the timing of receiving the instruction sentence based on the estimated emotions. In this way, the generation AI's processing can be started by inputting the instruction sentence input by the user to the generation AI.

[0067] The generation unit can generate specific instructions based on a specific analysis using the generation AI. The generation unit, for example, uses the generation AI to generate specific instructions based on the specific analysis. For example, the generation unit uses data analysis technology to generate instructions based on the specific analysis. The generation unit can also use the generation AI to generate instructions using pattern recognition technology. For example, the generation unit uses the generation AI to generate instructions based on a specific data pattern. The generation unit can also use the generation AI to generate detailed instructions based on the specific analysis. For example, the generation unit generates the instruction "Create a detailed report based on the specific analysis." In this way, by generating detailed instructions based on the specific analysis, the accuracy of the output of the generation AI can be improved.

[0068] The providing unit can provide the user with an appropriate output generated by the generation AI. For example, the providing unit provides the user with an appropriate output generated by the generation AI. For example, the providing unit displays a report created by the generation AI to the user. The providing unit can also select an optimal display method for providing the output generated by the generation AI to the user. For example, the providing unit estimates the user's emotions and adjusts the display method of the output based on the estimated emotions. Furthermore, the providing unit can establish a feedback loop for providing the user with the output generated by the generation AI. For example, the providing unit improves the display method of the output based on user feedback. This allows the user to use the results of the generation AI by providing the user with the optimal output generated by the generation AI.

[0069] The reception unit can estimate the user's emotion and adjust the timing of receiving the instruction sentence based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of receiving the instruction sentence based on the estimated emotion. For example, the reception unit estimates the user's emotion using facial expression recognition technology. The reception unit can also estimate the user's emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the reception unit adjusts the timing of receiving the instruction sentence based on the user's emotion. For example, if the user is feeling stressed, the reception unit temporarily delays receiving the instruction sentence and waits until the user is relaxed. Also, if the user is relaxed, the reception unit immediately accepts the instruction sentence and quickly starts processing. In this way, by adjusting the timing of receiving the instruction sentence according to the user's emotion, the instruction sentence can be accepted at a more appropriate timing.

[0070] The reception unit can analyze the user's past instruction input history and select an appropriate reception method. The reception unit, for example, analyzes the user's past instruction input history and selects the optimal reception method. For example, the reception unit preferentially receives instruction formats that the user has frequently used in the past. The reception unit can also predict instruction pattern to be used in a specific time period from the user's past input history and adjust the reception method. Furthermore, the reception unit can analyze the content of instruction statements input by the user in the past and automatically complete similar instruction statements. For example, the reception unit automatically generates related instruction statements based on the content of instruction statements input by the user in the past. In this way, the optimal reception method can be provided to the user by analyzing the past instruction input history.

[0071] The reception unit can filter the instruction sentences based on the user's current project or field of interest when receiving the instruction sentences. For example, the reception unit can preferentially receive only instruction sentences related to the user's current project or field of interest when receiving the instruction sentences. For example, the reception unit can preferentially receive only instruction sentences related to the user's current project or field of interest. The reception unit can also filter and receive highly relevant instruction sentences based on the user's field of interest. Furthermore, the reception unit can customize the reception of instruction sentences based on topics in which the user has shown interest in the past. For example, the reception unit preferentially receives instruction sentences related to topics in which the user has shown interest in the past. In this way, by filtering instruction sentences based on the user's current project or field of interest, highly relevant instruction sentences can be preferentially received.

[0072] The reception unit can select an appropriate reception means according to the user's input method when receiving an instruction. For example, the reception unit selects an appropriate reception means according to the user's input method when receiving an instruction. For example, when the user uses voice input, the reception unit receives the instruction using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also receive the instruction using text analysis technology. Furthermore, when the user uses image input, the reception unit can also receive the instruction using image recognition technology. For example, when the user uploads an image, the reception unit analyzes the image and recognizes it as an instruction. In this way, by selecting the optimal reception means according to the user's input method, it is possible to improve the accuracy of receiving instruction messages.

[0073] The reception unit can estimate the user's emotion and determine the priority of received instruction sentences based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of received instruction sentences based on the estimated emotion. For example, the reception unit estimates the user's emotion using facial expression recognition technology. The reception unit can also estimate the user's emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the reception unit determines the priority of instruction sentences based on the user's emotion. For example, when the user is feeling stressed, instruction sentences with low importance are postponed and instruction sentences with high importance are preferentially received. When the user is relaxed, all instruction sentences are equally received. In this way, by determining the priority of instruction sentences according to the user's emotion, important instruction sentences can be processed preferentially.

[0074] The reception unit can, when receiving a command, preferentially receive relevant command statements by taking into account the user's geographical location information. For example, when receiving a command, the reception unit preferentially receives relevant command statements by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially receives command statements related to that area. Furthermore, when the user is traveling, the reception unit can preferentially receive command statements related to the user's travel destination. Furthermore, when the user is at home, the reception unit can preferentially receive command statements related to the user's home. For example, the reception unit filters relevant command statements based on the user's geographical location information. In this way, highly relevant command statements can be preferentially received by taking into account the user's geographical location information.

[0075] The reception unit can analyze the user's social media activity when receiving the instruction and receive related instruction messages. For example, the reception unit analyzes the user's social media activity when receiving the instruction and receives related instruction messages. For example, the reception unit can preferentially receive related instruction messages based on information shared by the user on social media. The reception unit can also analyze the user's social media activity and filter and receive related instruction messages. Furthermore, the reception unit can also receive related instruction messages by referring to the activity of the user's friends on social media. For example, the reception unit preferentially receives related instruction messages based on information shared by the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant instruction messages.

[0076] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an instruction. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving an instruction. For example, the reception unit adjusts the reception method for the instruction based on feedback provided by the user in the past. The reception unit can also preferentially adopt a specific reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's feedback and suggest an optimal reception method. For example, the reception unit improves the reception method for the instruction based on the user's evaluation comments and improvement requests. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0077] The generation unit can estimate the user's emotion and adjust the expression method of the generated instruction sentence based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the generated instruction sentence based on the estimated emotion. For example, the generation unit estimates the user's emotion using facial expression recognition technology. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the generation unit adjusts the expression method of the instruction sentence based on the user's emotion. For example, if the user is relaxed, the generation unit generates an instruction sentence using gentle expression. On the other hand, if the user is in a hurry, the generation unit generates a concise and clear instruction sentence. In this way, by adjusting the expression method of the instruction sentence according to the user's emotion, more appropriate instruction sentences can be generated.

[0078] The generation unit can adjust the level of detail of the generated directive based on the importance of the directive when generating the directive. For example, the generation unit adjusts the level of detail of the generated directive based on the importance of the directive when generating the directive. For example, the generation unit generates a directive including a detailed explanation for a directive with high importance. The generation unit can also generate a concise directive for a directive with low importance. Furthermore, the generation unit can adjust the length and content of the generated directive based on the importance of the directive. For example, the generation unit evaluates the importance of the directive based on the urgency and scope of the task, and adjusts the level of detail of the directive accordingly. In this way, by adjusting the level of detail of the generated directive based on the importance of the directive, it is possible to generate a directive including a detailed explanation for an important directive.

[0079] The generation unit can apply different generation algorithms depending on the category of the directive when generating the directive. For example, the generation unit applies different generation algorithms depending on the category of the directive when generating the directive. For example, the generation unit applies a business generation algorithm to a business-related directive. The generation unit can also apply an academic generation algorithm to a directive related to an academic paper. Furthermore, the generation unit can apply a creative generation algorithm to a directive related to creative content. For example, the generation unit selects an optimal generation algorithm depending on the category of the directive and generates the directive. In this way, by applying different generation algorithms depending on the category of the directive, more appropriate directives can be generated.

[0080] The generation unit can improve the accuracy of generation when generating a command sentence by referring to the user's past generation results. For example, the generation unit can improve the accuracy of generation when generating a command sentence by referring to the user's past generation results. For example, the generation unit can analyze the results of command sentences generated by the user in the past and improve the accuracy of generation. The generation unit can also extract specific patterns from the user's past generation results and reflect them in the generation algorithm. Furthermore, the generation unit can generate an optimal command sentence based on the user's past generation results. For example, the generation unit can generate a similar command sentence by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0081] The generation unit can estimate the user's emotion and adjust the length of the generated instruction sentence based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the generated instruction sentence based on the estimated emotion. For example, the generation unit estimates the user's emotion using facial expression recognition technology. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the generation unit adjusts the length of the instruction sentence based on the user's emotion. For example, if the user is in a hurry, the generation unit generates short, to-the-point instruction sentences. On the other hand, if the user is relaxed, the generation unit generates longer instruction sentences that include detailed explanations. In this way, by adjusting the length of the instruction sentences according to the user's emotion, more appropriate instruction sentences can be generated.

[0082] The generation unit can determine the priority of generation when generating an instruction statement based on the submission time of the instruction statement. For example, the generation unit determines the priority of generation when generating an instruction statement based on the submission time of the instruction statement. For example, the generation unit generates instruction statements with an approaching deadline with priority. The generation unit can also postpone the generation of instruction statements with a more distant submission time. Furthermore, the generation unit can dynamically adjust the priority of generation according to the submission time. For example, the generation unit determines the priority of instruction statements based on the submission date and time and the degree of urgency. In this way, by determining the priority of generation based on the submission time of the instruction statement, instruction statements with a higher urgency can be generated with priority.

[0083] The generation unit can adjust the order of generation based on the relevance of the directives when generating the directives. For example, the generation unit adjusts the order of generation based on the relevance of the directives when generating the directives. For example, the generation unit generates directives with high relevance with priority. The generation unit can also generate directives with low relevance at a later date. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance of the directives. For example, the generation unit evaluates the relevance of the directives based on the degree of content similarity or related topics, and adjusts the order of generation accordingly. In this way, by adjusting the order of generation based on the relevance of the directives, highly relevant directives can be generated with priority.

[0084] The generation unit can adjust the use of technical terminology when generating an instruction sentence according to the user's level of expertise. For example, the generation unit adjusts the use of technical terminology when generating an instruction sentence according to the user's level of expertise. For example, if the user is an expert, the generation unit generates an instruction sentence that uses a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can generate a concise instruction sentence that avoids technical terminology. Furthermore, the generation unit can adjust the content of the instruction sentence to be generated according to the user's level of expertise. For example, the generation unit evaluates the user's level of expertise based on the user's occupation or past experience, and adjusts the content of the instruction sentence accordingly. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to generate an instruction sentence that is easier to understand.

[0085] The providing unit can estimate the user's emotion and adjust the display method of the output to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the display method of the output to be provided based on the estimated emotion. For example, the providing unit estimates the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the providing unit adjusts the display method of the output based on the user's emotion. For example, if the user is nervous, a simple and highly visible display method is provided. Also, if the user is relaxed, a display method including detailed information is provided. In this way, by adjusting the display method of the output according to the user's emotion, a more appropriate display method can be provided.

[0086] The providing unit can select an appropriate display method by referring to the user's past output results when providing output. For example, the providing unit selects an appropriate display method by referring to the user's past output results when providing output. For example, the providing unit provides an optimal display method based on display methods that the user has preferred in the past. The providing unit can also preferentially adopt a specific display method from the user's past output results. Furthermore, the providing unit can analyze the user's past output results and suggest an optimal display method. For example, the providing unit adjusts the display method based on the user's output result history and evaluation results. In this way, the optimal display method can be provided by referring to the user's past output results.

[0087] The providing unit can customize the display content according to the user's current task when providing output. For example, the providing unit customizes the display content according to the user's current task when providing output. For example, the providing unit prioritizes displaying information related to a task that the user is currently performing. The providing unit can also filter and display highly relevant information based on the user's current task. Furthermore, the providing unit can customize the display content based on tasks in which the user has previously shown interest. For example, the providing unit prioritizes displaying information related to tasks in which the user has previously shown interest. In this way, highly relevant information can be provided by customizing the display content according to the user's current task.

[0088] The providing unit can improve the providing method by reflecting user feedback when providing output. For example, the providing unit improves the providing method by reflecting user feedback when providing output. For example, the providing unit adjusts the output display method based on feedback provided by the user. The providing unit can also preferentially adopt a specific display method based on user feedback. Furthermore, the providing unit can analyze user feedback and suggest an optimal display method. For example, the providing unit improves the output display method based on user evaluation comments and improvement requests. In this way, the providing method can be improved by reflecting user feedback, and more appropriate output can be provided.

[0089] The providing unit can estimate the user's emotion and determine the priority of outputs to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority of outputs to be provided based on the estimated emotion. For example, the providing unit estimates the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the providing unit determines the priority of outputs based on the user's emotion. For example, if the user is feeling stressed, outputs with lower importance are postponed and outputs with higher importance are provided preferentially. Furthermore, if the user is relaxed, all outputs are provided equally. In this way, by determining the priority of outputs according to the user's emotion, important outputs can be provided preferentially.

[0090] The providing unit can select an appropriate output method by taking into consideration the user's geographical location information when providing output. For example, the providing unit selects an appropriate output method by taking into consideration the user's geographical location information when providing output. For example, when the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, when the user is traveling, the providing unit can prioritize providing information related to the travel destination. Furthermore, when the user is at home, the providing unit can prioritize providing information related to the user's home. For example, the providing unit filters relevant information based on the user's geographical location information. In this way, highly relevant output can be provided by taking into consideration the user's geographical location information.

[0091] The providing unit may provide relevant output by analyzing the user's social media activity when providing output. For example, the providing unit may provide relevant output by analyzing the user's social media activity when providing output. For example, the providing unit may provide relevant output preferentially based on information shared by the user on social media. The providing unit may also analyze the user's social media activity and filter and provide relevant output. Furthermore, the providing unit may provide relevant output by referring to the activity of the user's friends on social media. For example, the providing unit may provide relevant output preferentially based on information shared by the user's friends on social media. In this way, highly relevant output can be provided by analyzing the user's social media activity.

[0092] The providing unit can customize the output providing method by reflecting the user's past feedback when providing output. For example, the providing unit customizes the output providing method by reflecting the user's past feedback when providing output. For example, the providing unit adjusts the output providing method based on feedback provided by the user in the past. The providing unit can also preferentially adopt a specific providing method based on the user's past feedback. Furthermore, the providing unit can analyze the user's feedback and propose an optimal providing method. For example, the providing unit improves the output providing method based on the user's evaluation comments and improvement requests. In this way, the optimal providing method can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives a mediocre instruction sentence input by a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the mediocre instruction sentence received by the reception unit using a generation AI to generate an optimal instruction sentence. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the user with the optimal output generated by the generation AI. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a mediocre instruction sentence input by a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to analyze the mediocre instruction sentence received by the reception unit and generate an optimal instruction sentence. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the user with the optimal output generated by the generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives a mediocre instruction sentence input by the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to analyze the mediocre instruction sentence received by the reception unit and generate an optimal instruction sentence. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides the user with the optimal output generated by the generation AI. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a mediocre instruction sentence input by a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses a generation AI to analyze the mediocre instruction sentence received by the reception unit and generate an optimal instruction sentence. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the user with the optimal output generated by the generation AI.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The reception unit can analyze the user's input in real time and provide appropriate feedback even while the user is in the middle of input. For example, if the user is inputting "create a report," the reception unit can infer the user's intention and provide feedback such as "Please specify a specific topic." The reception unit can also point out errors or ambiguities in the input before the user completes the input and prompt the user to correct them. Furthermore, the reception unit can analyze the user's input speed and pattern and provide input assistance. For example, if the user is taking a long time to input, the reception unit can provide an auto-completion function to smoothly proceed with input. In this way, by analyzing the user's input in real time and providing appropriate feedback, it is possible to support the user in inputting instructions more accurately and efficiently.

[0095] The generation unit can learn the user's past command sentence generation history and customize the command sentence based on the user's preferences and tendencies. For example, the generation unit can analyze the expressions and styles that the user has used in the past and generate the command sentence based on the results. The generation unit can also generate optimal command sentences by taking into account the success rate and evaluation of the command sentences that the user has generated in the past. Furthermore, the generation unit can extract specific patterns and trends from the user's past command sentence generation history and generate command sentences based on the patterns and trends. For example, the generation unit can prioritize keywords and phrases that the user has frequently used in the past. In this way, by learning the user's past command sentence generation history, customized command sentences based on the user's preferences and tendencies can be generated.

[0096] The providing unit can optimize the output generated by the generation AI for the user's device and environment before providing it. For example, if the user is using a smartphone, the providing unit adjusts the output to fit the smartphone's screen size and resolution. In addition, if the user is using a voice assistant, the providing unit can also optimize the voice output. Furthermore, if the user is using a specific application or platform, the providing unit can provide the output in a format optimal for that environment. For example, if the user is using presentation software, the providing unit provides the output in slide format. In this way, by optimizing the output generated by the generation AI for the user's device and environment before providing it, the user can use the output more effectively.

[0097] The reception unit supports multiple languages ​​for user input and can accept instructions in different languages. For example, the reception unit translates instructions entered by the user in English into Japanese and inputs the instructions to the generation AI. The reception unit can also translate instructions entered by the user in French into English and input the instructions to the generation AI. Furthermore, the reception unit can automatically detect instructions entered by the user in different languages ​​and translate them into the appropriate language. For example, the reception unit automatically detects instructions entered by the user in Spanish and translates them into English. This allows users to enter instructions in different languages, expanding the scope of use of the generation AI.

[0098] The generation unit can estimate the user's emotion and adjust the tone and style of the generated instruction sentence based on the estimated emotion. For example, if the user is feeling stressed, the generation unit can generate the instruction sentence in a gentle tone. Also, if the user is relaxed, the generation unit can generate the instruction sentence in a casual tone. Furthermore, if the user is in a hurry, the generation unit can generate concise and clear instruction sentences. For example, the generation unit can estimate the user's emotion and adjust the tone and style of the instruction sentence based on the estimated emotion. In this way, more appropriate instruction sentences can be generated by adjusting the tone and style of the instruction sentence according to the user's emotion.

[0099] The providing unit can customize and provide the output generated by the generation AI based on the user's emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible output. Also, if the user is relaxed, the providing unit can provide an output containing detailed information. Furthermore, if the user is excited, the providing unit can provide an interactive output. For example, the providing unit can estimate the user's emotions and adjust the display method of the output based on the estimated emotions. In this way, the output can be customized according to the user's emotions to provide more appropriate output.

[0100] The reception unit can analyze the user's input content and automatically complete related information based on the input content. For example, if the user inputs "create a report," the reception unit automatically searches for and completes related data and materials. Also, if the user inputs "create minutes of a meeting," the reception unit can automatically search for and complete minutes of past meetings and related documents. Furthermore, if the user inputs "create a presentation," the reception unit can automatically search for and complete related templates and slides. This automatically completes related information based on the user's input content, thereby supporting more efficient input of instructions.

[0101] The generation unit can estimate the user's emotion and adjust the content of the generated instruction sentence based on the estimated emotion. For example, if the user is feeling stressed, the generation unit can generate a concise and to-the-point instruction sentence. Furthermore, if the user is relaxed, the generation unit can generate an instruction sentence including a detailed explanation. Furthermore, if the user is excited, the generation unit can generate an instruction sentence including an interactive element. For example, the generation unit can estimate the user's emotion and adjust the content of the instruction sentence based on the estimated emotion. In this way, more appropriate instruction sentences can be generated by adjusting the content of the instruction sentence according to the user's emotion.

[0102] The providing unit can customize and provide the output generated by the generation AI based on the user's past behavioral history. For example, the providing unit adjusts the output based on the format and style that the user has used in the past. The providing unit can also provide optimal output by taking into account the user's evaluation of outputs that he or she has generated in the past. Furthermore, the providing unit can extract specific patterns and trends from the user's past behavioral history and provide output based on them. For example, the providing unit prioritizes keywords and phrases that the user has used frequently in the past. This allows the output to be customized based on the user's past behavioral history, thereby providing more appropriate output.

[0103] The reception unit can estimate the user's emotion and adjust the method for receiving instructions based on the estimated emotion. For example, if the user is feeling stressed, the reception unit can preferentially receive concise and clear instructions. Furthermore, if the user is relaxed, the reception unit can also receive instructions that include detailed explanations. Furthermore, if the user is excited, the reception unit can also receive instructions that include interactive elements. For example, the reception unit can estimate the user's emotion and adjust the method for receiving instructions based on the estimated emotion. In this way, by adjusting the method for receiving instructions according to the user's emotion, more appropriate instructions can be received.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit receives a common instruction sentence entered by the user. Common instructions include general expressions and vague instructions. For example, the reception unit receives the instruction sentence "Write a report." The reception unit also supports various input methods, such as voice input and text input. Step 2: The generation unit uses a generation AI to analyze the mediocre instruction received by the reception unit and generate an optimal instruction. The generation AI analyzes the instruction using, for example, a text generation AI (e.g., LLM) and generates an optimal instruction. The generation unit also understands the intent of the instruction and generates an optimal instruction to maximize the output of the generation AI. For example, in response to the instruction "Write a report," the optimal instruction is generated: "Write a detailed report based on a specific analysis." Step 3: The providing unit re-inputs the optimal instruction sentence generated by the generating unit into the generating AI to obtain an optimal output. For example, the providing unit re-inputs the instruction sentence so that the generating AI creates a detailed report based on a specific analysis. The providing unit also provides the optimal output generated by the generating AI to a user. For example, the providing unit displays the report created by the generating AI to the user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives ordinary instruction sentences; a generation unit that analyzes the ordinary instruction sentence received by the reception unit and generates an appropriate instruction sentence; a providing unit that re-inputs the appropriate instruction sentence generated by the generating unit into the generating AI to obtain an appropriate output. A system characterized by:

2. The generation unit Generative AI understands the intent of the input instruction and generates appropriate instruction 2. The system of claim 1.

3. The providing unit Maximize the output of the generative AI based on appropriate instructions re-entered into the generative AI 2. The system of claim 1.

4. The reception unit Input the user's ordinary instruction sentence into the generative AI 2. The system of claim 1.

5. The generation unit Generative AI generates specific instructions based on specific analysis 2. The system of claim 1.

6. The providing unit Providing the user with the appropriate output generated by the generation AI 2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and adjusts the timing of receiving instructions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past instruction input history and select the appropriate reception method 2. The system of claim 1.

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