system

Abstract

The system according to this embodiment aims to streamline the design creation, research, and testing processes within a company. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a testing unit. The reception unit receives input from the user regarding the problem and the ideal state. The analysis unit analyzes past company data based on the information received by the reception unit. The proposal unit proposes the optimal design and necessary investigations and tests based on the analysis results obtained by the analysis unit. The testing unit conducts small-scale tests of the design and investigations / tests proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the processes of design creation, investigation, and testing within a company are inefficient, and time and resources are wasted.

[0005] The system according to the embodiment aims to improve the efficiency of the processes of design creation, investigation, and testing within a company.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a testing unit. The reception unit receives input from the user regarding the problem and the ideal state. The analysis unit analyzes past company data based on the information received by the reception unit. The proposal unit proposes the optimal design and necessary investigations and tests based on the analysis results obtained by the analysis unit. The testing unit conducts small-scale tests of the design and investigations / tests proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the design creation, research, and testing processes within a company. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The design creation, research, and testing efficiency system according to an embodiment of the present invention is a system that streamlines design creation, research, and testing within a company. When a user inputs a problem and an ideal state, the system analyzes past internal data and proposes the optimal design and necessary research / testing. Furthermore, it conducts small tests and evaluates the results. This system allows users to efficiently create designs, conduct research, and test, significantly reducing the time to service release. Additionally, by utilizing past data, it minimizes risk and realizes a user-first UI / UX. For example, a user inputs a specific problem, such as "I want to improve the UI of a new app," and an ideal state, such as "I want to create a UI that users can operate intuitively." This information is input into the system. Next, the system analyzes past internal data. It analyzes past design, test results, and user research data to identify similar cases. For example, it refers to data from past app UI improvement projects and extracts successful designs and testing methods. Based on the analysis results, the system proposes the optimal design and necessary research / testing. For example, based on past successes, it proposes specific design patterns or suggests necessary user research and live testing methods. This allows users to quickly select the optimal design and conduct the necessary research and testing. Furthermore, the system conducts small tests and evaluates the results. For example, it tests a proposed design with a select group of users and collects their feedback. This verifies the effectiveness of the design and allows for modifications as needed. This system enables users to efficiently create, research, and test designs, significantly reducing the time to service release. Additionally, leveraging historical data minimizes risk and enables a user-first UI / UX. Thus, the system streamlines design creation, research, and testing, proposing optimal designs, research, and testing based on the user's challenges and ideal state, and enabling efficient small-scale testing.

[0029] The design creation, research, and testing efficiency system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a testing unit. The reception unit receives input of the user's problem and ideal state. For example, the reception unit receives the problem and ideal state entered by the user. The reception unit receives input from the user, such as "I want to improve the UI of a new app" and "I want to realize a UI that users can operate intuitively." The analysis unit analyzes past internal data based on the information received by the reception unit. For example, the analysis unit analyzes data on past designs, test results, and user research to identify similar cases. The analysis unit refers to data from past app UI improvement projects and extracts successful designs and testing methods. The proposal unit proposes the optimal design and necessary research and testing based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes specific design patterns or proposes necessary user research and live testing methods based on past success stories. The proposal unit helps the user quickly select the optimal design and conduct the necessary research and testing. The testing department conducts small-scale tests of the designs and research / tests proposed by the proposal department. For example, the testing department tests the proposed design with a select group of users and collects their feedback. The testing department verifies the effectiveness of the design and makes modifications as needed. As a result, the design creation, research, and testing efficiency system according to this embodiment can propose optimal designs and research / tests based on the user's challenges and ideal state, and conduct small-scale tests efficiently.

[0030] The reception department receives input from users regarding their challenges and desired states. Specifically, users access the system and input their challenges and desired states through a dedicated interface. For example, a user can input a specific challenge such as "I want to improve the UI of a new app," and an ideal state such as "I want to create a UI that users can operate intuitively." To receive this input, the reception department provides a user-friendly interface and has a function to accurately record the input content. Furthermore, the reception department automatically categorizes the information entered by users and saves it in an appropriate format. For example, it saves challenges and ideal states in text format so that the subsequent analysis department can easily access them. The reception department also has a function to automatically send confirmation messages to the information entered by users, prompting them to check and correct the input content. In this way, the reception department can accurately and efficiently receive input from users and support the smooth operation of the entire system.

[0031] The analytics department analyzes past internal data based on information received by the reception department. Specifically, it analyzes past design, test results, and user survey data to identify similar cases. For example, the analytics department refers to data from past app UI improvement projects to extract successful designs and testing methods. This involves searching databases for relevant project data and analyzing the data using statistical methods and machine learning algorithms. For instance, natural language processing techniques are used to analyze user feedback and survey results to extract common challenges and success factors. Image recognition techniques can also be used to analyze past design patterns and evaluate the effectiveness of visual elements. Furthermore, the analytics department refers not only to past data but also to publicly available external data and industry best practices to conduct analysis from a broader perspective. This allows the analytics department to build a foundation for finding optimal solutions to user challenges and providing them to the proposal department.

[0032] The proposal department proposes optimal designs and necessary research and testing based on the analysis results obtained by the analysis department. Specifically, it proposes specific design patterns based on past success stories, and suggests methods for necessary user research and live testing. For example, the proposal department creates detailed proposals to help users quickly select the optimal design and conduct the necessary research and testing. These proposals include specific design proposals, research and testing procedures, and expected outcomes. The proposal department can also provide customized proposals, taking into account the user's needs and constraints. For example, it can adjust the scope of the optimal design and research / testing according to the user's budget and schedule. Furthermore, the proposal department maintains close communication with users and incorporates their feedback to maximize the effectiveness of the proposals. This allows the proposal department to provide optimal solutions to the user's challenges and support the efficiency of design creation, research, and testing.

[0033] The Testing Department conducts small-scale tests of designs, research, and tests proposed by the Proposal Department. Specifically, they test the proposed designs with a select group of users and collect their feedback. For example, the Testing Department conducts user tests and A / B tests to confirm the effectiveness of the design and make modifications as needed. In user tests, they have actual users use the design and collect their feedback and opinions. In A / B tests, they compare different design options and evaluate which is more effective. The Testing Department also uses quantitative and qualitative methods to analyze the collected feedback and identify areas for design improvement. For example, they statistically analyze survey results to evaluate user satisfaction and ease of use. They can also gain a deeper understanding of user behavior and emotions through interviews and observations. Furthermore, the Testing Department shares the test results with the Proposal Department and Analysis Department to help improve the design. In this way, the Testing Department can confirm the effectiveness of the proposed designs, research, and tests and play a crucial role in realizing the optimal design for solving users' problems.

[0034] The reception desk can analyze the user's past input history and suggest the optimal input format. For example, the reception desk can automatically display tasks and ideal states that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest tasks and ideal states to be used during a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input format based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input format.

[0035] The reception unit can filter input content based on the user's current projects and areas of interest when inputting issues and ideal states. For example, the reception unit may prioritize displaying issues and ideal states related to the user's current project. The reception unit can also suggest relevant issues and ideal states based on the user's areas of interest. The reception unit can also filter appropriate issues and ideal states according to the progress of the user's project. This allows the user to prioritize inputting highly relevant information by filtering input content based on their current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's project data into a generating AI and have the generating AI perform the filtering of the input content.

[0036] The reception unit can prioritize accepting highly relevant input content when users input problems and ideal states, taking into account the user's geographical location. For example, if a user is in a specific region, the reception unit will prioritize displaying problems and ideal states related to that region. The reception unit can also suggest relevant problems and ideal states based on the user's current location. The reception unit can also filter the most relevant input content based on the user's geographical location. This allows for the priority input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI perform the filtering of input content.

[0037] The reception unit can analyze the user's social media activity and accept relevant input content when the user inputs a problem and an ideal state. For example, the reception unit can analyze the user's social media posts and suggest relevant problems and ideal states. The reception unit can also filter the most relevant input content based on the user's social media activity history. The reception unit can also prioritize displaying relevant problems and ideal states based on the user's social media interests. This allows the user to input relevant information based on their social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform filtering of the input content.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of past data during the analysis process. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of past data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a design-specific analysis algorithm to design data. The analysis unit can also apply a test-specific analysis algorithm to test data. The analysis unit can also apply a survey-specific analysis algorithm to user survey data. This enables highly accurate analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0040] The analysis department can prioritize analyses based on the data submission date. For example, it might prioritize analyzing the most recent data. It can also postpone the analysis of older data. The analysis department can also adjust the analysis schedule based on the submission date. This allows for efficient analysis by prioritizing analyses based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0042] The proposal department can adjust the level of detail in its proposals based on the importance of the design, research, and testing. For example, it can provide detailed proposals for high-priority designs, research, and testing, and simplified proposals for lower-priority designs, research, and testing. The proposal department can also prioritize proposals according to the importance of the designs, research, and testing. This allows for efficient proposals by adjusting the level of detail based on the importance of the designs, research, and testing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of the designs, research, and testing into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0043] The proposal unit can apply different proposal algorithms depending on the design or research / test category when making a proposal. For example, the proposal unit can apply a design-specific proposal algorithm to designs. The proposal unit can also apply a research-specific proposal algorithm to research. The proposal unit can also apply a test-specific proposal algorithm to tests. This allows for highly accurate proposals by applying the appropriate proposal algorithm according to the design or research / test category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the design or research / test category into a generating AI and have the generating AI apply different proposal algorithms.

[0044] The proposal department can prioritize proposals based on the submission dates of designs, studies, and tests. For example, the proposal department will prioritize the most recent designs, studies, and tests. It can also postpone the submission of older designs, studies, and tests. The proposal department can also adjust the proposal schedule based on the submission dates. This allows for more efficient proposals by prioritizing proposals based on the submission dates of designs, studies, and tests. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission dates of designs, studies, and tests into a generating AI and have the generating AI determine the proposal priorities.

[0045] The proposal department can adjust the order of proposals based on the relevance of designs and research / tests during the proposal process. For example, the proposal department can prioritize proposing highly relevant designs and research / tests. It can also postpone proposing less relevant designs and research / tests. The proposal department can also adjust the proposal schedule based on the relevance of designs and research / tests. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of designs and research / tests. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the relevance of designs and research / tests into a generating AI and have the generating AI adjust the order of proposals.

[0046] The testing unit can optimize test algorithms by referring to past test results during small-scale testing. For example, the testing unit can apply the optimal test algorithm based on past successful test results. The testing unit can also apply an improved test algorithm based on past failed test results. The testing unit can also analyze past test results and select the most effective test algorithm. This enables highly accurate testing by optimizing the test algorithm by referring to past test results. Some or all of the above processes in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input past test result data into a generating AI and have the generating AI perform the optimization of the test algorithm.

[0047] The testing unit can apply different testing methods depending on the category of the test subject during small-scale testing. For example, the testing unit can apply design-specific testing methods to design tests. It can also apply research-specific testing methods to research tests. It can also apply usability-specific testing methods to usability tests. This allows for highly accurate testing by applying the appropriate testing method according to the category of the test subject. Some or all of the above-described processes in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input the category of the test subject into a generating AI and have the generating AI execute the application of different testing methods.

[0048] The testing unit can prioritize tests based on the submission dates of the test subjects during small-scale testing. For example, the testing unit will prioritize testing the most recent test subjects. It can also postpone testing older test subjects. The testing unit can also adjust the test schedule based on the submission dates. This enables efficient testing by prioritizing tests based on the submission dates of the test subjects. Some or all of the above processes in the testing unit may be performed using AI, for example, or not. For example, the testing unit can input the submission dates of the test subjects into a generating AI and have the generating AI determine the test priorities.

[0049] The testing unit can adjust the order of tests based on the relationships between test targets during small-scale testing. For example, the testing unit can prioritize testing highly relevant test targets. It can also postpone testing less relevant test targets. The testing unit can also adjust the test schedule based on the relationships between test targets. This allows for efficient testing by adjusting the order of tests based on the relationships between test targets. Some or all of the above processes in the testing unit may be performed using AI, for example, or not using AI. For example, the testing unit can input the relationships between test targets into a generating AI and have the generating AI adjust the order of tests.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can provide real-time feedback based on user input. For example, it can immediately present relevant past success stories and reference information in response to the user's inputted challenges or ideal states. This allows users to review their input and receive more specific and accurate information. The reception desk can also provide real-time feedback when users correct their input. Furthermore, after the user completes their input, the reception desk can display a summary of the input and a confirmation message, allowing the user to reconfirm their input. This reduces user input errors and enables efficient input.

[0052] The reception desk can analyze the user's past input history and provide optimal input support functions. For example, it can automatically display tasks and ideal states that the user has frequently entered in the past as suggestions. This allows the user to efficiently enter new information while referring to past input content. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest tasks and ideal states to be used during specific time periods based on the user's past input history. By providing optimal input support functions based on the user's past input history, the efficiency of input can be improved.

[0053] The input system can filter the input content based on the user's current projects and areas of interest when inputting issues and ideal states. For example, it can prioritize displaying issues and ideal states related to the user's current project. This allows the user to efficiently input information relevant to their current project. The input system can also suggest relevant issues and ideal states based on the user's areas of interest. Furthermore, the input system can filter appropriate issues and ideal states according to the progress of the user's project. By filtering the input content based on the user's current projects and areas of interest, it can prioritize the input of highly relevant information.

[0054] The reception system can prioritize input that is highly relevant to the user's geographical location when they input their challenges and ideal states. For example, if a user is in a specific region, it will prioritize displaying challenges and ideal states related to that region. This allows users to efficiently input information relevant to their current location. The reception system can also suggest relevant challenges and ideal states based on the user's current location. Furthermore, the reception system can filter the input content to the most relevant information based on the user's geographical location. This allows users to prioritize input that is highly relevant to their geographical location.

[0055] The reception desk can analyze the user's social media activity when they input their challenges and ideal states, and accept relevant input content. For example, it can analyze the user's social media posts and suggest relevant challenges and ideal states. This allows users to efficiently input information based on their social media activity. The reception desk can also filter the input content to the most relevant level based on the user's social media activity history. Furthermore, the reception desk can prioritize displaying relevant challenges and ideal states based on the user's areas of interest on social media. This allows users to input relevant information based on their social media activity.

[0056] The analysis unit can adjust the level of detail of the analysis based on the importance of historical data. For example, it can perform a detailed analysis on high-importance data, allowing users to gain deeper insights into important data. It can also perform a simplified analysis on less important data, enabling users to process data efficiently. Furthermore, the analysis unit can prioritize analyses based on data importance, allowing users to obtain analysis results starting with the most important data.

[0057] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, design-specific analysis algorithms can be applied to design data, thereby providing detailed insights into the design. Test-specific analysis algorithms can also be applied to test data, improving the accuracy of test results. Furthermore, user survey data can be subjected to survey-specific analysis algorithms, resulting in analysis results that accurately reflect user opinions and feedback.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives input from users regarding their challenges and desired state. For example, it accepts input from users regarding specific challenges such as "I want to improve the UI of a new app" and their desired state such as "I want to create a UI that users can operate intuitively." Step 2: The analysis department analyzes past internal data based on the information received by the reception department. For example, they analyze past design, test results, and user survey data to identify similar cases. They refer to data from past app UI improvement projects to extract successful designs and testing methods. Step 3: The proposal team proposes the optimal design and necessary research and testing based on the analysis results obtained by the analysis team. For example, they may suggest specific design patterns based on past success stories, or propose methods for necessary user research and live testing. This helps users quickly select the optimal design and conduct the necessary research and testing. Step 4: The testing team conducts small-scale tests of the designs, research, and tests proposed by the proposal team. For example, they test the proposed design with a select group of users and collect their feedback. They verify the effectiveness of the design and make modifications as needed.

[0060] (Example of form 2) The design creation, research, and testing efficiency system according to an embodiment of the present invention is a system that streamlines design creation, research, and testing within a company. When a user inputs a problem and an ideal state, the system analyzes past internal data and proposes the optimal design and necessary research / testing. Furthermore, it conducts small tests and evaluates the results. This system allows users to efficiently create designs, conduct research, and test, significantly reducing the time to service release. Additionally, by utilizing past data, it minimizes risk and realizes a user-first UI / UX. For example, a user inputs a specific problem, such as "I want to improve the UI of a new app," and an ideal state, such as "I want to create a UI that users can operate intuitively." This information is input into the system. Next, the system analyzes past internal data. It analyzes past design, test results, and user research data to identify similar cases. For example, it refers to data from past app UI improvement projects and extracts successful designs and testing methods. Based on the analysis results, the system proposes the optimal design and necessary research / testing. For example, based on past successes, it proposes specific design patterns or suggests necessary user research and live testing methods. This allows users to quickly select the optimal design and conduct the necessary research and testing. Furthermore, the system conducts small tests and evaluates the results. For example, it tests a proposed design with a select group of users and collects their feedback. This verifies the effectiveness of the design and allows for modifications as needed. This system enables users to efficiently create, research, and test designs, significantly reducing the time to service release. Additionally, leveraging historical data minimizes risk and enables a user-first UI / UX. Thus, the system streamlines design creation, research, and testing, proposing optimal designs, research, and testing based on the user's challenges and ideal state, and enabling efficient small-scale testing.

[0061] The design creation, research, and testing efficiency system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a testing unit. The reception unit receives input of the user's problem and ideal state. For example, the reception unit receives the problem and ideal state entered by the user. The reception unit receives input from the user, such as "I want to improve the UI of a new app" and "I want to realize a UI that users can operate intuitively." The analysis unit analyzes past internal data based on the information received by the reception unit. For example, the analysis unit analyzes data on past designs, test results, and user research to identify similar cases. The analysis unit refers to data from past app UI improvement projects and extracts successful designs and testing methods. The proposal unit proposes the optimal design and necessary research and testing based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes specific design patterns or proposes necessary user research and live testing methods based on past success stories. The proposal unit helps the user quickly select the optimal design and conduct the necessary research and testing. The testing department conducts small-scale tests of the designs and research / tests proposed by the proposal department. For example, the testing department tests the proposed design with a select group of users and collects their feedback. The testing department verifies the effectiveness of the design and makes modifications as needed. As a result, the design creation, research, and testing efficiency system according to this embodiment can propose optimal designs and research / tests based on the user's challenges and ideal state, and conduct small-scale tests efficiently.

[0062] The reception department receives input from users regarding their challenges and desired states. Specifically, users access the system and input their challenges and desired states through a dedicated interface. For example, a user can input a specific challenge such as "I want to improve the UI of a new app," and an ideal state such as "I want to create a UI that users can operate intuitively." To receive this input, the reception department provides a user-friendly interface and has a function to accurately record the input content. Furthermore, the reception department automatically categorizes the information entered by users and saves it in an appropriate format. For example, it saves challenges and ideal states in text format so that the subsequent analysis department can easily access them. The reception department also has a function to automatically send confirmation messages to the information entered by users, prompting them to check and correct the input content. In this way, the reception department can accurately and efficiently receive input from users and support the smooth operation of the entire system.

[0063] The analytics department analyzes past internal data based on information received by the reception department. Specifically, it analyzes past design, test results, and user survey data to identify similar cases. For example, the analytics department refers to data from past app UI improvement projects to extract successful designs and testing methods. This involves searching databases for relevant project data and analyzing the data using statistical methods and machine learning algorithms. For instance, natural language processing techniques are used to analyze user feedback and survey results to extract common challenges and success factors. Image recognition techniques can also be used to analyze past design patterns and evaluate the effectiveness of visual elements. Furthermore, the analytics department refers not only to past data but also to publicly available external data and industry best practices to conduct analysis from a broader perspective. This allows the analytics department to build a foundation for finding optimal solutions to user challenges and providing them to the proposal department.

[0064] The proposal department proposes optimal designs and necessary research and testing based on the analysis results obtained by the analysis department. Specifically, it proposes specific design patterns based on past success stories, and suggests methods for necessary user research and live testing. For example, the proposal department creates detailed proposals to help users quickly select the optimal design and conduct the necessary research and testing. These proposals include specific design proposals, research and testing procedures, and expected outcomes. The proposal department can also provide customized proposals, taking into account the user's needs and constraints. For example, it can adjust the scope of the optimal design and research / testing according to the user's budget and schedule. Furthermore, the proposal department maintains close communication with users and incorporates their feedback to maximize the effectiveness of the proposals. This allows the proposal department to provide optimal solutions to the user's challenges and support the efficiency of design creation, research, and testing.

[0065] The Testing Department conducts small-scale tests of designs, research, and tests proposed by the Proposal Department. Specifically, they test the proposed designs with a select group of users and collect their feedback. For example, the Testing Department conducts user tests and A / B tests to confirm the effectiveness of the design and make modifications as needed. In user tests, they have actual users use the design and collect their feedback and opinions. In A / B tests, they compare different design options and evaluate which is more effective. The Testing Department also uses quantitative and qualitative methods to analyze the collected feedback and identify areas for design improvement. For example, they statistically analyze survey results to evaluate user satisfaction and ease of use. They can also gain a deeper understanding of user behavior and emotions through interviews and observations. Furthermore, the Testing Department shares the test results with the Proposal Department and Analysis Department to help improve the design. In this way, the Testing Department can confirm the effectiveness of the proposed designs, research, and tests and play a crucial role in realizing the optimal design for solving users' problems.

[0066] The reception desk can estimate the user's emotions and adjust the input method for tasks and ideal states based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of tasks and ideal states. This reduces the user's burden and enables efficient input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0067] The reception desk can analyze the user's past input history and suggest the optimal input format. For example, the reception desk can automatically display tasks and ideal states that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest tasks and ideal states to be used during a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input format based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input format.

[0068] The reception unit can filter input content based on the user's current projects and areas of interest when inputting issues and ideal states. For example, the reception unit may prioritize displaying issues and ideal states related to the user's current project. The reception unit can also suggest relevant issues and ideal states based on the user's areas of interest. The reception unit can also filter appropriate issues and ideal states according to the progress of the user's project. This allows the user to prioritize inputting highly relevant information by filtering input content based on their current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's project data into a generating AI and have the generating AI perform the filtering of the input content.

[0069] The reception desk can estimate the user's emotions and prioritize input based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize inputting important tasks and ideal states. If the user is relaxed, the reception desk may also prioritize inputting detailed information. If the user is in a hurry, the reception desk may also prioritize inputting the most important tasks and ideal states. This allows important information to be prioritized by prioritizing input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0070] The reception unit can prioritize accepting highly relevant input content when users input problems and ideal states, taking into account the user's geographical location. For example, if a user is in a specific region, the reception unit will prioritize displaying problems and ideal states related to that region. The reception unit can also suggest relevant problems and ideal states based on the user's current location. The reception unit can also filter the most relevant input content based on the user's geographical location. This allows for the priority input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI perform the filtering of input content.

[0071] The reception unit can analyze the user's social media activity and accept relevant input content when the user inputs a problem and an ideal state. For example, the reception unit can analyze the user's social media posts and suggest relevant problems and ideal states. The reception unit can also filter the most relevant input content based on the user's social media activity history. The reception unit can also prioritize displaying relevant problems and ideal states based on the user's social media interests. This allows the user to input relevant information based on their social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform filtering of the input content.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple analysis method and display the results quickly. If the user is relaxed, the analysis unit can also provide a detailed analysis method and carefully explain the results. If the user is in a hurry, the analysis unit can also provide an analysis method that focuses on the most important data. This allows the analysis unit to provide analysis results that are appropriate for the user by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of past data during the analysis process. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of past data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a design-specific analysis algorithm to design data. The analysis unit can also apply a test-specific analysis algorithm to test data. The analysis unit can also apply a survey-specific analysis algorithm to user survey data. This enables highly accurate analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display that is appropriate for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0076] The analysis department can prioritize analyses based on the data submission date. For example, it might prioritize analyzing the most recent data. It can also postpone the analysis of older data. The analysis department can also adjust the analysis schedule based on the submission date. This allows for efficient analysis by prioritizing analyses based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0078] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily visible suggestion. If the user is relaxed, the suggestion unit can also provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion unit can also provide a concise suggestion. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide suggestions that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0079] The proposal department can adjust the level of detail in its proposals based on the importance of the design, research, and testing. For example, it can provide detailed proposals for high-priority designs, research, and testing, and simplified proposals for lower-priority designs, research, and testing. The proposal department can also prioritize proposals according to the importance of the designs, research, and testing. This allows for efficient proposals by adjusting the level of detail based on the importance of the designs, research, and testing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of the designs, research, and testing into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0080] The proposal unit can apply different proposal algorithms depending on the design or research / test category when making a proposal. For example, the proposal unit can apply a design-specific proposal algorithm to designs. The proposal unit can also apply a research-specific proposal algorithm to research. The proposal unit can also apply a test-specific proposal algorithm to tests. This allows for highly accurate proposals by applying the appropriate proposal algorithm according to the design or research / test category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the design or research / test category into a generating AI and have the generating AI apply different proposal algorithms.

[0081] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. By adjusting the length of the suggestion according to the user's emotions, it becomes possible to provide suggestions that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0082] The proposal department can prioritize proposals based on the submission dates of designs, studies, and tests. For example, the proposal department will prioritize the most recent designs, studies, and tests. It can also postpone the submission of older designs, studies, and tests. The proposal department can also adjust the proposal schedule based on the submission dates. This allows for more efficient proposals by prioritizing proposals based on the submission dates of designs, studies, and tests. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission dates of designs, studies, and tests into a generating AI and have the generating AI determine the proposal priorities.

[0083] The proposal department can adjust the order of proposals based on the relevance of designs and research / tests during the proposal process. For example, the proposal department can prioritize proposing highly relevant designs and research / tests. It can also postpone proposing less relevant designs and research / tests. The proposal department can also adjust the proposal schedule based on the relevance of designs and research / tests. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of designs and research / tests. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the relevance of designs and research / tests into a generating AI and have the generating AI adjust the order of proposals.

[0084] The testing unit can estimate the user's emotions and adjust the small-scale test method based on the estimated emotions. For example, if the user is nervous, the testing unit can provide a simple and highly visual test method. If the user is relaxed, the testing unit can also provide a test method that includes detailed information. If the user is in a hurry, the testing unit can provide a test method that gets straight to the point. This allows for user-appropriate testing by adjusting the small-scale test method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the testing unit may be performed using AI or not using AI. For example, the testing unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0085] The testing unit can optimize test algorithms by referring to past test results during small-scale testing. For example, the testing unit can apply the optimal test algorithm based on past successful test results. The testing unit can also apply an improved test algorithm based on past failed test results. The testing unit can also analyze past test results and select the most effective test algorithm. This enables highly accurate testing by optimizing the test algorithm by referring to past test results. Some or all of the above processes in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input past test result data into a generating AI and have the generating AI perform the optimization of the test algorithm.

[0086] The testing unit can apply different testing methods depending on the category of the test subject during small-scale testing. For example, the testing unit can apply design-specific testing methods to design tests. It can also apply research-specific testing methods to research tests. It can also apply usability-specific testing methods to usability tests. This allows for highly accurate testing by applying the appropriate testing method according to the category of the test subject. Some or all of the above-described processes in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input the category of the test subject into a generating AI and have the generating AI execute the application of different testing methods.

[0087] The testing unit can estimate the user's emotions and adjust how the results of the small test are displayed based on the estimated emotions. For example, if the user is nervous, the testing unit can provide a simple and highly visible display. If the user is relaxed, the testing unit can also provide a display that includes detailed information. If the user is in a hurry, the testing unit can also provide a display that gets straight to the point. This allows for a user-friendly display by adjusting how the results of the small test are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the testing unit may be performed using AI or not using AI. For example, the testing unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0088] The testing unit can prioritize tests based on the submission dates of the test subjects during small-scale testing. For example, the testing unit will prioritize testing the most recent test subjects. It can also postpone testing older test subjects. The testing unit can also adjust the test schedule based on the submission dates. This enables efficient testing by prioritizing tests based on the submission dates of the test subjects. Some or all of the above processes in the testing unit may be performed using AI, for example, or not. For example, the testing unit can input the submission dates of the test subjects into a generating AI and have the generating AI determine the test priorities.

[0089] The testing unit can adjust the order of tests based on the relationships between test targets during small-scale testing. For example, the testing unit can prioritize testing highly relevant test targets. It can also postpone testing less relevant test targets. The testing unit can also adjust the test schedule based on the relationships between test targets. This allows for efficient testing by adjusting the order of tests based on the relationships between test targets. Some or all of the above processes in the testing unit may be performed using AI, for example, or not using AI. For example, the testing unit can input the relationships between test targets into a generating AI and have the generating AI adjust the order of tests.

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

[0091] The reception desk can provide real-time feedback based on user input. For example, it can immediately present relevant past success stories and reference information in response to the user's inputted challenges or ideal states. This allows users to review their input and receive more specific and accurate information. The reception desk can also provide real-time feedback when users correct their input. Furthermore, after the user completes their input, the reception desk can display a summary of the input and a confirmation message, allowing the user to reconfirm their input. This reduces user input errors and enables efficient input.

[0092] The reception desk can estimate the user's emotions and adjust the tone of the input content based on the estimated emotions. For example, if the user is stressed, the reception desk will change the input content to a simple and friendly tone. If the user is relaxed, the reception desk may use a detailed and polite tone. If the user is in a hurry, the reception desk may use a short and to-the-point tone. This allows for a comfortable input experience for the user by adjusting the tone of the input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0093] The reception desk can analyze the user's past input history and provide optimal input support functions. For example, it can automatically display tasks and ideal states that the user has frequently entered in the past as suggestions. This allows the user to efficiently enter new information while referring to past input content. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest tasks and ideal states to be used during specific time periods based on the user's past input history. By providing optimal input support functions based on the user's past input history, the efficiency of input can be improved.

[0094] The input system can filter the input content based on the user's current projects and areas of interest when inputting issues and ideal states. For example, it can prioritize displaying issues and ideal states related to the user's current project. This allows the user to efficiently input information relevant to their current project. The input system can also suggest relevant issues and ideal states based on the user's areas of interest. Furthermore, the input system can filter appropriate issues and ideal states according to the progress of the user's project. By filtering the input content based on the user's current projects and areas of interest, it can prioritize the input of highly relevant information.

[0095] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, important tasks and ideal states can be prioritized. If the user is relaxed, detailed input can be prioritized. If the user is in a hurry, the most important tasks and ideal states can be prioritized. This allows important information to be prioritized by prioritizing input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0096] The reception system can prioritize input that is highly relevant to the user's geographical location when they input their challenges and ideal states. For example, if a user is in a specific region, it will prioritize displaying challenges and ideal states related to that region. This allows users to efficiently input information relevant to their current location. The reception system can also suggest relevant challenges and ideal states based on the user's current location. Furthermore, the reception system can filter the input content to the most relevant information based on the user's geographical location. This allows users to prioritize input that is highly relevant to their geographical location.

[0097] The reception desk can analyze the user's social media activity when they input their challenges and ideal states, and accept relevant input content. For example, it can analyze the user's social media posts and suggest relevant challenges and ideal states. This allows users to efficiently input information based on their social media activity. The reception desk can also filter the input content to the most relevant level based on the user's social media activity history. Furthermore, the reception desk can prioritize displaying relevant challenges and ideal states based on the user's areas of interest on social media. This allows users to input relevant information based on their social media activity.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, it can provide a simple analysis method and display the results quickly. If the user is relaxed, it can provide a detailed analysis method and carefully explain the results. If the user is in a hurry, it can provide an analysis method that focuses on the most important data. In this way, by adjusting the analysis method according to the user's emotions, it is possible to provide analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0099] The analysis unit can adjust the level of detail of the analysis based on the importance of historical data. For example, it can perform a detailed analysis on high-importance data, allowing users to gain deeper insights into important data. It can also perform a simplified analysis on less important data, enabling users to process data efficiently. Furthermore, the analysis unit can prioritize analyses based on data importance, allowing users to obtain analysis results starting with the most important data.

[0100] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, design-specific analysis algorithms can be applied to design data, thereby providing detailed insights into the design. Test-specific analysis algorithms can also be applied to test data, improving the accuracy of test results. Furthermore, user survey data can be subjected to survey-specific analysis algorithms, resulting in analysis results that accurately reflect user opinions and feedback.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives input from users regarding their challenges and desired state. For example, it accepts input from users regarding specific challenges such as "I want to improve the UI of a new app" and their desired state such as "I want to create a UI that users can operate intuitively." Step 2: The analysis department analyzes past internal data based on the information received by the reception department. For example, they analyze past design, test results, and user survey data to identify similar cases. They refer to data from past app UI improvement projects to extract successful designs and testing methods. Step 3: The proposal team proposes the optimal design and necessary research and testing based on the analysis results obtained by the analysis team. For example, they may suggest specific design patterns based on past success stories, or propose methods for necessary user research and live testing. This helps users quickly select the optimal design and conduct the necessary research and testing. Step 4: The testing team conducts small-scale tests of the designs, research, and tests proposed by the proposal team. For example, they test the proposed design with a select group of users and collect their feedback. They verify the effectiveness of the design and make modifications as needed.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and test unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from the user regarding the problem and the ideal state. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past company data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal design and necessary investigations and tests. The test unit is implemented by, for example, the control unit 46A of the smart device 14 and performs small tests on the proposed design and investigations and tests. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and testing unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input from the user regarding the problem and the ideal state. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes past company data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal design and necessary investigations and tests. The testing unit is implemented, for example, by the control unit 46A of the smart glasses 214 and performs small-scale tests of the proposed design and investigations and tests. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and test unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input from the user regarding the problem and the ideal state. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past company data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal design and necessary investigations and tests. The test unit is implemented by, for example, the control unit 46A of the headset terminal 314 and performs small-scale tests of the proposed design and investigations and tests. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and test unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from the user regarding the problem and the ideal state. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past company data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal design and necessary investigations and tests. The test unit is implemented by, for example, the control unit 46A of the robot 414 and performs small tests on the proposed design and investigations and tests. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

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

[0166] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) A reception desk that receives input from users regarding their challenges and ideal state, Based on the information received by the aforementioned reception department, an analysis department analyzes past company data. Based on the analysis results obtained by the aforementioned analysis department, the proposal department proposes the optimal design and necessary investigations and tests. The system includes a test unit that performs small-scale tests on the designs, investigations, and tests proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the input methods for the task and the ideal state based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When users input their challenges and ideal states, the system filters their input based on their current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users input their challenges and ideal states, the system prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users input their challenges and ideal states, the system analyzes their social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of historical data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the design, research, and testing. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the design, research, and testing categories. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of design, research, and testing submissions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the designs, research, and tests. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned test unit is We estimate the user's emotions and adjust the small-scale testing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned test unit is During small-scale testing, optimize the test algorithm by referring to past test results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned test unit is During small-scale testing, different testing methods are applied depending on the category being tested. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned test unit is We estimate the user's emotions and adjust how the results of small tests are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned test unit is During small-scale testing, prioritize tests based on the submission deadlines for the items being tested. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned test unit is During small-scale testing, adjust the order of tests based on the relevance of the test subjects. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives input from users regarding their challenges and ideal state, Based on the information received by the aforementioned reception department, an analysis department analyzes past company data. Based on the analysis results obtained by the aforementioned analysis department, the proposal department proposes the optimal design and necessary investigations and tests. The system includes a test unit that performs small-scale tests on the designs, investigations, and tests proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the input methods for the task and the ideal state based on the estimated user emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input format. The system according to feature 1.

4. The aforementioned reception unit is When users input their challenges and ideal states, the system filters their input based on their current projects and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When users input their challenges and ideal states, the system prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users input their challenges and ideal states, the system analyzes their social media activity and accepts relevant input. The system according to feature 1.

8. The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of historical data. The system according to feature 1.

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