System

Abstract

An object of the system according to the embodiment is to automate a design process in product development and achieve efficiency and optimization.SOLUTION: A system includes a reception unit, a learning unit, a generation unit, a trial unit, and a visualization unit. The reception unit inputs a performance target of a customer. The learning unit learns the past design data based on the performance target input by the reception unit. The generation unit generates a design based on the data learned by the learning unit. The trial unit performs trial and error of the design generated by the generation unit. The visualization unit visualizes the result obtained by the trial 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 technologies have not sufficiently streamlined and optimized the design process in product development, and there is room for improvement.

[0005] The system according to the embodiment aims to automate the design process in product development, thereby improving efficiency and optimization. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a learning unit, a generation unit, a trial unit, and a visualization unit. The reception unit inputs a customer's performance target. The learning unit learns past design data based on the performance target input by the reception unit. The generation unit generates a design based on the data learned by the learning unit. The trial unit performs trial and error on the design generated by the generation unit. The visualization unit visualizes the results obtained by the trial unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the design process in product development, thereby improving efficiency and optimization. [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 design process automation system according to an embodiment of the present invention automates the design process based on customer performance goals, improving efficiency and optimization. When a customer inputs their desired product performance goals into AI, the AI ​​generates the optimal design to achieve those goals. For example, the design process automation system learns from past product design data and generates new designs based on that data. This allows it to derive the optimal design by learning from past successes and failures. Furthermore, the design process automation system can simultaneously consider multiple parameters, taking into account factors that humans might overlook, and proposes optimal designs from multiple perspectives, including product performance, cost, and manufacturability. For example, it simultaneously selects materials and optimizes the manufacturing process. Furthermore, the design process automation system rapidly performs the trial-and-error process typically performed by humans. This significantly improves the efficiency of the design process and shortens time to market. For example, it can quickly test hundreds of design patterns and select the optimal design. Furthermore, the design process and results of the design process automation system are visualized, allowing users to understand why a design is optimal. This prevents AI from becoming a black box and makes it easier to gain internal understanding and approval. For example, each design step can be displayed in graphs and diagrams, clearly explaining the rationale for the design. This allows the design process automation system to significantly streamline the design process in the manufacturing industry, improving product quality and reducing time. This allows the design process automation system to significantly streamline the design process in the manufacturing industry, improving product quality and reducing time. For example, a design process automation system inputs the customer's desired product performance targets into an AI, which then generates the optimal design to achieve those targets. This streamlines and optimizes the design process, improving product quality and reducing time to market.

[0029] A design process automation system according to an embodiment includes a receiving unit, a learning unit, a generating unit, a trial unit, and a visualization unit. The receiving unit inputs a customer's performance goals. Examples of the performance goals include, but are not limited to, speed, efficiency, and durability. The receiving unit provides, for example, an interface for inputting the customer's desired product performance goals. The learning unit learns from past design data based on the performance goals input by the receiving unit. Examples of the past design data include, but are not limited to, design drawings, specifications, and test results. The learning unit learns from, for example, past successes and failures, and generates data for deriving an optimal design. The generating unit generates a design based on the data learned by the learning unit. The generating unit simultaneously selects materials and optimizes the manufacturing process, for example. The generating unit generates, for example, a design that balances strength and cost based on the past design data. The trial unit performs trial and error on the design generated by the generating unit. For example, the trial unit quickly tests hundreds of design patterns to select an optimal design. The trial unit, for example, performs trial and error design at high speed, thereby significantly improving the efficiency of the design process and shortening the time to market. The visualization unit visualizes the results obtained by the trial unit. For example, the visualization unit displays each design step in graphs and diagrams to clearly explain the design rationale. For example, by visualizing the design process and results, the visualization unit prevents AI from becoming a black box and makes it easier to gain understanding and approval within the company. As a result, the design process automation system according to the embodiment can automate the design process based on customer performance goals, thereby improving efficiency and optimization. For example, when the design process automation system inputs the customer's desired product performance goals into the AI, the AI ​​generates the optimal design to achieve those goals. This improves the efficiency and optimization of the design process, resulting in improved product quality and a shorter time to market.

[0030] The generation unit can learn from past successes and failures to derive a design. The generation unit, for example, learns from past successes and failures to derive an optimal design. For example, the generation unit generates a design that balances strength and cost based on past design data. The generation unit can also generate a design that balances durability and efficiency based on past design data. The generation unit can also simultaneously select materials and optimize manufacturing processes based on past design data. This makes it possible to generate an optimal design by learning from past successes and failures. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past design data into a generation AI and have the generation AI generate an optimal design.

[0031] The generation unit can simultaneously select materials and optimize the manufacturing process. The generation unit, for example, simultaneously selects materials and optimizes the manufacturing process. For example, the generation unit selects the optimal material taking into account the characteristics of the material. The generation unit can also select the optimal manufacturing process taking into account the efficiency of the manufacturing process. The generation unit can also improve the quality of the design by simultaneously selecting materials and optimizing the manufacturing process. This allows the quality of the design to be improved by simultaneously selecting materials and optimizing the manufacturing process. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input material characteristic data and manufacturing process data into the generation AI and cause the generation AI to select the optimal material and optimize the manufacturing process.

[0032] The trial unit can try out hundreds of design patterns in a short period of time and select a design. The trial unit, for example, tries out hundreds of design patterns in a short period of time and selects an optimal design. For example, the trial unit can significantly improve the efficiency of the design process and shorten the time to market by quickly trying out design patterns. The trial unit can also select an optimal design based on the results of the design pattern trials. The trial unit can also improve the accuracy of the design by repeatedly trying out design patterns. As a result, the efficiency of the design process can be improved by trying out hundreds of design patterns in a short period of time. Some or all of the above-mentioned processing in the trial unit may be performed using, or without, AI. For example, the trial unit can input design pattern data to a generation AI and cause the generation AI to select an optimal design.

[0033] The visualization unit can display each design step in a graph or diagram to show the rationale for the design. For example, the visualization unit can display each design step in a graph or diagram to clearly show the rationale for the design. For example, by visualizing the design process and results, the visualization unit can prevent AI from becoming a black box and make it easier to gain understanding and approval within the company. The visualization unit can also clearly show the rationale for the design by visually displaying each design step. The visualization unit can also deepen understanding of the design by displaying each design step in a graph or diagram. In this way, by visualizing each design step, the rationale for the design can be clearly shown and it can be easier to gain understanding and approval. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input design data into a generation AI and have the generation AI execute visualization that displays each design step in a graph or diagram.

[0034] The reception unit can analyze the customer's past performance target input history and provide an input interface. For example, the reception unit can automatically display performance targets that the customer has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the customer has used in the past. The reception unit can also predict and suggest performance targets to be used in a specific time period based on the customer's past input history. In this way, by analyzing the past input history, it is possible to provide the customer with an optimal input interface. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's past input history data into a generation AI and have the generation AI provide an optimal input interface.

[0035] When inputting performance goals, the reception unit can change the input items based on the customer's current project or areas of interest. For example, the reception unit can preferentially display performance goals related to the customer's ongoing project. The reception unit can also suggest related performance goals based on the customer's areas of interest. The reception unit can also customize optimal input items by referring to the customer's past project history. This allows for more appropriate performance goals to be input by customizing the input items based on the customer's projects and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input customer project data into a generation AI and have the generation AI customize the optimal input items.

[0036] When inputting performance targets, the reception unit can select an input means according to the customer's input method. For example, if the customer desires voice input, the reception unit can provide a voice recognition function and input the performance targets. Furthermore, if the customer desires text input, the reception unit can provide a text input field and input the performance targets. Furthermore, if the customer desires image input, the reception unit can provide an image recognition function and input the performance targets. This facilitates input of performance targets by selecting the optimal input means according to the customer's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's input method data to the generation AI and have the generation AI select the optimal input means.

[0037] When inputting performance targets, the reception unit can input highly relevant targets taking into account the customer's geographical location information. For example, if the customer is in a specific area, the reception unit can prioritize inputting performance targets related to that area. The reception unit can also suggest relevant performance targets based on the customer's geographical location information. The reception unit can also prioritize inputting optimal performance targets taking into account the distance from the customer's current location. In this way, highly relevant performance targets can be prioritized by taking the customer's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the customer's geographical location data to the generation AI and cause the generation AI to input highly relevant performance targets.

[0038] When inputting performance goals, the reception unit can analyze the customer's social media activity and input related goals. The reception unit can, for example, suggest related performance goals based on information shared by the customer on social media. The reception unit can also analyze the customer's social media activity history and input optimal performance goals. The reception unit can also suggest related performance goals by referring to the activities of the customer's friends on social media. In this way, related performance goals can be input by analyzing the customer's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's social media data to the generation AI and cause the generation AI to input related performance goals.

[0039] The reception unit can change the input method by reflecting the customer's past feedback when inputting performance targets. The reception unit can, for example, suggest an optimal input method based on feedback provided by the customer in the past. The reception unit can also customize the input interface by reflecting the customer's past feedback. The reception unit can also optimize the input procedure based on the customer's feedback. In this way, the optimal input method can be provided by reflecting the customer's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input customer feedback data to a generation AI and have the generation AI provide an optimal input method.

[0040] During learning, the learning unit can improve the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust parameters of the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to improve the learning algorithm.

[0041] During learning, the learning unit can improve the learning data by reflecting customer feedback. For example, the learning unit adds or modifies the learning data based on customer feedback. The learning unit can also improve the quality of the learning data by reflecting customer feedback. The learning unit can also review the criteria for selecting learning data based on customer feedback. In this way, the quality of the learning data can be improved by reflecting customer feedback. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input customer feedback data into the generation AI and cause the generation AI to improve the learning data.

[0042] During learning, the learning unit can integrate information from different data sources to increase the training data. For example, the learning unit collects information from different data sources and integrates it into the training data. The learning unit can also analyze information from different data sources to improve the diversity of the training data. The learning unit can also improve the accuracy of the training data based on information from different data sources. In this way, the diversity of the training data can be improved by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into a generation AI and cause the generation AI to integrate the training data.

[0043] During learning, the learning unit can set weighting of the learning data based on the submission time of the performance targets. For example, if the submission time of the performance targets is close, the learning unit sets a high weighting of the learning data. Also, if the submission time of the performance targets is far away, the learning unit can set a low weighting of the learning data. Also, the learning unit can dynamically adjust the weighting of the learning data according to the submission time of the performance targets. In this way, by weighting the learning data based on the submission time of the performance targets, the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input performance target submission time data to the generation AI and cause the generation AI to weight the learning data.

[0044] During learning, the learning unit can analyze the customer's past design data and change the learning algorithm. The learning unit can, for example, adjust the parameters of the learning algorithm based on the customer's past design data. The learning unit can also analyze the customer's past design data to improve the accuracy of the learning algorithm. The learning unit can also optimize the learning algorithm by referring to the customer's past design data. In this way, the accuracy of the learning algorithm can be improved by analyzing the customer's past design data. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input the customer's past design data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0045] During learning, the learning unit can improve the accuracy of learning by referring to design data from different industries. For example, the learning unit collects design data from different industries and integrates it into learning data. The learning unit can also analyze design data from different industries to improve the accuracy of the learning algorithm. The learning unit can also improve the diversity of the learning data based on design data from different industries. In this way, by referring to design data from different industries, the diversity of the learning data can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input design data from different industries into the generation AI and cause the generation AI to integrate the learning data.

[0046] The generation unit can change the level of detail of the generation based on the importance of the design during generation. For example, in the case of an important design, the generation unit generates a design including detailed information. In addition, in the case of a design with low importance, the generation unit can also generate a simple design. The generation unit can also dynamically adjust the level of detail of the generation according to the importance of the design. In this way, an appropriate design can be provided by adjusting the level of detail of the generation based on the importance of the design. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design importance data to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0047] During generation, the generation unit can use different generation algorithms depending on the design category. For example, in the case of mechanical design, the generation unit applies a specific generation algorithm. In addition, the generation unit can also apply a different generation algorithm in the case of electronic design. The generation unit can also select the optimal generation algorithm depending on the design category. In this way, by applying the optimal generation algorithm depending on the design category, the accuracy of the design can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design category data to the generation AI and cause the generation AI to apply the optimal generation algorithm.

[0048] During generation, the generation unit can improve the accuracy of generation by referring to the customer's past design results. The generation unit, for example, adjusts parameters of the generation algorithm based on the customer's past design results. The generation unit can also analyze the customer's past design results and improve the accuracy of generation. The generation unit can also optimize the generation algorithm by referring to the customer's past design results. In this way, the accuracy of generation can be improved by referring to the customer's past design results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the customer's past design result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0049] The generation unit can set generation priorities based on the submission dates of the designs at the time of generation. For example, the generation unit prioritizes the generation of designs that are due for submission soon. The generation unit can also postpone designs that are due for submission further away. The generation unit can also dynamically adjust the generation priorities according to the submission dates. This allows designs to be provided at an appropriate time by determining the generation priorities based on the submission dates of the designs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission date data into the generation AI and have the generation AI set the generation priorities.

[0050] The generation unit can change the order of generation based on the relevance of the designs during generation. For example, the generation unit prioritizes the generation of highly relevant designs. The generation unit can also postpone the generation of less relevant designs. The generation unit can also dynamically adjust the order of generation according to the relevance of the designs. In this way, by adjusting the order of generation based on the relevance of the designs, it is possible to provide highly relevant designs with priority. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0051] During generation, the generation unit can change the use of technical terminology in the generation depending on the customer's level of expertise. For example, if the customer has technical expertise, the generation unit can generate a design that uses a lot of technical terminology. Alternatively, if the customer does not have technical expertise, the generation unit can generate a design that avoids technical terminology. The generation unit can also dynamically adjust the use of technical terminology depending on the customer's level of expertise. This makes it possible to provide a more understandable design by adjusting the use of technical terminology depending on the customer's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0052] During a trial, the trial unit can select a trial method by analyzing the customer's past trial results. The trial unit, for example, selects the optimal trial method based on the customer's past trial results. The trial unit can also analyze the customer's past trial results and adjust the trial method parameters. The trial unit can also optimize the trial method by referring to the customer's past trial results. In this way, the optimal trial method can be provided by analyzing the customer's past trial results. Some or all of the above-described processing in the trial unit may be performed using, for example, AI, or may be performed without using AI. For example, the trial unit can input the customer's past trial result data into the generation AI and have the generation AI select a trial method.

[0053] During a trial, the trial unit can change the trial means based on the client's current project status. The trial unit, for example, selects the optimal trial means based on the client's current project status. The trial unit can also customize the trial means according to the client's project status. The trial unit can also optimize the trial means by referencing the client's project status. This enables more appropriate trials by customizing the trial means based on the client's project status. Some or all of the above-described processing in the trial unit may be performed using, or without, AI. For example, the trial unit can input client project status data into a generation AI and have the generation AI select a trial means.

[0054] The trial unit can improve the trial method by reflecting customer feedback during the trial. For example, the trial unit adds or modifies the trial method based on customer feedback. The trial unit can also improve the quality of the trial method by reflecting customer feedback. The trial unit can also review the selection criteria for the trial method based on customer feedback. In this way, the quality of the trial method can be improved by reflecting customer feedback. Some or all of the above-mentioned processing in the trial unit may be performed using, for example, AI, or may be performed without using AI. For example, the trial unit can input customer feedback data into a generation AI and have the generation AI improve the trial method.

[0055] During a trial, the trial unit can select a trial method taking into account the customer's geographical location information. For example, if the customer is in a specific area, the trial unit preferentially selects a trial method related to that area. The trial unit can also suggest a related trial method based on the customer's geographical location information. The trial unit can also select the optimal trial method taking into account the distance from the customer's current location. This makes it possible to provide the optimal trial method by taking into account the customer's geographical location information. Some or all of the above-described processing in the trial unit may be performed using, for example, AI, or may be performed without using AI. For example, the trial unit can input the customer's geographical location data into a generation AI and have the generation AI select a trial method.

[0056] During the trial, the trial unit can analyze the customer's social media activity and suggest trial measures. The trial unit can, for example, suggest relevant trial measures based on information shared by the customer on social media. The trial unit can also analyze the customer's social media activity history and suggest optimal trial measures. The trial unit can also suggest relevant trial measures by referring to the activities of the customer's friends on social media. In this way, relevant trial measures can be provided by analyzing the customer's social media activity. Some or all of the above-described processing in the trial unit can be performed using, for example, AI, or can be performed without using AI. For example, the trial unit can input the customer's social media data into a generation AI and have the generation AI execute the suggestion of trial measures.

[0057] The trial unit can change the trial method during the trial by reflecting past customer feedback. The trial unit can, for example, propose an optimal trial method based on feedback provided by the customer in the past. The trial unit can also customize the trial interface by reflecting past customer feedback. The trial unit can also optimize the trial procedure based on customer feedback. In this way, the optimal trial method can be provided by reflecting past customer feedback. Some or all of the above-described processing in the trial unit can be performed using, for example, AI, or can be performed without using AI. For example, the trial unit can input customer feedback data into a generation AI and cause the generation AI to improve the trial method.

[0058] During visualization, the visualization unit can select a display method by referring to the customer's past operation history. The visualization unit, for example, selects the optimal display method based on the customer's past operation history. The visualization unit can also analyze the customer's past operation history and adjust parameters of the display method. The visualization unit can also optimize the display method by referring to the customer's past operation history. This makes it possible to provide an optimal display method by referring to the customer's past operation history. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the customer's operation history data into the generation AI and have the generation AI select a display method.

[0059] The visualization unit can change the display content according to the customer's current task during visualization. The visualization unit, for example, selects optimal display content based on the customer's current task. The visualization unit can also customize the display content according to the customer's task. The visualization unit can also optimize the display content by referring to the customer's task. This enables more appropriate visualization by customizing the display content according to the customer's current task. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input customer task data into a generation AI and have the generation AI customize the display content.

[0060] The visualization unit can improve the display method by reflecting customer feedback during visualization. For example, the visualization unit adds or modifies the display method based on customer feedback. The visualization unit can also improve the quality of the display method by reflecting customer feedback. The visualization unit can also review the criteria for selecting the display method based on customer feedback. In this way, the quality of the display method can be improved by reflecting customer feedback. Some or all of the above-mentioned processing in the visualization unit may be performed using AI, for example, or may be performed without using AI. For example, the visualization unit can input customer feedback data into the generation AI and cause the generation AI to improve the display method.

[0061] The visualization unit can select a display method by taking into account the customer's device information during visualization. For example, if the customer is using a smartphone, the visualization unit can provide a display method that matches the screen size. Furthermore, if the customer is using a tablet, the visualization unit can also provide a display method that is optimized for a large screen. Furthermore, if the customer is using a smartwatch, the visualization unit can also provide a display method that is simple and highly visible. This makes it possible to provide an optimal display method by taking into account the customer's device information. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the customer's device information into the generation AI and cause the generation AI to select a display method.

[0062] The visualization unit can make the display content multilingual according to the customer's language setting during visualization. The visualization unit automatically sets the display content based on, for example, the language setting of the customer's device. The visualization unit can also provide a language switching function when the customer uses multiple languages. The visualization unit can also provide the display content in a specific language when the customer selects that language. This enables more appropriate visualization by making the display content multilingual according to the customer's language setting. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the customer's language setting data into a generation AI and cause the generation AI to make the display content multilingual.

[0063] The visualization unit can change the display content according to the customer's level of expertise during visualization. For example, if the customer has expertise, the visualization unit can provide display content that uses a lot of technical jargon. Furthermore, if the customer does not have expertise, the visualization unit can also provide display content that avoids technical jargon. The visualization unit can also dynamically adjust the use of technical jargon according to the customer's level of expertise. This allows for visualization that is easier to understand by adjusting the display content according to the customer's level of expertise. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input customer expertise level data into the generation AI and have the generation AI adjust the display content.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception department can analyze the history of a customer's past design projects and assist in the input of performance targets based on similar projects. For example, the reception department can refer to data from past successful projects and present recommended performance targets to the customer. The reception department can also suggest optimal options based on the materials and technologies used in past projects. Furthermore, the reception department can analyze examples of past project failures and provide advice on how to avoid similar failures. This allows the customer to set more appropriate performance targets by leveraging past experience.

[0066] The learning unit can collect design data from different industries and integrate it into learning data. For example, the learning unit can integrate design data from the automotive industry and the aviation industry to learn optimal designs from different perspectives. The learning unit can also combine design data from medical devices and home appliances to learn new designs that combine technologies from different fields. Furthermore, the learning unit can integrate design data from the architectural industry and the energy industry to learn sustainable designs. This makes it possible to create diverse designs that utilize knowledge from different industries.

[0067] The reception unit can input highly relevant performance targets taking into account the geographical location information of the customer. For example, if the customer is in a specific area, performance targets related to that area are preferentially input. The reception unit can also suggest relevant performance targets based on the geographical location information of the customer. Furthermore, the reception unit can also preferentially input optimal performance targets taking into account the distance from the customer's current location. In this way, highly relevant performance targets can be preferentially input by taking into account the geographical location information of the customer.

[0068] During generation, the generator can use different generation algorithms depending on the design category. For example, a specific generation algorithm is applied for mechanical design. Also, a different generation algorithm can be applied for electronic design. Furthermore, the generator can select the optimal generation algorithm depending on the design category. This allows for improved design accuracy by applying the optimal generation algorithm depending on the design category.

[0069] During a trial, the trial unit can analyze the past trial results of the customer and select a trial method. For example, the trial unit selects the optimal trial method based on the past trial results of the customer. It can also analyze the past trial results of the customer and adjust the parameters of the trial method. It can also optimize the trial method by referring to the past trial results of the customer. In this way, it is possible to provide the optimal trial method by analyzing the past trial results of the customer.

[0070] When visualizing, the visualization unit can select a display method taking into account the customer's device information. For example, if the customer is using a smartphone, a display method that matches the screen size can be provided. Also, if the customer is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the customer is using a smartwatch, a simple and highly visible display method can be provided. In this way, the optimal display method can be provided by taking into account the customer's device information.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit inputs the customer's performance goals. Performance goals include speed, efficiency, durability, etc. The reception unit provides an interface for the customer to input the performance goals of the product they are aiming for. Step 2: The learning unit learns from past design data based on the performance targets input by the reception unit. Past design data includes design drawings, specifications, test results, etc. The learning unit learns from past successes and failures and uses the data to derive the optimal design. Step 3: The generator generates a design based on the data learned by the learning unit. The generator simultaneously selects materials and optimizes the manufacturing process. Based on past design data, the generator generates a design that balances strength and cost. Step 4: The trial section performs trial and error on the design generated by the generation section. The trial section tries out hundreds of design patterns in a short period of time and selects the optimal design. By performing trial and error on the design at high speed, the trial section significantly improves the efficiency of the design process and shortens the time to market. Step 5: The visualization department visualizes the results obtained by the trial department. The visualization department displays each design step in graphs and diagrams, and clearly explains the design rationale. By visualizing the design process and results, the visualization department prevents AI from becoming a black box and makes it easier to gain understanding and approval within the company.

[0073] (Example 2) A design process automation system according to an embodiment of the present invention automates the design process based on customer performance goals, improving efficiency and optimization. When a customer inputs their desired product performance goals into AI, the AI ​​generates the optimal design to achieve those goals. For example, the design process automation system learns from past product design data and generates new designs based on that data. This allows it to derive the optimal design by learning from past successes and failures. Furthermore, the design process automation system can simultaneously consider multiple parameters, taking into account factors that humans might overlook, and proposes optimal designs from multiple perspectives, including product performance, cost, and manufacturability. For example, it simultaneously selects materials and optimizes the manufacturing process. Furthermore, the design process automation system rapidly performs the trial-and-error process typically performed by humans. This significantly improves the efficiency of the design process and shortens time to market. For example, it can quickly test hundreds of design patterns and select the optimal design. Furthermore, the design process and results of the design process automation system are visualized, allowing users to understand why a design is optimal. This prevents AI from becoming a black box and makes it easier to gain internal understanding and approval. For example, each design step can be displayed in graphs and diagrams, clearly explaining the rationale for the design. This allows the design process automation system to significantly streamline the design process in the manufacturing industry, improving product quality and reducing time. This allows the design process automation system to significantly streamline the design process in the manufacturing industry, improving product quality and reducing time. For example, a design process automation system inputs the customer's desired product performance targets into an AI, which then generates the optimal design to achieve those targets. This streamlines and optimizes the design process, improving product quality and reducing time to market.

[0074] A design process automation system according to an embodiment includes a receiving unit, a learning unit, a generating unit, a trial unit, and a visualization unit. The receiving unit inputs a customer's performance goals. Examples of the performance goals include, but are not limited to, speed, efficiency, and durability. The receiving unit provides, for example, an interface for inputting the customer's desired product performance goals. The learning unit learns from past design data based on the performance goals input by the receiving unit. Examples of the past design data include, but are not limited to, design drawings, specifications, and test results. The learning unit learns from, for example, past successes and failures, and generates data for deriving an optimal design. The generating unit generates a design based on the data learned by the learning unit. The generating unit simultaneously selects materials and optimizes the manufacturing process, for example. The generating unit generates, for example, a design that balances strength and cost based on the past design data. The trial unit performs trial and error on the design generated by the generating unit. For example, the trial unit quickly tests hundreds of design patterns to select an optimal design. The trial unit, for example, performs trial and error design at high speed, thereby significantly improving the efficiency of the design process and shortening the time to market. The visualization unit visualizes the results obtained by the trial unit. For example, the visualization unit displays each design step in graphs and diagrams to clearly explain the design rationale. For example, by visualizing the design process and results, the visualization unit prevents AI from becoming a black box and makes it easier to gain understanding and approval within the company. As a result, the design process automation system according to the embodiment can automate the design process based on customer performance goals, thereby improving efficiency and optimization. For example, when the design process automation system inputs the customer's desired product performance goals into the AI, the AI ​​generates the optimal design to achieve those goals. This improves the efficiency and optimization of the design process, resulting in improved product quality and a shorter time to market.

[0075] The generation unit can learn from past successes and failures to derive a design. The generation unit, for example, learns from past successes and failures to derive an optimal design. For example, the generation unit generates a design that balances strength and cost based on past design data. The generation unit can also generate a design that balances durability and efficiency based on past design data. The generation unit can also simultaneously select materials and optimize manufacturing processes based on past design data. This makes it possible to generate an optimal design by learning from past successes and failures. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past design data into a generation AI and have the generation AI generate an optimal design.

[0076] The generation unit can simultaneously select materials and optimize the manufacturing process. The generation unit, for example, simultaneously selects materials and optimizes the manufacturing process. For example, the generation unit selects the optimal material taking into account the characteristics of the material. The generation unit can also select the optimal manufacturing process taking into account the efficiency of the manufacturing process. The generation unit can also improve the quality of the design by simultaneously selecting materials and optimizing the manufacturing process. This allows the quality of the design to be improved by simultaneously selecting materials and optimizing the manufacturing process. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input material characteristic data and manufacturing process data into the generation AI and cause the generation AI to select the optimal material and optimize the manufacturing process.

[0077] The trial unit can try out hundreds of design patterns in a short period of time and select a design. The trial unit, for example, tries out hundreds of design patterns in a short period of time and selects an optimal design. For example, the trial unit can significantly improve the efficiency of the design process and shorten the time to market by quickly trying out design patterns. The trial unit can also select an optimal design based on the results of the design pattern trials. The trial unit can also improve the accuracy of the design by repeatedly trying out design patterns. As a result, the efficiency of the design process can be improved by trying out hundreds of design patterns in a short period of time. Some or all of the above-mentioned processing in the trial unit may be performed using, or without, AI. For example, the trial unit can input design pattern data to a generation AI and cause the generation AI to select an optimal design.

[0078] The visualization unit can display each design step in a graph or diagram to show the rationale for the design. For example, the visualization unit can display each design step in a graph or diagram to clearly show the rationale for the design. For example, by visualizing the design process and results, the visualization unit can prevent AI from becoming a black box and make it easier to gain understanding and approval within the company. The visualization unit can also clearly show the rationale for the design by visually displaying each design step. The visualization unit can also deepen understanding of the design by displaying each design step in a graph or diagram. In this way, by visualizing each design step, the rationale for the design can be clearly shown and it can be easier to gain understanding and approval. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input design data into a generation AI and have the generation AI execute visualization that displays each design step in a graph or diagram.

[0079] The reception unit can estimate the customer's emotions and adjust the performance goal input method based on the emotion data. For example, if the customer is stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the customer is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Alternatively, if the customer is in a hurry, the reception unit can prioritize voice input to enable the customer to quickly input performance goals. This allows the input method to be adjusted according to the customer's emotions, enabling more appropriate performance goal input. 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 reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the customer's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] The reception unit can analyze the customer's past performance target input history and provide an input interface. For example, the reception unit can automatically display performance targets that the customer has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the customer has used in the past. The reception unit can also predict and suggest performance targets to be used in a specific time period based on the customer's past input history. In this way, by analyzing the past input history, it is possible to provide the customer with an optimal input interface. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's past input history data into a generation AI and have the generation AI provide an optimal input interface.

[0081] When inputting performance goals, the reception unit can change the input items based on the customer's current project or areas of interest. For example, the reception unit can preferentially display performance goals related to the customer's ongoing project. The reception unit can also suggest related performance goals based on the customer's areas of interest. The reception unit can also customize optimal input items by referring to the customer's past project history. This allows for more appropriate performance goals to be input by customizing the input items based on the customer's projects and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input customer project data into a generation AI and have the generation AI customize the optimal input items.

[0082] When inputting performance targets, the reception unit can select an input means according to the customer's input method. For example, if the customer desires voice input, the reception unit can provide a voice recognition function and input the performance targets. Furthermore, if the customer desires text input, the reception unit can provide a text input field and input the performance targets. Furthermore, if the customer desires image input, the reception unit can provide an image recognition function and input the performance targets. This facilitates input of performance targets by selecting the optimal input means according to the customer's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's input method data to the generation AI and have the generation AI select the optimal input means.

[0083] The reception unit can estimate the customer's emotions and prioritize the input performance goals based on the estimated customer emotions. For example, if the customer is stressed, the reception unit can prioritize important performance goals. Furthermore, if the customer is relaxed, the reception unit can prioritize detailed performance goals. Furthermore, if the customer is in a hurry, the reception unit can prioritize performance goals that require rapid processing. Thus, by determining the priorities of performance goals based on the customer's emotions, important performance goals can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0084] When inputting performance targets, the reception unit can input highly relevant targets taking into account the customer's geographical location information. For example, if the customer is in a specific area, the reception unit can prioritize inputting performance targets related to that area. The reception unit can also suggest relevant performance targets based on the customer's geographical location information. The reception unit can also prioritize inputting optimal performance targets taking into account the distance from the customer's current location. In this way, highly relevant performance targets can be prioritized by taking the customer's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the customer's geographical location data to the generation AI and cause the generation AI to input highly relevant performance targets.

[0085] When inputting performance goals, the reception unit can analyze the customer's social media activity and input related goals. The reception unit can, for example, suggest related performance goals based on information shared by the customer on social media. The reception unit can also analyze the customer's social media activity history and input optimal performance goals. The reception unit can also suggest related performance goals by referring to the activities of the customer's friends on social media. In this way, related performance goals can be input by analyzing the customer's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the customer's social media data to the generation AI and cause the generation AI to input related performance goals.

[0086] The reception unit can change the input method by reflecting the customer's past feedback when inputting performance targets. The reception unit can, for example, suggest an optimal input method based on feedback provided by the customer in the past. The reception unit can also customize the input interface by reflecting the customer's past feedback. The reception unit can also optimize the input procedure based on the customer's feedback. In this way, the optimal input method can be provided by reflecting the customer's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input customer feedback data to a generation AI and have the generation AI provide an optimal input method.

[0087] The learning unit can estimate a customer's emotions and select training data based on the estimated customer emotions. For example, if a customer is feeling stressed, the learning unit can select a simple data set and perform training. Alternatively, if a customer is relaxed, the learning unit can select a detailed data set and perform training. Alternatively, if a customer is in a hurry, the learning unit can select a data set that can be quickly learned. This allows for more appropriate training by selecting training data based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Examples of generative AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using AI, or may be performed without AI. For example, the learning unit can input customer facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0088] During learning, the learning unit can improve the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust parameters of the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. This makes it possible to optimize the learning algorithm by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to improve the learning algorithm.

[0089] During learning, the learning unit can improve the learning data by reflecting customer feedback. For example, the learning unit adds or modifies the learning data based on customer feedback. The learning unit can also improve the quality of the learning data by reflecting customer feedback. The learning unit can also review the criteria for selecting learning data based on customer feedback. In this way, the quality of the learning data can be improved by reflecting customer feedback. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input customer feedback data into the generation AI and cause the generation AI to improve the learning data.

[0090] During learning, the learning unit can integrate information from different data sources to increase the training data. For example, the learning unit collects information from different data sources and integrates it into the training data. The learning unit can also analyze information from different data sources to improve the diversity of the training data. The learning unit can also improve the accuracy of the training data based on information from different data sources. In this way, the diversity of the training data can be improved by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into a generation AI and cause the generation AI to integrate the training data.

[0091] The learning unit can estimate the customer's emotions and change the learning frequency based on the estimated customer emotions. For example, if the customer is stressed, the learning unit can set the learning frequency low. Furthermore, if the customer is relaxed, the learning unit can also set the learning frequency high. Furthermore, if the customer is in a hurry, the learning unit can optimize the learning frequency to quickly complete the learning. This enables more appropriate learning by adjusting the learning frequency based on the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit can be performed using, for example, an AI, or without an AI. For example, the learning unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] During learning, the learning unit can set weighting of the learning data based on the submission time of the performance targets. For example, if the submission time of the performance targets is close, the learning unit sets a high weighting of the learning data. Also, if the submission time of the performance targets is far away, the learning unit can set a low weighting of the learning data. Also, the learning unit can dynamically adjust the weighting of the learning data according to the submission time of the performance targets. In this way, by weighting the learning data based on the submission time of the performance targets, the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input performance target submission time data to the generation AI and cause the generation AI to weight the learning data.

[0093] During learning, the learning unit can analyze the customer's past design data and change the learning algorithm. The learning unit can, for example, adjust the parameters of the learning algorithm based on the customer's past design data. The learning unit can also analyze the customer's past design data to improve the accuracy of the learning algorithm. The learning unit can also optimize the learning algorithm by referring to the customer's past design data. In this way, the accuracy of the learning algorithm can be improved by analyzing the customer's past design data. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input the customer's past design data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0094] During learning, the learning unit can improve the accuracy of learning by referring to design data from different industries. For example, the learning unit collects design data from different industries and integrates it into learning data. The learning unit can also analyze design data from different industries to improve the accuracy of the learning algorithm. The learning unit can also improve the diversity of the learning data based on design data from different industries. In this way, by referring to design data from different industries, the diversity of the learning data can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input design data from different industries into the generation AI and cause the generation AI to integrate the learning data.

[0095] The generation unit can estimate the customer's emotions and change the expression method of the generated design based on the estimated customer emotions. For example, if the customer is stressed, the generation unit can generate a simple, highly visible design. If the customer is relaxed, the generation unit can also generate a design that includes detailed information. If the customer is in a hurry, the generation unit can also generate a design that can be quickly understood. This allows for adjusting the expression method of the design based on the customer's emotions to provide a more appropriate design. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input customer facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0096] The generation unit can change the level of detail of the generation based on the importance of the design during generation. For example, in the case of an important design, the generation unit generates a design including detailed information. In addition, in the case of a design with low importance, the generation unit can also generate a simple design. The generation unit can also dynamically adjust the level of detail of the generation according to the importance of the design. In this way, an appropriate design can be provided by adjusting the level of detail of the generation based on the importance of the design. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design importance data to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0097] During generation, the generation unit can use different generation algorithms depending on the design category. For example, in the case of mechanical design, the generation unit applies a specific generation algorithm. In addition, the generation unit can also apply a different generation algorithm in the case of electronic design. The generation unit can also select the optimal generation algorithm depending on the design category. In this way, by applying the optimal generation algorithm depending on the design category, the accuracy of the design can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design category data to the generation AI and cause the generation AI to apply the optimal generation algorithm.

[0098] During generation, the generation unit can improve the accuracy of generation by referring to the customer's past design results. The generation unit, for example, adjusts parameters of the generation algorithm based on the customer's past design results. The generation unit can also analyze the customer's past design results and improve the accuracy of generation. The generation unit can also optimize the generation algorithm by referring to the customer's past design results. In this way, the accuracy of generation can be improved by referring to the customer's past design results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the customer's past design result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0099] The generation unit can estimate the customer's emotions and change the length of the design to be generated based on the estimated customer emotions. For example, if the customer is stressed, the generation unit can generate a short, concise design. If the customer is relaxed, the generation unit can generate a longer design with detailed explanations. If the customer is in a hurry, the generation unit can generate a short design that can be quickly understood. This allows for adjusting the length of the design based on the customer's emotions to provide a more appropriate design. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input customer facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0100] The generation unit can set generation priorities based on the submission dates of the designs at the time of generation. For example, the generation unit prioritizes the generation of designs that are due for submission soon. The generation unit can also postpone designs that are due for submission further away. The generation unit can also dynamically adjust the generation priorities according to the submission dates. This allows designs to be provided at an appropriate time by determining the generation priorities based on the submission dates of the designs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission date data into the generation AI and have the generation AI set the generation priorities.

[0101] The generation unit can change the order of generation based on the relevance of the designs during generation. For example, the generation unit prioritizes the generation of highly relevant designs. The generation unit can also postpone the generation of less relevant designs. The generation unit can also dynamically adjust the order of generation according to the relevance of the designs. In this way, by adjusting the order of generation based on the relevance of the designs, it is possible to provide highly relevant designs with priority. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input design relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0102] During generation, the generation unit can change the use of technical terminology in the generation depending on the customer's level of expertise. For example, if the customer has technical expertise, the generation unit can generate a design that uses a lot of technical terminology. Alternatively, if the customer does not have technical expertise, the generation unit can generate a design that avoids technical terminology. The generation unit can also dynamically adjust the use of technical terminology depending on the customer's level of expertise. This makes it possible to provide a more understandable design by adjusting the use of technical terminology depending on the customer's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0103] The trial unit can estimate the customer's emotions and change the trial method based on the estimated customer emotions. For example, if the customer is stressed, the trial unit can provide a simple trial method. If the customer is relaxed, the trial unit can also provide a detailed trial method. If the customer is in a hurry, the trial unit can also provide a method that allows the trial to be completed quickly. This allows for more appropriate trials by adjusting the trial method based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the trial unit can be performed using AI, for example, or without AI. For example, the trial unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0104] During a trial, the trial unit can select a trial method by analyzing the customer's past trial results. The trial unit, for example, selects the optimal trial method based on the customer's past trial results. The trial unit can also analyze the customer's past trial results and adjust the trial method parameters. The trial unit can also optimize the trial method by referring to the customer's past trial results. In this way, the optimal trial method can be provided by analyzing the customer's past trial results. Some or all of the above-described processing in the trial unit may be performed using, for example, AI, or may be performed without using AI. For example, the trial unit can input the customer's past trial result data into the generation AI and have the generation AI select a trial method.

[0105] During a trial, the trial unit can change the trial means based on the client's current project status. The trial unit, for example, selects the optimal trial means based on the client's current project status. The trial unit can also customize the trial means according to the client's project status. The trial unit can also optimize the trial means by referencing the client's project status. This enables more appropriate trials by customizing the trial means based on the client's project status. Some or all of the above-described processing in the trial unit may be performed using, or without, AI. For example, the trial unit can input client project status data into a generation AI and have the generation AI select a trial means.

[0106] The trial unit can improve the trial method by reflecting customer feedback during the trial. For example, the trial unit adds or modifies the trial method based on customer feedback. The trial unit can also improve the quality of the trial method by reflecting customer feedback. The trial unit can also review the selection criteria for the trial method based on customer feedback. In this way, the quality of the trial method can be improved by reflecting customer feedback. Some or all of the above-mentioned processing in the trial unit may be performed using, for example, AI, or may be performed without using AI. For example, the trial unit can input customer feedback data into a generation AI and have the generation AI improve the trial method.

[0107] The trial unit can estimate the customer's emotions and prioritize trials based on the estimated customer emotions. For example, if the customer is stressed, the trial unit can prioritize important trials. Furthermore, if the customer is relaxed, the trial unit can prioritize detailed trials. Furthermore, if the customer is in a hurry, the trial unit can prioritize trials that require quick processing. Thus, by determining the trial priority based on the customer's emotions, important trials can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the trial unit can be performed using, for example, an AI, or without an AI. For example, the trial unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0108] During a trial, the trial unit can select a trial method taking into account the customer's geographical location information. For example, if the customer is in a specific area, the trial unit preferentially selects a trial method related to that area. The trial unit can also suggest a related trial method based on the customer's geographical location information. The trial unit can also select the optimal trial method taking into account the distance from the customer's current location. This makes it possible to provide the optimal trial method by taking into account the customer's geographical location information. Some or all of the above-described processing in the trial unit may be performed using, for example, AI, or may be performed without using AI. For example, the trial unit can input the customer's geographical location data into a generation AI and have the generation AI select a trial method.

[0109] During the trial, the trial unit can analyze the customer's social media activity and suggest trial measures. The trial unit can, for example, suggest relevant trial measures based on information shared by the customer on social media. The trial unit can also analyze the customer's social media activity history and suggest optimal trial measures. The trial unit can also suggest relevant trial measures by referring to the activities of the customer's friends on social media. In this way, relevant trial measures can be provided by analyzing the customer's social media activity. Some or all of the above-described processing in the trial unit can be performed using, for example, AI, or can be performed without using AI. For example, the trial unit can input the customer's social media data into a generation AI and have the generation AI execute the suggestion of trial measures.

[0110] The trial unit can change the trial method during the trial by reflecting past customer feedback. The trial unit can, for example, propose an optimal trial method based on feedback provided by the customer in the past. The trial unit can also customize the trial interface by reflecting past customer feedback. The trial unit can also optimize the trial procedure based on customer feedback. In this way, the optimal trial method can be provided by reflecting past customer feedback. Some or all of the above-described processing in the trial unit can be performed using, for example, AI, or can be performed without using AI. For example, the trial unit can input customer feedback data into a generation AI and cause the generation AI to improve the trial method.

[0111] The visualization unit can estimate the customer's emotions and change the visualization display method based on the estimated customer emotions. For example, if the customer is stressed, the visualization unit can provide a simple, highly visible display method. Furthermore, if the customer is relaxed, the visualization unit can provide a display method that includes detailed information. Furthermore, if the customer is in a hurry, the visualization unit can provide a display method that focuses on the main points. This enables more appropriate visualization by adjusting the display method based on the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the visualization unit can be performed using, for example, AI, or without AI. For example, the visualization unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0112] During visualization, the visualization unit can select a display method by referring to the customer's past operation history. The visualization unit, for example, selects the optimal display method based on the customer's past operation history. The visualization unit can also analyze the customer's past operation history and adjust parameters of the display method. The visualization unit can also optimize the display method by referring to the customer's past operation history. This makes it possible to provide an optimal display method by referring to the customer's past operation history. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the customer's operation history data into the generation AI and have the generation AI select a display method.

[0113] The visualization unit can change the display content according to the customer's current task during visualization. The visualization unit, for example, selects optimal display content based on the customer's current task. The visualization unit can also customize the display content according to the customer's task. The visualization unit can also optimize the display content by referring to the customer's task. This enables more appropriate visualization by customizing the display content according to the customer's current task. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input customer task data into a generation AI and have the generation AI customize the display content.

[0114] The visualization unit can improve the display method by reflecting customer feedback during visualization. For example, the visualization unit adds or modifies the display method based on customer feedback. The visualization unit can also improve the quality of the display method by reflecting customer feedback. The visualization unit can also review the criteria for selecting the display method based on customer feedback. In this way, the quality of the display method can be improved by reflecting customer feedback. Some or all of the above-mentioned processing in the visualization unit may be performed using AI, for example, or may be performed without using AI. For example, the visualization unit can input customer feedback data into the generation AI and cause the generation AI to improve the display method.

[0115] The visualization unit can estimate the customer's emotions and change the visualization operation procedure based on the estimated customer emotions. For example, if the customer is stressed, the visualization unit can provide simple operation procedures. Furthermore, if the customer is relaxed, the visualization unit can provide detailed operation procedures. Furthermore, if the customer is in a hurry, the visualization unit can provide procedures that allow operations to be completed quickly. This allows for more appropriate visualization by adjusting the operation procedures based on the customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 visualization unit can be performed using, for example, AI, or without AI. For example, the visualization unit can input customer facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0116] The visualization unit can select a display method by taking into account the customer's device information during visualization. For example, if the customer is using a smartphone, the visualization unit can provide a display method that matches the screen size. Furthermore, if the customer is using a tablet, the visualization unit can also provide a display method that is optimized for a large screen. Furthermore, if the customer is using a smartwatch, the visualization unit can also provide a display method that is simple and highly visible. This makes it possible to provide an optimal display method by taking into account the customer's device information. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the customer's device information into the generation AI and cause the generation AI to select a display method.

[0117] The visualization unit can make the display content multilingual according to the customer's language setting during visualization. The visualization unit automatically sets the display content based on, for example, the language setting of the customer's device. The visualization unit can also provide a language switching function when the customer uses multiple languages. The visualization unit can also provide the display content in a specific language when the customer selects that language. This enables more appropriate visualization by making the display content multilingual according to the customer's language setting. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the customer's language setting data into a generation AI and cause the generation AI to make the display content multilingual.

[0118] The visualization unit can change the display content according to the customer's level of expertise during visualization. For example, if the customer has expertise, the visualization unit can provide display content that uses a lot of technical jargon. Furthermore, if the customer does not have expertise, the visualization unit can also provide display content that avoids technical jargon. The visualization unit can also dynamically adjust the use of technical jargon according to the customer's level of expertise. This allows for visualization that is easier to understand by adjusting the display content according to the customer's level of expertise. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input customer expertise level data into the generation AI and have the generation AI adjust the display content. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, learning unit, generation unit, trial unit, and visualization unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit provides an interface for inputting customer performance goals using the reception device 38 of the smart device 14. For example, the learning unit learns past design data using the specific processing unit 290 of the data processing device 12 to learn data for deriving an optimal design. For example, the generation unit generates a design based on the data learned by the specific processing unit 290 of the data processing device 12, and simultaneously selects materials and optimizes the manufacturing process. For example, the trial unit performs trial and error on the design generated by the specific processing unit 290 of the data processing device 12, quickly testing hundreds of design patterns and selecting the optimal design. For example, the visualization unit displays each design step in graphs and charts using the output device 40 of the smart device 14 to clearly explain the rationale for the design. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, learning unit, generation unit, trial unit, and visualization unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit provides an interface for inputting a customer's performance goals using the microphone 238 of the smart glasses 214. For example, the learning unit learns past design data using the specific processing unit 290 of the data processing device 12 and learns data for deriving an optimal design. For example, the generation unit generates a design based on the data learned by the specific processing unit 290 of the data processing device 12, and simultaneously selects materials and optimizes the manufacturing process. For example, the trial unit performs trial and error on the design generated by the specific processing unit 290 of the data processing device 12, quickly testing hundreds of design patterns and selecting the optimal design. For example, the visualization unit uses the speaker 240 of the smart glasses 214 to explain each design step aloud and clearly explain the rationale for the design. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, learning unit, generation unit, trial unit, and visualization unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit provides an interface for inputting customer performance goals using the microphone 238 of the headset terminal 314. For example, the learning unit learns past design data using the specific processing unit 290 of the data processing device 12 and learns data for deriving an optimal design. For example, the generation unit generates a design based on the data learned by the specific processing unit 290 of the data processing device 12, and simultaneously selects materials and optimizes the manufacturing process. For example, the trial unit performs trial and error on the design generated by the specific processing unit 290 of the data processing device 12, quickly trying out hundreds of design patterns and selecting the optimal design. For example, the visualization unit displays each design step in graphs and charts using the display 343 of the headset terminal 314 to clearly explain the rationale for the design. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, learning unit, generation unit, trial unit, and visualization unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit provides an interface for inputting customer performance goals using the microphone 238 of the robot 414. For example, the learning unit uses the specific processing unit 290 of the data processing device 12 to learn past design data and generate data for deriving an optimal design. For example, the generation unit generates a design based on the data learned by the specific processing unit 290 of the data processing device 12, and simultaneously selects materials and optimizes the manufacturing process. For example, the trial unit performs trial and error on the design generated by the specific processing unit 290 of the data processing device 12, quickly testing hundreds of design patterns and selecting the optimal design. For example, the visualization unit uses the display device of the robot 414 to display each design step in graphs and charts, clearly explaining the rationale for the design.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The reception department can analyze the history of a customer's past design projects and assist in the input of performance targets based on similar projects. For example, the reception department can refer to data from past successful projects and present recommended performance targets to the customer. The reception department can also suggest optimal options based on the materials and technologies used in past projects. Furthermore, the reception department can analyze examples of past project failures and provide advice on how to avoid similar failures. This allows the customer to set more appropriate performance targets by leveraging past experience.

[0121] The learning unit can collect design data from different industries and integrate it into learning data. For example, the learning unit can integrate design data from the automotive industry and the aviation industry to learn optimal designs from different perspectives. The learning unit can also combine design data from medical devices and home appliances to learn new designs that combine technologies from different fields. Furthermore, the learning unit can integrate design data from the architectural industry and the energy industry to learn sustainable designs. This makes it possible to create diverse designs that utilize knowledge from different industries.

[0122] The generation unit can estimate the customer's emotions and change the way the design is expressed based on the estimated customer emotions. For example, if the customer is feeling stressed, a simple, highly visible design can be generated. If the customer is relaxed, a design including detailed information can be generated. Furthermore, if the customer is in a hurry, a design that can be quickly understood can be generated. In this way, by adjusting the way the design is expressed based on the customer's emotions, it is possible to provide a more appropriate design.

[0123] The trial unit can estimate the customer's emotions and change the trial method based on the estimated customer's emotions. For example, if the customer is feeling stressed, a simple trial method can be provided. If the customer is relaxed, a detailed trial method can be provided. Furthermore, if the customer is in a hurry, a method for quickly completing the trial can be provided. This allows for more appropriate trials by adjusting the trial method based on the customer's emotions.

[0124] The visualization unit can estimate the customer's emotions and change the visualization display method based on the estimated customer emotions. For example, if the customer is feeling stressed, a simple, highly visible display method can be provided. If the customer is relaxed, a display method including detailed information can be provided. Furthermore, if the customer is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate visualization by adjusting the display method based on the customer's emotions.

[0125] The reception unit can input highly relevant performance targets taking into account the geographical location information of the customer. For example, if the customer is in a specific area, performance targets related to that area are preferentially input. The reception unit can also suggest relevant performance targets based on the geographical location information of the customer. Furthermore, the reception unit can also preferentially input optimal performance targets taking into account the distance from the customer's current location. In this way, highly relevant performance targets can be preferentially input by taking into account the geographical location information of the customer.

[0126] The learning unit can estimate the customer's emotions and select learning data based on the estimated customer emotions. For example, if the customer is feeling stressed, a simple data set can be selected for learning. Alternatively, if the customer is relaxed, a detailed data set can be selected for learning. Furthermore, if the customer is in a hurry, a data set that can be quickly learned can be selected. This allows for more appropriate learning by selecting learning data based on the customer's emotions.

[0127] During generation, the generator can use different generation algorithms depending on the design category. For example, a specific generation algorithm is applied for mechanical design. Also, a different generation algorithm can be applied for electronic design. Furthermore, the generator can select the optimal generation algorithm depending on the design category. This allows for improved design accuracy by applying the optimal generation algorithm depending on the design category.

[0128] During a trial, the trial unit can analyze the past trial results of the customer and select a trial method. For example, the trial unit selects the optimal trial method based on the past trial results of the customer. It can also analyze the past trial results of the customer and adjust the parameters of the trial method. It can also optimize the trial method by referring to the past trial results of the customer. In this way, it is possible to provide the optimal trial method by analyzing the past trial results of the customer.

[0129] When visualizing, the visualization unit can select a display method taking into account the customer's device information. For example, if the customer is using a smartphone, a display method that matches the screen size can be provided. Also, if the customer is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the customer is using a smartwatch, a simple and highly visible display method can be provided. In this way, the optimal display method can be provided by taking into account the customer's device information.

[0130] The processing flow of the second embodiment will be briefly explained below.

[0131] Step 1: The reception unit inputs the customer's performance goals. Performance goals include speed, efficiency, durability, etc. The reception unit provides an interface for the customer to input the performance goals of the product they are aiming for. Step 2: The learning unit learns from past design data based on the performance targets input by the reception unit. Past design data includes design drawings, specifications, test results, etc. The learning unit learns from past successes and failures and uses the data to derive the optimal design. Step 3: The generator generates a design based on the data learned by the learning unit. The generator simultaneously selects materials and optimizes the manufacturing process. Based on past design data, the generator generates a design that balances strength and cost. Step 4: The trial section performs trial and error on the design generated by the generation section. The trial section tries out hundreds of design patterns in a short period of time and selects the optimal design. By performing trial and error on the design at high speed, the trial section significantly improves the efficiency of the design process and shortens the time to market. Step 5: The visualization department visualizes the results obtained by the trial department. The visualization department displays each design step in graphs and diagrams, and clearly explains the design rationale. By visualizing the design process and results, the visualization department prevents AI from becoming a black box and makes it easier to gain understanding and approval within the company.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting customer performance targets; a learning unit that learns past design data based on the performance target input by the receiving unit; a generation unit that generates a design based on the data learned by the learning unit; a trial unit that performs trial and error on the design generated by the generation unit; a visualization unit that visualizes the results obtained by the trial unit. A system characterized by:

2. The generation unit Learning from past successes and failures to derive a design 2. The system of claim 1.

3. The generation unit Material selection and manufacturing process are carried out simultaneously 2. The system of claim 1.

4. The trial unit Rapidly try out hundreds of design patterns and select the design 2. The system of claim 1.

5. The visualization unit Each step of the design is displayed in graphs and diagrams to provide a rationale for the design.

2. The system of claim 1.

6. The reception unit Estimate customer sentiment and change the method of inputting performance goals based on the estimated customer sentiment 2. The system of claim 1.

7. The reception unit Analyze the customer's past performance goal input history and provide an input interface 2. The system of claim 1.

8. The reception unit When entering performance goals, modify the input based on the customer's current projects and areas of interest 2. The system of claim 1.

Images