System

Abstract

An object of a system according to an embodiment is to efficiently collect and utilize know-how obtained from failure cases.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information on failure cases. The analysis unit analyzes the information collected by the collection unit and identifies the cause of the failure. The generation unit generates know-how based on the cause of the failure specified by the analysis unit. The providing unit provides the know-how generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not been able to efficiently collect and utilize the know-how gained from failure cases, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect and utilize know-how obtained from failure cases. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information on failure cases. The analysis unit analyzes the information collected by the collection unit and identifies the cause of the failure. The generation unit generates know-how based on the cause of the failure identified by the analysis unit. The provision unit provides the know-how generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and utilize know-how obtained from failure cases. [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 product development support system according to an embodiment of the present invention accumulates many product failure cases created within a company and manages them using a generation AI, allowing even inexperienced individuals to easily acquire know-how from failures and rapidly commercialize products. The product development support system collects product failure cases that occur within a company, analyzes them using a generation AI, and identifies the causes of the failures and areas for improvement. Furthermore, based on the causes of failures and areas for improvement identified by the generation AI, product development know-how is generated and provided to inexperienced individuals. For example, the product development support system collects product failure cases that occur within a company. Specific details of the failures, such as product design errors or insufficient market research, are collected. Next, the product development support system uses a generation AI to analyze the collected failure cases and identify the causes of failures and areas for improvement. For example, if a design error is the cause, the system identifies which part of the design was problematic and suggests areas for improvement. Furthermore, the product development support system generates product development know-how based on the causes of failures and areas for improvement identified by the generation AI. For example, based on areas for improvement regarding design errors, it creates guidelines summarizing points to pay attention to during design. Finally, the product development support system provides the generated know-how to less experienced developers, helping to expedite the commercialization of a product. For example, guidelines created by the generative AI can be provided in the early stages of product development to prevent design errors. This allows the product development support system to reduce failures in in-house product development and achieve rapid commercialization. For example, by utilizing know-how based on past failure cases, such as design errors and insufficient market research, it is expected that product quality will improve and development time will be shortened.

[0029] A product development support system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information on failure cases. Examples of the information on failure cases include, but are not limited to, technical failures, business failures, and operational failures. The collection unit collects detailed information on failure cases, such as product design flaws and insufficient market research. The collection unit can also estimate a user's emotions and adjust the timing of failure case collection based on the estimated user emotions. For example, if a user is feeling stressed, the timing of collection can be delayed to collect data when the user is relaxed. The analysis unit analyzes the collected data and identifies causes of failure and areas for improvement. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods. For example, the analysis unit identifies causes of failure based on the collected data and suggests areas for improvement. The generation unit generates product development know-how based on the identified causes of failure and areas for improvement. The know-how can include, but is not limited to, procedure manuals, checklists, best practices, and other methods. For example, the generation unit creates guidelines summarizing points to pay attention to during design based on improvements to be made to design errors. The provision unit provides the generated know-how to inexperienced people to support product development. Methods of provision include, but are not limited to, online manuals, video tutorials, workshops, etc. For example, the provision unit provides the generated know-how in the early stages of product development to prevent design errors. As a result, the product development support system according to the embodiment collects and analyzes information on failure cases, generates and provides know-how, allowing even inexperienced people to efficiently advance product development.

[0030] The collection unit can collect specific information about failure cases, such as product design errors or insufficient market research. Specific information includes, but is not limited to, details of the failure, the scope of its impact, and the people involved. For example, the collection unit collects detailed information about product design errors. The collection unit can also collect specific information about insufficient market research. For example, the collection unit collects detailed information about failure cases caused by insufficient market research. The collection unit can also collect specific information about failure cases that occurred during the testing phase. For example, the collection unit collects detailed information about failure cases based on test results. By collecting detailed information about failure cases, more specific areas for improvement can be identified. Some or all of the above-described processing by the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input detailed information about product design errors into a generation AI, which then performs analysis.

[0031] The analysis unit can analyze the collected data and identify the cause of the failure and areas for improvement. The analysis can be performed using methods such as, but not limited to, data mining, statistical analysis, and machine learning algorithms. The analysis unit can identify the cause of the failure based on the collected data. The analysis unit can also identify areas for improvement based on the collected data. For example, if the cause is a design error, the analysis unit can identify which part of the design was problematic and suggest areas for improvement. For example, if the cause is insufficient market research, the analysis unit can identify which part of the market research was insufficient and suggest areas for improvement. For example, the analysis unit can analyze data on failure cases that occurred due to insufficient market research and suggest areas for improvement. The analysis unit can also analyze data on failure cases that occurred in the testing phase and identify areas for improvement. For example, the analysis unit can analyze data on failure cases based on test results and identify areas for improvement. By identifying the cause of the failure and areas for improvement, measures to prevent recurrence can be taken. Some or all of the above-described processing by the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input collected data into the generation AI, which can then identify the cause of failure and areas for improvement.

[0032] The generation unit can generate know-how for product development based on the identified causes of failure and improvements. Product development know-how includes, but is not limited to, procedures, checklists, and best practices. For example, the generation unit creates guidelines summarizing points to pay attention to during design based on improvements to design errors. The generation unit can also generate know-how summarizing points to pay attention to during market research based on improvements to insufficient market research. For example, the generation unit generates know-how based on improvements to failure cases caused by insufficient market research. The generation unit can also generate know-how regarding testing methods based on improvements to failure cases that occurred during the testing phase. For example, the generation unit generates know-how based on improvements to failure cases based on test results. Generating know-how for product development can improve development efficiency and quality. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the identified causes of failure and improvements to a generation AI, which then generates know-how.

[0033] The provision unit can provide the generated know-how to less experienced individuals to support product development. Examples of less experienced individuals include, but are not limited to, new employees, transferred employees, and trainees. For example, the provision unit can provide the generated know-how in the early stages of product development to prevent design errors. The provision unit can also provide the generated know-how as an online manual. For example, the provision unit can provide the generated know-how as a video tutorial. The provision unit can also provide the generated know-how in a workshop format. For example, the provision unit can share the generated know-how in a workshop to support practical learning. By providing the generated know-how, even less experienced individuals can efficiently advance product development. Some or all of the above-described processing in the provision unit can be performed using, or without, a generation AI. For example, the provision unit can input the generated know-how into a generation AI, which then provides the know-how.

[0034] When collecting failure cases, the collection unit can apply different collection methods for each product development phase. For example, in the initial design phase, the collection unit collects detailed information about design errors. In addition, in the market research phase, the collection unit can focus on collecting information about insufficient market research. For example, the collection unit collects information about failure cases that occurred due to insufficient market research. In addition, in the testing phase, the collection unit can collect failure cases based on test results and identify areas for improvement. For example, the collection unit collects detailed information about failure cases based on test results. This enables more effective collection of failure cases by applying an appropriate collection method for each development phase. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input different collection methods for each product development phase into the generation AI, and the generation AI can apply the collection methods.

[0035] When collecting failure cases, the collection unit can adjust the level of detail of the information to be collected depending on the type and scale of the product. For example, in the case of a large-scale product, the collection unit collects detailed technical information and market data. In addition, in the case of a small-scale product, the collection unit can collect concise summaries of failure cases. For example, the collection unit collects summaries of failure cases of small-scale products. In addition, in the case of a product targeted at a specific industry, the collection unit can focus on collecting failure cases specific to that industry. For example, the collection unit collects failure cases of products targeted at a specific industry. In this way, appropriate information can be collected by adjusting the level of detail of the information depending on the type and scale of the product. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the level of detail of the information to be collected depending on the type and scale of the product to the generation AI, and the generation AI can adjust the level of detail of the information.

[0036] When collecting failure cases, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the items of information to be collected based on feedback provided by the user in the past. The collection unit can also prioritize the collection of particularly important failure cases from the user's past feedback. For example, the collection unit analyzes the user's feedback and optimizes the collection method. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's past feedback into the generation AI, which can then customize the collection method.

[0037] When collecting failure cases, the collection unit can prioritize collecting highly relevant cases by taking into account the timing of product launch. For example, the collection unit prioritizes collecting failure cases related to products that are about to be launched on the market. The collection unit can also collect related failure cases based on feedback after the market launch. For example, the collection unit focuses on collecting failure cases related to products that are about to be launched on the market. This makes it possible to prioritize collecting highly relevant cases by taking into account the timing of market launch. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the timing of product launch into the generation AI, which can then prioritize collecting highly relevant cases.

[0038] When collecting failure cases, the collection unit can analyze the user's social media activities and collect related cases. For example, the collection unit collects failure cases mentioned by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related failure cases. For example, the collection unit collects related failure cases by referring to the activities of the user's friends on social media. In this way, related failure cases can be effectively collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's social media activities into a generation AI, which then collects related cases.

[0039] When collecting failure cases, the collection unit can select cases to collect by taking into account the user's geographical location information. For example, the collection unit collects failure cases that occurred in a specific area where the user was located. The collection unit can also collect related failure cases based on the user's geographical location information. For example, the collection unit collects failure cases in areas that the user has visited in the past. This makes it possible to collect highly relevant failure cases by taking the geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI and select cases for the generation AI to collect.

[0040] During analysis, the analysis unit can determine the analysis priority based on the frequency of occurrence of failure cases. For example, the analysis unit prioritizes analysis of failure cases that occur frequently. The analysis unit can also postpone analysis of failure cases that occur less frequently. For example, the analysis unit optimally allocates analysis resources based on the frequency of occurrence. This enables efficient analysis by determining the analysis priority based on the frequency of occurrence. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the frequency of occurrence of failure cases into the generation AI, and the generation AI can determine the analysis priority.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the technical elements of the product. For example, in the case of a software product, the analysis unit can apply a bug analysis algorithm. Furthermore, in the case of a hardware product, the analysis unit can apply a physical failure analysis algorithm. For example, in the case of a service product, the analysis unit can apply a customer satisfaction analysis algorithm. This enables more accurate analysis by applying an analysis algorithm depending on the technical elements. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the technical elements of the product into a generation AI, which then applies different analysis algorithms.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit improves the analysis accuracy of similar failure cases based on past analysis results. The analysis unit can also optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit improves the efficiency of the analysis by utilizing past analysis results. In this way, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis results into the generation AI, which can improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the failure case. For example, the analysis unit identifies the cause of the failure case based on the related literature. The analysis unit can also optimize the analysis algorithm by referring to the related literature. For example, the analysis unit improves the accuracy of the analysis by utilizing the related literature. In this way, the accuracy of the analysis is improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input literature related to the failure case into the generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis taking into account the market value of the product. For example, the analysis unit prioritizes analysis of failure cases of products with high market value. The analysis unit can also postpone failure cases of products with low market value. For example, the analysis unit optimally allocates analysis resources based on market value. This enables efficient analysis by determining the priority of analysis based on market value. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the market value of the product into the generation AI, and the generation AI can determine the priority of analysis.

[0045] The analysis unit can perform analysis while taking into account attribute information of the product's development team. The analysis unit can adjust the analysis approach based on, for example, the years of experience of the development team. The analysis unit can also select an analysis algorithm depending on the development team's area of ​​expertise. For example, the analysis unit can improve the accuracy of the analysis by referring to the development team's past project history. This enables more appropriate analysis by taking into account the development team's attribute information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the development team's attribute information into the generation AI, which can then perform the analysis.

[0046] When generating know-how, the generation unit can apply different generation algorithms depending on the category of the failure case. For example, in the case of a failure case related to a design error, the generation unit generates design guidelines. Furthermore, in the case of a failure case related to insufficient market research, the generation unit can generate know-how summarizing key points of the market research. For example, in the case of a failure case in the test phase, the generation unit generates know-how related to test methods. In this way, by applying a generation algorithm depending on the category of the failure case, more effective know-how can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the category of the failure case into the generation AI, and the generation AI can apply different generation algorithms.

[0047] When generating know-how, the generation unit can generate different know-how for each product development phase. For example, in the initial design phase, the generation unit generates know-how that summarizes design points. Furthermore, in the market research phase, the generation unit can generate know-how that summarizes market research methods. For example, in the test phase, the generation unit generates know-how related to test methods. This allows appropriate know-how to be generated for each development phase, thereby improving the efficiency of product development. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the product development phase into the generation AI, and the generation AI can generate different know-how.

[0048] When generating know-how, the generation unit can improve the accuracy of generation by referring to past know-how generation results. The generation unit generates know-how for similar failure cases, for example, based on past know-how generation results. The generation unit can also optimize the generation algorithm by referring to past know-how generation results. For example, the generation unit improves the efficiency of generation by utilizing past know-how generation results. As a result, the accuracy of generation is improved by referring to past know-how generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past know-how generation results into the generation AI, which can improve the accuracy of generation.

[0049] When generating know-how, the generation unit can select know-how to be generated taking into consideration the market launch timing of the product. For example, the generation unit can prioritize generating know-how related to products that are about to be launched on the market. The generation unit can also generate related know-how based on feedback after the market launch. For example, the generation unit can focus on generating know-how related to products that are about to be launched on the market. This allows highly relevant know-how to be generated preferentially by taking the market launch timing into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input the market launch timing of the product into the generation AI, which can then generate highly relevant know-how preferentially.

[0050] When generating know-how, the generation unit can customize the know-how to be generated by taking into account the technical elements of the product. For example, in the case of a software product, the generation unit generates know-how related to bug fixes. Furthermore, in the case of a hardware product, the generation unit can generate know-how related to physical fault repairs. For example, in the case of a service product, the generation unit generates know-how related to customer support. This makes it possible to generate more appropriate know-how by taking technical elements into account. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the technical elements of the product into the generation AI, which can then customize the know-how.

[0051] When generating know-how, the generation unit can adjust the know-how to be generated by taking into account attribute information of the product's development team. The generation unit can adjust the level of detail of the know-how based on, for example, the years of experience of the development team. The generation unit can also customize the content of the know-how according to the development team's field of expertise. For example, the generation unit generates know-how by referring to the development team's past project history. This makes it possible to generate more appropriate know-how by taking into account the attribute information of the development team. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input attribute information of the development team into the generation AI, which can then adjust the know-how.

[0052] When providing know-how, the providing unit can optimize the providing method by referring to the user's past feedback. The providing unit, for example, adjusts the content of the know-how to be provided based on feedback provided by the user in the past. Furthermore, the providing unit can prioritize providing particularly important know-how from the user's past feedback. For example, the providing unit analyzes the user's feedback and optimizes the providing method. In this way, the providing method can be optimized by referring to the past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past feedback into the generation AI, which can then optimize the providing method.

[0053] When providing know-how, the providing unit can customize the content to be provided according to the user's current project status. For example, the providing unit can provide basic know-how in the early stages of a project. Furthermore, the providing unit can provide know-how that is useful for solving specific problems in the middle stages of a project. For example, the providing unit can provide know-how related to final confirmation and testing in the final stages of a project. In this way, by customizing the content to be provided according to the project status, more appropriate know-how can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the user's current project status into the generation AI, which can then customize the content to be provided.

[0054] When providing know-how, the providing unit can adjust the level of detail of the information to be provided according to the user's level of expertise. For example, the providing unit can provide basic information to an inexperienced user. Furthermore, the providing unit can provide detailed technical information to an experienced user. For example, the providing unit customizes the level of detail of the information according to the user's level of expertise. This allows more appropriate know-how to be provided by adjusting the level of detail of the information according to the level of expertise. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's level of expertise to the generation AI, which can then adjust the level of detail of the information.

[0055] When providing know-how, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method that is optimized for a large screen. For example, if the user is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. This allows the optimal providing method to be selected by taking the device information into account. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's device information into the generation AI, which can then select the optimal providing method.

[0056] When providing know-how, the providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit, for example, automatically sets the language of the know-how based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the know-how in that language. This makes the provided content multilingual in accordance with the language setting, thereby making it possible to accommodate a larger number of users. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's language setting into the generation AI, which can then make the provided content multilingual.

[0057] When providing know-how, the providing unit can select know-how to provide taking into consideration the user's geographical location information. For example, the providing unit provides know-how based on failure cases that occurred in a specific area to the user. The providing unit can also provide related know-how based on the user's geographical location information. For example, the providing unit provides know-how based on failure cases in areas that the user has visited in the past. This makes it possible to provide more relevant know-how by taking the geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's geographical location information into the generation AI and select know-how to be provided by the generation AI.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The product development support system can also collect and analyze users' past success stories. For example, the collection unit collects detailed information on successful product designs and market research. The analysis unit analyzes the data on success stories and can identify the factors behind success and effective methods. The generation unit generates best practices for product development based on the factors behind the success stories. The provision unit can provide the generated best practices to less experienced people, improving the success rate of product development. This makes it possible to provide more comprehensive know-how by utilizing not only failure stories but also success stories.

[0060] The collection unit can collect user feedback in real time and provide it to the analysis unit. For example, the collection unit collects real-time feedback while the user is using the product. The collection unit can also collect emotions and opinions that the user has about the product. The analysis unit can analyze the real-time feedback and immediately identify areas for improvement. The generation unit can quickly generate know-how based on the real-time feedback. The provision unit can immediately provide the generated know-how to the user to support product improvement. This makes it possible to make rapid improvements by utilizing real-time feedback.

[0061] The data collection department can apply different collection methods to each product development phase. For example, in the initial design phase, detailed information on design errors can be collected. In the market research phase, information on insufficient market research can be focused on. Furthermore, in the testing phase, failure cases based on test results can be collected and areas for improvement can be identified. This makes it possible to collect failure cases more effectively by applying the appropriate collection method to each development phase.

[0062] During analysis, the analysis unit can determine the priority of analysis based on the frequency of occurrence of failure cases. For example, failure cases that occur frequently can be analyzed first. Also, failure cases that occur less frequently can be postponed. Furthermore, analysis resources can be optimally allocated based on the frequency of occurrence. Thus, by determining the priority of analysis based on the frequency of occurrence, efficient analysis becomes possible.

[0063] When generating know-how, the generation unit can apply different generation algorithms depending on the category of the failure case. For example, in the case of a failure case related to a design error, design guidelines can be generated. In addition, in the case of a failure case related to insufficient market research, know-how summarizing key points of the market research can be generated. Furthermore, in the case of a failure case in the test phase, know-how related to test methods can be generated. In this way, by applying a generation algorithm according to the category of the failure case, more effective know-how can be generated.

[0064] When providing know-how, the providing unit can adjust the level of detail of the information to be provided according to the user's level of expertise. For example, basic information can be provided to an inexperienced user, while detailed technical information can be provided to an experienced user. Furthermore, the level of detail of the information can be customized according to the user's level of expertise. This allows more appropriate know-how to be provided by adjusting the level of detail of the information according to the level of expertise.

[0065] When providing know-how, the providing unit can select the optimal providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. In this way, the optimal providing method can be selected by taking into consideration the device information.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects information on failure cases. Information on failure cases includes technical failures, business failures, operational failures, etc. The collection unit collects detailed information on failure cases, such as product design flaws and insufficient market research. The collection unit can also estimate the user's emotions and adjust the timing of collecting failure cases based on the estimated user emotions. For example, if the user is feeling stressed, the timing of collection can be delayed and collection can be performed when the user is relaxed. Step 2: The analysis unit analyzes the collected data and identifies the cause of the failure and areas for improvement. Analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit uses the collected data to identify the cause of the failure and suggest areas for improvement. Step 3: The generation department generates know-how for product development based on the identified causes of failure and areas for improvement. This know-how includes procedures, checklists, best practices, etc. For example, the generation department creates guidelines that summarize points to pay attention to during design based on areas for improvement of design errors. Step 4: The delivery department provides the generated know-how to less experienced developers to support product development. Delivery methods include online manuals, video tutorials, and workshops. For example, the delivery department provides the generated know-how in the early stages of product development to prevent design errors.

[0068] (Example 2) A product development support system according to an embodiment of the present invention accumulates many product failure cases created within a company and manages them using a generation AI, allowing even inexperienced individuals to easily acquire know-how from failures and rapidly commercialize products. The product development support system collects product failure cases that occur within a company, analyzes them using a generation AI, and identifies the causes of the failures and areas for improvement. Furthermore, based on the causes of failures and areas for improvement identified by the generation AI, product development know-how is generated and provided to inexperienced individuals. For example, the product development support system collects product failure cases that occur within a company. Specific details of the failures, such as product design errors or insufficient market research, are collected. Next, the product development support system uses a generation AI to analyze the collected failure cases and identify the causes of failures and areas for improvement. For example, if a design error is the cause, the system identifies which part of the design was problematic and suggests areas for improvement. Furthermore, the product development support system generates product development know-how based on the causes of failures and areas for improvement identified by the generation AI. For example, based on areas for improvement regarding design errors, it creates guidelines summarizing points to pay attention to during design. Finally, the product development support system provides the generated know-how to less experienced developers, helping to expedite the commercialization of a product. For example, guidelines created by the generative AI can be provided in the early stages of product development to prevent design errors. This allows the product development support system to reduce failures in in-house product development and achieve rapid commercialization. For example, by utilizing know-how based on past failure cases, such as design errors and insufficient market research, it is expected that product quality will improve and development time will be shortened.

[0069] A product development support system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information on failure cases. Examples of the information on failure cases include, but are not limited to, technical failures, business failures, and operational failures. The collection unit collects detailed information on failure cases, such as product design flaws and insufficient market research. The collection unit can also estimate a user's emotions and adjust the timing of failure case collection based on the estimated user emotions. For example, if a user is feeling stressed, the timing of collection can be delayed to collect data when the user is relaxed. The analysis unit analyzes the collected data and identifies causes of failure and areas for improvement. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods. For example, the analysis unit identifies causes of failure based on the collected data and suggests areas for improvement. The generation unit generates product development know-how based on the identified causes of failure and areas for improvement. The know-how can include, but is not limited to, procedure manuals, checklists, best practices, and other methods. For example, the generation unit creates guidelines summarizing points to pay attention to during design based on improvements to be made to design errors. The provision unit provides the generated know-how to inexperienced people to support product development. Methods of provision include, but are not limited to, online manuals, video tutorials, workshops, etc. For example, the provision unit provides the generated know-how in the early stages of product development to prevent design errors. As a result, the product development support system according to the embodiment collects and analyzes information on failure cases, generates and provides know-how, allowing even inexperienced people to efficiently advance product development.

[0070] The collection unit can collect specific information about failure cases, such as product design errors or insufficient market research. Specific information includes, but is not limited to, details of the failure, the scope of its impact, and the people involved. For example, the collection unit collects detailed information about product design errors. The collection unit can also collect specific information about insufficient market research. For example, the collection unit collects detailed information about failure cases caused by insufficient market research. The collection unit can also collect specific information about failure cases that occurred during the testing phase. For example, the collection unit collects detailed information about failure cases based on test results. By collecting detailed information about failure cases, more specific areas for improvement can be identified. Some or all of the above-described processing by the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input detailed information about product design errors into a generation AI, which then performs analysis.

[0071] The analysis unit can analyze the collected data and identify the cause of the failure and areas for improvement. The analysis can be performed using methods such as, but not limited to, data mining, statistical analysis, and machine learning algorithms. The analysis unit can identify the cause of the failure based on the collected data. The analysis unit can also identify areas for improvement based on the collected data. For example, if the cause is a design error, the analysis unit can identify which part of the design was problematic and suggest areas for improvement. For example, if the cause is insufficient market research, the analysis unit can identify which part of the market research was insufficient and suggest areas for improvement. For example, the analysis unit can analyze data on failure cases that occurred due to insufficient market research and suggest areas for improvement. The analysis unit can also analyze data on failure cases that occurred in the testing phase and identify areas for improvement. For example, the analysis unit can analyze data on failure cases based on test results and identify areas for improvement. By identifying the cause of the failure and areas for improvement, measures to prevent recurrence can be taken. Some or all of the above-described processing by the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input collected data into the generation AI, which can then identify the cause of failure and areas for improvement.

[0072] The generation unit can generate know-how for product development based on the identified causes of failure and improvements. Product development know-how includes, but is not limited to, procedures, checklists, and best practices. For example, the generation unit creates guidelines summarizing points to pay attention to during design based on improvements to design errors. The generation unit can also generate know-how summarizing points to pay attention to during market research based on improvements to insufficient market research. For example, the generation unit generates know-how based on improvements to failure cases caused by insufficient market research. The generation unit can also generate know-how regarding testing methods based on improvements to failure cases that occurred during the testing phase. For example, the generation unit generates know-how based on improvements to failure cases based on test results. Generating know-how for product development can improve development efficiency and quality. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the identified causes of failure and improvements to a generation AI, which then generates know-how.

[0073] The provision unit can provide the generated know-how to less experienced individuals to support product development. Examples of less experienced individuals include, but are not limited to, new employees, transferred employees, and trainees. For example, the provision unit can provide the generated know-how in the early stages of product development to prevent design errors. The provision unit can also provide the generated know-how as an online manual. For example, the provision unit can provide the generated know-how as a video tutorial. The provision unit can also provide the generated know-how in a workshop format. For example, the provision unit can share the generated know-how in a workshop to support practical learning. By providing the generated know-how, even less experienced individuals can efficiently advance product development. Some or all of the above-described processing in the provision unit can be performed using, or without, a generation AI. For example, the provision unit can input the generated know-how into a generation AI, which then provides the know-how.

[0074] The collection unit can estimate the user's emotions and determine the timing of collecting failure cases based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately collect failure cases to obtain detailed information. For example, if the user is in a hurry, the collection unit shortens the collection timing and quickly collects the minimum amount of information necessary. This allows failure cases to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, and the generation AI can determine the collection timing.

[0075] When collecting failure cases, the collection unit can apply different collection methods for each product development phase. For example, in the initial design phase, the collection unit collects detailed information about design errors. In addition, in the market research phase, the collection unit can focus on collecting information about insufficient market research. For example, the collection unit collects information about failure cases that occurred due to insufficient market research. In addition, in the testing phase, the collection unit can collect failure cases based on test results and identify areas for improvement. For example, the collection unit collects detailed information about failure cases based on test results. This enables more effective collection of failure cases by applying an appropriate collection method for each development phase. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input different collection methods for each product development phase into the generation AI, and the generation AI can apply the collection methods.

[0076] When collecting failure cases, the collection unit can adjust the level of detail of the information to be collected depending on the type and scale of the product. For example, in the case of a large-scale product, the collection unit collects detailed technical information and market data. In addition, in the case of a small-scale product, the collection unit can collect concise summaries of failure cases. For example, the collection unit collects summaries of failure cases of small-scale products. In addition, in the case of a product targeted at a specific industry, the collection unit can focus on collecting failure cases specific to that industry. For example, the collection unit collects failure cases of products targeted at a specific industry. In this way, appropriate information can be collected by adjusting the level of detail of the information depending on the type and scale of the product. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the level of detail of the information to be collected depending on the type and scale of the product to the generation AI, and the generation AI can adjust the level of detail of the information.

[0077] When collecting failure cases, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the items of information to be collected based on feedback provided by the user in the past. The collection unit can also prioritize the collection of particularly important failure cases from the user's past feedback. For example, the collection unit analyzes the user's feedback and optimizes the collection method. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's past feedback into the generation AI, which can then customize the collection method.

[0078] The collection unit can estimate the user's emotions and determine the importance of failure cases to be collected based on the estimated user emotions. For example, if the user is stressed, the collection unit can postpone collecting failure cases with low importance. Furthermore, if the user is relaxed, the collection unit can prioritize collecting failure cases with high importance. For example, if the user is in a hurry, the collection unit prioritizes failure cases that can be collected quickly. This allows important cases to be collected preferentially by determining the priority of failure cases according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, which can then determine the importance of the failure cases.

[0079] When collecting failure cases, the collection unit can prioritize collecting highly relevant cases by taking into account the timing of product launch. For example, the collection unit prioritizes collecting failure cases related to products that are about to be launched on the market. The collection unit can also collect related failure cases based on feedback after the market launch. For example, the collection unit focuses on collecting failure cases related to products that are about to be launched on the market. This makes it possible to prioritize collecting highly relevant cases by taking into account the timing of market launch. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the timing of product launch into the generation AI, which can then prioritize collecting highly relevant cases.

[0080] When collecting failure cases, the collection unit can analyze the user's social media activities and collect related cases. For example, the collection unit collects failure cases mentioned by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related failure cases. For example, the collection unit collects related failure cases by referring to the activities of the user's friends on social media. In this way, related failure cases can be effectively collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's social media activities into a generation AI, which then collects related cases.

[0081] When collecting failure cases, the collection unit can select cases to collect by taking into account the user's geographical location information. For example, the collection unit collects failure cases that occurred in a specific area where the user was located. The collection unit can also collect related failure cases based on the user's geographical location information. For example, the collection unit collects failure cases in areas that the user has visited in the past. This makes it possible to collect highly relevant failure cases by taking the geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI and select cases for the generation AI to collect.

[0082] The analysis unit can estimate the user's emotions and change the analysis method for the cause of failure or areas for improvement based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a concise and easy-to-understand analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can quickly provide an analysis result. This allows for adjusting the analysis method according to the user's emotions to provide more appropriate analysis results. 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 analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the analysis method.

[0083] During analysis, the analysis unit can determine the analysis priority based on the frequency of occurrence of failure cases. For example, the analysis unit prioritizes analysis of failure cases that occur frequently. The analysis unit can also postpone analysis of failure cases that occur less frequently. For example, the analysis unit optimally allocates analysis resources based on the frequency of occurrence. This enables efficient analysis by determining the analysis priority based on the frequency of occurrence. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the frequency of occurrence of failure cases into the generation AI, and the generation AI can determine the analysis priority.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the technical elements of the product. For example, in the case of a software product, the analysis unit can apply a bug analysis algorithm. Furthermore, in the case of a hardware product, the analysis unit can apply a physical failure analysis algorithm. For example, in the case of a service product, the analysis unit can apply a customer satisfaction analysis algorithm. This enables more accurate analysis by applying an analysis algorithm depending on the technical elements. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the technical elements of the product into a generation AI, which then applies different analysis algorithms.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit improves the analysis accuracy of similar failure cases based on past analysis results. The analysis unit can also optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit improves the efficiency of the analysis by utilizing past analysis results. In this way, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis results into the generation AI, which can improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for adjusting the display method according to the user's emotions to provide more appropriate analysis results. 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 analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the display method.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the failure case. For example, the analysis unit identifies the cause of the failure case based on the related literature. The analysis unit can also optimize the analysis algorithm by referring to the related literature. For example, the analysis unit improves the accuracy of the analysis by utilizing the related literature. In this way, the accuracy of the analysis is improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input literature related to the failure case into the generation AI, which can improve the accuracy of the analysis.

[0088] During analysis, the analysis unit can determine the priority of analysis taking into account the market value of the product. For example, the analysis unit prioritizes analysis of failure cases of products with high market value. The analysis unit can also postpone failure cases of products with low market value. For example, the analysis unit optimally allocates analysis resources based on market value. This enables efficient analysis by determining the priority of analysis based on market value. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the market value of the product into the generation AI, and the generation AI can determine the priority of analysis.

[0089] The analysis unit can perform analysis while taking into account attribute information of the product's development team. The analysis unit can adjust the analysis approach based on, for example, the years of experience of the development team. The analysis unit can also select an analysis algorithm depending on the development team's area of ​​expertise. For example, the analysis unit can improve the accuracy of the analysis by referring to the development team's past project history. This enables more appropriate analysis by taking into account the development team's attribute information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the development team's attribute information into the generation AI, which can then perform the analysis.

[0090] The generation unit can estimate the user's emotions and change the know-how generation method based on the estimated user's emotions. For example, when the user is feeling stressed, the generation unit generates concise and easy-to-understand know-how. Furthermore, when the user is relaxed, the generation unit can generate detailed know-how. For example, when the user is in a hurry, the generation unit quickly generates know-how. This allows the know-how generation method to be adjusted according to the user's emotions, thereby providing more appropriate know-how. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which then adjusts the know-how generation method.

[0091] When generating know-how, the generation unit can apply different generation algorithms depending on the category of the failure case. For example, in the case of a failure case related to a design error, the generation unit generates design guidelines. Furthermore, in the case of a failure case related to insufficient market research, the generation unit can generate know-how summarizing key points of the market research. For example, in the case of a failure case in the test phase, the generation unit generates know-how related to test methods. In this way, by applying a generation algorithm depending on the category of the failure case, more effective know-how can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the category of the failure case into the generation AI, and the generation AI can apply different generation algorithms.

[0092] When generating know-how, the generation unit can generate different know-how for each product development phase. For example, in the initial design phase, the generation unit generates know-how that summarizes design points. Furthermore, in the market research phase, the generation unit can generate know-how that summarizes market research methods. For example, in the test phase, the generation unit generates know-how related to test methods. This allows appropriate know-how to be generated for each development phase, thereby improving the efficiency of product development. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the product development phase into the generation AI, and the generation AI can generate different know-how.

[0093] When generating know-how, the generation unit can improve the accuracy of generation by referring to past know-how generation results. The generation unit generates know-how for similar failure cases, for example, based on past know-how generation results. The generation unit can also optimize the generation algorithm by referring to past know-how generation results. For example, the generation unit improves the efficiency of generation by utilizing past know-how generation results. As a result, the accuracy of generation is improved by referring to past know-how generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past know-how generation results into the generation AI, which can improve the accuracy of generation.

[0094] The generation unit can estimate the user's emotions and determine the importance of the know-how to be generated based on the estimated user's emotions. For example, when the user is feeling stressed, the generation unit postpones know-how with a lower importance. Furthermore, when the user is relaxed, the generation unit can prioritize generating know-how with a higher importance. For example, when the user is in a hurry, the generation unit prioritizes know-how that can be generated quickly. This allows important know-how to be generated preferentially by determining the priority of know-how according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which then determines the importance of the know-how.

[0095] When generating know-how, the generation unit can select know-how to be generated taking into consideration the market launch timing of the product. For example, the generation unit can prioritize generating know-how related to products that are about to be launched on the market. The generation unit can also generate related know-how based on feedback after the market launch. For example, the generation unit can focus on generating know-how related to products that are about to be launched on the market. This allows highly relevant know-how to be generated preferentially by taking the market launch timing into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input the market launch timing of the product into the generation AI, which can then generate highly relevant know-how preferentially.

[0096] When generating know-how, the generation unit can customize the know-how to be generated by taking into account the technical elements of the product. For example, in the case of a software product, the generation unit generates know-how related to bug fixes. Furthermore, in the case of a hardware product, the generation unit can generate know-how related to physical fault repairs. For example, in the case of a service product, the generation unit generates know-how related to customer support. This makes it possible to generate more appropriate know-how by taking technical elements into account. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the technical elements of the product into the generation AI, which can then customize the know-how.

[0097] When generating know-how, the generation unit can adjust the know-how to be generated by taking into account attribute information of the product's development team. The generation unit can adjust the level of detail of the know-how based on, for example, the years of experience of the development team. The generation unit can also customize the content of the know-how according to the development team's field of expertise. For example, the generation unit generates know-how by referring to the development team's past project history. This makes it possible to generate more appropriate know-how by taking into account the attribute information of the development team. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input attribute information of the development team into the generation AI, which can then adjust the know-how.

[0098] The providing unit can estimate the user's emotions and change the know-how provision method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide concise and easy-to-understand know-how. Furthermore, if the user is relaxed, the providing unit can provide detailed know-how. For example, if the user is in a hurry, the providing unit prioritizes know-how that can be provided quickly. This allows the provision method to be adjusted according to the user's emotions, thereby providing more appropriate know-how. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then adjust the provision method.

[0099] When providing know-how, the providing unit can optimize the providing method by referring to the user's past feedback. The providing unit, for example, adjusts the content of the know-how to be provided based on feedback provided by the user in the past. Furthermore, the providing unit can prioritize providing particularly important know-how from the user's past feedback. For example, the providing unit analyzes the user's feedback and optimizes the providing method. In this way, the providing method can be optimized by referring to the past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past feedback into the generation AI, which can then optimize the providing method.

[0100] When providing know-how, the providing unit can customize the content to be provided according to the user's current project status. For example, the providing unit can provide basic know-how in the early stages of a project. Furthermore, the providing unit can provide know-how that is useful for solving specific problems in the middle stages of a project. For example, the providing unit can provide know-how related to final confirmation and testing in the final stages of a project. In this way, by customizing the content to be provided according to the project status, more appropriate know-how can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input the user's current project status into the generation AI, which can then customize the content to be provided.

[0101] When providing know-how, the providing unit can adjust the level of detail of the information to be provided according to the user's level of expertise. For example, the providing unit can provide basic information to an inexperienced user. Furthermore, the providing unit can provide detailed technical information to an experienced user. For example, the providing unit customizes the level of detail of the information according to the user's level of expertise. This allows more appropriate know-how to be provided by adjusting the level of detail of the information according to the level of expertise. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's level of expertise to the generation AI, which can then adjust the level of detail of the information.

[0102] The providing unit can estimate the user's emotions and change the timing of providing know-how based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit delays the timing of providing know-how and provides it when the user is relaxed. Furthermore, if the user is relaxed, the providing unit can provide know-how immediately. For example, if the user is in a hurry, the providing unit prioritizes know-how that can be provided quickly. This allows the timing of providing know-how to be adjusted according to the user's emotions, thereby providing know-how at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can adjust the timing of providing know-how.

[0103] When providing know-how, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method that is optimized for a large screen. For example, if the user is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. This allows the optimal providing method to be selected by taking the device information into account. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's device information into the generation AI, which can then select the optimal providing method.

[0104] When providing know-how, the providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit, for example, automatically sets the language of the know-how based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the know-how in that language. This makes the provided content multilingual in accordance with the language setting, thereby making it possible to accommodate a larger number of users. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's language setting into the generation AI, which can then make the provided content multilingual.

[0105] When providing know-how, the providing unit can select know-how to provide taking into consideration the user's geographical location information. For example, the providing unit provides know-how based on failure cases that occurred in a specific area to the user. The providing unit can also provide related know-how based on the user's geographical location information. For example, the providing unit provides know-how based on failure cases in areas that the user has visited in the past. This makes it possible to provide more relevant know-how by taking the geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's geographical location information into the generation AI and select know-how to be provided by the generation AI. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on failure cases using the camera 42 and microphone 38B of the smart device 14, and adjusts the collection timing using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to identify causes of failure and areas for improvement. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and generates know-how based on the identified causes of failure and areas for improvement. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated know-how to inexperienced users. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 collection unit collects information on failure cases using the camera 42 and microphone 238 of the smart glasses 214, and adjusts the collection timing using the control unit 46A. The analysis unit, realized, for example, by the identification processing unit 290 of the data processing device 12, analyzes the collected data and identifies causes of failure and areas for improvement. The generation unit, realized, for example, by the identification processing unit 290 of the data processing device 12, generates know-how based on the identified causes of failure and areas for improvement. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the generated know-how to inexperienced users. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects information on failure cases using the camera 42 and microphone 238 of the headset-type terminal 314, and adjusts the collection timing using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to identify causes of failure and areas for improvement. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and generates know-how based on the identified causes of failure and areas for improvement. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the generated know-how to inexperienced people. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on failure cases using the camera 42 and microphone 238 of the robot 414, and adjusts the collection timing using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify causes of failure and areas for improvement. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates know-how based on the identified causes of failure and areas for improvement. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated know-how to inexperienced people.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The product development support system can also collect and analyze users' past success stories. For example, the collection unit collects detailed information on successful product designs and market research. The analysis unit analyzes the data on success stories and can identify the factors behind success and effective methods. The generation unit generates best practices for product development based on the factors behind the success stories. The provision unit can provide the generated best practices to less experienced people, improving the success rate of product development. This makes it possible to provide more comprehensive know-how by utilizing not only failure stories but also success stories.

[0108] The collection unit can collect user feedback in real time and provide it to the analysis unit. For example, the collection unit collects real-time feedback while the user is using the product. The collection unit can also collect emotions and opinions that the user has about the product. The analysis unit can analyze the real-time feedback and immediately identify areas for improvement. The generation unit can quickly generate know-how based on the real-time feedback. The provision unit can immediately provide the generated know-how to the user to support product improvement. This makes it possible to make rapid improvements by utilizing real-time feedback.

[0109] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method according to the user's emotions, more appropriate analysis results can be provided.

[0110] The generation unit can estimate the user's emotions and change the know-how generation method based on the estimated user's emotions. For example, if the user is feeling stressed, concise and easy-to-understand know-how can be generated. If the user is relaxed, detailed know-how can be generated. Furthermore, if the user is in a hurry, know-how can be generated quickly. In this way, by adjusting the know-how generation method according to the user's emotions, more appropriate know-how can be provided.

[0111] The providing unit can estimate the user's emotions and change the method of providing know-how based on the estimated user's emotions. For example, if the user is feeling stressed, concise and easy-to-understand know-how can be provided. If the user is relaxed, detailed know-how can be provided. Furthermore, if the user is in a hurry, know-how that can be provided quickly can be prioritized. In this way, by adjusting the method of providing know-how according to the user's emotions, more appropriate know-how can be provided.

[0112] The data collection department can apply different collection methods to each product development phase. For example, in the initial design phase, detailed information on design errors can be collected. In the market research phase, information on insufficient market research can be focused on. Furthermore, in the testing phase, failure cases based on test results can be collected and areas for improvement can be identified. This makes it possible to collect failure cases more effectively by applying the appropriate collection method to each development phase.

[0113] During analysis, the analysis unit can determine the priority of analysis based on the frequency of occurrence of failure cases. For example, failure cases that occur frequently can be analyzed first. Also, failure cases that occur less frequently can be postponed. Furthermore, analysis resources can be optimally allocated based on the frequency of occurrence. Thus, by determining the priority of analysis based on the frequency of occurrence, efficient analysis becomes possible.

[0114] When generating know-how, the generation unit can apply different generation algorithms depending on the category of the failure case. For example, in the case of a failure case related to a design error, design guidelines can be generated. In addition, in the case of a failure case related to insufficient market research, know-how summarizing key points of the market research can be generated. Furthermore, in the case of a failure case in the test phase, know-how related to test methods can be generated. In this way, by applying a generation algorithm according to the category of the failure case, more effective know-how can be generated.

[0115] When providing know-how, the providing unit can adjust the level of detail of the information to be provided according to the user's level of expertise. For example, basic information can be provided to an inexperienced user, while detailed technical information can be provided to an experienced user. Furthermore, the level of detail of the information can be customized according to the user's level of expertise. This allows more appropriate know-how to be provided by adjusting the level of detail of the information according to the level of expertise.

[0116] When providing know-how, the providing unit can select the optimal providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. In this way, the optimal providing method can be selected by taking into consideration the device information.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects information on failure cases. Information on failure cases includes technical failures, business failures, operational failures, etc. The collection unit collects detailed information on failure cases, such as product design flaws and insufficient market research. The collection unit can also estimate the user's emotions and adjust the timing of collecting failure cases based on the estimated user emotions. For example, if the user is feeling stressed, the timing of collection can be delayed and collection can be performed when the user is relaxed. Step 2: The analysis unit analyzes the collected data and identifies the cause of the failure and areas for improvement. Analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit uses the collected data to identify the cause of the failure and suggest areas for improvement. Step 3: The generation department generates know-how for product development based on the identified causes of failure and areas for improvement. This know-how includes procedures, checklists, best practices, etc. For example, the generation department creates guidelines that summarize points to pay attention to during design based on areas for improvement of design errors. Step 4: The delivery department provides the generated know-how to less experienced developers to support product development. Delivery methods include online manuals, video tutorials, and workshops. For example, the delivery department provides the generated know-how in the early stages of product development to prevent design errors.

[0119] 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.

[0120] 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.

[0121] 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.

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

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0137] The data processing system 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.

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

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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 AI 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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, in order to avoid confusion and to 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 collection department that collects information on failure cases; an analysis unit that analyzes the information collected by the collection unit and identifies the cause of the failure; a generation unit that generates know-how based on the cause of the failure identified by the analysis unit; a providing unit that provides the know-how generated by the generating unit. A system characterized by:

2. The collecting unit Collect specific information on failure cases such as product design flaws and insufficient market research 2. The system of claim 1.

3. The analysis unit Analyze the collected data to identify causes of failure and areas for improvement 2. The system of claim 1.

4. The generation unit Generate know-how for product development based on the identified causes of failure and areas for improvement 2. The system of claim 1.

5. The providing unit Provide the generated know-how to those with less experience to support product development 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and determine the timing of collecting failure cases based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When collecting failure cases, apply different collection methods for each product development phase.

2. The system of claim 1.

8. The collecting unit When collecting failure cases, adjust the specificity of the information collected depending on the type and scale of the product.

2. The system of claim 1.

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