System

Abstract

An object of a system according to an embodiment is to efficiently perform a quality check before a product is released.SOLUTION: A system according to an embodiment includes a user viewpoint simulation unit, a problem solving confirmation unit, and an operability confirmation unit. The user viewpoint simulator simulates a user viewpoint. The problem solution confirmation unit confirms the rationality of the problem solution, which is the essence of the service, based on the operation procedure simulated by the user viewpoint simulation unit. The operability confirmation unit confirms the operability at the user's viewpoint based on the operation procedure simulated by the user's viewpoint simulation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies require quality checks before product release, which takes a lot of time and resources, and there is room for improvement in efficiency.

[0005] The system according to the embodiment aims to efficiently perform quality checks before the release of a product. [Means for solving the problem]

[0006] The system according to the embodiment includes a user-perspective simulation unit, a problem-solution confirmation unit, and an operability confirmation unit. The user-perspective simulation unit simulates a user's perspective. The problem-solution confirmation unit confirms the rationality of the problem solution, which is the essence of the service, based on the operation procedures simulated by the user-perspective simulation unit. The operability confirmation unit confirms operability from the user's perspective based on the operation procedures simulated by the user-perspective simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently perform quality checks before the release of a product. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 quality check system according to an embodiment of the present invention utilizes AI walkthroughs to test the overall user experience of a product, thereby enabling the quality check system to efficiently and effectively perform quality checks before the release of a product.

[0029] A quality check system according to an embodiment includes a user-perspective simulation unit, a problem-solving confirmation unit, and an operability confirmation unit. The user-perspective simulation unit simulates a user's perspective. For example, the generation AI reproduces a series of operations from when a user launches an application to when a specific function is used. Furthermore, when a user uses an application to solve a specific problem, the generation AI checks whether the process is smooth and whether the necessary functions are properly provided. Furthermore, the generation AI checks whether the user interface is intuitive and easy to use, and whether the operation procedures are overly complicated. For example, the generation AI simulates a series of procedures a user performs when operating an application and evaluates the operability. The problem-solving confirmation unit checks the rationality of the problem-solving, which is the essence of the service, based on the operation procedures simulated by the user-perspective simulation unit. For example, the generation AI simulates operation procedures according to the user's purpose and evaluates the results. Furthermore, when a user uses an application to solve a specific problem, the generation AI checks whether the process is smooth and whether the necessary functions are properly provided. The operability confirmation unit checks the operability from the user's perspective based on the operation procedures simulated by the user-perspective simulation unit. For example, the generation AI checks whether the user interface is intuitive and easy to use, and whether the operation procedures are too complicated. The generation AI also simulates a series of steps a user takes to operate an application and evaluates the operability. This allows the quality check system according to the embodiment to efficiently and effectively perform quality checks before a product is released. For example, the generation AI checks the operability of an application in advance and corrects any problems, thereby improving user satisfaction after release. The generation AI also checks whether the essential problem-solving capabilities of the service can be rationally implemented, thereby maximizing the value of the product. Furthermore, the generation AI streamlines the quality assurance process, enabling the delivery of high-quality products while adhering to release schedules.

[0030] The user-perspective simulation unit can learn the user's behavior patterns in real time and incorporate new behavior patterns during the simulation. In the user-perspective simulation unit, for example, the generation AI learns the user's behavior patterns in real time and incorporates new behavior patterns during the simulation. For example, operations that the user frequently performs are reflected in the simulation on a priority basis. The generation AI also learns the user's behavior patterns in real time and incorporates new behavior patterns during the simulation. For example, it can also reflect new operations performed by the user in the simulation. This makes it possible to learn the user's behavior patterns in real time and incorporate new behavior patterns during the simulation.

[0031] The user-perspective simulation unit can reproduce an individual user experience based on the user's past operation history. In the user-perspective simulation unit, for example, the generation AI analyzes the user's past operation history and reproduces an individual user experience. For example, it reproduces the operation procedures that the user previously performed and provides the same experience. The generation AI also analyzes the user's past operation history and reproduces an individual user experience. For example, it can also reproduce similar operation procedures based on operations that the user previously performed. In this way, it is possible to reproduce an individual user experience based on the user's past operation history.

[0032] The user-perspective simulation unit can include voice commands and gesture operations in the simulation, allowing for testing of a wider variety of user interfaces. In the user-perspective simulation unit, for example, the generation AI analyzes voice commands and reflects them in the simulation. For example, it can recreate a scenario in which a user instructs an operation by voice. The generation AI can also analyze gesture operations and reflect them in the simulation. For example, it can recreate a scenario in which a user instructs an operation by gesture. This allows for testing of a wider variety of user interfaces, including voice commands and gesture operations.

[0033] The user-perspective simulation unit can simultaneously simulate operability on different devices (smartphones, tablets, PCs, etc.). In the user-perspective simulation unit, for example, the generation AI simultaneously simulates operability on different devices. For example, it can reproduce the operation procedures on a smartphone, tablet, and PC, and evaluate the operability of each device. The generation AI can also simultaneously simulate operability on different devices. For example, it can reproduce the operation procedures on a smartphone, tablet, and PC, and evaluate the operability of each device. This makes it possible to simultaneously simulate operability on different devices.

[0034] The problem-solving confirmation unit can collect user feedback in real time and improve the problem-solving process based on that feedback. In the problem-solving confirmation unit, for example, the generation AI collects user feedback in real time and improves the problem-solving process based on that data. For example, it can optimize the process by reflecting user opinions. In addition, the generation AI collects user feedback in real time and improves the problem-solving process based on that data. For example, it can also optimize the process by reflecting user opinions. In this way, it is possible to collect user feedback in real time and improve the problem-solving process based on that feedback.

[0035] The problem-solving confirmation unit can generate different scenarios and compare the efficiency of problem-solving in each scenario. In the problem-solving confirmation unit, for example, the generation AI generates different scenarios and compares the efficiency of problem-solving in each scenario. For example, it can simulate multiple solution methods and identify the optimal method. The generation AI can also generate different scenarios and compare the efficiency of problem-solving in each scenario. For example, it can simulate multiple solution methods and identify the optimal method. This makes it possible to generate different scenarios and compare the efficiency of problem-solving in each scenario.

[0036] The problem-solving confirmation unit can simulate different problem-solving processes for different user groups (beginners, advanced users, etc.). In the problem-solving confirmation unit, for example, the generation AI simulates different problem-solving processes for different user groups. For example, it can reproduce a simplified process for beginners and a detailed process for advanced users. The generation AI can also simulate different problem-solving processes for different user groups. For example, it can reproduce a simplified process for beginners and a detailed process for advanced users. This makes it possible to simulate different problem-solving processes for different user groups.

[0037] The operability confirmation unit can detect user operation errors in real time and analyze the causes thereof. In the operability confirmation unit, for example, the generation AI detects user operation errors in real time and analyzes the causes thereof. For example, if a user presses the wrong button, the cause can be identified. In addition, the generation AI can detect user operation errors in real time and analyze the causes thereof. For example, if a user presses the wrong button, the cause can be identified. This makes it possible to detect user operation errors in real time and analyze the causes thereof.

[0038] The operability confirmation unit can measure the user's operation speed and reaction time and reflect this in the evaluation of operability. In the operability confirmation unit, for example, the generation AI measures the user's operation speed and reaction time and reflects this in the evaluation of operability. For example, it measures the time it takes for the user to complete a specific operation. The generation AI can also measure the user's operation speed and reaction time and reflect this in the evaluation of operability. For example, it can measure the time it takes for the user to complete a specific operation. This makes it possible to measure the user's operation speed and reaction time and reflect this in the evaluation of operability.

[0039] The operability checking unit can simulate different user interface designs and identify the optimal design. In the operability checking unit, for example, the generation AI simulates different user interface designs and identifies the optimal design. For example, it can compare multiple design proposals and select the design that is easiest for the user to use. The generation AI can also simulate different user interface designs and identify the optimal design. For example, it can compare multiple design proposals and select the design that is easiest for the user to use. In this way, it is possible to simulate different user interface designs and identify the optimal design.

[0040] The operability checking unit can evaluate operability in different operating environments (brightness, volume, etc.). In the operability checking unit, for example, the generation AI evaluates operability in different operating environments. For example, it can simulate operating procedures in environments with different brightness and volume. The generation AI also evaluates operability in different operating environments. For example, it can simulate operating procedures in environments with different brightness and volume. This makes it possible to evaluate operability in different operating environments.

[0041] The quality assurance process efficiency improvement unit can simulate multiple scenarios in parallel and efficiently perform quality checks. In the quality assurance process efficiency improvement unit, for example, the generation AI simulates multiple scenarios in parallel and efficiently performs quality checks. For example, different user behavior patterns can be tested simultaneously. The generation AI can also simulate multiple scenarios in parallel and efficiently perform quality checks. For example, different user behavior patterns can also be tested simultaneously. This allows multiple scenarios to be simulated in parallel and efficiently perform quality checks.

[0042] The quality assurance process efficiency improvement unit can automatically generate a report of the simulation results and provide it to the quality assurance personnel. In the quality assurance process efficiency improvement unit, for example, the generation AI automatically generates a report of the simulation results and provides it to the quality assurance personnel. For example, the test results can be visually displayed in graphs or tables. The generation AI can also automatically generate a report of the simulation results and provide it to the quality assurance personnel. For example, the test results can also be visually displayed in graphs or tables. In this way, the simulation results can be automatically generated as a report and provided to the quality assurance personnel.

[0043] The quality assurance process efficiency improvement unit can simultaneously perform quality checks between different product versions and evaluate the differences between the versions. In the quality assurance process efficiency improvement unit, for example, the generation AI simultaneously performs quality checks between different product versions and evaluates the differences between the versions. For example, it can compare the operability of an old version with that of a new version. The generation AI can also simultaneously perform quality checks between different product versions and evaluate the differences between the versions. For example, it can also compare the operability of an old version with that of a new version. This makes it possible to simultaneously perform quality checks between different product versions and evaluate the differences between the versions.

[0044] The quality assurance process efficiency improvement unit can perform integrated quality checks at different development phases (design, implementation, testing). In the quality assurance process efficiency improvement unit, for example, the generation AI performs integrated quality checks at different development phases. For example, it can simulate a series of processes from the design stage to the testing stage. The generation AI also performs integrated quality checks at different development phases. For example, it can also simulate a series of processes from the design stage to the testing stage. This allows integrated quality checks at different development phases.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The quality check system can propose operation procedures optimized for individual users based on the user's operation history. For example, it can analyze past operation history and prioritize proposals for operations that the user frequently performs. It can also simplify operation procedures based on the user's operation history. This makes it possible to propose operation procedures optimized for individual users based on the user's operation history.

[0047] The quality check system can provide an appropriate user interface to users from different cultural and linguistic regions. For example, it can simulate operation procedures in different languages ​​and evaluate operability in each language. It can also provide an interface that takes into account the cultural background of users from different cultural regions. This makes it possible to provide an appropriate user interface to users from different cultural and linguistic regions.

[0048] The quality check system can optimize operation procedures based on the user's operation speed and reaction time. For example, it can measure the time it takes the user to complete a specific operation and simplify the operation procedure. It can also optimize operation procedures based on the user's reaction time. This makes it possible to optimize operation procedures based on the user's operation speed and reaction time.

[0049] The quality check system can provide an appropriate user interface for different user groups (age, gender, occupation, etc.). For example, it can simulate operation procedures by age and evaluate the operability for each age group. It can also simulate operation procedures by gender and occupation and evaluate the operability for each group. This makes it possible to provide an appropriate user interface for different user groups.

[0050] The quality check system can evaluate operability in different operating environments (brightness, volume, network speed, etc.). For example, it can simulate operating procedures in environments with different brightness and volume, and evaluate operability in each environment. It can also simulate operating procedures in environments with different network speeds, and evaluate operability in each environment. This makes it possible to evaluate operability in different operating environments.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The user perspective simulation unit simulates the user's perspective. For example, the generation AI reproduces the series of operations a user goes through from launching an application to using a specific function. The generation AI also checks whether the process is smooth and whether the necessary functions are properly provided when the user uses the application to solve a specific problem. The generation AI also checks whether the user interface is intuitive and easy to use, and whether the operating procedures are too complicated. Step 2: The problem-solving verification unit verifies the rationality of the problem-solving that is the essence of the service based on the operating procedures simulated by the user-perspective simulation unit. For example, the generation AI simulates operating procedures according to the user's purpose and evaluates the results. The generation AI also checks whether the process is smooth and whether the necessary functions are properly provided when the user uses the application to solve a specific problem. Step 3: The operability verification unit checks the operability from the user's perspective based on the operation procedures simulated by the user's perspective simulation unit. For example, the generation AI checks whether the user interface is intuitive and easy to use, and whether the operation procedures are too complicated. The generation AI also simulates a series of procedures that a user would take to operate the application and evaluates its operability.

[0053] (Example 2) A quality check system according to an embodiment of the present invention utilizes AI walkthroughs to test the overall user experience of a product, thereby enabling the quality check system to efficiently and effectively perform quality checks before the release of a product.

[0054] A quality check system according to an embodiment includes a user-perspective simulation unit, a problem-solving confirmation unit, and an operability confirmation unit. The user-perspective simulation unit simulates a user's perspective. For example, the generation AI reproduces a series of operations from when a user launches an application to when a specific function is used. Furthermore, when a user uses an application to solve a specific problem, the generation AI checks whether the process is smooth and whether the necessary functions are properly provided. Furthermore, the generation AI checks whether the user interface is intuitive and easy to use, and whether the operation procedures are overly complicated. For example, the generation AI simulates a series of procedures a user performs when operating an application and evaluates the operability. The problem-solving confirmation unit checks the rationality of the problem-solving, which is the essence of the service, based on the operation procedures simulated by the user-perspective simulation unit. For example, the generation AI simulates operation procedures according to the user's purpose and evaluates the results. Furthermore, when a user uses an application to solve a specific problem, the generation AI checks whether the process is smooth and whether the necessary functions are properly provided. The operability confirmation unit checks the operability from the user's perspective based on the operation procedures simulated by the user-perspective simulation unit. For example, the generation AI checks whether the user interface is intuitive and easy to use, and whether the operation procedures are too complicated. The generation AI also simulates a series of steps a user takes to operate an application and evaluates the operability. This allows the quality check system according to the embodiment to efficiently and effectively perform quality checks before a product is released. For example, the generation AI checks the operability of an application in advance and corrects any problems, thereby improving user satisfaction after release. The generation AI also checks whether the essential problem-solving capabilities of the service can be rationally implemented, thereby maximizing the value of the product. Furthermore, the generation AI streamlines the quality assurance process, enabling the delivery of high-quality products while adhering to release schedules.

[0055] The user-perspective simulation unit can estimate the user's emotions and dynamically change the simulation scenario according to changes in emotions. In the user-perspective simulation unit, for example, the generation AI analyzes the user's emotions in real time and dynamically changes the simulation scenario according to changes in emotions. For example, if the user feels stressed, the scenario is simplified. The generation AI also analyzes the user's emotions and dynamically changes the simulation scenario according to changes in emotions. For example, if the user feels satisfied, the scenario can be made more complex. This makes it possible to dynamically change the simulation scenario according to the user's emotions.

[0056] The user-perspective simulation unit can learn the user's behavior patterns in real time and incorporate new behavior patterns during the simulation. In the user-perspective simulation unit, for example, the generation AI learns the user's behavior patterns in real time and incorporates new behavior patterns during the simulation. For example, operations that the user frequently performs are reflected in the simulation on a priority basis. The generation AI also learns the user's behavior patterns in real time and incorporates new behavior patterns during the simulation. For example, it can also reflect new operations performed by the user in the simulation. This makes it possible to learn the user's behavior patterns in real time and incorporate new behavior patterns during the simulation.

[0057] The user-perspective simulation unit can reproduce an individual user experience based on the user's past operation history. In the user-perspective simulation unit, for example, the generation AI analyzes the user's past operation history and reproduces an individual user experience. For example, it reproduces the operation procedures that the user previously performed and provides the same experience. The generation AI also analyzes the user's past operation history and reproduces an individual user experience. For example, it can also reproduce similar operation procedures based on operations that the user previously performed. In this way, it is possible to reproduce an individual user experience based on the user's past operation history.

[0058] The user-perspective simulation unit can include voice commands and gesture operations in the simulation, allowing for testing of a wider variety of user interfaces. In the user-perspective simulation unit, for example, the generation AI analyzes voice commands and reflects them in the simulation. For example, it can recreate a scenario in which a user instructs an operation by voice. The generation AI can also analyze gesture operations and reflect them in the simulation. For example, it can recreate a scenario in which a user instructs an operation by gesture. This allows for testing of a wider variety of user interfaces, including voice commands and gesture operations.

[0059] The user-perspective simulation unit can simultaneously simulate operability on different devices (smartphones, tablets, PCs, etc.). In the user-perspective simulation unit, for example, the generation AI simultaneously simulates operability on different devices. For example, it can reproduce the operation procedures on a smartphone, tablet, and PC, and evaluate the operability of each device. The generation AI can also simultaneously simulate operability on different devices. For example, it can reproduce the operation procedures on a smartphone, tablet, and PC, and evaluate the operability of each device. This makes it possible to simultaneously simulate operability on different devices.

[0060] The user-perspective simulation unit uses the emotion estimation function to measure the stress level when the user performs a specific operation and can propose a less stressful operation procedure. The user-perspective simulation unit, for example, uses the emotion estimation function to measure the stress level when the user performs a specific operation. For example, it analyzes the stress when the user performs a complex operation. The generation AI also uses the emotion estimation function to measure the stress level when the user performs a specific operation. For example, it can also analyze the stress when the user performs a simple operation. This makes it possible to measure the stress level when the user performs a specific operation and propose a less stressful operation procedure.

[0061] The problem-solving confirmation unit can estimate the user's emotions and evaluate the problem-solving process based on the emotions. In the problem-solving confirmation unit, for example, the generation AI analyzes the user's emotions and evaluates the problem-solving process based on the emotions. For example, it can identify a process that makes the user feel satisfied. The generation AI can also analyze the user's emotions and evaluate the problem-solving process based on the emotions. For example, it can also identify a process that makes the user feel dissatisfied. This makes it possible to evaluate the problem-solving process based on the user's emotions.

[0062] The problem-solving confirmation unit can collect user feedback in real time and improve the problem-solving process based on that feedback. In the problem-solving confirmation unit, for example, the generation AI collects user feedback in real time and improves the problem-solving process based on that data. For example, it can optimize the process by reflecting user opinions. In addition, the generation AI collects user feedback in real time and improves the problem-solving process based on that data. For example, it can also optimize the process by reflecting user opinions. In this way, it is possible to collect user feedback in real time and improve the problem-solving process based on that feedback.

[0063] The problem-solving confirmation unit can generate different scenarios and compare the efficiency of problem-solving in each scenario. In the problem-solving confirmation unit, for example, the generation AI generates different scenarios and compares the efficiency of problem-solving in each scenario. For example, it can simulate multiple solution methods and identify the optimal method. The generation AI can also generate different scenarios and compare the efficiency of problem-solving in each scenario. For example, it can simulate multiple solution methods and identify the optimal method. This makes it possible to generate different scenarios and compare the efficiency of problem-solving in each scenario.

[0064] The problem-solving confirmation unit can simulate different problem-solving processes for different user groups (beginners, advanced users, etc.). In the problem-solving confirmation unit, for example, the generation AI simulates different problem-solving processes for different user groups. For example, it can reproduce a simplified process for beginners and a detailed process for advanced users. The generation AI can also simulate different problem-solving processes for different user groups. For example, it can reproduce a simplified process for beginners and a detailed process for advanced users. This makes it possible to simulate different problem-solving processes for different user groups.

[0065] The problem-solving confirmation unit uses the emotion estimation function to measure the level of satisfaction the user feels during the problem-solving process and can identify processes that provide high satisfaction. The problem-solving confirmation unit, for example, uses the emotion estimation function to measure the level of satisfaction the user feels during the problem-solving process. For example, it can analyze the user's facial expressions and voice and calculate a satisfaction score. The generation AI also uses the emotion estimation function to measure the level of satisfaction the user feels during the problem-solving process. For example, it can analyze the user's facial expressions and voice and calculate a satisfaction score. This makes it possible to measure the level of satisfaction the user feels during the problem-solving process and identify processes that provide high satisfaction.

[0066] The operability confirmation unit can estimate the user's emotions and evaluate the operability based on the emotions. In the operability confirmation unit, for example, the generation AI analyzes the user's emotions and evaluates the operability based on the emotions. For example, it can measure the stress and satisfaction the user feels during operation. The generation AI can also analyze the user's emotions and evaluate the operability based on the emotions. For example, it can measure the stress and satisfaction the user feels during operation. This makes it possible to evaluate the operability based on the user's emotions.

[0067] The operability confirmation unit can detect user operation errors in real time and analyze the causes thereof. In the operability confirmation unit, for example, the generation AI detects user operation errors in real time and analyzes the causes thereof. For example, if a user presses the wrong button, the cause can be identified. In addition, the generation AI can detect user operation errors in real time and analyze the causes thereof. For example, if a user presses the wrong button, the cause can be identified. This makes it possible to detect user operation errors in real time and analyze the causes thereof.

[0068] The operability confirmation unit can measure the user's operation speed and reaction time and reflect this in the evaluation of operability. In the operability confirmation unit, for example, the generation AI measures the user's operation speed and reaction time and reflects this in the evaluation of operability. For example, it measures the time it takes for the user to complete a specific operation. The generation AI can also measure the user's operation speed and reaction time and reflect this in the evaluation of operability. For example, it can measure the time it takes for the user to complete a specific operation. This makes it possible to measure the user's operation speed and reaction time and reflect this in the evaluation of operability.

[0069] The operability checking unit can simulate different user interface designs and identify the optimal design. In the operability checking unit, for example, the generation AI simulates different user interface designs and identifies the optimal design. For example, it can compare multiple design proposals and select the design that is easiest for the user to use. The generation AI can also simulate different user interface designs and identify the optimal design. For example, it can compare multiple design proposals and select the design that is easiest for the user to use. In this way, it is possible to simulate different user interface designs and identify the optimal design.

[0070] The operability checking unit can evaluate operability in different operating environments (brightness, volume, etc.). In the operability checking unit, for example, the generation AI evaluates operability in different operating environments. For example, it can simulate operating procedures in environments with different brightness and volume. The generation AI also evaluates operability in different operating environments. For example, it can simulate operating procedures in environments with different brightness and volume. This makes it possible to evaluate operability in different operating environments.

[0071] The operability confirmation unit uses the emotion estimation function to measure the frustration felt by the user during operation and can propose an operation procedure that causes less frustration. The operability confirmation unit, for example, uses the emotion estimation function to measure the frustration felt by the user during operation. For example, it can analyze the user's facial expressions and voice and calculate a frustration score. The generation AI also uses the emotion estimation function to measure the frustration felt by the user during operation. For example, it can analyze the user's facial expressions and voice and calculate a frustration score. This makes it possible to measure the frustration felt by the user during operation and propose an operation procedure that causes less frustration.

[0072] The quality assurance process efficiency improvement unit can use the generation AI to estimate the user's emotions and determine the priority of the quality assurance process based on the emotions. For example, the quality assurance process efficiency improvement unit uses the generation AI to analyze the user's emotions and determine the priority of the quality assurance process based on the emotions. For example, it can prioritize checking parts that make the user feel dissatisfied. The generation AI can also analyze the user's emotions and determine the priority of the quality assurance process based on the emotions. For example, it can also prioritize checking parts that make the user feel satisfied. In this way, it is possible to determine the priority of the quality assurance process based on the user's emotions.

[0073] The quality assurance process efficiency improvement unit can simulate multiple scenarios in parallel and efficiently perform quality checks. In the quality assurance process efficiency improvement unit, for example, the generation AI simulates multiple scenarios in parallel and efficiently performs quality checks. For example, different user behavior patterns can be tested simultaneously. The generation AI can also simulate multiple scenarios in parallel and efficiently perform quality checks. For example, different user behavior patterns can also be tested simultaneously. This allows multiple scenarios to be simulated in parallel and efficiently perform quality checks.

[0074] The quality assurance process efficiency improvement unit can automatically generate a report of the simulation results and provide it to the quality assurance personnel. In the quality assurance process efficiency improvement unit, for example, the generation AI automatically generates a report of the simulation results and provides it to the quality assurance personnel. For example, the test results can be visually displayed in graphs or tables. The generation AI can also automatically generate a report of the simulation results and provide it to the quality assurance personnel. For example, the test results can also be visually displayed in graphs or tables. In this way, the simulation results can be automatically generated as a report and provided to the quality assurance personnel.

[0075] The quality assurance process efficiency improvement unit can simultaneously perform quality checks between different product versions and evaluate the differences between the versions. In the quality assurance process efficiency improvement unit, for example, the generation AI simultaneously performs quality checks between different product versions and evaluates the differences between the versions. For example, it can compare the operability of an old version with that of a new version. The generation AI can also simultaneously perform quality checks between different product versions and evaluate the differences between the versions. For example, it can also compare the operability of an old version with that of a new version. This makes it possible to simultaneously perform quality checks between different product versions and evaluate the differences between the versions.

[0076] The quality assurance process efficiency improvement unit can perform integrated quality checks at different development phases (design, implementation, testing). In the quality assurance process efficiency improvement unit, for example, the generation AI performs integrated quality checks at different development phases. For example, it can simulate a series of processes from the design stage to the testing stage. The generation AI also performs integrated quality checks at different development phases. For example, it can also simulate a series of processes from the design stage to the testing stage. This allows integrated quality checks at different development phases.

[0077] The quality assurance process efficiency improvement unit uses the emotion estimation function to measure the sense of security felt by users during the quality assurance process and can identify processes that provide a high sense of security. The quality assurance process efficiency improvement unit, for example, uses the emotion estimation function to measure the sense of security felt by users during the quality assurance process. For example, it can analyze the user's facial expressions and voice and calculate a sense of security score. The generation AI also uses the emotion estimation function to measure the sense of security felt by users during the quality assurance process. For example, it can analyze the user's facial expressions and voice and calculate a sense of security score. This makes it possible to measure the sense of security felt by users during the quality assurance process and identify processes that provide a high sense of security.

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

[0079] The quality check system can acquire a user's biometric information (heart rate, galvanic skin response, etc.) in real time and measure the user's stress level and concentration level. For example, it can analyze heart rate fluctuations to evaluate the user's stress level when performing a specific operation. It can also analyze galvanic skin response to evaluate the user's concentration level when performing a specific operation. This makes it possible to evaluate operability and problem-solving processes in more detail based on the user's biometric information.

[0080] The quality check system can propose operation procedures optimized for individual users based on the user's operation history. For example, it can analyze past operation history and prioritize proposals for operations that the user frequently performs. It can also simplify operation procedures based on the user's operation history. This makes it possible to propose operation procedures optimized for individual users based on the user's operation history.

[0081] The quality check system can estimate the user's emotions and dynamically change the design of the user interface based on the emotions. For example, if the user feels stressed, the interface can be simplified. On the other hand, if the user feels satisfied, the interface can be made more complex. This allows the design of the user interface to be dynamically changed according to the user's emotions.

[0082] The quality check system can provide an appropriate user interface to users from different cultural and linguistic regions. For example, it can simulate operation procedures in different languages ​​and evaluate operability in each language. It can also provide an interface that takes into account the cultural background of users from different cultural regions. This makes it possible to provide an appropriate user interface to users from different cultural and linguistic regions.

[0083] The quality check system can estimate user emotions, collect user feedback based on emotions in real time, and improve the product based on the feedback. For example, if a user feels dissatisfied, the system can collect the feedback immediately and identify areas for improvement. Also, if a user feels satisfied, the system can collect the feedback and strengthen the strengths of the product. This makes it possible to collect feedback in real time based on user emotions and improve the product.

[0084] The quality check system can optimize operation procedures based on the user's operation speed and reaction time. For example, it can measure the time it takes the user to complete a specific operation and simplify the operation procedure. It can also optimize operation procedures based on the user's reaction time. This makes it possible to optimize operation procedures based on the user's operation speed and reaction time.

[0085] The quality check system can estimate user emotions and optimize the timing of product releases based on those emotions. For example, if there are many areas where users feel dissatisfied, the release can be postponed and improvements can be made. Also, if there are many areas where users feel satisfied, the release can be brought forward. This makes it possible to optimize the timing of product releases based on user emotions.

[0086] The quality check system can provide an appropriate user interface for different user groups (age, gender, occupation, etc.). For example, it can simulate operation procedures by age and evaluate the operability for each age group. It can also simulate operation procedures by gender and occupation and evaluate the operability for each group. This makes it possible to provide an appropriate user interface for different user groups.

[0087] The quality check system can estimate the user's emotions and evaluate the user's learning effectiveness based on the emotions. For example, it can measure the stress and satisfaction the user feels while studying and evaluate the learning effectiveness. It can also analyze the changes in the user's emotions while studying and evaluate the learning effectiveness. This makes it possible to evaluate the learning effectiveness based on the user's emotions.

[0088] The quality check system can evaluate operability in different operating environments (brightness, volume, network speed, etc.). For example, it can simulate operating procedures in environments with different brightness and volume, and evaluate operability in each environment. It can also simulate operating procedures in environments with different network speeds, and evaluate operability in each environment. This makes it possible to evaluate operability in different operating environments.

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

[0090] Step 1: The user perspective simulation unit simulates the user's perspective. For example, the generation AI reproduces the series of operations a user goes through from launching an application to using a specific function. The generation AI also checks whether the process is smooth and whether the necessary functions are properly provided when the user uses the application to solve a specific problem. The generation AI also checks whether the user interface is intuitive and easy to use, and whether the operating procedures are too complicated. Step 2: The problem-solving verification unit verifies the rationality of the problem-solving that is the essence of the service based on the operating procedures simulated by the user-perspective simulation unit. For example, the generation AI simulates operating procedures according to the user's purpose and evaluates the results. The generation AI also checks whether the process is smooth and whether the necessary functions are properly provided when the user uses the application to solve a specific problem. Step 3: The operability verification unit checks the operability from the user's perspective based on the operation procedures simulated by the user's perspective simulation unit. For example, the generation AI checks whether the user interface is intuitive and easy to use, and whether the operation procedures are too complicated. The generation AI also simulates a series of procedures that a user would take to operate the application and evaluates its operability.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] 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. [Explanation of symbols]

[0158] 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 user viewpoint simulation unit that simulates a user viewpoint; a problem-solution confirmation unit that confirms the rationality of the problem solution, which is the essence of the service, based on the operation procedure simulated by the user-perspective simulation unit; an operability confirmation unit that confirms operability from a user's viewpoint based on the operation procedure simulated by the user's viewpoint simulation unit. A system characterized by:

2. The user viewpoint simulation unit The user's emotions are estimated, and the simulation scenario is dynamically changed according to the change in the emotions.

2. The system of claim 1.

3. The problem solution confirmation unit Estimating user emotions and evaluating the problem-solving process based on said emotions 2. The system of claim 1.

4. The operability confirmation unit Estimate the user's emotions and evaluate the operability based on the emotions.

2. The system of claim 1.

5. The Quality Assurance Process Efficiency Department Using generative AI to infer user sentiment and prioritize quality assurance processes based on that sentiment 2. The system of claim 1.

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