System

Abstract

An object of the system according to the embodiment is to easily convert a requirement or an idea of a user into a specific design drawing, and to determine patentability or review whether an existing patent is infringed.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, and a review unit. The reception unit receives a requirement and an idea in a natural language from a user. The generation unit generates a specific design drawing based on the requirement received by the reception unit. The review unit reviews patentability and infringement of an existing patent based on the design drawing generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of requiring specialized knowledge to convert users' requirements and ideas into concrete design drawings, and then to determine patentability and review existing patents for infringement.

[0005] The system according to the embodiment aims to easily convert a user's requirements and ideas into concrete design drawings, and to determine patentability and review whether or not there is an infringement of an existing patent. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a review unit. The receiving unit receives requirements and ideas in natural language from a user. The generating unit generates specific design drawings based on the requirements received by the receiving unit. The review unit determines patentability and reviews whether or not there is an infringement of an existing patent based on the design drawings generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily convert a user's requirements and ideas into concrete design drawings, and can review whether the designs are patentable or whether they infringe existing patents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A hardware design support system according to an embodiment of the present invention generates specific design drawings based on requirements entered by a user in natural language, and then determines patentability and reviews whether or not there is infringement of existing patents. In the hardware design support system, a user enters design requirements and ideas in natural language, and a generation AI analyzes the requirements and generates specific design drawings. The generation AI then determines patentability and reviews whether or not there is infringement of existing patents. For example, a user might enter a requirement such as "I want to create a one-of-a-kind toy for my elementary school-aged child." This requirement is then input into the generation AI. The hardware design support system then uses the generation AI to analyze the input requirements and generate specific design drawings. The generation AI refines the idea using a large-scale language model and translates it into requirements. For example, it generates specific design elements such as the toy's shape, function, and materials used. The hardware design support system then uses the generation AI to determine patentability and review whether or not there is infringement of existing patents based on the generated design drawings. The generation AI uses a patent-specialized model to determine whether a design is patentable and check for infringement of existing patents. This allows users to create products with peace of mind. This allows the hardware design support system to enable anyone to create the product they want, even without specialized hardware design knowledge. This will greatly simplify the hardware design process, realizing a future in which anyone can create the product they want. For example, when subcontracted component manufacturers develop their own products, they can use generative AI to create high-quality products even without design know-how or skills. In addition, major telecommunications equipment manufacturers can rapidly develop new products while utilizing their vast past design know-how.

[0029] A hardware design support system according to an embodiment includes a receiving unit, a generating unit, and a review unit. The receiving unit receives requirements and ideas in natural language from a user. The requirements and ideas in natural language from a user include, but are not limited to, technical requirements and business ideas. For example, the receiving unit receives a requirement such as, "I want to create a one-of-a-kind toy for my elementary school-aged child." The generating unit uses a generation AI to generate specific design drawings based on the requirements received by the receiving unit. For example, the generation AI refines the idea using a large-scale language model and converts it into requirements. The generating unit generates specific design elements such as the shape, function, and materials of the toy. The generating unit can also include a verification process using the generation AI to ensure the accuracy of the design drawings. For example, the generating unit checks the accuracy of the dimensions and the consistency of the design of the generated design drawings. The review unit reviews the design drawings generated by the generating unit to determine patentability and whether or not there is infringement of existing patents. The review unit, for example, uses a patent-specialized model to determine whether a design is patentable by the generation AI. The review unit also checks whether the generation AI infringes any existing patents. For example, the review unit searches a patent database to evaluate whether the design infringes any existing patents. This allows the hardware design support system according to the embodiment to generate specific design drawings based on requirements entered by a user in natural language, and to review the drawings to determine patentability and determine whether or not an existing patent has been infringed.

[0030] The generation unit may include a verification process for verifying the accuracy of the design drawings. Examples of the accuracy of the design drawings include, but are not limited to, dimensional accuracy and design consistency. For example, the generation unit may verify the dimensional accuracy of the generated design drawings. The generation unit may also verify the consistency of the design. For example, the generation unit may verify whether each element of the design drawings is consistent. The generation unit may also include an automatic verification tool for ensuring the accuracy of the design drawings. For example, the generation unit may automatically verify each element of the design drawings and detect errors. Thus, by providing a verification process for ensuring the accuracy of the design drawings, the reliability of the generated design drawings is improved. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit may input the generated design drawings into the generation AI and have the generation AI verify the accuracy of the design drawings.

[0031] The generation unit may include an interface for a user to edit the design drawing. Examples of the interface include, but are not limited to, a drag-and-drop function and a zoom function. For example, the generation unit may provide an interface that allows a user to edit the design drawing by dragging and dropping. The generation unit may also provide an interface that allows a user to zoom in and out of the design drawing. For example, the generation unit may allow a user to enlarge a specific portion of the design drawing to view the details. The generation unit may also provide an interface that allows a user to select and edit each element of the design drawing. For example, the generation unit may allow a user to select a specific element of the design drawing and change its attributes. This allows the user to easily modify the generated design drawing by providing an interface for modifying the design drawing. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit may input the design drawing edited by the user into the generation AI and have the generation AI confirm the edited content.

[0032] The review department can determine whether a design is patentable using a patent-specific model. Examples of patent-specific models include, but are not limited to, machine learning models and rule-based models. The review department can determine whether a design is patentable using, for example, a machine learning model. The review department can also determine patentability using a rule-based model. For example, the review department can evaluate the patentability of a design by applying rules based on the Patent Act. The review department can also evaluate the novelty and inventive step of a design using a patent-specific model. For example, the review department can determine whether a design is new compared to existing technology. Thus, using a patent-specific model can accurately determine whether a design is patentable. Some or all of the above-described processing in the review department can be performed using or without a generative AI. For example, the review department can input design drawings into a generative AI and have the generative AI perform a patentability determination.

[0033] The review unit can check for infringement of existing patents. Examples of existing patents include, but are not limited to, patent classification and keyword search. The review unit can, for example, search a patent database to check for existing patents. The review unit can also identify existing patents using a keyword search. For example, the review unit can search a patent database using keywords related to the design. The review unit can also identify existing patents using patent classification. For example, the review unit can search a patent database based on the patent classification to which the design belongs. This allows users to create products with peace of mind by checking for infringement of existing patents. Some or all of the above-mentioned processing in the review unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the review unit can input design drawings into the generation AI and have the generation AI check for infringement of existing patents.

[0034] The reception unit can analyze the user's past requirement submission history and select the optimal reception method. For example, the reception unit automatically displays requirements that the user has frequently submitted in the past as candidates. The reception unit can also prioritize suggesting input methods (such as voice or text) that the user has used in the past. For example, the reception unit prioritizes suggesting input methods that the user has used in the past. The reception unit can also predict and suggest requirements to be used in a specific time period based on the user's past requirement submission history. For example, the reception unit predicts requirements to be used in a specific time period based on the user's past requirement submission history. In this way, the optimal reception method can be provided by analyzing the user's past requirement submission history. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past requirement submission history into the generation AI and have the generation AI select the optimal reception method.

[0035] When receiving requirements, the reception unit can filter them based on the user's current project or areas of interest. For example, the reception unit preferentially receives requirements related to the user's current project. The reception unit can also filter and display related requirements based on the user's areas of interest. For example, the reception unit can filter and display related requirements based on the user's areas of interest. The reception unit can also suggest related requirements based on areas in which the user has previously shown interest. For example, the reception unit can suggest related requirements based on areas in which the user has previously shown interest. In this way, by filtering requirements based on the user's current project or areas of interest, it is possible to preferentially receive highly relevant requirements. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input data on the user's current project or areas of interest into the generation AI and have the generation AI perform the filtering.

[0036] When accepting requirements, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user inputs requirements by voice, the acceptance unit accepts the requirements using voice recognition technology. Furthermore, when the user inputs requirements in text, the acceptance unit can also accept the requirements using text analysis technology. For example, when the user inputs requirements in text, the acceptance unit accepts the requirements using text analysis technology. Furthermore, when the user inputs requirements in image form, the acceptance unit can also accept the requirements using image recognition technology. For example, when the user inputs requirements in image form, the acceptance unit accepts the requirements using image recognition technology. This allows for the selection of an optimal acceptance means depending on the user's input method, thereby providing an interface that is easy for the user to use. Some or all of the above-described processing in the acceptance unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data to a generation AI and have the generation AI select the optimal acceptance means.

[0037] When accepting requirements, the reception unit can prioritize accepting highly relevant requirements by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes accepting requirements related to that area. Furthermore, when the user is moving, the reception unit can also prioritize accepting requirements related to the user's current location. For example, when the user is moving, the reception unit prioritizes accepting requirements related to the user's current location. Furthermore, when the user is in a specific location, the reception unit can also prioritize accepting requirements related to the location. For example, when the user is in a specific location, the reception unit prioritizes accepting requirements related to the location. In this way, by taking the user's geographical location information into account, highly relevant requirements can be prioritized. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to identify highly relevant requirements.

[0038] When receiving a requirement, the reception unit can analyze the user's social media activity and receive related requirements. For example, the reception unit preferentially receives requirements shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related requirements. For example, the reception unit analyzes the content of the user's social media posts and receive related requirements. The reception unit can also receive related requirements by referring to the activities of the user's friends on social media. For example, the reception unit receives related requirements by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related requirements can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to identify related requirements.

[0039] The reception unit can change the reception method by reflecting the user's past feedback when receiving requirements. The reception unit adjusts the reception method based on, for example, feedback provided by the user in the past. The reception unit can also propose an optimal reception method based on the user's past feedback. For example, the reception unit proposes an optimal reception method based on the user's past feedback. The reception unit can also customize the reception interface by reflecting the user's feedback. For example, the reception unit customizes the reception interface by reflecting the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the reception method.

[0040] The generation unit can change the level of detail of the generation based on the importance of the requirements when generating the design drawings. For example, the generation unit generates detailed design drawings for requirements with high importance. The generation unit can also generate simplified design drawings for requirements with low importance. For example, the generation unit generates simplified design drawings for requirements with low importance. The generation unit can also dynamically adjust the level of detail of the design drawings according to the importance of the requirements. For example, the generation unit dynamically adjusts the level of detail of the design drawings according to the importance of the requirements. In this way, by adjusting the level of detail of the generation based on the importance of the requirements, detailed design drawings can be generated for important requirements. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input requirement importance data to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0041] When generating design drawings, the generation unit can use different generation algorithms depending on the category of requirements. For example, the generation unit applies a dedicated generation algorithm to design requirements for mechanical parts. The generation unit can also apply a dedicated generation algorithm to design requirements for electronic circuits. For example, the generation unit applies a dedicated generation algorithm to design requirements for electronic circuits. The generation unit can also apply a dedicated generation algorithm to design requirements for software. For example, the generation unit applies a dedicated generation algorithm to software design requirements. In this way, by applying different generation algorithms depending on the category of requirements, optimal design drawings can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input requirement category data to the generation AI and have the generation AI select a generation algorithm.

[0042] When generating design drawings, the generation unit can improve the accuracy of generation by referring to the user's past design results. For example, the generation unit improves the accuracy of generation by referring to design drawings created by the user in the past. The generation unit can also extract and apply an optimal design pattern from the user's past design results. For example, the generation unit extracts and applies an optimal design pattern from the user's past design results. The generation unit can also analyze the user's past design history and dynamically adjust the accuracy of generation. For example, the generation unit analyzes the user's past design history and dynamically adjusts the accuracy of generation. In this way, the accuracy of generation can be improved by referring to the user's past design results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past design result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0043] The generation unit can set generation priorities based on the submission dates of requirements when generating design drawings. For example, the generation unit prioritizes processing of requirements with an earlier submission date. The generation unit can also postpone requirements with a later submission date. For example, the generation unit postpones requirements with a later submission date. The generation unit can also dynamically adjust generation priorities based on the submission dates. For example, the generation unit dynamically adjusts generation priorities based on the submission dates. In this way, by determining generation priorities based on the submission dates of requirements, requirements with an earlier submission date can be preferentially processed. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input requirement submission date data into the generation AI and cause the generation AI to set generation priorities.

[0044] The generation unit can change the order of generation based on the relevance of requirements when generating design drawings. For example, the generation unit prioritizes processing of highly relevant requirements. The generation unit can also postpone processing of less relevant requirements. For example, the generation unit postpones processing of less relevant requirements. The generation unit can also dynamically adjust the order of generation based on the relevance of requirements. For example, the generation unit dynamically adjusts the order of generation based on the relevance of requirements. In this way, by adjusting the order of generation based on the relevance of requirements, highly relevant requirements can be processed preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input requirement relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0045] When generating design drawings, the generation unit can change the use of technical terminology used in the generation according to the user's level of expertise. For example, the generation unit generates design drawings that use a lot of technical terminology for a user with high technical expertise. The generation unit can also generate design drawings that use concise terminology for a user with low technical expertise. For example, the generation unit generates design drawings that use concise terminology for a user with low technical expertise. The generation unit can also dynamically adjust the use of technical terminology according to the user's level of expertise. For example, the generation unit dynamically adjusts the use of technical terminology according to the user's level of expertise. This makes it possible to generate design drawings that are easy for the user to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0046] The review unit can improve the accuracy of the review by taking into account the interrelationships between design drawings during the review. For example, the review unit analyzes the interrelationships between design drawings and checks consistency. The review unit can also improve the accuracy of the review by taking into account the interrelationships between design drawings. For example, the review unit improves the accuracy of the review by taking into account the interrelationships between design drawings. The review unit can also apply optimal review criteria based on the interrelationships between design drawings. For example, the review unit applies optimal review criteria based on the interrelationships between design drawings. In this way, the accuracy of the review can be improved by taking the interrelationships between design drawings into consideration. Some or all of the above-mentioned processing in the review unit may be performed using or without the generation AI. For example, the review unit can input interrelationship data between design drawings into the generation AI and cause the generation AI to improve the accuracy of the review.

[0047] During the review, the review unit can conduct the review while taking into account the attribute information of the person who submitted the design drawings. The review unit can adjust the review criteria, for example, depending on the submitter's level of expertise. The review unit can also improve the accuracy of the review by referring to the submitter's past review history. For example, the review unit can improve the accuracy of the review by referring to the submitter's past review history. The review unit can also apply optimal review criteria based on the submitter's attribute information. For example, the review unit applies optimal review criteria based on the submitter's attribute information. In this way, by taking into account the attribute information of the submitter of the design drawings, it is possible to perform an optimal review according to the submitter. Some or all of the above-mentioned processing in the review unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the review unit can input the submitter's attribute information data into the generation AI and have the generation AI adjust the review criteria.

[0048] During review, the review unit can weight the reviews based on the submission frequency of the design drawings. For example, the review unit prioritizes reviewing design drawings that are submitted frequently. The review unit can also postpone reviewing design drawings that are submitted infrequently. For example, the review unit postpones reviewing design drawings that are submitted infrequently. The review unit can also dynamically adjust the review weighting based on the submission frequency. For example, the review unit dynamically adjusts the review weighting based on the submission frequency. In this way, by weighting the reviews based on the submission frequency of the design drawings, it is possible to prioritize reviewing design drawings that are submitted frequently. Some or all of the above-mentioned processing in the review unit may be performed using or without the generation AI. For example, the review unit can input submission frequency data into the generation AI and have the generation AI perform the review weighting.

[0049] The review unit can conduct a review taking into account the geographic distribution of the design drawings. For example, the review unit analyzes the geographic distribution of the design drawings and conducts the review taking into account the characteristics of each region. The review unit can also evaluate the relevance of the design drawings based on the geographic distribution. For example, the review unit evaluates the relevance of the design drawings based on the geographic distribution. The review unit can also apply optimal review criteria taking into account the geographic distribution. For example, the review unit applies optimal review criteria taking into account the geographic distribution. In this way, by taking into account the geographic distribution of the design drawings, a review taking into account the characteristics of each region can be conducted. Some or all of the above-described processing in the review unit may be performed using or without the generation AI. For example, the review unit can input geographic distribution data of the design drawings into the generation AI and have the generation AI execute the review.

[0050] During the review, the review unit can improve the accuracy of the review by referring to literature related to the design drawings. For example, the review unit improves the accuracy of the review by referring to literature related to the design drawings. The review unit can also evaluate the patentability of the design drawings based on the related literature. For example, the review unit evaluates the patentability of the design drawings based on the related literature. The review unit can also confirm the technical validity of the design drawings by referring to the related literature. For example, the review unit confirms the technical validity of the design drawings by referring to the related literature. In this way, by referring to the literature related to the design drawings, the accuracy of the review can be improved. Some or all of the above-mentioned processing in the review unit may be performed using or without the generation AI. For example, the review unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the review.

[0051] The review department can conduct the review taking into account the market value of the design drawings. For example, the review department evaluates the market value of the design drawings and prioritizes reviewing high-value designs. The review department can also evaluate the patentability of the design drawings based on market value. For example, the review department evaluates the patentability of the design drawings based on market value. The review department can also confirm the technical validity of the design drawings taking market value into account. For example, the review department confirms the technical validity of the design drawings taking market value into account. In this way, by taking the market value of the design drawings into account, high-value designs can be prioritized for review. Some or all of the above-mentioned processing in the review department may be performed using or without the generation AI. For example, the review department can input market value data into the generation AI and have the generation AI perform the review.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The generation unit can change the level of detail of the generation based on the importance of the requirements when generating design drawings. For example, the generation unit can generate detailed design drawings for requirements with high importance. The generation unit can also generate simplified design drawings for requirements with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the design drawings according to the importance of the requirements. As a result, detailed design drawings can be generated for important requirements by adjusting the level of detail of the generation based on the importance of the requirements. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input requirement importance data to the generation AI and have the generation AI adjust the level of detail of the generation.

[0054] When generating design drawings, the generation unit can use different generation algorithms depending on the category of requirements. For example, the generation unit can apply a dedicated generation algorithm to the design requirements of mechanical parts. The generation unit can also apply a dedicated generation algorithm to the design requirements of electronic circuits. Furthermore, the generation unit can also apply a dedicated generation algorithm to the design requirements of software. In this way, by applying different generation algorithms depending on the category of requirements, optimal design drawings can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input requirement category data into the generation AI and have the generation AI select a generation algorithm.

[0055] When generating design drawings, the generation unit can improve the accuracy of generation by referring to the user's past design results. For example, the generation unit can improve the accuracy of generation by referring to design drawings created by the user in the past. The generation unit can also extract and apply optimal design patterns from the user's past design results. Furthermore, the generation unit can analyze the user's past design history and dynamically adjust the accuracy of generation. In this way, the accuracy of generation can be improved by referring to the user's past design results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past design result data into the generation AI and have the generation AI improve the accuracy of generation.

[0056] When generating design drawings, the generation unit can set generation priorities based on the submission dates of requirements. For example, the generation unit can prioritize requirements that are submitted early. Also, the generation unit can postpone requirements that are submitted late. Furthermore, the generation unit can dynamically adjust the generation priorities based on the submission dates. In this way, by determining the generation priorities based on the submission dates of requirements, requirements that are submitted early can be processed preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input requirement submission date data into the generation AI and have the generation AI execute the setting of generation priorities.

[0057] When generating design drawings, the generation unit can change the order of generation based on the relevance of requirements. For example, the generation unit can prioritize processing of highly relevant requirements. Also, the generation unit can postpone processing of less relevant requirements. Furthermore, the generation unit can dynamically adjust the order of generation based on the relevance of requirements. In this way, by adjusting the order of generation based on the relevance of requirements, highly relevant requirements can be prioritized. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input requirement relevance data into the generation AI and have the generation AI adjust the order of generation.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives requirements and ideas in natural language from the user. The requirements and ideas in natural language from the user include, for example, technical requirements and business ideas. Specifically, the reception unit receives requirements such as "I want to make a one-of-a-kind toy for my elementary school-aged child." Step 2: The generation unit uses a generation AI to generate specific design drawings based on the requirements received by the reception unit. For example, the generation AI uses a large-scale language model to refine ideas and turn them into requirements. Specifically, it generates design elements such as the shape, function, and materials used for the toy. The generation unit can also use the generation AI to implement a verification process to ensure the accuracy of the design drawings. For example, it checks the accuracy of the dimensions of the generated design drawings and the consistency of the design. Step 3: The review unit determines patentability and reviews the design drawings generated by the generation unit to determine whether or not the design is patentable and whether or not it infringes existing patents. For example, the generation AI uses a patent-specific model to determine whether the design is patentable. The review unit also checks whether the generation AI infringes any existing patents. Specifically, it searches a patent database and evaluates whether the design infringes any existing patents.

[0060] (Example 2) A hardware design support system according to an embodiment of the present invention generates specific design drawings based on requirements entered by a user in natural language, and then determines patentability and reviews whether or not there is infringement of existing patents. In the hardware design support system, a user enters design requirements and ideas in natural language, and a generation AI analyzes the requirements and generates specific design drawings. The generation AI then determines patentability and reviews whether or not there is infringement of existing patents. For example, a user might enter a requirement such as "I want to create a one-of-a-kind toy for my elementary school-aged child." This requirement is then input into the generation AI. The hardware design support system then uses the generation AI to analyze the input requirements and generate specific design drawings. The generation AI refines the idea using a large-scale language model and translates it into requirements. For example, it generates specific design elements such as the toy's shape, function, and materials used. The hardware design support system then uses the generation AI to determine patentability and review whether or not there is infringement of existing patents based on the generated design drawings. The generation AI uses a patent-specialized model to determine whether a design is patentable and check for infringement of existing patents. This allows users to create products with peace of mind. This allows the hardware design support system to enable anyone to create the product they want, even without specialized hardware design knowledge. This will greatly simplify the hardware design process, realizing a future in which anyone can create the product they want. For example, when subcontracted component manufacturers develop their own products, they can use generative AI to create high-quality products even without design know-how or skills. In addition, major telecommunications equipment manufacturers can rapidly develop new products while utilizing their vast past design know-how.

[0061] A hardware design support system according to an embodiment includes a receiving unit, a generating unit, and a review unit. The receiving unit receives requirements and ideas in natural language from a user. The requirements and ideas in natural language from a user include, but are not limited to, technical requirements and business ideas. For example, the receiving unit receives a requirement such as, "I want to create a one-of-a-kind toy for my elementary school-aged child." The generating unit uses a generation AI to generate specific design drawings based on the requirements received by the receiving unit. For example, the generation AI refines the idea using a large-scale language model and converts it into requirements. The generating unit generates specific design elements such as the shape, function, and materials of the toy. The generating unit can also include a verification process using the generation AI to ensure the accuracy of the design drawings. For example, the generating unit checks the accuracy of the dimensions and the consistency of the design of the generated design drawings. The review unit reviews the design drawings generated by the generating unit to determine patentability and whether or not there is infringement of existing patents. The review unit, for example, uses a patent-specialized model to determine whether a design is patentable by the generation AI. The review unit also checks whether the generation AI infringes any existing patents. For example, the review unit searches a patent database to evaluate whether the design infringes any existing patents. This allows the hardware design support system according to the embodiment to generate specific design drawings based on requirements entered by a user in natural language, and to review the drawings to determine patentability and determine whether or not an existing patent has been infringed.

[0062] The generation unit may include a verification process for verifying the accuracy of the design drawings. Examples of the accuracy of the design drawings include, but are not limited to, dimensional accuracy and design consistency. For example, the generation unit may verify the dimensional accuracy of the generated design drawings. The generation unit may also verify the consistency of the design. For example, the generation unit may verify whether each element of the design drawings is consistent. The generation unit may also include an automatic verification tool for ensuring the accuracy of the design drawings. For example, the generation unit may automatically verify each element of the design drawings and detect errors. Thus, by providing a verification process for ensuring the accuracy of the design drawings, the reliability of the generated design drawings is improved. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit may input the generated design drawings into the generation AI and have the generation AI verify the accuracy of the design drawings.

[0063] The generation unit may include an interface for a user to edit the design drawing. Examples of the interface include, but are not limited to, a drag-and-drop function and a zoom function. For example, the generation unit may provide an interface that allows a user to edit the design drawing by dragging and dropping. The generation unit may also provide an interface that allows a user to zoom in and out of the design drawing. For example, the generation unit may allow a user to enlarge a specific portion of the design drawing to view the details. The generation unit may also provide an interface that allows a user to select and edit each element of the design drawing. For example, the generation unit may allow a user to select a specific element of the design drawing and change its attributes. This allows the user to easily modify the generated design drawing by providing an interface for modifying the design drawing. Some or all of the above-described processing in the generation unit may be performed using or without the generation AI. For example, the generation unit may input the design drawing edited by the user into the generation AI and have the generation AI confirm the edited content.

[0064] The review department can determine whether a design is patentable using a patent-specific model. Examples of patent-specific models include, but are not limited to, machine learning models and rule-based models. The review department can determine whether a design is patentable using, for example, a machine learning model. The review department can also determine patentability using a rule-based model. For example, the review department can evaluate the patentability of a design by applying rules based on the Patent Act. The review department can also evaluate the novelty and inventive step of a design using a patent-specific model. For example, the review department can determine whether a design is new compared to existing technology. Thus, using a patent-specific model can accurately determine whether a design is patentable. Some or all of the above-described processing in the review department can be performed using or without a generative AI. For example, the review department can input design drawings into a generative AI and have the generative AI perform a patentability determination.

[0065] The review unit can check for infringement of existing patents. Examples of existing patents include, but are not limited to, patent classification and keyword search. The review unit can, for example, search a patent database to check for existing patents. The review unit can also identify existing patents using a keyword search. For example, the review unit can search a patent database using keywords related to the design. The review unit can also identify existing patents using patent classification. For example, the review unit can search a patent database based on the patent classification to which the design belongs. This allows users to create products with peace of mind by checking for infringement of existing patents. Some or all of the above-mentioned processing in the review unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the review unit can input design drawings into the generation AI and have the generation AI check for infringement of existing patents.

[0066] The reception unit can estimate the user's emotions and change the method for accepting requirements based on the estimated user emotions. For example, when the user is stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, when the user is relaxed, the reception unit provides detailed input options. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to enable quick input of requirements. For example, when the user is in a hurry, the reception unit prioritizes voice input. This allows the method for accepting requirements to be adjusted according to the user's emotions, thereby providing an optimal reception method for the user. 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 reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0067] The reception unit can analyze the user's past requirement submission history and select the optimal reception method. For example, the reception unit automatically displays requirements that the user has frequently submitted in the past as candidates. The reception unit can also prioritize suggesting input methods (such as voice or text) that the user has used in the past. For example, the reception unit prioritizes suggesting input methods that the user has used in the past. The reception unit can also predict and suggest requirements to be used in a specific time period based on the user's past requirement submission history. For example, the reception unit predicts requirements to be used in a specific time period based on the user's past requirement submission history. In this way, the optimal reception method can be provided by analyzing the user's past requirement submission history. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past requirement submission history into the generation AI and have the generation AI select the optimal reception method.

[0068] When receiving requirements, the reception unit can filter them based on the user's current project or areas of interest. For example, the reception unit preferentially receives requirements related to the user's current project. The reception unit can also filter and display related requirements based on the user's areas of interest. For example, the reception unit can filter and display related requirements based on the user's areas of interest. The reception unit can also suggest related requirements based on areas in which the user has previously shown interest. For example, the reception unit can suggest related requirements based on areas in which the user has previously shown interest. In this way, by filtering requirements based on the user's current project or areas of interest, it is possible to preferentially receive highly relevant requirements. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input data on the user's current project or areas of interest into the generation AI and have the generation AI perform the filtering.

[0069] When accepting requirements, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user inputs requirements by voice, the acceptance unit accepts the requirements using voice recognition technology. Furthermore, when the user inputs requirements in text, the acceptance unit can also accept the requirements using text analysis technology. For example, when the user inputs requirements in text, the acceptance unit accepts the requirements using text analysis technology. Furthermore, when the user inputs requirements in image form, the acceptance unit can also accept the requirements using image recognition technology. For example, when the user inputs requirements in image form, the acceptance unit accepts the requirements using image recognition technology. This allows for the selection of an optimal acceptance means depending on the user's input method, thereby providing an interface that is easy for the user to use. Some or all of the above-described processing in the acceptance unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data to a generation AI and have the generation AI select the optimal acceptance means.

[0070] The reception unit can estimate the user's emotions and set a priority order for the requirements to be received based on the estimated user's emotions. For example, when the user is stressed, the reception unit prioritizes receiving requirements with high importance. Furthermore, when the user is relaxed, the reception unit can also prioritize receiving detailed requirements. For example, when the user is relaxed, the reception unit prioritizes receiving detailed requirements. Furthermore, when the user is in a hurry, the reception unit can also prioritize receiving requirements that require prompt processing. For example, when the user is in a hurry, the reception unit prioritizes receiving requirements that require prompt processing. Thus, by determining the priority order of requirements according to the user's emotions, it is possible to prioritize receiving important requirements. 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 reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0071] When accepting requirements, the reception unit can prioritize accepting highly relevant requirements by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes accepting requirements related to that area. Furthermore, when the user is moving, the reception unit can also prioritize accepting requirements related to the user's current location. For example, when the user is moving, the reception unit prioritizes accepting requirements related to the user's current location. Furthermore, when the user is in a specific location, the reception unit can also prioritize accepting requirements related to the location. For example, when the user is in a specific location, the reception unit prioritizes accepting requirements related to the location. In this way, by taking the user's geographical location information into account, highly relevant requirements can be prioritized. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to identify highly relevant requirements.

[0072] When receiving a requirement, the reception unit can analyze the user's social media activity and receive related requirements. For example, the reception unit preferentially receives requirements shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related requirements. For example, the reception unit analyzes the content of the user's social media posts and receive related requirements. The reception unit can also receive related requirements by referring to the activities of the user's friends on social media. For example, the reception unit receives related requirements by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related requirements can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to identify related requirements.

[0073] The reception unit can change the reception method by reflecting the user's past feedback when receiving requirements. The reception unit adjusts the reception method based on, for example, feedback provided by the user in the past. The reception unit can also propose an optimal reception method based on the user's past feedback. For example, the reception unit proposes an optimal reception method based on the user's past feedback. The reception unit can also customize the reception interface by reflecting the user's feedback. For example, the reception unit customizes the reception interface by reflecting the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the reception method.

[0074] The generation unit can estimate the user's emotions and change the method for generating the design drawings based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates design drawings that proceed at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate design drawings that emphasize the shortest route. For example, if the user is in a hurry, the generation unit can generate design drawings that emphasize the shortest route. Furthermore, if the user is excited, the generation unit can generate design drawings that add visually stimulating effects. For example, if the user is excited, the generation unit generates design drawings that add visually stimulating effects. In this way, by adjusting the method for generating the design drawings according to the user's emotions, it is possible to generate design drawings that are optimal for the user. 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 generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the method for generating design drawings.

[0075] The generation unit can change the level of detail of the generation based on the importance of the requirements when generating the design drawings. For example, the generation unit generates detailed design drawings for requirements with high importance. The generation unit can also generate simplified design drawings for requirements with low importance. For example, the generation unit generates simplified design drawings for requirements with low importance. The generation unit can also dynamically adjust the level of detail of the design drawings according to the importance of the requirements. For example, the generation unit dynamically adjusts the level of detail of the design drawings according to the importance of the requirements. In this way, by adjusting the level of detail of the generation based on the importance of the requirements, detailed design drawings can be generated for important requirements. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input requirement importance data to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0076] When generating design drawings, the generation unit can use different generation algorithms depending on the category of requirements. For example, the generation unit applies a dedicated generation algorithm to design requirements for mechanical parts. The generation unit can also apply a dedicated generation algorithm to design requirements for electronic circuits. For example, the generation unit applies a dedicated generation algorithm to design requirements for electronic circuits. The generation unit can also apply a dedicated generation algorithm to design requirements for software. For example, the generation unit applies a dedicated generation algorithm to software design requirements. In this way, by applying different generation algorithms depending on the category of requirements, optimal design drawings can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input requirement category data to the generation AI and have the generation AI select a generation algorithm.

[0077] When generating design drawings, the generation unit can improve the accuracy of generation by referring to the user's past design results. For example, the generation unit improves the accuracy of generation by referring to design drawings created by the user in the past. The generation unit can also extract and apply an optimal design pattern from the user's past design results. For example, the generation unit extracts and applies an optimal design pattern from the user's past design results. The generation unit can also analyze the user's past design history and dynamically adjust the accuracy of generation. For example, the generation unit analyzes the user's past design history and dynamically adjusts the accuracy of generation. In this way, the accuracy of generation can be improved by referring to the user's past design results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past design result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0078] The generation unit can estimate the user's emotions and change the length of the design drawings based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates detailed design drawings. Furthermore, if the user is in a hurry, the generation unit can also generate concise design drawings. For example, if the user is in a hurry, the generation unit generates concise design drawings. Furthermore, if the user is excited, the generation unit can also generate design drawings with visually stimulating effects. For example, if the user is excited, the generation unit generates design drawings with visually stimulating effects. This allows the generation of optimal design drawings for the user by adjusting the length of the design drawings 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 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 generation unit may be performed using the generation AI, or may be performed without the generation AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the design drawings.

[0079] The generation unit can set generation priorities based on the submission dates of requirements when generating design drawings. For example, the generation unit prioritizes processing of requirements with an earlier submission date. The generation unit can also postpone requirements with a later submission date. For example, the generation unit postpones requirements with a later submission date. The generation unit can also dynamically adjust generation priorities based on the submission dates. For example, the generation unit dynamically adjusts generation priorities based on the submission dates. In this way, by determining generation priorities based on the submission dates of requirements, requirements with an earlier submission date can be preferentially processed. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input requirement submission date data into the generation AI and cause the generation AI to set generation priorities.

[0080] The generation unit can change the order of generation based on the relevance of requirements when generating design drawings. For example, the generation unit prioritizes processing of highly relevant requirements. The generation unit can also postpone processing of less relevant requirements. For example, the generation unit postpones processing of less relevant requirements. The generation unit can also dynamically adjust the order of generation based on the relevance of requirements. For example, the generation unit dynamically adjusts the order of generation based on the relevance of requirements. In this way, by adjusting the order of generation based on the relevance of requirements, highly relevant requirements can be processed preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input requirement relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0081] When generating design drawings, the generation unit can change the use of technical terminology used in the generation according to the user's level of expertise. For example, the generation unit generates design drawings that use a lot of technical terminology for a user with high technical expertise. The generation unit can also generate design drawings that use concise terminology for a user with low technical expertise. For example, the generation unit generates design drawings that use concise terminology for a user with low technical expertise. The generation unit can also dynamically adjust the use of technical terminology according to the user's level of expertise. For example, the generation unit dynamically adjusts the use of technical terminology according to the user's level of expertise. This makes it possible to generate design drawings that are easy for the user to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0082] The review unit can estimate the user's emotions and change the review criteria based on the estimated user emotions. For example, the review unit applies detailed review criteria when the user is relaxed. The review unit can also apply concise review criteria when the user is in a hurry. For example, the review unit applies concise review criteria when the user is in a hurry. The review unit can also apply review criteria with visually stimulating effects when the user is excited. For example, the review unit applies review criteria with visually stimulating effects when the user is excited. This allows the review criteria to be adjusted according to the user's emotions, thereby providing optimal review criteria for the user. Emotion estimation is achieved 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 review unit can be performed using the generation AI, or can be performed without the generation AI. For example, the review unit can input the user's emotion data into the generation AI and have the generation AI adjust the review criteria.

[0083] The review unit can improve the accuracy of the review by taking into account the interrelationships between design drawings during the review. For example, the review unit analyzes the interrelationships between design drawings and checks consistency. The review unit can also improve the accuracy of the review by taking into account the interrelationships between design drawings. For example, the review unit improves the accuracy of the review by taking into account the interrelationships between design drawings. The review unit can also apply optimal review criteria based on the interrelationships between design drawings. For example, the review unit applies optimal review criteria based on the interrelationships between design drawings. In this way, the accuracy of the review can be improved by taking the interrelationships between design drawings into consideration. Some or all of the above-mentioned processing in the review unit may be performed using or without the generation AI. For example, the review unit can input interrelationship data between design drawings into the generation AI and cause the generation AI to improve the accuracy of the review.

[0084] During the review, the review unit can conduct the review while taking into account the attribute information of the person who submitted the design drawings. The review unit can adjust the review criteria, for example, depending on the submitter's level of expertise. The review unit can also improve the accuracy of the review by referring to the submitter's past review history. For example, the review unit can improve the accuracy of the review by referring to the submitter's past review history. The review unit can also apply optimal review criteria based on the submitter's attribute information. For example, the review unit applies optimal review criteria based on the submitter's attribute information. In this way, by taking into account the attribute information of the submitter of the design drawings, it is possible to perform an optimal review according to the submitter. Some or all of the above-mentioned processing in the review unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the review unit can input the submitter's attribute information data into the generation AI and have the generation AI adjust the review criteria.

[0085] During review, the review unit can weight the reviews based on the submission frequency of the design drawings. For example, the review unit prioritizes reviewing design drawings that are submitted frequently. The review unit can also postpone reviewing design drawings that are submitted infrequently. For example, the review unit postpones reviewing design drawings that are submitted infrequently. The review unit can also dynamically adjust the review weighting based on the submission frequency. For example, the review unit dynamically adjusts the review weighting based on the submission frequency. In this way, by weighting the reviews based on the submission frequency of the design drawings, it is possible to prioritize reviewing design drawings that are submitted frequently. Some or all of the above-mentioned processing in the review unit may be performed using or without the generation AI. For example, the review unit can input submission frequency data into the generation AI and have the generation AI perform the review weighting.

[0086] The review unit can estimate the user's emotions and change the order in which review results are displayed based on the estimated user emotions. For example, when the user is relaxed, the review unit prioritizes displaying detailed review results. Furthermore, when the user is in a hurry, the review unit can prioritize displaying review results that focus on the main points. For example, when the user is in a hurry, the review unit prioritizes displaying review results that focus on the main points. Furthermore, when the user is excited, the review unit can prioritize displaying review results that add visually stimulating effects. For example, when the user is excited, the review unit prioritizes displaying review results that add visually stimulating effects. This allows the order in which review results are displayed to be adjusted according to the user's emotions, thereby providing optimal review results for the user. 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 review unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the review unit can input user emotional data into the generation AI and have the generation AI adjust the display order of the review results.

[0087] The review unit can conduct a review taking into account the geographic distribution of the design drawings. For example, the review unit analyzes the geographic distribution of the design drawings and conducts the review taking into account the characteristics of each region. The review unit can also evaluate the relevance of the design drawings based on the geographic distribution. For example, the review unit evaluates the relevance of the design drawings based on the geographic distribution. The review unit can also apply optimal review criteria taking into account the geographic distribution. For example, the review unit applies optimal review criteria taking into account the geographic distribution. In this way, by taking into account the geographic distribution of the design drawings, a review taking into account the characteristics of each region can be conducted. Some or all of the above-described processing in the review unit may be performed using or without the generation AI. For example, the review unit can input geographic distribution data of the design drawings into the generation AI and have the generation AI execute the review.

[0088] During the review, the review unit can improve the accuracy of the review by referring to literature related to the design drawings. For example, the review unit improves the accuracy of the review by referring to literature related to the design drawings. The review unit can also evaluate the patentability of the design drawings based on the related literature. For example, the review unit evaluates the patentability of the design drawings based on the related literature. The review unit can also confirm the technical validity of the design drawings by referring to the related literature. For example, the review unit confirms the technical validity of the design drawings by referring to the related literature. In this way, by referring to the literature related to the design drawings, the accuracy of the review can be improved. Some or all of the above-mentioned processing in the review unit may be performed using or without the generation AI. For example, the review unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the review.

[0089] The review department can conduct the review taking into account the market value of the design drawings. For example, the review department evaluates the market value of the design drawings and prioritizes reviewing high-value designs. The review department can also evaluate the patentability of the design drawings based on market value. For example, the review department evaluates the patentability of the design drawings based on market value. The review department can also confirm the technical validity of the design drawings taking market value into account. For example, the review department confirms the technical validity of the design drawings taking market value into account. In this way, by taking the market value of the design drawings into account, high-value designs can be prioritized for review. Some or all of the above-mentioned processing in the review department may be performed using or without the generation AI. For example, the review department can input market value data into the generation AI and have the generation AI perform the review. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and review unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives requirements and ideas in natural language from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates specific design drawings using a generation AI. The review unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines patentability and reviews whether or not there is an infringement of an existing patent based on the generated design drawings. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and review unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives requirements and ideas in natural language from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates specific design drawings using a generation AI. The review unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines patentability and reviews whether or not there is an infringement of an existing patent based on the generated design drawings. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and review unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives requirements and ideas in natural language from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates specific design drawings using a generation AI. The review unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines patentability and reviews whether or not there is an infringement of an existing patent based on the generated design drawings. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and review unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives requirements and ideas in natural language from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates specific design drawings using a generation AI. The review unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines patentability and reviews whether or not there is an infringement of an existing patent based on the generated design drawings.

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

[0091] The generation unit can estimate the user's emotions and change the method for generating design drawings based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate detailed design drawings. If the user is in a hurry, the generation unit can generate simple design drawings. Furthermore, if the user is excited, the generation unit can generate design drawings with visually stimulating effects. This allows the generation of optimal design drawings for the user by adjusting the design drawing generation method 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, 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 generation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the design drawing generation method.

[0092] The review unit can estimate the user's emotions and change the review criteria based on the estimated user emotions. For example, if the user is relaxed, the review unit can apply detailed review criteria. If the user is in a hurry, the review unit can apply brief review criteria. Furthermore, if the user is excited, the review unit can apply review criteria with visually stimulating effects. This allows the review criteria to be adjusted according to the user's emotions, thereby providing optimal review criteria for the user. Emotion estimation is achieved 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 review unit can be performed using the generation AI, or can be performed without the generation AI. For example, the review unit can input the user's emotion data into the generation AI and have the generation AI adjust the review criteria.

[0093] The reception unit can estimate the user's emotions and change the method for receiving requirements based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of requirements. This allows the optimal reception method for the user to be provided by adjusting the method for receiving requirements 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 reception unit may be performed using the generation AI, or may be performed without the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0094] The generation unit can estimate the user's emotions and change the length of the design drawings based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate detailed design drawings. If the user is in a hurry, the generation unit can generate concise design drawings. Furthermore, if the user is excited, the generation unit can generate design drawings with visually stimulating effects. This allows the length of the design drawings to be adjusted according to the user's emotions, thereby generating optimal design drawings for the user. Emotion estimation is achieved 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 generation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the design drawings.

[0095] The review unit can estimate the user's emotions and change the display order of review results based on the estimated user emotions. For example, if the user is relaxed, the review unit can prioritize displaying detailed review results. If the user is in a hurry, the review unit can prioritize displaying review results that focus on the main points. If the user is excited, the review unit can prioritize displaying review results with visually stimulating effects. This allows the display order of review results to be adjusted according to the user's emotions, thereby providing optimal review results for the user. 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 review unit can be performed using the generation AI, or without the generation AI. For example, the review unit can input the user's emotion data into the generation AI and have the generation AI adjust the display order of the review results.

[0096] The generation unit can change the level of detail of the generation based on the importance of the requirements when generating design drawings. For example, the generation unit can generate detailed design drawings for requirements with high importance. The generation unit can also generate simplified design drawings for requirements with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the design drawings according to the importance of the requirements. As a result, detailed design drawings can be generated for important requirements by adjusting the level of detail of the generation based on the importance of the requirements. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input requirement importance data to the generation AI and have the generation AI adjust the level of detail of the generation.

[0097] When generating design drawings, the generation unit can use different generation algorithms depending on the category of requirements. For example, the generation unit can apply a dedicated generation algorithm to the design requirements of mechanical parts. The generation unit can also apply a dedicated generation algorithm to the design requirements of electronic circuits. Furthermore, the generation unit can also apply a dedicated generation algorithm to the design requirements of software. In this way, by applying different generation algorithms depending on the category of requirements, optimal design drawings can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input requirement category data into the generation AI and have the generation AI select a generation algorithm.

[0098] When generating design drawings, the generation unit can improve the accuracy of generation by referring to the user's past design results. For example, the generation unit can improve the accuracy of generation by referring to design drawings created by the user in the past. The generation unit can also extract and apply optimal design patterns from the user's past design results. Furthermore, the generation unit can analyze the user's past design history and dynamically adjust the accuracy of generation. In this way, the accuracy of generation can be improved by referring to the user's past design results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past design result data into the generation AI and have the generation AI improve the accuracy of generation.

[0099] When generating design drawings, the generation unit can set generation priorities based on the submission dates of requirements. For example, the generation unit can prioritize requirements that are submitted early. Also, the generation unit can postpone requirements that are submitted late. Furthermore, the generation unit can dynamically adjust the generation priorities based on the submission dates. In this way, by determining the generation priorities based on the submission dates of requirements, requirements that are submitted early can be processed preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input requirement submission date data into the generation AI and have the generation AI execute the setting of generation priorities.

[0100] When generating design drawings, the generation unit can change the order of generation based on the relevance of requirements. For example, the generation unit can prioritize processing of highly relevant requirements. Also, the generation unit can postpone processing of less relevant requirements. Furthermore, the generation unit can dynamically adjust the order of generation based on the relevance of requirements. In this way, by adjusting the order of generation based on the relevance of requirements, highly relevant requirements can be prioritized. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input requirement relevance data into the generation AI and have the generation AI adjust the order of generation.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives requirements and ideas in natural language from the user. The requirements and ideas in natural language from the user include, for example, technical requirements and business ideas. Specifically, the reception unit receives requirements such as "I want to make a one-of-a-kind toy for my elementary school-aged child." Step 2: The generation unit uses a generation AI to generate specific design drawings based on the requirements received by the reception unit. For example, the generation AI uses a large-scale language model to refine ideas and turn them into requirements. Specifically, it generates design elements such as the shape, function, and materials used for the toy. The generation unit can also use the generation AI to implement a verification process to ensure the accuracy of the design drawings. For example, it checks the accuracy of the dimensions of the generated design drawings and the consistency of the design. Step 3: The review unit determines patentability and reviews the design drawings generated by the generation unit to determine whether or not the design is patentable and whether or not it infringes existing patents. For example, the generation AI uses a patent-specific model to determine whether the design is patentable. The review unit also checks whether the generation AI infringes any existing patents. Specifically, it searches a patent database and evaluates whether the design infringes any existing patents.

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

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

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

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

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

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

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

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

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

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

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

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

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 receiving unit for receiving requirements and ideas in natural language from a user; a generation unit that generates specific design drawings based on the requirements accepted by the acceptance unit; a review unit that determines patentability and reviews whether or not there is an infringement of an existing patent based on the design drawings generated by the generation unit. A system characterized by:

2. The generation unit Have a verification process to ensure the accuracy of design drawings 2. The system of claim 1.

3. The generation unit Provides an interface for users to edit design drawings 2. The system of claim 1.

4. The review unit Determine whether a design is patentable using a patent-specific model 2. The system of claim 1.

5. The review unit Check for infringement of existing patents 2. The system of claim 1.

6. The reception unit Infer user sentiment and change the way requirements are accepted based on the estimated sentiment 2. The system of claim 1.

7. The reception unit Analyze the user's past requirements submission history and select the appropriate acceptance method 2. The system of claim 1.

8. The reception unit As requirements are received, filtering is performed based on the user's current projects and areas of interest.

2. The system of claim 1.

9. The reception unit When accepting requirements, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

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