System

Abstract

An object of a system according to an embodiment is to automatically generate a new generative AI in which functions of a plurality of generative AI are merged.SOLUTION: A system includes an interpretation unit and a generation unit. The interpretation unit interprets the document. The generation unit automatically generates an input example based on the information interpreted by the interpretation unit. The interpretation part interprets output contents on the basis of the input example generated by the generation part. The generation unit automatically generates the generated AI based on the information interpreted by the interpretation unit.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, when using multiple generation AIs at the same time, the user had to share the conversation context with each generation AI, which was a time-consuming process.

[0005] The system according to the embodiment aims to automatically generate a new generation AI that combines the functions of multiple generation AIs. [Means for solving the problem]

[0006] The system according to the embodiment includes an interpretation unit and a generation unit. The interpretation unit interprets a document. The generation unit automatically generates an input example based on the information interpreted by the interpretation unit. The interpretation unit interprets output content based on the input example generated by the generation unit. The generation unit automatically generates a generation AI based on the information interpreted by the interpretation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a new generation AI that combines the functions of multiple generation AIs. [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) A platform according to an embodiment of the present invention is a system that combines multiple generative AIs to automatically generate a new generative AI. This system interprets the document of a generative AI selected by the user, automatically generates input examples, and interprets the output content to gain a detailed understanding of the functions of each generative AI and automatically generate a new generative AI that combines the functions of all the generative AIs. This eliminates the need for users to operate multiple generative AIs individually, making generative AIs more convenient to use. For example, while previously requiring separate instructions for each generative AI to simultaneously generate text and images, the present invention allows users to use both functions with a single instruction. Furthermore, when interpreting the document of a generative AI, the platform uses natural language processing technology to understand the context and automatically generate appropriate input examples. This allows users to easily use generative AIs without specialized knowledge.

[0029] A generative AI automatic generation system according to an embodiment includes an interpretation unit and a generation unit. The interpretation unit interprets a document. The interpretation unit interprets the generative AI's document using, for example, natural language processing technology. The interpretation unit can also interpret output content based on example inputs generated by the generation unit. For example, the interpretation unit analyzes the output content of the generative AI and understands its functions in detail. The generation unit automatically generates example inputs based on the information interpreted by the interpretation unit. The generation unit, for example, combines functions of the generative AI selected by a user. The generation unit can also interpret the generative AI's document and automatically generate example inputs. The generation unit automatically generates a new generative AI based on the information interpreted by the interpretation unit. For example, the generation unit combines a generative AI specialized in text generation with a generative AI specialized in image generation to automatically generate a new generative AI that combines the functions of both text generation and image generation. This allows the generative AI automatic generation system according to an embodiment to automatically generate a new generative AI that combines the functions of multiple generative AIs.

[0030] The interpretation unit can interpret the generative AI's document using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The interpretation unit, for example, interprets the generative AI's document using morphological analysis. The interpretation unit can also interpret the generative AI's document using grammatical analysis. The interpretation unit can also interpret the generative AI's document using semantic analysis. For example, the interpretation unit performs morphological analysis by dividing a sentence into words and identifying the part of speech of each word. Grammatical analysis analyzes the structure of the sentence and clarifies grammatical relationships. Semantic analysis analyzes the meaning of the sentence and understands the context. As a result, the interpretation unit can accurately interpret the generative AI's document by using natural language processing technology.

[0031] The generation unit can combine the functions of the generation AI selected by the user. Specific methods for combining functions include, but are not limited to, function selection criteria and combination algorithms. For example, the generation unit generates a new generation AI based on the functions of the generation AI selected by the user. The generation unit can also select an optimal combination based on the function selection criteria. The generation unit can also combine the functions of multiple generation AIs using a combination algorithm. For example, the generation unit lists the functions of the generation AI selected by the user and evaluates the importance of each function. The function selection criteria are set according to the user's needs and purposes. The combination algorithm optimally combines the selected functions to generate a new generation AI. In this way, the generation unit can generate a new generation AI by combining the functions of the generation AI selected by the user.

[0032] The generation unit can interpret the document of the generation AI and generate example inputs. Specific methods for generating example inputs include, but are not limited to, the type of example input to be generated and the generation algorithm. For example, the generation unit interprets the document of the generation AI and automatically generates appropriate example inputs. The generation unit can also generate example inputs using a generation algorithm. The generation unit can also apply different generation algorithms depending on the type of example input to be generated. For example, the generation unit analyzes the document of the generation AI and extracts appropriate data as an example input. The generation algorithm generates an example input based on the extracted data. The type of example input to be generated is set according to the function and purpose of the generation AI. In this way, the generation unit can understand the function of the generation AI in detail by interpreting the document of the generation AI and automatically generating example inputs.

[0033] The interpretation unit can interpret the output content of the generation AI. Specific methods for interpreting the output content include, but are not limited to, the type of output content and the interpretation algorithm. For example, the interpretation unit analyzes the output content of the generation AI to understand its function in detail. The interpretation unit can also interpret the output content using an interpretation algorithm. The interpretation unit can also apply different interpretation algorithms depending on the type of output content. For example, the interpretation unit classifies the output content of the generation AI and interprets it according to each category. The interpretation algorithm analyzes the characteristics of the output content and understands its meaning. The type of output content is set according to the function and purpose of the generation AI. In this way, the interpretation unit can understand the function of the generation AI in detail by interpreting the output content of the generation AI.

[0034] The generation unit can automatically generate a new generation AI. Specific methods for automatically generating a new generation AI include, for example, the algorithm used and the generation criteria, but are not limited to these examples. The generation unit, for example, automatically generates a new generation AI based on information interpreted by the interpretation unit. The generation unit can also generate a new generation AI using a generation algorithm. The generation unit can also generate an optimal generation AI based on the generation criteria. For example, the generation unit designs a generation AI based on the information interpreted by the interpretation unit. The generation algorithm automatically generates the designed generation AI. The generation criteria are set according to the user's needs and purposes. In this way, the generation unit improves user convenience by automatically generating a new generation AI.

[0035] The interpretation unit can improve the accuracy of interpretation by referring to past interpretation history. For example, the interpretation unit can refer to the history of similar documents that have been interpreted in the past to maintain consistency in interpretation. The interpretation unit can also learn frequently occurring terms and phrases from the past interpretation history to improve the accuracy of interpretation. The interpretation unit can also apply an interpretation algorithm specialized for a specific field based on the past interpretation history. For example, the interpretation unit stores the history of documents that have been interpreted in the past in a database and refers to it during interpretation. To maintain consistency in interpretation, new documents are interpreted based on past interpretation results. The interpretation unit automatically learns frequently occurring terms and phrases to improve the accuracy of interpretation. An interpretation algorithm specialized for a specific field is optimized based on the past interpretation history. In this way, the interpretation unit can improve the accuracy of interpretation by referring to the past interpretation history.

[0036] The interpretation unit can apply different interpretation algorithms depending on the type of document. For example, in the case of a technical document, the interpretation unit applies an algorithm that accurately interprets technical terms. In addition, in the case of a legal document, the interpretation unit can also apply an algorithm that accurately interprets legal terms and provisions. In addition, in the case of a general text, the interpretation unit can understand the context and perform interpretation using natural language processing technology. For example, the interpretation unit performs interpretation by referring to a technical terminology dictionary for technical documents. For legal documents, the interpretation unit performs interpretation by referring to a legal terminology dictionary and a provision database for legal documents. For general text, the interpretation unit uses natural language processing technology to understand the context and perform interpretation. The interpretation algorithm is optimized depending on the type of document. As a result, the interpretation unit can apply an appropriate interpretation algorithm depending on the type of document, thereby improving the accuracy of interpretation.

[0037] The interpretation unit can adjust the level of detail of the interpretation according to the user's level of expertise. For example, if the user is an expert, the interpretation unit provides an interpretation including detailed technical information. Furthermore, if the user is a beginner, the interpretation unit can also provide a concise and easy-to-understand interpretation. Furthermore, the interpretation unit can dynamically adjust the level of detail of the interpretation according to the user's level of expertise. For example, the interpretation unit evaluates the user's level of expertise and performs an interpretation accordingly. For experts, the interpretation unit provides an interpretation including detailed technical information and technical terminology. For beginners, the interpretation unit provides a concise and easy-to-understand interpretation. The level of detail of the interpretation is dynamically adjusted according to the user's level of expertise. In this way, the interpretation unit can adjust the level of detail of the interpretation according to the user's level of expertise, thereby enabling a more appropriate interpretation.

[0038] The interpretation unit can determine the priority of interpretation based on the submission time of the document. For example, the interpretation unit gives priority to interpreting documents that have been submitted recently. The interpretation unit can also postpone interpreting documents that have been submitted recently. The interpretation unit can also dynamically adjust the priority of interpretation based on the submission time. For example, the interpretation unit determines the priority of interpretation based on the submission date and time. Documents that have been submitted recently are given priority to interpretation. Documents that have been submitted recently are postponed. The priority of interpretation is dynamically adjusted based on the submission time. This allows the interpretation unit to determine the priority of interpretation based on the submission time of the document, thereby enabling efficient interpretation.

[0039] The interpretation unit can adjust the order of interpretation based on the relevance of the documents. For example, the interpretation unit gives priority to interpreting highly relevant documents. The interpretation unit can also postpone interpreting less relevant documents. The interpretation unit can also dynamically adjust the order of interpretation based on the relevance of the documents. For example, the interpretation unit evaluates the relevance of the documents and determines the order of interpretation based on the evaluation. Highly relevant documents are interpreted with priority. Less relevant documents are postponed. The order of interpretation is dynamically adjusted based on the relevance of the documents. This allows the interpretation unit to adjust the order of interpretation based on the relevance of the documents, thereby enabling efficient interpretation.

[0040] The interpretation unit can customize the interpretation method by reflecting the user's past feedback. The interpretation unit, for example, adjusts the interpretation method based on feedback provided by the user in the past. The interpretation unit can also improve the accuracy of the interpretation by reflecting the user's feedback. The interpretation unit can also customize the interpretation algorithm based on the user's feedback. For example, the interpretation unit stores the user's past feedback in a database and refers to it during interpretation. The interpretation method is adjusted based on the feedback, and interpretation is performed according to the user's needs. The accuracy of the interpretation is improved by reflecting the user's feedback. The interpretation algorithm is customized based on the user's feedback. This allows the interpretation unit to customize the interpretation method by reflecting the user's past feedback.

[0041] The generation unit can improve the accuracy of generation by referring to past generation history. For example, the generation unit generates similar input examples based on input examples generated in the past. The generation unit can also learn frequently occurring patterns from the past generation history and improve the accuracy of generation. The generation unit can also generate input examples specialized in a specific field based on the past generation history. For example, the generation unit stores the history of input examples generated in the past in a database and refers to it at the time of generation. The generation unit automatically learns frequently occurring patterns and improves the accuracy of generation. Input examples specialized in a specific field are optimized based on the past generation history. In this way, the generation unit improves the accuracy of generation by referring to the past generation history.

[0042] The generation unit can apply different generation algorithms depending on the category of content to be generated. For example, in the case of technical documents, the generation unit applies an algorithm that accurately generates technical terms. Furthermore, in the case of legal documents, the generation unit can also apply an algorithm that accurately generates legal terms and provisions. Furthermore, in the case of general text, the generation unit can understand the context and generate example inputs using natural language processing technology. For example, for technical documents, the generation unit generates example inputs by referring to a technical terminology dictionary. For legal documents, the generation unit generates example inputs by referring to a legal terminology dictionary and a provision database. For general text, the generation unit understands the context and generates example inputs using natural language processing technology. The generation algorithm is optimized depending on the category of content to be generated. As a result, the generation unit applies an appropriate generation algorithm depending on the category of content to be generated, thereby improving the accuracy of generation.

[0043] The generation unit can adjust the level of detail of the generation according to the user's level of expertise. For example, if the user is an expert, the generation unit generates input examples that include detailed technical information. Furthermore, if the user is a beginner, the generation unit can generate concise and easy-to-understand input examples. Furthermore, the generation unit can dynamically adjust the level of detail of the generation according to the user's level of expertise. For example, the generation unit evaluates the user's level of expertise and generates input examples accordingly. For experts, input examples that include detailed technical information and technical terminology are provided. For beginners, concise and easy-to-understand input examples are provided. The level of detail of the generation is dynamically adjusted according to the user's level of expertise. In this way, the generation unit adjusts the level of detail of the generation according to the user's level of expertise, thereby generating more appropriate input examples.

[0044] The generation unit can determine the generation priority based on the submission time of the content to be generated. For example, the generation unit generates content that has been submitted recently with priority. The generation unit can also postpone content that has been submitted recently. The generation unit can also dynamically adjust the generation priority based on the submission time. For example, the generation unit determines the generation priority based on the submission date and time. Content that has been submitted recently is generated with priority. Content that has been submitted recently is postponed. The generation priority is dynamically adjusted based on the submission time. In this way, the generation unit can determine the generation priority based on the submission time of the content to be generated, thereby enabling efficient generation.

[0045] The generation unit can adjust the order of generation based on the relevance of the content to be generated. For example, the generation unit generates highly relevant content with priority. The generation unit can also postpone content with low relevance. The generation unit can also dynamically adjust the order of generation based on the relevance of the content. For example, the generation unit evaluates the relevance of the content and determines the order of generation based on the evaluation. Highly relevant content is generated with priority. Content with low relevance is postponed. The order of generation is dynamically adjusted based on the relevance of the content. In this way, the generation unit can adjust the order of generation based on the relevance of the content to be generated, thereby enabling efficient generation.

[0046] The generation unit can customize the generation method by reflecting the user's past feedback. The generation unit, for example, adjusts the generation method based on feedback provided by the user in the past. The generation unit can also improve the accuracy of generation by reflecting the user's feedback. The generation unit can also customize the generation algorithm based on the user's feedback. For example, the generation unit stores the user's past feedback in a database and refers to it during generation. The generation method is adjusted based on the feedback, and an input example that meets the user's needs is generated. The accuracy of generation is improved by reflecting the user's feedback. The generation algorithm is customized based on the user's feedback. This allows the generation unit to customize the generation method by reflecting the user's past feedback.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The interpretation unit can improve the accuracy of the interpretation by referring to the user's past behavior history. For example, it can record what documents the user has interpreted in the past and perform similar interpretations for similar documents. It can also learn what interpretation results the user has preferred in the past and adjust the interpretation based on that preference. It can also refer to what feedback the user has provided in the past and reflect that feedback to improve the accuracy of the interpretation. In this way, the interpretation unit can provide more accurate interpretations by referring to the user's past behavior history.

[0049] The generator can adjust the characteristics of the generated AI based on the user's current environment. For example, if the user is in a quiet environment, the generator can generate AI that generates quiet and calming content. Alternatively, if the user is in a noisy environment, the generator can generate AI that generates bright and loud content to attract attention. Furthermore, if the user is on the move, the generator can generate AI that generates content that can be used in a short time. This allows the generator to provide more appropriate content by adjusting the characteristics of the generated AI according to the user's current environment.

[0050] The generator can adjust the characteristics of the generated AI based on the user's past feedback. For example, the generator can customize the characteristics of the generated AI based on feedback provided by the user in the past. It can also generate AI that reflects a specific style or tone preferred by the user. It can also adjust the generated AI to avoid characteristics that the user has previously expressed dissatisfaction with. This allows the generator to provide a more satisfying experience by adjusting the characteristics of the generated AI based on the user's past feedback.

[0051] The generator can adjust the characteristics of the generated AI based on the user's current task. For example, if the user is creating a presentation, the generator can generate an AI that generates visually appealing slides. Alternatively, if the user is creating a report, the generator can generate an AI that provides detailed and accurate data. Furthermore, if the user is working on a creative project, the generator can generate an AI that provides creative ideas. This allows the generator to provide more appropriate support by adjusting the characteristics of the generated AI according to the user's current task.

[0052] The generator can adjust the characteristics of the generated AI based on the user's current device. For example, if the user is using a smartphone, the generator can generate AI that generates mobile-friendly content. Alternatively, if the user is using a desktop, the generator can generate AI that generates detailed and complex content. Furthermore, if the user is using a tablet, the generator can generate AI that generates content suitable for touch operation. This allows the generator to provide more appropriate content by adjusting the characteristics of the generated AI according to the user's current device.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The interpretation unit interprets the document. The interpretation unit interprets the document of the generation AI using, for example, natural language processing technology. The interpretation unit can also interpret the output content based on the input example generated by the generation unit. For example, the interpretation unit analyzes the output content of the generation AI and understands its functions in detail. Step 2: The generation unit automatically generates input examples based on the information interpreted by the interpretation unit. The generation unit, for example, combines the functions of the generation AI selected by the user. The generation unit can also interpret the document of the generation AI and automatically generate input examples. Step 3: The generation unit automatically generates a new generative AI based on the information interpreted by the interpretation unit. For example, the generation unit may combine a generative AI specialized in text generation with a generative AI specialized in image generation to automatically generate a new generative AI that combines the functions of both text generation and image generation.

[0055] (Example 2) A platform according to an embodiment of the present invention is a system that combines multiple generative AIs to automatically generate a new generative AI. This system interprets the document of a generative AI selected by the user, automatically generates input examples, and interprets the output content to gain a detailed understanding of the functions of each generative AI and automatically generate a new generative AI that combines the functions of all the generative AIs. This eliminates the need for users to operate multiple generative AIs individually, making generative AIs more convenient to use. For example, while previously requiring separate instructions for each generative AI to simultaneously generate text and images, the present invention allows users to use both functions with a single instruction. Furthermore, when interpreting the document of a generative AI, the platform uses natural language processing technology to understand the context and automatically generate appropriate input examples. This allows users to easily use generative AIs without specialized knowledge.

[0056] A generative AI automatic generation system according to an embodiment includes an interpretation unit and a generation unit. The interpretation unit interprets a document. The interpretation unit interprets the generative AI's document using, for example, natural language processing technology. The interpretation unit can also interpret output content based on example inputs generated by the generation unit. For example, the interpretation unit analyzes the output content of the generative AI and understands its functions in detail. The generation unit automatically generates example inputs based on the information interpreted by the interpretation unit. The generation unit, for example, combines functions of the generative AI selected by a user. The generation unit can also interpret the generative AI's document and automatically generate example inputs. The generation unit automatically generates a new generative AI based on the information interpreted by the interpretation unit. For example, the generation unit combines a generative AI specialized in text generation with a generative AI specialized in image generation to automatically generate a new generative AI that combines the functions of both text generation and image generation. This allows the generative AI automatic generation system according to an embodiment to automatically generate a new generative AI that combines the functions of multiple generative AIs.

[0057] The interpretation unit can interpret the generative AI's document using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The interpretation unit, for example, interprets the generative AI's document using morphological analysis. The interpretation unit can also interpret the generative AI's document using grammatical analysis. The interpretation unit can also interpret the generative AI's document using semantic analysis. For example, the interpretation unit performs morphological analysis by dividing a sentence into words and identifying the part of speech of each word. Grammatical analysis analyzes the structure of the sentence and clarifies grammatical relationships. Semantic analysis analyzes the meaning of the sentence and understands the context. As a result, the interpretation unit can accurately interpret the generative AI's document by using natural language processing technology.

[0058] The generation unit can combine the functions of the generation AI selected by the user. Specific methods for combining functions include, but are not limited to, function selection criteria and combination algorithms. For example, the generation unit generates a new generation AI based on the functions of the generation AI selected by the user. The generation unit can also select an optimal combination based on the function selection criteria. The generation unit can also combine the functions of multiple generation AIs using a combination algorithm. For example, the generation unit lists the functions of the generation AI selected by the user and evaluates the importance of each function. The function selection criteria are set according to the user's needs and purposes. The combination algorithm optimally combines the selected functions to generate a new generation AI. In this way, the generation unit can generate a new generation AI by combining the functions of the generation AI selected by the user.

[0059] The generation unit can interpret the document of the generation AI and generate example inputs. Specific methods for generating example inputs include, but are not limited to, the type of example input to be generated and the generation algorithm. For example, the generation unit interprets the document of the generation AI and automatically generates appropriate example inputs. The generation unit can also generate example inputs using a generation algorithm. The generation unit can also apply different generation algorithms depending on the type of example input to be generated. For example, the generation unit analyzes the document of the generation AI and extracts appropriate data as an example input. The generation algorithm generates an example input based on the extracted data. The type of example input to be generated is set according to the function and purpose of the generation AI. In this way, the generation unit can understand the function of the generation AI in detail by interpreting the document of the generation AI and automatically generating example inputs.

[0060] The interpretation unit can interpret the output content of the generation AI. Specific methods for interpreting the output content include, but are not limited to, the type of output content and the interpretation algorithm. For example, the interpretation unit analyzes the output content of the generation AI to understand its function in detail. The interpretation unit can also interpret the output content using an interpretation algorithm. The interpretation unit can also apply different interpretation algorithms depending on the type of output content. For example, the interpretation unit classifies the output content of the generation AI and interprets it according to each category. The interpretation algorithm analyzes the characteristics of the output content and understands its meaning. The type of output content is set according to the function and purpose of the generation AI. In this way, the interpretation unit can understand the function of the generation AI in detail by interpreting the output content of the generation AI.

[0061] The generation unit can automatically generate a new generation AI. Specific methods for automatically generating a new generation AI include, for example, the algorithm used and the generation criteria, but are not limited to these examples. The generation unit, for example, automatically generates a new generation AI based on information interpreted by the interpretation unit. The generation unit can also generate a new generation AI using a generation algorithm. The generation unit can also generate an optimal generation AI based on the generation criteria. For example, the generation unit designs a generation AI based on the information interpreted by the interpretation unit. The generation algorithm automatically generates the designed generation AI. The generation criteria are set according to the user's needs and purposes. In this way, the generation unit improves user convenience by automatically generating a new generation AI.

[0062] The interpretation unit can estimate a user's emotion and change the document interpretation method based on the estimated user emotion. For example, the interpretation unit estimates a user's emotion and adjusts the document interpretation method based on the estimated user emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is stressed, the interpretation unit can provide a concise and to-the-point interpretation. If the user is relaxed, the interpretation unit can provide a detailed interpretation and additional information. If the user is in a hurry, the interpretation unit can provide a quick interpretation and prioritize the most important information. This allows the interpretation unit to adjust the document interpretation method according to the user's emotion, enabling more appropriate interpretation.

[0063] The interpretation unit can improve the accuracy of interpretation by referring to past interpretation history. For example, the interpretation unit can refer to the history of similar documents that have been interpreted in the past to maintain consistency in interpretation. The interpretation unit can also learn frequently occurring terms and phrases from the past interpretation history to improve the accuracy of interpretation. The interpretation unit can also apply an interpretation algorithm specialized for a specific field based on the past interpretation history. For example, the interpretation unit stores the history of documents that have been interpreted in the past in a database and refers to it during interpretation. To maintain consistency in interpretation, new documents are interpreted based on past interpretation results. The interpretation unit automatically learns frequently occurring terms and phrases to improve the accuracy of interpretation. An interpretation algorithm specialized for a specific field is optimized based on the past interpretation history. In this way, the interpretation unit can improve the accuracy of interpretation by referring to the past interpretation history.

[0064] The interpretation unit can apply different interpretation algorithms depending on the type of document. For example, in the case of a technical document, the interpretation unit applies an algorithm that accurately interprets technical terms. In addition, in the case of a legal document, the interpretation unit can also apply an algorithm that accurately interprets legal terms and provisions. In addition, in the case of a general text, the interpretation unit can understand the context and perform interpretation using natural language processing technology. For example, the interpretation unit performs interpretation by referring to a technical terminology dictionary for technical documents. For legal documents, the interpretation unit performs interpretation by referring to a legal terminology dictionary and a provision database for legal documents. For general text, the interpretation unit uses natural language processing technology to understand the context and perform interpretation. The interpretation algorithm is optimized depending on the type of document. As a result, the interpretation unit can apply an appropriate interpretation algorithm depending on the type of document, thereby improving the accuracy of interpretation.

[0065] The interpretation unit can adjust the level of detail of the interpretation according to the user's level of expertise. For example, if the user is an expert, the interpretation unit provides an interpretation including detailed technical information. Furthermore, if the user is a beginner, the interpretation unit can also provide a concise and easy-to-understand interpretation. Furthermore, the interpretation unit can dynamically adjust the level of detail of the interpretation according to the user's level of expertise. For example, the interpretation unit evaluates the user's level of expertise and performs an interpretation accordingly. For experts, the interpretation unit provides an interpretation including detailed technical information and technical terminology. For beginners, the interpretation unit provides a concise and easy-to-understand interpretation. The level of detail of the interpretation is dynamically adjusted according to the user's level of expertise. In this way, the interpretation unit can adjust the level of detail of the interpretation according to the user's level of expertise, thereby enabling a more appropriate interpretation.

[0066] The interpretation unit can estimate the user's emotions and change the display method of the interpretation result based on the estimated user emotions. The interpretation unit, for example, estimates the user's emotions and adjusts the display method of the interpretation result based on the estimated user emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the interpretation unit provides a simple, highly visible display method. If the user is relaxed, the interpretation unit can also provide a display method that includes detailed information. If the user is in a hurry, the interpretation unit can also provide a display method that focuses on the main points. This allows the interpretation unit to adjust the display method of the interpretation result according to the user's emotions, enabling a more appropriate display.

[0067] The interpretation unit can determine the priority of interpretation based on the submission time of the document. For example, the interpretation unit gives priority to interpreting documents that have been submitted recently. The interpretation unit can also postpone interpreting documents that have been submitted recently. The interpretation unit can also dynamically adjust the priority of interpretation based on the submission time. For example, the interpretation unit determines the priority of interpretation based on the submission date and time. Documents that have been submitted recently are given priority to interpretation. Documents that have been submitted recently are postponed. The priority of interpretation is dynamically adjusted based on the submission time. This allows the interpretation unit to determine the priority of interpretation based on the submission time of the document, thereby enabling efficient interpretation.

[0068] The interpretation unit can adjust the order of interpretation based on the relevance of the documents. For example, the interpretation unit gives priority to interpreting highly relevant documents. The interpretation unit can also postpone interpreting less relevant documents. The interpretation unit can also dynamically adjust the order of interpretation based on the relevance of the documents. For example, the interpretation unit evaluates the relevance of the documents and determines the order of interpretation based on the evaluation. Highly relevant documents are interpreted with priority. Less relevant documents are postponed. The order of interpretation is dynamically adjusted based on the relevance of the documents. This allows the interpretation unit to adjust the order of interpretation based on the relevance of the documents, thereby enabling efficient interpretation.

[0069] The interpretation unit can customize the interpretation method by reflecting the user's past feedback. The interpretation unit, for example, adjusts the interpretation method based on feedback provided by the user in the past. The interpretation unit can also improve the accuracy of the interpretation by reflecting the user's feedback. The interpretation unit can also customize the interpretation algorithm based on the user's feedback. For example, the interpretation unit stores the user's past feedback in a database and refers to it during interpretation. The interpretation method is adjusted based on the feedback, and interpretation is performed according to the user's needs. The accuracy of the interpretation is improved by reflecting the user's feedback. The interpretation algorithm is customized based on the user's feedback. This allows the interpretation unit to customize the interpretation method by reflecting the user's past feedback.

[0070] The generation unit can estimate the user's emotion and change the input example generation method based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the input example generation method based on the estimated user emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation unit generates detailed input examples when the user is relaxed. The generation unit can also generate concise input examples when the user is in a hurry. The generation unit can also generate visually stimulating input examples when the user is excited. In this way, the generation unit adjusts the input example generation method according to the user's emotion, thereby generating more appropriate input examples.

[0071] The generation unit can improve the accuracy of generation by referring to past generation history. For example, the generation unit generates similar input examples based on input examples generated in the past. The generation unit can also learn frequently occurring patterns from the past generation history and improve the accuracy of generation. The generation unit can also generate input examples specialized in a specific field based on the past generation history. For example, the generation unit stores the history of input examples generated in the past in a database and refers to it at the time of generation. The generation unit automatically learns frequently occurring patterns and improves the accuracy of generation. Input examples specialized in a specific field are optimized based on the past generation history. In this way, the generation unit improves the accuracy of generation by referring to the past generation history.

[0072] The generation unit can apply different generation algorithms depending on the category of content to be generated. For example, in the case of technical documents, the generation unit applies an algorithm that accurately generates technical terms. Furthermore, in the case of legal documents, the generation unit can also apply an algorithm that accurately generates legal terms and provisions. Furthermore, in the case of general text, the generation unit can understand the context and generate example inputs using natural language processing technology. For example, for technical documents, the generation unit generates example inputs by referring to a technical terminology dictionary. For legal documents, the generation unit generates example inputs by referring to a legal terminology dictionary and a provision database. For general text, the generation unit understands the context and generates example inputs using natural language processing technology. The generation algorithm is optimized depending on the category of content to be generated. As a result, the generation unit applies an appropriate generation algorithm depending on the category of content to be generated, thereby improving the accuracy of generation.

[0073] The generation unit can adjust the level of detail of the generation according to the user's level of expertise. For example, if the user is an expert, the generation unit generates input examples that include detailed technical information. Furthermore, if the user is a beginner, the generation unit can generate concise and easy-to-understand input examples. Furthermore, the generation unit can dynamically adjust the level of detail of the generation according to the user's level of expertise. For example, the generation unit evaluates the user's level of expertise and generates input examples accordingly. For experts, input examples that include detailed technical information and technical terminology are provided. For beginners, concise and easy-to-understand input examples are provided. The level of detail of the generation is dynamically adjusted according to the user's level of expertise. In this way, the generation unit adjusts the level of detail of the generation according to the user's level of expertise, thereby generating more appropriate input examples.

[0074] The generation unit can estimate the user's emotion and set a priority order for the example inputs to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and determines a priority order for the example inputs to be generated based on the estimated user's emotion. The emotion estimation is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the generation unit may prioritize generating example inputs that are concise and to the point. Also, if the user is relaxed, the generation unit may prioritize generating example inputs that are detailed. Also, if the user is in a hurry, the generation unit may prioritize generating example inputs that can be generated quickly. In this way, the generation unit prioritizes the example inputs according to the user's emotion, thereby generating more appropriate example inputs.

[0075] The generation unit can determine the generation priority based on the submission time of the content to be generated. For example, the generation unit generates content that has been submitted recently with priority. The generation unit can also postpone content that has been submitted recently. The generation unit can also dynamically adjust the generation priority based on the submission time. For example, the generation unit determines the generation priority based on the submission date and time. Content that has been submitted recently is generated with priority. Content that has been submitted recently is postponed. The generation priority is dynamically adjusted based on the submission time. In this way, the generation unit can determine the generation priority based on the submission time of the content to be generated, thereby enabling efficient generation.

[0076] The generation unit can adjust the order of generation based on the relevance of the content to be generated. For example, the generation unit generates highly relevant content with priority. The generation unit can also postpone content with low relevance. The generation unit can also dynamically adjust the order of generation based on the relevance of the content. For example, the generation unit evaluates the relevance of the content and determines the order of generation based on the evaluation. Highly relevant content is generated with priority. Content with low relevance is postponed. The order of generation is dynamically adjusted based on the relevance of the content. In this way, the generation unit can adjust the order of generation based on the relevance of the content to be generated, thereby enabling efficient generation.

[0077] The generation unit can customize the generation method by reflecting the user's past feedback. The generation unit, for example, adjusts the generation method based on feedback provided by the user in the past. The generation unit can also improve the accuracy of generation by reflecting the user's feedback. The generation unit can also customize the generation algorithm based on the user's feedback. For example, the generation unit stores the user's past feedback in a database and refers to it during generation. The generation method is adjusted based on the feedback, and an input example that meets the user's needs is generated. The accuracy of generation is improved by reflecting the user's feedback. The generation algorithm is customized based on the user's feedback. This allows the generation unit to customize the generation method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the interpretation unit and the generation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the interpretation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the interpretation unit interprets the document of the generation AI using the camera 42 or microphone 38B of the smart device 14 and understands the context using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates example inputs based on the information interpreted by the interpretation unit. The generation unit is also realized, for example, by the control unit 46A of the smart device 14 and can interpret the document of the generation AI and automatically generate example inputs. === Hard Collateral 1-2 === Each of the multiple elements, including the interpretation unit and the generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the interpretation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the interpretation unit interprets the document of the generation AI using the camera 42 or the microphone 238 of the smart glasses 214 and understands the context using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates example inputs based on the information interpreted by the interpretation unit. The generation unit is also realized, for example, by the control unit 46A of the smart glasses 214 and can interpret the document of the generation AI and automatically generate example inputs. === Hard Collateral 1-3 === Each of the multiple elements including the interpretation unit and the generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the interpretation unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the interpretation unit interprets the document of the generation AI using the camera 42 or the microphone 238 of the headset-type terminal 314 and understands the context using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates example inputs based on the information interpreted by the interpretation unit. The generation unit is also realized, for example, by the control unit 46A of the headset-type terminal 314 and can interpret the document of the generation AI and automatically generate example inputs. === Hard Collateral 1-4 === Each of the multiple elements including the interpretation unit and the generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the interpretation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the interpretation unit interprets the document of the generation AI using the camera 42 or the microphone 238 of the robot 414 and understands the context using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates example inputs based on the information interpreted by the interpretation unit. The generation unit is also realized, for example, by the control unit 46A of the robot 414 and can interpret the document of the generation AI and automatically generate example inputs.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The generator can estimate the user's emotions and adjust the characteristics of the generated AI based on the estimated user emotions. For example, if the user is feeling stressed, the generator can preferentially generate AI that generates content with a relaxing effect. Alternatively, if the user is excited, the generator can generate AI that generates energetic content. Furthermore, if the user is sad, the generator can generate AI that generates messages of comfort and encouragement. In this way, the generator can provide a more personalized experience by adjusting the characteristics of the generated AI according to the user's emotions.

[0080] The interpretation unit can improve the accuracy of the interpretation by referring to the user's past behavior history. For example, it can record what documents the user has interpreted in the past and perform similar interpretations for similar documents. It can also learn what interpretation results the user has preferred in the past and adjust the interpretation based on that preference. It can also refer to what feedback the user has provided in the past and reflect that feedback to improve the accuracy of the interpretation. In this way, the interpretation unit can provide more accurate interpretations by referring to the user's past behavior history.

[0081] The generator can adjust the characteristics of the generated AI based on the user's current environment. For example, if the user is in a quiet environment, the generator can generate AI that generates quiet and calming content. Alternatively, if the user is in a noisy environment, the generator can generate AI that generates bright and loud content to attract attention. Furthermore, if the user is on the move, the generator can generate AI that generates content that can be used in a short time. This allows the generator to provide more appropriate content by adjusting the characteristics of the generated AI according to the user's current environment.

[0082] The interpretation unit can estimate the user's emotions and adjust the level of detail of the interpretation result based on the estimated user's emotions. For example, if the user is relaxed, a detailed interpretation result can be provided. If the user is in a hurry, a concise and to-the-point interpretation result can be provided. Furthermore, if the user is feeling anxious, an interpretation result that gives a sense of security can be provided. In this way, the interpretation unit can provide a more appropriate interpretation by adjusting the level of detail of the interpretation result according to the user's emotions.

[0083] The generator can adjust the characteristics of the generated AI based on the user's past feedback. For example, the generator can customize the characteristics of the generated AI based on feedback provided by the user in the past. It can also generate AI that reflects a specific style or tone preferred by the user. It can also adjust the generated AI to avoid characteristics that the user has previously expressed dissatisfaction with. This allows the generator to provide a more satisfying experience by adjusting the characteristics of the generated AI based on the user's past feedback.

[0084] The interpretation unit can estimate the user's emotions and change the display format of the interpretation results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display format can be provided. If the user is relaxed, a display format including detailed information can be provided. Furthermore, if the user is in a hurry, a display format that focuses on the main points can be provided. This allows the interpretation unit to change the display format of the interpretation results according to the user's emotions, enabling more appropriate display.

[0085] The generator can adjust the characteristics of the generated AI based on the user's current task. For example, if the user is creating a presentation, the generator can generate an AI that generates visually appealing slides. Alternatively, if the user is creating a report, the generator can generate an AI that provides detailed and accurate data. Furthermore, if the user is working on a creative project, the generator can generate an AI that provides creative ideas. This allows the generator to provide more appropriate support by adjusting the characteristics of the generated AI according to the user's current task.

[0086] The interpretation unit can estimate the user's emotions and change the priority of interpretations based on the estimated user's emotions. For example, if the user is feeling stressed, important information can be given priority in interpretation. Also, if the user is relaxed, an interpretation including detailed information can be provided. Furthermore, if the user is in a hurry, an interpretation can be performed quickly and the most important information can be given priority. In this way, the interpretation unit can change the priority of interpretations according to the user's emotions, enabling more appropriate interpretations.

[0087] The generator can adjust the characteristics of the generated AI based on the user's current device. For example, if the user is using a smartphone, the generator can generate AI that generates mobile-friendly content. Alternatively, if the user is using a desktop, the generator can generate AI that generates detailed and complex content. Furthermore, if the user is using a tablet, the generator can generate AI that generates content suitable for touch operation. This allows the generator to provide more appropriate content by adjusting the characteristics of the generated AI according to the user's current device.

[0088] The interpretation unit can estimate the user's emotions and change the interpretation feedback method based on the estimated user's emotions. For example, if the user feels anxious, the interpretation unit can provide reassuring feedback. If the user feels relaxed, the interpretation unit can provide detailed feedback. If the user is in a hurry, the interpretation unit can provide concise, to-the-point feedback. This allows the interpretation unit to provide more appropriate feedback by changing the interpretation feedback method according to the user's emotions.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The interpretation unit interprets the document. The interpretation unit interprets the document of the generation AI using, for example, natural language processing technology. The interpretation unit can also interpret the output content based on the input example generated by the generation unit. For example, the interpretation unit analyzes the output content of the generation AI and understands its functions in detail. Step 2: The generation unit automatically generates input examples based on the information interpreted by the interpretation unit. The generation unit, for example, combines the functions of the generation AI selected by the user. The generation unit can also interpret the document of the generation AI and automatically generate input examples. Step 3: The generation unit automatically generates a new generative AI based on the information interpreted by the interpretation unit. For example, the generation unit may combine a generative AI specialized in text generation with a generative AI specialized in image generation to automatically generate a new generative AI that combines the functions of both text generation and image generation.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] [Explanation of symbols]

[0163] 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. an interpretation unit for interpreting the document; a generation unit that automatically generates input examples based on the information interpreted by the interpretation unit; an interpretation unit that interprets output content based on the example input generated by the generation unit; a generation unit that automatically generates a generation AI based on the information interpreted by the interpretation unit. A system characterized by:

2. The interpretation unit Using natural language processing techniques to interpret documents for generative AI 2. The system of claim 1.

3. The generation unit Combine user-selected generative AI functions 2. The system of claim 1.

4. The generation unit Interpret the document for generative AI and generate example inputs 2. The system of claim 1.

5. The interpretation unit Interpreting the output of the generated AI 2. The system of claim 1.

6. The generation unit Automatically generate new AI 2. The system of claim 1.

7. The interpretation unit Inferring user sentiment and changing how a document is interpreted based on the inferred sentiment 2. The system of claim 1.

8. The interpretation unit Improve interpretation accuracy by referring to past interpretation history 2. The system of claim 1.

Images