system

Abstract

The system according to the embodiment aims to extract appropriate parameters from vendor documents and to create an error-free design. [Solution] A system according to an embodiment includes a reading unit, an extraction unit, a sharing unit, a design unit, and a verification unit. The reading unit reads vendor documents. The extraction unit extracts the information read by the reading unit. The sharing unit shares a parameter design concept for an SB based on the information extracted by the extraction unit. The design unit designs parameters based on the concept shared by the sharing unit. The verification unit verifies the parameters designed by the design unit and performs a design to prevent errors by referring to constraints and parameter descriptions.

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] Conventional technology has had the problem of making it difficult to extract appropriate parameters from vendor documents and create error-free designs.

[0005] The system according to the embodiment aims to extract appropriate parameters from vendor documents and to create an error-free design. [Means for solving the problem]

[0006] The system according to the embodiment includes a reading unit, an extraction unit, a sharing unit, a design unit, and a verification unit. The reading unit reads vendor documents. The extraction unit extracts the information read by the reading unit. The sharing unit shares a parameter design concept for the SB based on the information extracted by the extraction unit. The design unit designs parameters based on the concept shared by the sharing unit. The verification unit verifies the parameters designed by the design unit and performs a design to prevent errors by referring to constraints and parameter descriptions. [Effects of the Invention]

[0007] The system according to the embodiment can extract the appropriate parameters from the vendor documentation to produce an error-free design. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI system according to an embodiment of the present invention proposes appropriate parameters by reading vendor documents and sharing the SB parameter design concept with the AI. This AI system reads vendor documents, extracts necessary information, shares the SB parameter design concept, designs parameters based on that concept, and performs an error-free design by referring to constraints and parameter descriptions. For example, it can propose optimal parameters under specific constraint conditions. This system enables efficient and accurate parameter design. First, a "reading unit" is provided to read vendor documents, followed by an "extraction unit" to extract necessary information. Next, a "sharing unit" is provided to share the SB parameter design concept, followed by a "design unit" to design parameters. Finally, a "verification unit" is provided to perform an error-free design by referring to constraints and parameter descriptions. The "reading unit" reads specific parts of the vendor documents and extracts necessary information. The "extraction unit" extracts necessary data based on the information provided by the reading unit. The "sharing unit" shares the SB parameter design concept with the AI ​​based on the data provided by the extraction unit. The "design department" designs parameters based on the concept passed to it by the shared department. The "verification department" verifies the parameters designed by the design department and performs the design without errors, taking into account constraints and parameter descriptions. This system efficiently and accurately carries out the entire process from reading vendor documents to parameter design. For example, it can propose optimal parameters under specific constraint conditions. This enables efficient and accurate parameter design. This allows the AI ​​system to efficiently and accurately carry out the entire process from reading vendor documents to parameter design.

[0029] The AI ​​system according to the embodiment includes a reading unit, an extraction unit, a sharing unit, a design unit, and a verification unit. The reading unit reads vendor documents. Examples of vendor documents include, but are not limited to, technical documents, product catalogs, and contracts. For example, the reading unit digitizes and reads the technical documents using scanning technology. The reading unit can also directly read documents submitted in digital format. The reading unit can also read printed documents using OCR technology. For example, the reading unit scans technical documents with a high-resolution scanner and converts them into text information using OCR technology. Digital documents submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The extraction unit extracts necessary data based on the information read by the reading unit. Extraction can be performed using, for example, keyword search or pattern matching, but is not limited to, examples. For example, the extraction unit searches for specific keywords in technical documents and extracts related information. The extraction unit can also use pattern matching technology to extract information that matches a specific pattern. Furthermore, the extraction unit can also use natural language processing technology to analyze the content of a document and extract necessary information. For example, the extraction unit analyzes the content of a technical document and extracts specific technical specifications and price information. The sharing unit shares the SB parameter design concept with the AI ​​based on the information extracted by the extraction unit. Sharing is performed, for example, based on the format of the information and the means of sharing, but is not limited to such examples. For example, the sharing unit shares the extracted information with the AI ​​in text format. The sharing unit can also share the extracted information with the AI ​​in visual format. Furthermore, the sharing unit can share the extracted information with the AI ​​in real time. For example, the sharing unit transmits the extracted information to the AI ​​in real time, and the AI ​​instantly designs parameters based on that information. The design unit designs parameters based on the concept shared by the sharing unit. The design is performed, for example, based on the type of parameters to be designed and the design process, but is not limited to such examples. For example, the design unit designs numerical parameters.The design unit can also design setting parameters. Furthermore, the design unit can simultaneously design multiple parameters based on the design process. For example, the design unit simultaneously designs numerical parameters and setting parameters. The verification unit verifies the parameters designed by the design unit and designs them without errors, taking into account constraints and parameter descriptions. Verification is performed, for example, based on the items to be verified and the verification method, but is not limited to such examples. For example, the verification unit verifies the technical constraints of the designed parameters. The verification unit can also verify the legal constraints of the designed parameters. Furthermore, the verification unit can verify the usage of the designed parameters. For example, the verification unit verifies the technical specifications of the designed parameters and confirms that there are no errors. This allows the AI ​​system according to the embodiment to efficiently and accurately perform a series of processes, from reading vendor materials to parameter design.

[0030] The reading unit can read specific portions of vendor documents and extract necessary information. The reading unit, for example, reads specific chapters or sections of vendor documents and extracts necessary information. For example, the reading unit scans a specific chapter of a technical document and extracts technical specifications contained in that chapter. The reading unit can also read specific sections of a product catalog and extract product information contained in that section. For example, the reading unit scans a specific section of a product catalog and extracts product pricing information contained in that section. The reading unit can also read specific paragraphs of a contract and extract contract terms contained in that paragraph. For example, the reading unit scans a specific paragraph of a contract and extracts contract terms contained in that paragraph. This allows the reading unit to efficiently read specific portions of vendor documents and extract necessary information. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can scan specific portions of vendor documents, input the scanned data into AI, and allow the AI ​​to extract necessary information.

[0031] The extraction unit can extract necessary data based on the information passed from the reading unit. The extraction unit extracts necessary technical data based on, for example, information in a technical document passed from the reading unit. For example, the extraction unit extracts specific technical specifications from the technical document. The extraction unit can also extract necessary product data based on information in a product catalog passed from the reading unit. For example, the extraction unit extracts price information for a specific product from the product catalog. The extraction unit can also extract necessary contract data based on information in a contract passed from the reading unit. For example, the extraction unit extracts specific contract terms from the contract. This allows the extraction unit to efficiently extract necessary data based on the information passed from the reading unit. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the information passed from the reading unit into AI, and the AI ​​can extract the necessary data.

[0032] The sharing unit can share the SB parameter design concept with the AI ​​based on the data passed from the extraction unit. The sharing unit shares the SB parameter design concept with the AI, for example, based on the technical data passed from the extraction unit. For example, the sharing unit shares the technical data with the AI ​​in text format. The sharing unit can also share the SB parameter design concept with the AI ​​based on the product data passed from the extraction unit. For example, the sharing unit shares the product data with the AI ​​in a visual format. The sharing unit can also share the SB parameter design concept with the AI ​​based on the contract data passed from the extraction unit. For example, the sharing unit shares the contract data with the AI ​​in real time. This allows the sharing unit to efficiently share the SB parameter design concept with the AI ​​based on the data passed from the extraction unit. Some or all of the above-mentioned processing in the sharing unit may be performed using, or without, AI. For example, the sharing unit inputs the data passed from the extraction unit into the AI, allowing the AI ​​to understand and share the SB parameter design concept.

[0033] The design department can design parameters based on the concept passed from the shared department. The design department, for example, designs technical parameters based on the technical concept passed from the shared department. For example, the design department designs numerical parameters based on the technical concept. The design department can also design product parameters based on the product concept passed from the shared department. For example, the design department designs setting parameters based on the product concept. The design department can also design contract parameters based on the contract concept passed from the shared department. For example, the design department simultaneously designs multiple parameters based on the contract concept. This allows the design department to efficiently design parameters based on the concept passed from the shared department. Some or all of the above-mentioned processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input the concept passed from the shared department into AI, which then designs the parameters.

[0034] The verification unit verifies the parameters designed by the design unit and can design the product without errors by referring to constraints and parameter descriptions. The verification unit, for example, verifies the technical parameters designed by the design unit and checks the technical constraints. For example, the verification unit verifies the technical specifications of the technical parameters and checks that there are no errors. The verification unit can also verify the product parameters designed by the design unit and check legal constraints. For example, the verification unit verifies the legal requirements of the product parameters and checks that there are no errors. The verification unit can also verify the contract parameters designed by the design unit and check how they are used. For example, the verification unit verifies how the contract parameters are used and checks that there are no errors. In this way, the verification unit verifies the parameters designed by the design unit and designs the product without errors, thereby enabling accurate parameter design. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without AI. For example, the verification unit can input the parameters designed by the design unit into AI, which can then verify the parameters.

[0035] When reading vendor materials, the reading unit can determine the reading priority based on the importance of the materials. The reading unit, for example, evaluates the importance of the vendor materials and determines the reading priority based on the evaluation. For example, materials with high importance are read first. Materials with low importance can also be postponed. Materials with medium importance can also be read as appropriate depending on the reading status of other materials. In this way, by determining the reading priority based on the importance of the materials, important materials can be read first. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the importance of the vendor materials into AI, which evaluates the importance, and determine the reading priority based on the result.

[0036] When reading vendor documents, the reading unit can apply different reading algorithms depending on the category of the document. For example, the reading unit evaluates the category of the vendor document and applies different reading algorithms based on the evaluation. For example, a detailed reading algorithm can be applied to technical documents. A simplified reading algorithm can also be applied to marketing documents. Furthermore, an algorithm that prioritizes reading specific numerical data can also be applied to financial documents. In this way, by applying an appropriate reading algorithm depending on the document category, documents can be read efficiently. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the category of the vendor document into AI, which evaluates the category, and apply an appropriate reading algorithm based on the evaluation result.

[0037] When reading vendor documents, the reading unit can adjust the reading order based on the submission dates of the documents. The reading unit, for example, evaluates the submission dates of the vendor documents and adjusts the reading order based on the evaluation. For example, the reading unit prioritizes reading the most recent documents. Older documents can also be postponed. Documents that are close in date of submission can also be read simultaneously. In this way, by adjusting the reading order based on the submission dates of the documents, the most recent documents can be prioritized for reading. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the submission dates of the vendor documents into AI, which evaluates the submission dates, and adjust the reading order based on the results.

[0038] When reading vendor materials, the reading unit can adjust the level of detail of the reading based on the relevance of the materials. The reading unit, for example, evaluates the relevance of the vendor materials and adjusts the level of detail of the reading based on the evaluation. For example, highly relevant materials are read in detail. Low relevant materials can also be read more simply. For medium relevant materials, the level of detail can be adjusted appropriately depending on the reading status of other materials. In this way, by adjusting the level of detail of the reading based on the relevance of the materials, materials can be read efficiently. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the relevance of the vendor materials to AI, which evaluates the relevance, and adjust the level of detail of the reading based on the result.

[0039] The extraction unit can adjust the level of extraction detail based on the importance of the information during extraction. For example, the extraction unit evaluates the importance of the information and adjusts the level of extraction detail based on the evaluation. For example, information of high importance is extracted in detail. Information of low importance can also be extracted simply. Information of medium importance can also have its level of detail appropriately adjusted depending on the extraction status of other information. In this way, by adjusting the level of extraction detail based on the importance of the information, information can be extracted efficiently. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the importance of the information into AI, which evaluates the importance, and adjust the level of extraction detail based on the result.

[0040] The extraction unit can apply different extraction algorithms depending on the category of information during extraction. For example, the extraction unit evaluates the category of information and applies different extraction algorithms based on the evaluation. For example, a detailed extraction algorithm can be applied to technical information. A simplified extraction algorithm can also be applied to marketing information. Furthermore, an algorithm that preferentially extracts specific numerical data can also be applied to financial information. This allows information to be extracted efficiently by applying an appropriate extraction algorithm depending on the category of information. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the category of information into AI, which evaluates the category, and apply an appropriate extraction algorithm based on the evaluation result.

[0041] The extraction unit can determine the extraction priority based on the time of information submission during extraction. The extraction unit, for example, evaluates the time of information submission and determines the extraction priority based on the evaluation. For example, the extraction unit prioritizes the most recent information. Older information can also be postponed. Information that is close in time of submission can also be extracted simultaneously. In this way, by determining the extraction priority based on the time of information submission, the most recent information can be preferentially extracted. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the time of information submission into AI, which evaluates the submission time, and determine the extraction priority based on the result.

[0042] The extraction unit can adjust the extraction order based on the relevance of information during extraction. The extraction unit, for example, evaluates the relevance of information and adjusts the extraction order based on the evaluation. For example, highly relevant information can be extracted preferentially. Information with low relevance can also be postponed. The order of information with medium relevance can also be adjusted appropriately depending on the extraction status of other information. In this way, by adjusting the extraction order based on the relevance of information, information can be extracted efficiently. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the relevance of information to AI, which evaluates the relevance, and adjust the extraction order based on the result.

[0043] The sharing unit can adjust the level of detail of the sharing based on the importance of the concept when sharing. For example, the sharing unit evaluates the importance of the concept and adjusts the level of detail of the sharing based on the evaluation. For example, concepts with high importance are shared in detail. Concepts with low importance can also be shared simply. For concepts with medium importance, the level of detail can also be adjusted appropriately depending on the sharing status of other concepts. In this way, concepts can be shared efficiently by adjusting the level of detail of the sharing based on the importance of the concept. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the importance of the concept to AI, which evaluates the importance, and adjust the level of detail of the sharing based on the result.

[0044] The sharing unit can apply different sharing algorithms depending on the concept category when sharing. For example, the sharing unit evaluates the concept category and applies different sharing algorithms based on the evaluation. For example, a detailed sharing algorithm can be applied to technical concepts. A simplified sharing algorithm can also be applied to marketing concepts. Furthermore, an algorithm that preferentially shares specific numerical data can also be applied to financial concepts. In this way, concepts can be shared efficiently by applying an appropriate sharing algorithm depending on the concept category. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the concept category into AI, which evaluates the category, and apply an appropriate sharing algorithm based on the result.

[0045] The sharing unit can adjust the order of sharing based on the submission date of the concepts when sharing. The sharing unit, for example, evaluates the submission date of the concepts and adjusts the order of sharing based on the evaluation. For example, the sharing unit prioritizes sharing the most recent concepts. Older concepts can also be postponed. Concepts that were submitted recently can also be shared simultaneously. In this way, by adjusting the order of sharing based on the submission date of the concepts, the most recent concepts can be shared preferentially. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the submission date of the concepts into AI, which can evaluate the submission date and adjust the order of sharing based on the result.

[0046] The sharing unit can adjust the level of detail of sharing based on the relevance of concepts when sharing. The sharing unit, for example, evaluates the relevance of concepts and adjusts the level of detail of sharing based on the evaluation. For example, highly relevant concepts are shared in detail. Low-relevance concepts can also be shared simply. For moderately relevant concepts, the level of detail can also be adjusted appropriately depending on the sharing status of other concepts. In this way, concepts can be shared efficiently by adjusting the level of detail of sharing based on the relevance of concepts. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the relevance of concepts to AI, which evaluates the relevance, and adjust the level of detail of sharing based on the result.

[0047] The design department can adjust the level of detail of the design based on the importance of the parameters during design. For example, the design department evaluates the importance of the parameters and adjusts the level of detail of the design based on the evaluation. For example, parameters with high importance are designed in detail. Parameters with low importance can also be designed simply. Parameters with medium importance can also have their level of detail appropriately adjusted depending on the design status of other parameters. In this way, by adjusting the level of detail of the design based on the importance of the parameters, parameters can be designed efficiently. Some or all of the above-mentioned processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input the importance of the parameters to AI, which evaluates the importance, and adjust the level of detail of the design based on the result.

[0048] During design, the design department can apply different design algorithms depending on the category of the parameter. For example, the design department evaluates the category of the parameter and applies different design algorithms based on the evaluation. For example, a detailed design algorithm can be applied to technical parameters. A simplified design algorithm can also be applied to marketing parameters. Furthermore, an algorithm that prioritizes designing specific numerical data can also be applied to financial parameters. In this way, by applying an appropriate design algorithm depending on the category of the parameter, parameters can be designed efficiently. Some or all of the above-mentioned processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input the category of the parameter into AI, which evaluates the category, and apply an appropriate design algorithm based on the result.

[0049] During design, the design department can determine design priorities based on the submission dates of parameters. The design department, for example, evaluates the submission dates of parameters and determines design priorities based on the evaluation. For example, the latest parameters are given priority in design. Older parameters can also be postponed. Parameters with similar submission dates can also be designed simultaneously. In this way, by determining design priorities based on the submission dates of parameters, the latest parameters can be given priority in design. Some or all of the above-described processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input the submission dates of parameters into AI, which evaluates the submission dates, and determine design priorities based on the results.

[0050] The design unit can adjust the design order based on the relevance of parameters during design. The design unit, for example, evaluates the relevance of parameters and adjusts the design order based on the evaluation. For example, highly relevant parameters are given priority in design. Parameters with low relevance can also be postponed. The order of parameters with medium relevance can also be adjusted appropriately depending on the design status of other parameters. In this way, by adjusting the design order based on the relevance of parameters, parameters can be designed efficiently. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input the relevance of parameters to AI, which evaluates the relevance, and adjust the design order based on the result.

[0051] The verification unit can adjust the level of verification detail based on the importance of the parameter during verification. The verification unit, for example, evaluates the importance of the parameter and adjusts the level of verification detail based on the evaluation. For example, parameters with high importance are verified in detail. Parameters with low importance can also be verified simply. Parameters with medium importance can also have their level of detail appropriately adjusted depending on the verification status of other parameters. In this way, by adjusting the level of verification detail based on the importance of the parameter, parameters can be verified efficiently. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the importance of the parameter to AI, which evaluates the importance, and adjust the level of verification detail based on the result.

[0052] During verification, the verification unit can apply different verification algorithms depending on the parameter category. For example, the verification unit evaluates the parameter category and applies different verification algorithms based on the evaluation. For example, a detailed verification algorithm can be applied to technical parameters. A simplified verification algorithm can also be applied to marketing parameters. Furthermore, an algorithm that prioritizes verification of specific numerical data can also be applied to financial parameters. In this way, parameters can be efficiently verified by applying an appropriate verification algorithm depending on the parameter category. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the parameter category into AI, which evaluates the category, and apply an appropriate verification algorithm based on the result.

[0053] During verification, the verification unit can determine the priority of verification based on the time of submission of parameters. The verification unit, for example, evaluates the time of submission of parameters and determines the priority of verification based on the evaluation. For example, the verification unit prioritizes verification of the latest parameters. Older parameters can also be postponed. Parameters that have been submitted recently can also be verified simultaneously. In this way, by determining the priority of verification based on the time of submission of parameters, the latest parameters can be verified preferentially. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the time of submission of parameters to AI, have the AI ​​evaluate the submission time, and determine the priority of verification based on the result.

[0054] The verification unit can adjust the verification order based on the relevance of parameters during verification. The verification unit, for example, evaluates the relevance of parameters and adjusts the verification order based on the evaluation. For example, highly relevant parameters are verified first. Parameters with low relevance can also be postponed. The order of parameters with medium relevance can also be adjusted appropriately depending on the verification status of other parameters. In this way, by adjusting the verification order based on the relevance of parameters, parameters can be verified efficiently. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the relevance of parameters to AI, which evaluates the relevance, and adjust the verification order based on the result.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When reading vendor documents, the reading unit can determine the reading priority based on the importance of the documents. For example, documents with high importance can be read first. Documents with low importance can also be postponed. Documents with medium importance can also be read as appropriate depending on the reading status of other documents. In this way, by determining the reading priority based on the importance of the documents, important documents can be read first. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the importance of the vendor documents into AI, which evaluates the importance, and determine the reading priority based on the evaluation result.

[0057] When reading vendor documents, the reading unit can apply different reading algorithms depending on the category of the document. For example, a detailed reading algorithm can be applied to technical documents. A simplified reading algorithm can be applied to marketing documents. An algorithm that prioritizes reading specific numerical data can be applied to financial documents. This allows documents to be read efficiently by applying an appropriate reading algorithm depending on the document category. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the category of the vendor document into AI, which evaluates the category and applies an appropriate reading algorithm based on the evaluation result.

[0058] The extraction unit can adjust the level of extraction detail based on the importance of the information during extraction. For example, information of high importance can be extracted in detail. Information of low importance can also be extracted simply. Information of medium importance can also have its level of detail adjusted appropriately depending on the extraction status of other information. In this way, by adjusting the level of extraction detail based on the importance of the information, information can be extracted efficiently. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the importance of the information into AI, which evaluates the importance, and adjust the level of extraction detail based on the evaluation result.

[0059] During extraction, the extraction unit can apply different extraction algorithms depending on the category of information. For example, a detailed extraction algorithm can be applied to technical information. A simplified extraction algorithm can be applied to marketing information. An algorithm that preferentially extracts specific numerical data can be applied to financial information. This allows information to be extracted efficiently by applying an appropriate extraction algorithm depending on the category of information. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the category of information into AI, which evaluates the category and then applies an appropriate extraction algorithm based on the evaluation result.

[0060] During design, the design department can adjust the level of detail of the design based on the importance of the parameters. For example, parameters with high importance can be designed in detail. Parameters with low importance can also be designed simply. Parameters with medium importance can also have their level of detail adjusted appropriately depending on the design status of other parameters. In this way, by adjusting the level of detail of the design based on the importance of the parameters, parameters can be designed efficiently. Some or all of the above-mentioned processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input the importance of the parameters into AI, which evaluates the importance and adjusts the level of detail of the design based on the results.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reader reads vendor documents. Vendor documents include technical documents, product catalogs, contracts, etc. The reader uses scanning technology to digitize and read the technical documents. It can also directly read documents submitted in digital format. It can also read printed documents using OCR technology. For example, technical documents can be scanned with a high-resolution scanner and converted into text information using OCR technology. Digital documents submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: The extraction unit extracts the necessary data based on the information read by the reading unit. Extraction is performed using methods such as keyword search and pattern matching. For example, specific keywords can be searched for in technical documents to extract related information. Pattern matching technology can also be used to extract information that matches a specific pattern. Furthermore, natural language processing technology can be used to analyze the contents of documents and extract the necessary information. For example, the contents of technical documents can be analyzed to extract specific technical specifications or price information. Step 3: The sharing unit shares the SB parameter design concept with the AI ​​based on the information extracted by the extraction unit. Sharing is performed based on the format and means of information. For example, the extracted information can be shared with the AI ​​in text format. It can also be shared with the AI ​​in visual format. It can also be shared with the AI ​​in real time. For example, the extracted information can be sent to the AI ​​in real time, and the AI ​​can instantly design parameters based on that information. Step 4: The design department designs parameters based on the concepts shared by the sharing department. The design is performed based on the type of parameters to be designed and the design process. For example, numerical parameters are designed. Setting parameters can also be designed. Furthermore, multiple parameters can be designed simultaneously. For example, numerical parameters and setting parameters can be designed simultaneously. Step 5: The Verification Department verifies the parameters designed by the Design Department, and designs without errors, taking into account constraints and parameter descriptions. Verification is performed based on the items to be verified and the verification method. For example, the technical constraints of the designed parameters are verified. It is also possible to verify legal constraints of the designed parameters. It is also possible to verify how the designed parameters are used. For example, the technical specifications of the designed parameters are verified to ensure there are no errors.

[0063] (Example 2) An AI system according to an embodiment of the present invention proposes appropriate parameters by reading vendor documents and sharing the SB parameter design concept with the AI. This AI system reads vendor documents, extracts necessary information, shares the SB parameter design concept, designs parameters based on that concept, and performs an error-free design by referring to constraints and parameter descriptions. For example, it can propose optimal parameters under specific constraint conditions. This system enables efficient and accurate parameter design. First, a "reading unit" is provided to read vendor documents, followed by an "extraction unit" to extract necessary information. Next, a "sharing unit" is provided to share the SB parameter design concept, followed by a "design unit" to design parameters. Finally, a "verification unit" is provided to perform an error-free design by referring to constraints and parameter descriptions. The "reading unit" reads specific parts of the vendor documents and extracts necessary information. The "extraction unit" extracts necessary data based on the information provided by the reading unit. The "sharing unit" shares the SB parameter design concept with the AI ​​based on the data provided by the extraction unit. The "design department" designs parameters based on the concept passed to it by the shared department. The "verification department" verifies the parameters designed by the design department and performs the design without errors, taking into account constraints and parameter descriptions. This system efficiently and accurately carries out the entire process from reading vendor documents to parameter design. For example, it can propose optimal parameters under specific constraint conditions. This enables efficient and accurate parameter design. This allows the AI ​​system to efficiently and accurately carry out the entire process from reading vendor documents to parameter design.

[0064] The AI ​​system according to the embodiment includes a reading unit, an extraction unit, a sharing unit, a design unit, and a verification unit. The reading unit reads vendor documents. Examples of vendor documents include, but are not limited to, technical documents, product catalogs, and contracts. For example, the reading unit digitizes and reads the technical documents using scanning technology. The reading unit can also directly read documents submitted in digital format. The reading unit can also read printed documents using OCR technology. For example, the reading unit scans technical documents with a high-resolution scanner and converts them into text information using OCR technology. Digital documents submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The extraction unit extracts necessary data based on the information read by the reading unit. Extraction can be performed using, for example, keyword search or pattern matching, but is not limited to, examples. For example, the extraction unit searches for specific keywords in technical documents and extracts related information. The extraction unit can also use pattern matching technology to extract information that matches a specific pattern. Furthermore, the extraction unit can also use natural language processing technology to analyze the content of a document and extract necessary information. For example, the extraction unit analyzes the content of a technical document and extracts specific technical specifications and price information. The sharing unit shares the SB parameter design concept with the AI ​​based on the information extracted by the extraction unit. Sharing is performed, for example, based on the format of the information and the means of sharing, but is not limited to such examples. For example, the sharing unit shares the extracted information with the AI ​​in text format. The sharing unit can also share the extracted information with the AI ​​in visual format. Furthermore, the sharing unit can share the extracted information with the AI ​​in real time. For example, the sharing unit transmits the extracted information to the AI ​​in real time, and the AI ​​instantly designs parameters based on that information. The design unit designs parameters based on the concept shared by the sharing unit. The design is performed, for example, based on the type of parameters to be designed and the design process, but is not limited to such examples. For example, the design unit designs numerical parameters.The design unit can also design setting parameters. Furthermore, the design unit can simultaneously design multiple parameters based on the design process. For example, the design unit simultaneously designs numerical parameters and setting parameters. The verification unit verifies the parameters designed by the design unit and designs them without errors, taking into account constraints and parameter descriptions. Verification is performed, for example, based on the items to be verified and the verification method, but is not limited to such examples. For example, the verification unit verifies the technical constraints of the designed parameters. The verification unit can also verify the legal constraints of the designed parameters. Furthermore, the verification unit can verify the usage of the designed parameters. For example, the verification unit verifies the technical specifications of the designed parameters and confirms that there are no errors. This allows the AI ​​system according to the embodiment to efficiently and accurately perform a series of processes, from reading vendor materials to parameter design.

[0065] The reading unit can read specific portions of vendor documents and extract necessary information. The reading unit, for example, reads specific chapters or sections of vendor documents and extracts necessary information. For example, the reading unit scans a specific chapter of a technical document and extracts technical specifications contained in that chapter. The reading unit can also read specific sections of a product catalog and extract product information contained in that section. For example, the reading unit scans a specific section of a product catalog and extracts product pricing information contained in that section. The reading unit can also read specific paragraphs of a contract and extract contract terms contained in that paragraph. For example, the reading unit scans a specific paragraph of a contract and extracts contract terms contained in that paragraph. This allows the reading unit to efficiently read specific portions of vendor documents and extract necessary information. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can scan specific portions of vendor documents, input the scanned data into AI, and allow the AI ​​to extract necessary information.

[0066] The extraction unit can extract necessary data based on the information passed from the reading unit. The extraction unit extracts necessary technical data based on, for example, information in a technical document passed from the reading unit. For example, the extraction unit extracts specific technical specifications from the technical document. The extraction unit can also extract necessary product data based on information in a product catalog passed from the reading unit. For example, the extraction unit extracts price information for a specific product from the product catalog. The extraction unit can also extract necessary contract data based on information in a contract passed from the reading unit. For example, the extraction unit extracts specific contract terms from the contract. This allows the extraction unit to efficiently extract necessary data based on the information passed from the reading unit. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the information passed from the reading unit into AI, and the AI ​​can extract the necessary data.

[0067] The sharing unit can share the SB parameter design concept with the AI ​​based on the data passed from the extraction unit. The sharing unit shares the SB parameter design concept with the AI, for example, based on the technical data passed from the extraction unit. For example, the sharing unit shares the technical data with the AI ​​in text format. The sharing unit can also share the SB parameter design concept with the AI ​​based on the product data passed from the extraction unit. For example, the sharing unit shares the product data with the AI ​​in a visual format. The sharing unit can also share the SB parameter design concept with the AI ​​based on the contract data passed from the extraction unit. For example, the sharing unit shares the contract data with the AI ​​in real time. This allows the sharing unit to efficiently share the SB parameter design concept with the AI ​​based on the data passed from the extraction unit. Some or all of the above-mentioned processing in the sharing unit may be performed using, or without, AI. For example, the sharing unit inputs the data passed from the extraction unit into the AI, allowing the AI ​​to understand and share the SB parameter design concept.

[0068] The design department can design parameters based on the concept passed from the shared department. The design department, for example, designs technical parameters based on the technical concept passed from the shared department. For example, the design department designs numerical parameters based on the technical concept. The design department can also design product parameters based on the product concept passed from the shared department. For example, the design department designs setting parameters based on the product concept. The design department can also design contract parameters based on the contract concept passed from the shared department. For example, the design department simultaneously designs multiple parameters based on the contract concept. This allows the design department to efficiently design parameters based on the concept passed from the shared department. Some or all of the above-mentioned processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input the concept passed from the shared department into AI, which then designs the parameters.

[0069] The verification unit verifies the parameters designed by the design unit and can design the product without errors by referring to constraints and parameter descriptions. The verification unit, for example, verifies the technical parameters designed by the design unit and checks the technical constraints. For example, the verification unit verifies the technical specifications of the technical parameters and checks that there are no errors. The verification unit can also verify the product parameters designed by the design unit and check legal constraints. For example, the verification unit verifies the legal requirements of the product parameters and checks that there are no errors. The verification unit can also verify the contract parameters designed by the design unit and check how they are used. For example, the verification unit verifies how the contract parameters are used and checks that there are no errors. In this way, the verification unit verifies the parameters designed by the design unit and designs the product without errors, thereby enabling accurate parameter design. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without AI. For example, the verification unit can input the parameters designed by the design unit into AI, which can then verify the parameters.

[0070] The reading unit can estimate the user's emotion and adjust the timing of loading vendor materials based on the estimated user emotion. For example, the reading unit can estimate the user's emotion and adjust the timing of loading vendor materials based on the estimated emotion. For example, if the user is stressed, the timing of loading vendor materials can be delayed to reduce the user's burden. Also, if the user is relaxed, the timing of loading vendor materials can be accelerated to provide information quickly if the user is in a hurry. This reduces the user's burden by adjusting the timing of loading vendor materials according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a 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. Some or all of the above-described processing in the reading unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reading unit can input the user's facial expression data into the generation AI, which can estimate the emotion and adjust the timing of loading based on the result.

[0071] When reading vendor materials, the reading unit can determine the reading priority based on the importance of the materials. The reading unit, for example, evaluates the importance of the vendor materials and determines the reading priority based on the evaluation. For example, materials with high importance are read first. Materials with low importance can also be postponed. Materials with medium importance can also be read as appropriate depending on the reading status of other materials. In this way, by determining the reading priority based on the importance of the materials, important materials can be read first. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the importance of the vendor materials into AI, which evaluates the importance, and determine the reading priority based on the result.

[0072] When reading vendor documents, the reading unit can apply different reading algorithms depending on the category of the document. For example, the reading unit evaluates the category of the vendor document and applies different reading algorithms based on the evaluation. For example, a detailed reading algorithm can be applied to technical documents. A simplified reading algorithm can also be applied to marketing documents. Furthermore, an algorithm that prioritizes reading specific numerical data can also be applied to financial documents. In this way, by applying an appropriate reading algorithm depending on the document category, documents can be read efficiently. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the category of the vendor document into AI, which evaluates the category, and apply an appropriate reading algorithm based on the evaluation result.

[0073] The reading unit can estimate the user's emotions and determine the priority of materials to be read based on the estimated user emotions. For example, the reading unit can estimate the user's emotions and determine the priority of materials to be read based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize reading less important materials. Also, if the user is relaxed, it can prioritize reading more important materials. Also, if the user is in a hurry, it can prioritize reading the most important materials. This reduces the burden on the user by determining the priority of materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the reading unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reading unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and determine the priority of materials based on the result.

[0074] When reading vendor documents, the reading unit can adjust the reading order based on the submission dates of the documents. The reading unit, for example, evaluates the submission dates of the vendor documents and adjusts the reading order based on the evaluation. For example, the reading unit prioritizes reading the most recent documents. Older documents can also be postponed. Documents that are close in date of submission can also be read simultaneously. In this way, by adjusting the reading order based on the submission dates of the documents, the most recent documents can be prioritized for reading. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the submission dates of the vendor documents into AI, which evaluates the submission dates, and adjust the reading order based on the results.

[0075] When reading vendor materials, the reading unit can adjust the level of detail of the reading based on the relevance of the materials. The reading unit, for example, evaluates the relevance of the vendor materials and adjusts the level of detail of the reading based on the evaluation. For example, highly relevant materials are read in detail. Low relevant materials can also be read more simply. For medium relevant materials, the level of detail can be adjusted appropriately depending on the reading status of other materials. In this way, by adjusting the level of detail of the reading based on the relevance of the materials, materials can be read efficiently. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the relevance of the vendor materials to AI, which evaluates the relevance, and adjust the level of detail of the reading based on the result.

[0076] The extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated user emotions. The extraction unit, for example, estimates the user's emotions and determines the priority of information to be extracted based on the estimated emotions. For example, if the user is stressed, it can prioritize extracting less important information. Also, if the user is relaxed, it can prioritize extracting more important information. Also, if the user is in a hurry, it can prioritize extracting the most important information. This reduces the burden on the user by determining the priority of information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or without AI. For example, the extraction unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and determine the priority of information based on the result.

[0077] The extraction unit can adjust the level of extraction detail based on the importance of the information during extraction. For example, the extraction unit evaluates the importance of the information and adjusts the level of extraction detail based on the evaluation. For example, information of high importance is extracted in detail. Information of low importance can also be extracted simply. Information of medium importance can also have its level of detail appropriately adjusted depending on the extraction status of other information. In this way, by adjusting the level of extraction detail based on the importance of the information, information can be extracted efficiently. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the importance of the information into AI, which evaluates the importance, and adjust the level of extraction detail based on the result.

[0078] The extraction unit can apply different extraction algorithms depending on the category of information during extraction. For example, the extraction unit evaluates the category of information and applies different extraction algorithms based on the evaluation. For example, a detailed extraction algorithm can be applied to technical information. A simplified extraction algorithm can also be applied to marketing information. Furthermore, an algorithm that preferentially extracts specific numerical data can also be applied to financial information. This allows information to be extracted efficiently by applying an appropriate extraction algorithm depending on the category of information. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the category of information into AI, which evaluates the category, and apply an appropriate extraction algorithm based on the evaluation result.

[0079] The extraction unit can estimate the user's emotion and adjust the display method of the extracted information based on the estimated user emotion. For example, the extraction unit can estimate the user's emotion and adjust the display method of the extracted information based on the estimated emotion. For example, if the user is stressed, a simple display method can be provided. If the user is relaxed, a detailed display method can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This reduces the burden on the user by adjusting the display method of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the extraction unit can be performed using, for example, an AI, or without an AI. For example, the extraction unit can input the user's facial expression data into a generation AI, which can estimate the emotion and adjust the display method of the information based on the result.

[0080] The extraction unit can determine the extraction priority based on the time of information submission during extraction. The extraction unit, for example, evaluates the time of information submission and determines the extraction priority based on the evaluation. For example, the extraction unit prioritizes the most recent information. Older information can also be postponed. Information that is close in time of submission can also be extracted simultaneously. In this way, by determining the extraction priority based on the time of information submission, the most recent information can be preferentially extracted. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the time of information submission into AI, which evaluates the submission time, and determine the extraction priority based on the result.

[0081] The extraction unit can adjust the extraction order based on the relevance of information during extraction. The extraction unit, for example, evaluates the relevance of information and adjusts the extraction order based on the evaluation. For example, highly relevant information can be extracted preferentially. Information with low relevance can also be postponed. The order of information with medium relevance can also be adjusted appropriately depending on the extraction status of other information. In this way, by adjusting the extraction order based on the relevance of information, information can be extracted efficiently. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the relevance of information to AI, which evaluates the relevance, and adjust the extraction order based on the result.

[0082] The sharing unit can estimate the user's emotions and adjust the expression method of the shared concept based on the estimated user emotions. For example, the sharing unit can estimate the user's emotions and adjust the expression method of the shared concept based on the estimated emotions. For example, if the user is stressed, the sharing unit can provide a simple expression method. If the user is relaxed, the sharing unit can provide a detailed expression method. If the user is in a hurry, the sharing unit can provide a basic expression method. This reduces the burden on the user by adjusting the expression method of the concept according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the sharing unit can be performed using, for example, AI, or without AI. For example, the sharing unit can input the user's facial expression data into a generation AI, which can estimate the emotion and adjust the expression method of the concept based on the result.

[0083] The sharing unit can adjust the level of detail of the sharing based on the importance of the concept when sharing. For example, the sharing unit evaluates the importance of the concept and adjusts the level of detail of the sharing based on the evaluation. For example, concepts with high importance are shared in detail. Concepts with low importance can also be shared simply. For concepts with medium importance, the level of detail can also be adjusted appropriately depending on the sharing status of other concepts. In this way, concepts can be shared efficiently by adjusting the level of detail of the sharing based on the importance of the concept. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the importance of the concept to AI, which evaluates the importance, and adjust the level of detail of the sharing based on the result.

[0084] The sharing unit can apply different sharing algorithms depending on the concept category when sharing. For example, the sharing unit evaluates the concept category and applies different sharing algorithms based on the evaluation. For example, a detailed sharing algorithm can be applied to technical concepts. A simplified sharing algorithm can also be applied to marketing concepts. Furthermore, an algorithm that preferentially shares specific numerical data can also be applied to financial concepts. In this way, concepts can be shared efficiently by applying an appropriate sharing algorithm depending on the concept category. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the concept category into AI, which evaluates the category, and apply an appropriate sharing algorithm based on the result.

[0085] The sharing unit can estimate the user's emotions and determine the priority of concepts to be shared based on the estimated user emotions. The sharing unit, for example, estimates the user's emotions and determines the priority of concepts to be shared based on the estimated emotions. For example, if the user is stressed, it can prioritize sharing less important concepts. Also, if the user is relaxed, it can prioritize sharing more important concepts. Also, if the user is in a hurry, it can prioritize sharing the most important concepts. This reduces the burden on the user by determining the priority of concepts according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the sharing unit can be performed using, for example, AI, or without AI. For example, the sharing unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and determine the priority of concepts based on the result.

[0086] The sharing unit can adjust the order of sharing based on the submission date of the concepts when sharing. The sharing unit, for example, evaluates the submission date of the concepts and adjusts the order of sharing based on the evaluation. For example, the sharing unit prioritizes sharing the most recent concepts. Older concepts can also be postponed. Concepts that were submitted recently can also be shared simultaneously. In this way, by adjusting the order of sharing based on the submission date of the concepts, the most recent concepts can be shared preferentially. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the submission date of the concepts into AI, which can evaluate the submission date and adjust the order of sharing based on the result.

[0087] The sharing unit can adjust the level of detail of sharing based on the relevance of concepts when sharing. The sharing unit, for example, evaluates the relevance of concepts and adjusts the level of detail of sharing based on the evaluation. For example, highly relevant concepts are shared in detail. Low-relevance concepts can also be shared simply. For moderately relevant concepts, the level of detail can also be adjusted appropriately depending on the sharing status of other concepts. In this way, concepts can be shared efficiently by adjusting the level of detail of sharing based on the relevance of concepts. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the relevance of concepts to AI, which evaluates the relevance, and adjust the level of detail of sharing based on the result.

[0088] The design unit can estimate the user's emotions and determine the priority of parameters to be designed based on the estimated user emotions. For example, the design unit can estimate the user's emotions and determine the priority of parameters to be designed based on the estimated emotions. For example, if the user is stressed, it can prioritize less important parameters. Also, if the user is relaxed, it can prioritize more important parameters. Also, if the user is in a hurry, it can prioritize the most important parameters. This reduces the burden on the user by determining the priority of parameters according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the design unit can be performed using, for example, an AI, or without an AI. For example, the design unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and determine the priority of parameters based on the results.

[0089] The design department can adjust the level of detail of the design based on the importance of the parameters during design. For example, the design department evaluates the importance of the parameters and adjusts the level of detail of the design based on the evaluation. For example, parameters with high importance are designed in detail. Parameters with low importance can also be designed simply. Parameters with medium importance can also have their level of detail appropriately adjusted depending on the design status of other parameters. In this way, by adjusting the level of detail of the design based on the importance of the parameters, parameters can be designed efficiently. Some or all of the above-mentioned processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input the importance of the parameters to AI, which evaluates the importance, and adjust the level of detail of the design based on the result.

[0090] During design, the design department can apply different design algorithms depending on the category of the parameter. For example, the design department evaluates the category of the parameter and applies different design algorithms based on the evaluation. For example, a detailed design algorithm can be applied to technical parameters. A simplified design algorithm can also be applied to marketing parameters. Furthermore, an algorithm that prioritizes designing specific numerical data can also be applied to financial parameters. In this way, by applying an appropriate design algorithm depending on the category of the parameter, parameters can be designed efficiently. Some or all of the above-mentioned processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input the category of the parameter into AI, which evaluates the category, and apply an appropriate design algorithm based on the result.

[0091] The design unit can estimate the user's emotions and adjust the display method of the parameters to be designed based on the estimated user emotions. For example, the design unit can estimate the user's emotions and adjust the display method of the parameters to be designed based on the estimated emotions. For example, if the user is stressed, a simple display method can be provided. If the user is relaxed, a detailed display method can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This reduces the burden on the user by adjusting the display method of the parameters according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the design unit can be performed using, for example, an AI, or without an AI. For example, the design unit can input the user's facial expression data into the generation AI, which can estimate the emotion and adjust the display method of the parameters based on the result.

[0092] During design, the design department can determine design priorities based on the submission dates of parameters. The design department, for example, evaluates the submission dates of parameters and determines design priorities based on the evaluation. For example, the latest parameters are given priority in design. Older parameters can also be postponed. Parameters with similar submission dates can also be designed simultaneously. In this way, by determining design priorities based on the submission dates of parameters, the latest parameters can be given priority in design. Some or all of the above-described processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input the submission dates of parameters into AI, which evaluates the submission dates, and determine design priorities based on the results.

[0093] The design unit can adjust the design order based on the relevance of parameters during design. The design unit, for example, evaluates the relevance of parameters and adjusts the design order based on the evaluation. For example, highly relevant parameters are given priority in design. Parameters with low relevance can also be postponed. The order of parameters with medium relevance can also be adjusted appropriately depending on the design status of other parameters. In this way, by adjusting the design order based on the relevance of parameters, parameters can be designed efficiently. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input the relevance of parameters to AI, which evaluates the relevance, and adjust the design order based on the result.

[0094] The verification unit can estimate the user's emotions and determine the priority of parameters to be verified based on the estimated user emotions. For example, the verification unit can estimate the user's emotions and determine the priority of parameters to be verified based on the estimated emotions. For example, if the user is stressed, it can prioritize verification of less important parameters. Also, if the user is relaxed, it can prioritize verification of more important parameters. Also, if the user is in a hurry, it can prioritize verification of the most important parameters. This reduces the burden on the user by determining the priority of parameters according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the verification unit can be performed using, for example, an AI, or without an AI. For example, the verification unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and determine the priority of parameters based on the result.

[0095] The verification unit can adjust the level of verification detail based on the importance of the parameter during verification. The verification unit, for example, evaluates the importance of the parameter and adjusts the level of verification detail based on the evaluation. For example, parameters with high importance are verified in detail. Parameters with low importance can also be verified simply. Parameters with medium importance can also have their level of detail appropriately adjusted depending on the verification status of other parameters. In this way, by adjusting the level of verification detail based on the importance of the parameter, parameters can be verified efficiently. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the importance of the parameter to AI, which evaluates the importance, and adjust the level of verification detail based on the result.

[0096] During verification, the verification unit can apply different verification algorithms depending on the parameter category. For example, the verification unit evaluates the parameter category and applies different verification algorithms based on the evaluation. For example, a detailed verification algorithm can be applied to technical parameters. A simplified verification algorithm can also be applied to marketing parameters. Furthermore, an algorithm that prioritizes verification of specific numerical data can also be applied to financial parameters. In this way, parameters can be efficiently verified by applying an appropriate verification algorithm depending on the parameter category. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the parameter category into AI, which evaluates the category, and apply an appropriate verification algorithm based on the result.

[0097] The verification unit can estimate the user's emotion and adjust the display method of the parameters to be verified based on the estimated user emotion. For example, the verification unit can estimate the user's emotion and adjust the display method of the parameters to be verified based on the estimated emotion. For example, if the user is stressed, a simple display method can be provided. If the user is relaxed, a detailed display method can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This reduces the burden on the user by adjusting the display method of the parameters according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as 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. Some or all of the above-described processing in the verification unit can be performed using, for example, an AI, or without an AI. For example, the verification unit can input the user's facial expression data into the generation AI, which can estimate the emotion and adjust the display method of the parameters based on the result.

[0098] During verification, the verification unit can determine the priority of verification based on the time of submission of parameters. The verification unit, for example, evaluates the time of submission of parameters and determines the priority of verification based on the evaluation. For example, the verification unit prioritizes verification of the latest parameters. Older parameters can also be postponed. Parameters that have been submitted recently can also be verified simultaneously. In this way, by determining the priority of verification based on the time of submission of parameters, the latest parameters can be verified preferentially. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the time of submission of parameters to AI, have the AI ​​evaluate the submission time, and determine the priority of verification based on the result.

[0099] The verification unit can adjust the verification order based on the relevance of parameters during verification. The verification unit, for example, evaluates the relevance of parameters and adjusts the verification order based on the evaluation. For example, highly relevant parameters are verified first. Parameters with low relevance can also be postponed. The order of parameters with medium relevance can also be adjusted appropriately depending on the verification status of other parameters. In this way, by adjusting the verification order based on the relevance of parameters, parameters can be verified efficiently. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the relevance of parameters to AI, which evaluates the relevance, and adjust the verification order based on the result. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reading unit, extraction unit, sharing unit, design unit, and verification unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reading unit is realized by the computer 36 of the smart device 14 and reads vendor materials. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts necessary data. The sharing unit is realized by the control unit 46A of the smart device 14 and shares the SB parameter design concept with the AI. The design unit is realized by the specific processing unit 290 of the data processing device 12 and designs parameters. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the designed parameters. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reading unit, extraction unit, sharing unit, design unit, and verification unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reading unit is realized by the computer 36 of the smart glasses 214 and reads vendor materials. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts necessary data. The sharing unit is realized by the control unit 46A of the smart glasses 214 and shares the SB parameter design concept with the AI. The design unit is realized by the specific processing unit 290 of the data processing device 12 and designs parameters. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the designed parameters. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reading unit, extraction unit, sharing unit, design unit, and verification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reading unit is realized by the computer 36 of the headset type terminal 314 and reads vendor materials. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts necessary data. The sharing unit is realized by the control unit 46A of the headset type terminal 314 and shares the SB parameter design concept with the AI. The design unit is realized by the specific processing unit 290 of the data processing device 12 and designs parameters. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the designed parameters. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reading unit, extraction unit, sharing unit, design unit, and verification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reading unit is realized by the computer 36 of the robot 414 and reads vendor materials. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts necessary data. The sharing unit is realized by the control unit 46A of the robot 414 and shares the SB parameter design concept with the AI. The design unit is realized by the specific processing unit 290 of the data processing device 12 and designs parameters. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and verifies the designed parameters.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The reading unit can estimate the user's emotions and adjust the timing of loading vendor materials based on the estimated emotions. For example, if the user is stressed, the timing of loading can be delayed to reduce the user's burden. Also, if the user is relaxed, the timing of loading vendor materials can be accelerated to provide information quickly if the user is in a hurry. This reduces the user's burden by adjusting the timing of loading vendor materials according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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. Some or all of the above-described processing in the reading unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reading unit can input the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the timing of loading based on the estimation result.

[0102] The extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated emotions. For example, if the user is stressed, it can prioritize extracting less important information. Also, if the user is relaxed, it can prioritize extracting more important information. Also, if the user is in a hurry, it can prioritize extracting the most important information. This reduces the burden on the user by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or without AI. For example, the extraction unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and determine the priority of information based on the results.

[0103] The sharing unit can estimate the user's emotions and adjust the expression method of the shared concept based on the estimated emotions. For example, if the user is stressed, a simple expression method can be provided. If the user is relaxed, a detailed expression method can be provided. If the user is in a hurry, a more concise expression method can be provided. This reduces the burden on the user by adjusting the expression method of the concept according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or without AI. For example, the sharing unit can input the user's facial expression data into the generation AI, which can estimate the emotion and adjust the expression method of the concept based on the result.

[0104] The design unit can estimate the user's emotions and prioritize the parameters to be designed based on the estimated emotions. For example, if the user is stressed, it can prioritize less important parameters. Also, if the user is relaxed, it can prioritize more important parameters. Also, if the user is in a hurry, it can prioritize the most important parameters. This reduces the burden on the user by prioritizing parameters according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the design unit can be performed using, for example, AI, or without AI. For example, the design unit can input the user's facial expression data into a generation AI, which can estimate the user's emotions and prioritize the parameters based on the results.

[0105] The verification unit can estimate the user's emotions and determine the priority of parameters to be verified based on the estimated emotions. For example, if the user is stressed, it can prioritize verification of less important parameters. Also, if the user is relaxed, it can prioritize verification of more important parameters. Also, if the user is in a hurry, it can prioritize verification of the most important parameters. This reduces the burden on the user by determining the priority of parameters according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the verification unit can be performed using, for example, AI, or without AI. For example, the verification unit can input the user's facial expression data into a generation AI, which then estimates the user's emotions and determines the priority of parameters based on the results.

[0106] When reading vendor documents, the reading unit can determine the reading priority based on the importance of the documents. For example, documents with high importance can be read first. Documents with low importance can also be postponed. Documents with medium importance can also be read as appropriate depending on the reading status of other documents. In this way, by determining the reading priority based on the importance of the documents, important documents can be read first. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the importance of the vendor documents into AI, which evaluates the importance, and determine the reading priority based on the evaluation result.

[0107] When reading vendor documents, the reading unit can apply different reading algorithms depending on the category of the document. For example, a detailed reading algorithm can be applied to technical documents. A simplified reading algorithm can be applied to marketing documents. An algorithm that prioritizes reading specific numerical data can be applied to financial documents. This allows documents to be read efficiently by applying an appropriate reading algorithm depending on the document category. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the category of the vendor document into AI, which evaluates the category and applies an appropriate reading algorithm based on the evaluation result.

[0108] The extraction unit can adjust the level of extraction detail based on the importance of the information during extraction. For example, information of high importance can be extracted in detail. Information of low importance can also be extracted simply. Information of medium importance can also have its level of detail adjusted appropriately depending on the extraction status of other information. In this way, by adjusting the level of extraction detail based on the importance of the information, information can be extracted efficiently. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the importance of the information into AI, which evaluates the importance, and adjust the level of extraction detail based on the evaluation result.

[0109] During extraction, the extraction unit can apply different extraction algorithms depending on the category of information. For example, a detailed extraction algorithm can be applied to technical information. A simplified extraction algorithm can be applied to marketing information. An algorithm that preferentially extracts specific numerical data can be applied to financial information. This allows information to be extracted efficiently by applying an appropriate extraction algorithm depending on the category of information. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the category of information into AI, which evaluates the category and then applies an appropriate extraction algorithm based on the evaluation result.

[0110] During design, the design department can adjust the level of detail of the design based on the importance of the parameters. For example, parameters with high importance can be designed in detail. Parameters with low importance can also be designed simply. Parameters with medium importance can also have their level of detail adjusted appropriately depending on the design status of other parameters. In this way, by adjusting the level of detail of the design based on the importance of the parameters, parameters can be designed efficiently. Some or all of the above-mentioned processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input the importance of the parameters into AI, which evaluates the importance and adjusts the level of detail of the design based on the results.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reader reads vendor documents. Vendor documents include technical documents, product catalogs, contracts, etc. The reader uses scanning technology to digitize and read the technical documents. It can also directly read documents submitted in digital format. It can also read printed documents using OCR technology. For example, technical documents can be scanned with a high-resolution scanner and converted into text information using OCR technology. Digital documents submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: The extraction unit extracts the necessary data based on the information read by the reading unit. Extraction is performed using methods such as keyword search and pattern matching. For example, specific keywords can be searched for in technical documents to extract related information. Pattern matching technology can also be used to extract information that matches a specific pattern. Furthermore, natural language processing technology can be used to analyze the contents of documents and extract the necessary information. For example, the contents of technical documents can be analyzed to extract specific technical specifications or price information. Step 3: The sharing unit shares the SB parameter design concept with the AI ​​based on the information extracted by the extraction unit. Sharing is performed based on the format and means of information. For example, the extracted information can be shared with the AI ​​in text format. It can also be shared with the AI ​​in visual format. It can also be shared with the AI ​​in real time. For example, the extracted information can be sent to the AI ​​in real time, and the AI ​​can instantly design parameters based on that information. Step 4: The design department designs parameters based on the concepts shared by the sharing department. The design is performed based on the type of parameters to be designed and the design process. For example, numerical parameters are designed. Setting parameters can also be designed. Furthermore, multiple parameters can be designed simultaneously. For example, numerical parameters and setting parameters can be designed simultaneously. Step 5: The Verification Department verifies the parameters designed by the Design Department, and designs without errors, taking into account constraints and parameter descriptions. Verification is performed based on the items to be verified and the verification method. For example, the technical constraints of the designed parameters are verified. It is also possible to verify legal constraints of the designed parameters. It is also possible to verify how the designed parameters are used. For example, the technical specifications of the designed parameters are verified to ensure there are no errors.

[0113] 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.

[0114] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reading unit for reading vendor documents; an extracting unit that extracts the information read by the reading unit; a sharing unit that shares a concept of parameter design of the SB based on the information extracted by the extraction unit; a design unit that designs parameters based on the concept shared by the sharing unit; a verification unit that verifies the parameters designed by the design unit and performs a design so that errors do not occur, by referring to the constraints and explanations of the parameters. A system characterized by:

2. The reading unit Read specific sections of vendor documentation and extract the information you need 2. The system of claim 1.

3. The extraction unit Extract the necessary data based on the information passed from the reading unit.

2. The system of claim 1.

4. The common part is Based on the data passed from the extraction unit, the SB parameter design concept is shared with the AI.

2. The system of claim 1.

5. The design unit Design parameters based on the concepts passed from the shared part 2. The system of claim 1.

6. The verification unit The design department verifies the parameters designed and performs the design to prevent errors by referring to the constraints and parameter explanations.

2. The system of claim 1.

7. The reading unit Estimate user emotions and adjust the timing of loading vendor documents based on the estimated user emotions 2. The system of claim 1.

8. The reading unit When loading vendor documents, prioritize the loading based on the importance of the documents.

2. The system of claim 1.

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