system

Abstract

The system according to this embodiment aims to generate and provide an optimal design according to the user's requirements. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an improvement unit. The reception unit receives user requests as input. The analysis unit analyzes consumer preferences based on the information received by the reception unit. The generation unit generates an optimal design based on the analysis results obtained by the analysis unit. The improvement unit collects user feedback based on the design generated by the generation unit, and the AI ​​performs self-improvement.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including 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, it is difficult to quickly generate a design according to the user's requirements, and there is room for improvement in providing a design that meets the preferences of consumers.

[0005] The system according to the embodiment aims to generate and provide an optimal design according to the user's requirements.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an improvement unit. The reception unit receives user requests as input. The analysis unit analyzes consumer preferences based on the information received by the reception unit. The generation unit generates an optimal design based on the analysis results obtained by the analysis unit. The improvement unit collects user feedback based on the design generated by the generation unit, and the AI ​​performs self-improvement. [Effects of the Invention]

[0007] The system according to this embodiment can generate and provide an optimal design according to the user's requirements. [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 multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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 generation agent according to an embodiment of the present invention is a system that uses AI to instantly generate designs that meet user requests. This design generation agent receives user requests as input, the AI ​​analyzes consumer preferences, and proposes the optimal design based on that analysis. The AI ​​analyzes consumer behavior using deep learning and generates designs in real time. Furthermore, the AI ​​performs self-improvement based on user feedback, continuously improving design quality. For example, the design generation agent includes a reception unit that receives user requests as input. The reception unit receives user requests as input and then includes an analysis unit that analyzes consumer preferences based on the information received by the reception unit. The analysis unit analyzes consumer behavior using deep learning. Next, it includes a generation unit that generates the optimal design based on the analysis results obtained by the analysis unit. The generation unit generates designs in real time and proposes them to the user. Furthermore, it includes an improvement unit in which the AI ​​performs self-improvement based on user feedback. The improvement unit collects user feedback, and the AI ​​performs self-improvement, continuously improving design quality. As a result, the design generation agent can reduce design production time and costs, and is expected to improve customer satisfaction through design proposals based on market data. This allows the design generation agent to efficiently generate designs that meet user requirements and continuously improve them.

[0029] The design generation agent according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an improvement unit. The reception unit receives user requests as input. User requests include, but are not limited to, text input, voice input, and image input. The reception unit receives, for example, text data entered by the user. The reception unit can also convert voice data into text data using voice recognition technology when the user enters a request by voice. Furthermore, the reception unit can analyze image data using image recognition technology and extract requests when the user uploads an image. For example, the reception unit analyzes text data entered by the user using natural language processing technology and extracts requests. Voice recognition technology converts the user's voice into text with high accuracy. Image recognition technology extracts requests from images uploaded by the user. The analysis unit uses deep learning to analyze consumer preferences based on the information received by the reception unit. Consumer preferences include, but are not limited to, past purchase history and survey results. For example, the analysis unit analyzes consumer preferences based on past purchase history. The analysis unit can also analyze consumer preferences based on survey results. Furthermore, the analysis unit can analyze consumer preferences based on social media data. For example, the analysis unit inputs past purchase history into a deep learning model to predict consumer preferences. Survey results are used in the analysis as data reflecting consumer preferences. Social media data is used in the analysis as data reflecting consumer interests. The generation unit generates the optimal design based on the analysis results obtained by the analysis unit. The generation unit generates designs in real time, for example. Real time includes, but is not limited to, response times of a few seconds. For example, the generation unit generates a design within a few seconds in response to user requests. The generation unit can also generate a design within a few minutes in response to user requests. Furthermore, the generation unit can generate a design within a few hours in response to user requests. For example, the generation unit uses a deep learning model to generate designs that meet user requests.Deep learning models have learned from large amounts of design data and possess advanced design generation capabilities. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit inputs user requests as prompts to the generation AI, and the generation AI generates a design. The improvement unit collects user feedback based on the design generated by the generation unit, and the AI ​​performs self-improvement. Self-improvement includes, but is not limited to, methods for collecting feedback and improvement algorithms. For example, the improvement unit collects feedback from users, and the AI ​​performs self-improvement. The improvement unit can also collect user behavior data, and the AI ​​can perform self-improvement. The improvement unit can also collect user evaluation data, and the AI ​​can perform self-improvement. For example, the improvement unit analyzes user feedback using natural language processing techniques and extracts areas for improvement. Behavioral data is collected as data reflecting the user's operation history and usage status. Evaluation data is collected as data reflecting user satisfaction and evaluation. As a result, the design generation agent according to the embodiment can efficiently generate designs that meet user requirements and continuously improve them.

[0030] The reception desk receives user requests as input. User requests include, but are not limited to, text input, voice input, and image input. For example, the reception desk receives text data entered by the user. The reception desk can also convert voice data into text data using speech recognition technology when the user enters a request by voice. Furthermore, the reception desk can analyze image data using image recognition technology and extract requests when the user uploads an image. For example, the reception desk analyzes text data entered by the user using natural language processing technology and extracts requests. Speech recognition technology converts the user's voice into text with high accuracy. Image recognition technology extracts requests from images uploaded by the user. Specifically, in the case of text input, natural language processing technology is used to analyze the user's request and extract keywords and important phrases. In the case of voice input, speech recognition technology is used to convert voice data into text data, and then it is analyzed using natural language processing technology. In the case of image input, image recognition technology is used to analyze image data and extract objects and text within the image. This allows the reception desk to handle diverse user input formats and accurately understand requests. Furthermore, the reception desk can verify the data entered by the user through the user interface and prompt for corrections or additional input as needed. For example, if there is an error in the text data entered by the user, the reception desk will display a message prompting the user to make corrections. In the case of voice input, the reception desk can present the voice recognition results to the user and ask for confirmation. This allows the reception desk to accurately receive user requests and prepare to proceed to the next processing step.

[0031] The analysis department uses deep learning to analyze consumer preferences based on information received by the reception department. Consumer preferences include, but are not limited to, past purchase history and survey results. For example, the analysis department can analyze consumer preferences based on past purchase history. The analysis department can also analyze consumer preferences based on survey results. Furthermore, the analysis department can analyze consumer preferences based on social media data. For example, the analysis department inputs past purchase history into a deep learning model to predict consumer preferences. Survey results are used in the analysis as data that reflects consumer preferences. Social media data is used in the analysis as data that reflects consumer interests and concerns. Specifically, the deep learning model learns from past purchase history data and extracts patterns to predict consumer preferences and trends. Survey results are input into the deep learning model as data that reflects consumer preferences and opinions and are used to analyze consumer preferences in more detail. Social media data is input into the deep learning model as data that reflects consumers' real-time interests and concerns and is used to predict consumer preferences. This allows the analytics department to predict consumer preferences with high accuracy and prepare for the next processing step. Furthermore, the analytics department can update consumer preferences in real time and perform analysis based on the latest information. For example, if new purchase history or survey results are added, the deep learning model retrains on this data to keep consumer preferences up-to-date. In addition, social media data is collected in real time, allowing for rapid reflection of changes in consumer interests and preferences. This enables the analytics department to always perform highly accurate analysis based on the latest information and prepare for the next processing step.

[0032] The generation unit generates the optimal design based on the analysis results obtained by the analysis unit. The generation unit generates designs in real time, for example. Real time includes, but is not limited to, a response time of a few seconds. For example, the generation unit generates a design within a few seconds according to the user's request. The generation unit can also generate a design within a few minutes according to the user's request. The generation unit can also generate a design within a few hours according to the user's request. For example, the generation unit uses a deep learning model to generate a design according to the user's request. The deep learning model has learned from a large amount of design data and has advanced design generation capabilities. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's request as a prompt to the generation AI, and the generation AI generates a design. Specifically, the generation unit uses a deep learning model to generate a design according to the user's request. The deep learning model has learned from a large amount of design data and has advanced design generation capabilities. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's request as a prompt to the generation AI, and the generation AI generates a design. Based on the user's request, the generation AI generates prompts to generate the optimal design, and generates a design based on those prompts. The generation AI has learned from a large amount of design data and has advanced design generation capabilities. As a result, the generation unit can quickly generate the optimal design that meets the user's request. Furthermore, the generation unit can present the generated design to the user and collect user feedback. For example, the generation unit can present the generated design to the user and allow the user to input evaluations and comments on the design. As a result, the generation unit can collect user feedback and prepare to proceed to the next process.

[0033] The improvement unit collects user feedback based on the design generated by the generation unit, and the AI ​​performs self-improvement. Self-improvement includes, but is not limited to, the methods for collecting feedback and improvement algorithms. For example, the improvement unit collects user feedback, and the AI ​​performs self-improvement. The improvement unit can also collect user behavior data, and the AI ​​can perform self-improvement. The improvement unit can also collect user evaluation data, and the AI ​​can perform self-improvement. For example, the improvement unit analyzes user feedback using natural language processing technology and extracts areas for improvement. Behavioral data is collected as data reflecting the user's operation history and usage status. Evaluation data is collected as data reflecting the user's satisfaction and evaluation. Specifically, the improvement unit analyzes user feedback using natural language processing technology and extracts areas for improvement. Behavioral data is collected as data reflecting the user's operation history and usage status. Evaluation data is collected as data reflecting the user's satisfaction and evaluation. This allows the improvement unit to perform self-improvement based on user feedback. Furthermore, the improvement unit can provide feedback on the results of its self-improvement to the user, thereby improving user satisfaction. For example, the improvement unit can present the results of its self-improvement to the user, allowing the user to review and evaluate the improvements. This enables the improvement unit to use user feedback to allow the AI ​​to perform self-improvement and prepare to proceed to the next process.

[0034] The analysis unit can analyze consumer behavior using deep learning. Deep learning includes, but is not limited to, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). For example, the analysis unit can analyze consumer behavior using convolutional neural networks. For example, the analysis unit can input consumer purchase history data into a convolutional neural network to predict consumer preferences. The analysis unit can also analyze consumer behavior using recurrent neural networks. For example, the analysis unit can input consumer behavior data into a recurrent neural network to predict consumer behavior patterns. The analysis unit can also cluster consumer preferences using deep learning. For example, the analysis unit can input consumer preference data into a deep learning model to classify consumers into multiple clusters. This improves the accuracy of consumer behavior analysis by using deep learning. Specific implementation methods of deep learning include, but are not limited to, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input consumer behavior data into an AI model and have the AI ​​perform an analysis of consumer behavior patterns.

[0035] The generation unit can generate designs in real time. Real time includes, but is not limited to, a response time of a few seconds. The generation unit can generate a design within a few seconds in response to a user's request. For example, the generation unit can use a deep learning model to generate a design that meets the user's request. The generation unit can also generate a design within a few minutes in response to a user's request. For example, the generation unit can use a generation AI to generate a design that meets the user's request. The generation unit can also generate a design within a few hours in response to a user's request. For example, the generation unit can use a generation AI to generate a design that meets the user's request. This allows for a rapid response to user requests by generating designs in real time. A specific definition of real time includes, but is not limited to, a response time of a few seconds. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit inputs the user's request as a prompt to the generation AI, and the generation AI generates a design.

[0036] The improvement unit allows the AI ​​to perform self-improvement based on user feedback. Self-improvement includes, but is not limited to, methods for collecting feedback and improvement algorithms. For example, the improvement unit can collect user feedback and the AI ​​can perform self-improvement. For example, the improvement unit can analyze user feedback using natural language processing technology and extract areas for improvement. The improvement unit can also collect user behavior data and the AI ​​can perform self-improvement. For example, the improvement unit can collect user operation history and usage status and the AI ​​can perform self-improvement. The improvement unit can also collect user evaluation data and the AI ​​can perform self-improvement. For example, the improvement unit can collect user satisfaction and evaluation data and the AI ​​can perform self-improvement. As a result, design quality is continuously improved by performing self-improvement based on user feedback. Specific methods of self-improvement include, but are not limited to, methods for collecting feedback and improvement algorithms. Some or all of the above-described processes in the improvement unit may be performed using, for example, AI, or not using AI. For example, the improvement department can input user feedback into an AI model and have the AI ​​extract areas for improvement.

[0037] The reception desk can analyze the user's past request history and select the most suitable reception method. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can analyze the user's past request history and select the most suitable reception method. The reception desk can also suggest the most suitable reception method for a specific time period based on the user's past request history. For example, the reception desk can analyze the user's past request history and select the most efficient reception method. This allows for the selection of the most suitable reception method by analyzing the user's past request history. Past request history includes, but is not limited to, the data format and analysis algorithm of past requests. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past request history into an AI model and have the AI ​​select the most suitable reception method.

[0038] The reception desk can filter requests based on the user's current projects and areas of interest when receiving them. For example, the reception desk can prioritize requests related to the user's current projects. For example, the reception desk can filter requests based on the user's current projects and areas of interest. The reception desk can also filter and receive relevant requests based on the user's areas of interest. For example, the reception desk can receive the most relevant requests according to the progress of the user's current projects. This allows for the priority of receiving highly relevant requests by filtering requests based on the user's current projects and areas of interest. Current projects include, but are not limited to, the type of project and filtering criteria. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input data from the user's current projects into an AI model and have the AI ​​perform the filtering.

[0039] The reception desk can prioritize requests based on their relevance, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize requests related to that region. The reception desk can also filter and accept the most relevant requests based on the user's geographical location. For example, if the user is on the move, the reception desk can prioritize requests related to their current location. This allows for prioritizing requests based on the user's geographical location. Geographical location information includes, but is not limited to, methods for prioritizing requests based on the user's location. 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 geographical location into an AI model and have the AI ​​determine the priority of requests.

[0040] The reception desk can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception desk can prioritize requests related to the user's current interests based on their social media activity. For example, the reception desk can analyze the user's social media activity and accept relevant requests. The reception desk can also analyze the content of the user's social media posts and filter and accept relevant requests. For example, the reception desk can refer to the activities of the user's social media followers and friends to accept relevant requests. This allows the reception desk to prioritize requests by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and comments. 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 social media activity data into an AI model and have the AI ​​perform the request filtering.

[0041] The analytics department can adjust the level of detail of its analysis based on the importance of the consumer. For example, it can provide detailed analysis results to important consumers. For example, it can identify important consumers based on their purchase frequency or customer rank. The analytics department can also provide standard analysis results to general consumers. For example, it can provide standard analysis results based on consumers' purchase history data. The analytics department can also provide concise analysis results to less important consumers. For example, it can provide concise analysis results based on consumers' behavioral data. This allows for the provision of detailed analysis results to important consumers by adjusting the level of detail based on the importance of the consumer. Consumer importance includes, but is not limited to, purchase frequency and customer rank. Some or all of the above processes in the analytics department may be performed using, for example, AI, or not using AI. For example, the analytics department can input consumer importance data into an AI model and have the AI ​​adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analytical algorithms depending on the consumer category during analysis. For example, the analysis unit can perform a detailed purchasing behavior analysis for consumers who purchase expensive goods. For example, the analysis unit can perform a detailed purchasing behavior analysis based on the consumer's purchase history data. The analysis unit can also perform a standard purchasing behavior analysis for consumers who purchase general goods. For example, the analysis unit can perform a standard purchasing behavior analysis based on the consumer's purchase history data. Furthermore, the analysis unit can apply category-specific analytical algorithms to consumers who purchase products in a specific category. For example, the analysis unit can apply category-specific analytical algorithms based on the consumer's purchase history data. By applying different analytical algorithms depending on the consumer category, more appropriate analytical results can be provided. Consumer categories include, but are not limited to, age groups and purchase history. Analytical algorithms include, but are not limited to, clustering algorithms and regression analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis department can input consumer category data into an AI model and have the AI ​​apply the analysis algorithm.

[0043] The analysis department can determine the priority of analysis based on the timing of consumer submissions. For example, the analysis department can prioritize the analysis of urgent submissions. For example, the analysis department can identify urgent submissions based on consumer submission timing data. The analysis department can also set a higher priority for consumers whose submission deadlines are approaching. For example, the analysis department can identify consumers whose submission deadlines are approaching based on consumer submission timing data. The analysis department can also perform analysis with normal priority for consumers whose submission deadlines are far off. For example, the analysis department can identify consumers whose submission deadlines are far off based on consumer submission timing data. This allows for priority analysis of urgent submissions by determining the priority of analysis based on consumer submission timing. Consumer submission timing includes, but is not limited to, submission date and time, and submission frequency. Some or all of the above processing in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can input consumer submission timing data into an AI model and have the AI ​​determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on consumer relevance. For example, the analysis unit may perform analysis on important consumers first. For example, the analysis unit can identify important consumers based on consumer relevance data. The analysis unit can also perform analysis on general consumers in a standard order. For example, the analysis unit can identify general consumers based on consumer relevance data. The analysis unit can also postpone analysis on less relevant consumers. For example, the analysis unit can identify less relevant consumers based on consumer relevance data. This allows for prioritizing analysis on important consumers by adjusting the order of analysis based on consumer relevance. Consumer relevance includes, but is not limited to, highly relevant consumers and less relevant consumers. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input consumer relevance data into an AI model and have the AI ​​adjust the order of analysis.

[0045] The generation unit can adjust the level of detail of the generated designs based on their importance. For example, the generation unit can generate detailed designs for important designs. For example, the generation unit can identify important designs based on design importance data. The generation unit can also generate standard designs for general designs. For example, the generation unit can identify general designs based on design importance data. The generation unit can also generate concise designs for less important designs. For example, the generation unit can identify less important designs based on design importance data. This allows for the generation of detailed designs for important designs by adjusting the level of detail based on design importance. Design importance includes, but is not limited to, evaluation criteria for importance based on user requests. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input design importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the generated designs.

[0046] The generation unit can apply different generation algorithms depending on the design category during generation. For example, the generation unit can apply a detailed design algorithm to designs for expensive products. For example, the generation unit can apply a design algorithm for expensive products based on design category data. The generation unit can also apply a standard design algorithm to designs for general products. For example, the generation unit can apply a design algorithm for general products based on design category data. The generation unit can also apply a design algorithm specific to a particular category of product. For example, the generation unit can apply a design algorithm specific to a particular category based on design category data. By applying different generation algorithms depending on the design category, more appropriate designs can be generated. Design categories include, but are not limited to, web design and graphic design. Generation algorithms include, but are not limited to, genetic algorithms and neural networks. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input design category data into an AI model and have the AI ​​perform the application of the generation algorithm.

[0047] The generation unit can determine the generation priority based on the design submission timing during generation. For example, the generation unit can prioritize the generation of urgent designs. For example, the generation unit can identify urgent designs based on design submission timing data. The generation unit can also set a higher priority for designs with approaching submission deadlines. For example, the generation unit can identify designs with approaching submission deadlines based on design submission timing data. The generation unit can also generate designs with distant submission deadlines with normal priority. For example, the generation unit can identify designs with distant submission deadlines based on design submission timing data. This allows for priority generation of urgent designs by determining the generation priority based on the design submission timing. The design submission timing includes, but is not limited to, the submission date and time, and submission frequency. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input design submission timing data into an AI model and have the AI ​​determine the generation priority.

[0048] The generation unit can adjust the generation order based on the relevance of the designs during generation. For example, the generation unit may generate important designs first. For example, the generation unit can identify important designs based on design relevance data. The generation unit can also generate general designs in a standard order. For example, the generation unit can identify general designs based on design relevance data. The generation unit can also postpone the generation of less relevant designs. For example, the generation unit can identify less relevant designs based on design relevance data. This allows for priority generation of important designs by adjusting the generation order based on design relevance. Design relevance includes, but is not limited to, highly relevant and less relevant designs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input design relevance data into an AI model and have the AI ​​adjust the generation order.

[0049] The improvement unit can select the optimal improvement method by analyzing past user feedback during the improvement process. For example, the improvement unit can propose the optimal improvement method based on feedback previously provided by the user. For example, the improvement unit can select the optimal improvement method based on past user feedback data. The improvement unit can also select a method to improve a specific problem from past user feedback. For example, the improvement unit can select a method to improve a specific problem based on past user feedback data. The improvement unit can also analyze past user feedback and select the most effective improvement method. For example, the improvement unit can select the most effective improvement method based on past user feedback data. This allows the optimal improvement method to be selected by analyzing past user feedback. Past feedback includes, but is not limited to, the data format of the feedback and the analysis algorithm. Some or all of the above-described processes in the improvement unit may be performed using, for example, AI, or not using AI. For example, the improvement unit can input past user feedback data into an AI model and have the AI ​​select the improvement method.

[0050] The improvement unit can customize the means of improvement based on the user's current living situation when making improvements. For example, if the user is busy, the improvement unit can suggest improvement measures that can be implemented in a short amount of time. For example, the improvement unit can suggest improvement measures that can be implemented in a short amount of time based on the user's current living situation data. The improvement unit can also suggest detailed improvement measures if the user is relaxed. For example, the improvement unit can suggest detailed improvement measures based on the user's current living situation data. Furthermore, if the user is in a specific living situation, the improvement unit can customize and suggest improvement measures that are best suited to that situation. For example, the improvement unit can customize and suggest improvement measures that are best suited to that situation based on the user's current living situation data. This allows for the provision of more appropriate improvement measures by customizing the means of improvement based on the user's current living situation. Current living situation includes, but is not limited to, living environment and lifestyle. Some or all of the above processing in the improvement unit may be performed using, for example, AI, or not using AI. For example, the improvement unit can input the user's current living situation data into an AI model and have the AI ​​perform the customization of improvement measures.

[0051] The improvement unit can select the optimal improvement method when making improvements, taking into account the user's geographical location information. For example, if the user is in a specific region, the improvement unit can suggest improvement methods related to that region. For example, the improvement unit can select improvement methods related to a region based on the user's geographical location information. The improvement unit can also select the optimal improvement method based on the user's geographical location information. For example, if the user is on the move, the improvement unit can suggest improvement methods related to the user's current location. This allows the optimal improvement method to be selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, selection criteria for improvement methods based on the user's location information. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the user's geographical location information into an AI model and have the AI ​​perform the selection of improvement methods.

[0052] The improvement unit can analyze the user's social media activity and propose improvement measures during the improvement process. For example, the improvement unit can propose improvement measures related to the user's current interests based on the user's social media activity. For example, the improvement unit can analyze the user's social media activity and propose improvement measures related to the user's current interests. The improvement unit can also analyze the content of the user's social media posts and propose relevant improvement measures. For example, the improvement unit can refer to the activities of the user's social media followers and friends and propose relevant improvement measures. In this way, relevant improvement measures can be proposed by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and comments. Some or all of the above processing in the improvement unit may be performed using, for example, AI, or not using AI. For example, the improvement unit can input the user's social media activity data into an AI model and have the AI ​​execute the proposal of improvement measures.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze a user's past design history and suggest designs based on their preferences. For example, it can analyze the patterns and colors of designs the user has previously selected and suggest similar designs. It can also extract specific design elements from the user's past design history and generate new designs based on them. Furthermore, it can predict the user's preferred design trends based on their past design history and suggest designs based on those trends. This allows for more personalized design suggestions by leveraging the user's past design history.

[0055] The generation unit can propose designs based on the user's current project progress. For example, in the early stages of a project, the generation unit can propose designs suitable for idea generation and brainstorming. In the middle stages of the project, it can also propose specific design proposals. Furthermore, in the final stages of the project, it can propose designs suitable for final adjustments and finishing touches. This allows for appropriate design proposals according to the project's progress.

[0056] The reception desk can take the user's geographical location into consideration to provide region-specific design suggestions. For example, if a user is in a specific region, it can suggest designs based on the culture and trends of that region. If a user is traveling, it can also suggest designs related to the region they are visiting. Furthermore, if a user is attending a specific event, it can suggest designs related to that event. In this way, by utilizing the user's geographical location information, it becomes possible to provide more relevant design suggestions.

[0057] The reception desk can analyze users' social media activity and propose designs based on their interests. For example, it can suggest designs related to topics users frequently mention on social media. It can also suggest designs based on the activity of accounts and groups users follow. Furthermore, it can analyze users' social media posts and suggest designs related to their current interests. This allows for more personalized design suggestions by leveraging users' social media activity.

[0058] The improvement team can analyze past user feedback and propose improvements based on feedback trends. For example, they can improve the system to prevent similar problems from occurring based on issues users have previously pointed out. They can also incorporate design elements that users have previously given high ratings to. Furthermore, they can extract specific areas for improvement from past user feedback and propose improvements based on those. This allows for more effective improvement proposals by utilizing past user feedback.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives user requests as input. User requests include text input, voice input, and image input. The reception desk receives the text data entered by the user. It can also convert voice data into text data using speech recognition technology. Furthermore, it can analyze image data using image recognition technology and extract requests. Step 2: The analysis department uses deep learning to analyze consumer preferences based on the information received by the reception department. Consumer preferences include past purchase history, survey results, and social media data. The analysis department inputs this data into a deep learning model to predict consumer preferences. Step 3: The generation unit generates the optimal design based on the analysis results obtained by the analysis unit. The generation unit can generate designs in real time and uses deep learning models to generate designs that meet user requirements. Processing in the generation unit may also be performed using generation AI. Step 4: The improvement unit collects user feedback based on the design generated by the generation unit, and the AI ​​performs self-improvement. The improvement unit collects user feedback, behavioral data, and evaluation data, analyzes them using natural language processing technology, and extracts areas for improvement.

[0061] (Example of form 2) The design generation agent according to an embodiment of the present invention is a system that uses AI to instantly generate designs that meet user requests. This design generation agent receives user requests as input, the AI ​​analyzes consumer preferences, and proposes the optimal design based on that analysis. The AI ​​analyzes consumer behavior using deep learning and generates designs in real time. Furthermore, the AI ​​performs self-improvement based on user feedback, continuously improving design quality. For example, the design generation agent includes a reception unit that receives user requests as input. The reception unit receives user requests as input and then includes an analysis unit that analyzes consumer preferences based on the information received by the reception unit. The analysis unit analyzes consumer behavior using deep learning. Next, it includes a generation unit that generates the optimal design based on the analysis results obtained by the analysis unit. The generation unit generates designs in real time and proposes them to the user. Furthermore, it includes an improvement unit in which the AI ​​performs self-improvement based on user feedback. The improvement unit collects user feedback, and the AI ​​performs self-improvement, continuously improving design quality. As a result, the design generation agent can reduce design production time and costs, and is expected to improve customer satisfaction through design proposals based on market data. This allows the design generation agent to efficiently generate designs that meet user requirements and continuously improve them.

[0062] The design generation agent according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an improvement unit. The reception unit receives user requests as input. User requests include, but are not limited to, text input, voice input, and image input. The reception unit receives, for example, text data entered by the user. The reception unit can also convert voice data into text data using voice recognition technology when the user enters a request by voice. Furthermore, the reception unit can analyze image data using image recognition technology and extract requests when the user uploads an image. For example, the reception unit analyzes text data entered by the user using natural language processing technology and extracts requests. Voice recognition technology converts the user's voice into text with high accuracy. Image recognition technology extracts requests from images uploaded by the user. The analysis unit uses deep learning to analyze consumer preferences based on the information received by the reception unit. Consumer preferences include, but are not limited to, past purchase history and survey results. For example, the analysis unit analyzes consumer preferences based on past purchase history. The analysis unit can also analyze consumer preferences based on survey results. Furthermore, the analysis unit can analyze consumer preferences based on social media data. For example, the analysis unit inputs past purchase history into a deep learning model to predict consumer preferences. Survey results are used in the analysis as data reflecting consumer preferences. Social media data is used in the analysis as data reflecting consumer interests. The generation unit generates the optimal design based on the analysis results obtained by the analysis unit. The generation unit generates designs in real time, for example. Real time includes, but is not limited to, response times of a few seconds. For example, the generation unit generates a design within a few seconds in response to user requests. The generation unit can also generate a design within a few minutes in response to user requests. Furthermore, the generation unit can generate a design within a few hours in response to user requests. For example, the generation unit uses a deep learning model to generate designs that meet user requests.Deep learning models have learned from large amounts of design data and possess advanced design generation capabilities. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit inputs user requests as prompts to the generation AI, and the generation AI generates a design. The improvement unit collects user feedback based on the design generated by the generation unit, and the AI ​​performs self-improvement. Self-improvement includes, but is not limited to, methods for collecting feedback and improvement algorithms. For example, the improvement unit collects feedback from users, and the AI ​​performs self-improvement. The improvement unit can also collect user behavior data, and the AI ​​can perform self-improvement. The improvement unit can also collect user evaluation data, and the AI ​​can perform self-improvement. For example, the improvement unit analyzes user feedback using natural language processing techniques and extracts areas for improvement. Behavioral data is collected as data reflecting the user's operation history and usage status. Evaluation data is collected as data reflecting user satisfaction and evaluation. As a result, the design generation agent according to the embodiment can efficiently generate designs that meet user requirements and continuously improve them.

[0063] The reception desk receives user requests as input. User requests include, but are not limited to, text input, voice input, and image input. For example, the reception desk receives text data entered by the user. The reception desk can also convert voice data into text data using speech recognition technology when the user enters a request by voice. Furthermore, the reception desk can analyze image data using image recognition technology and extract requests when the user uploads an image. For example, the reception desk analyzes text data entered by the user using natural language processing technology and extracts requests. Speech recognition technology converts the user's voice into text with high accuracy. Image recognition technology extracts requests from images uploaded by the user. Specifically, in the case of text input, natural language processing technology is used to analyze the user's request and extract keywords and important phrases. In the case of voice input, speech recognition technology is used to convert voice data into text data, and then it is analyzed using natural language processing technology. In the case of image input, image recognition technology is used to analyze image data and extract objects and text within the image. This allows the reception desk to handle diverse user input formats and accurately understand requests. Furthermore, the reception desk can verify the data entered by the user through the user interface and prompt for corrections or additional input as needed. For example, if there is an error in the text data entered by the user, the reception desk will display a message prompting the user to make corrections. In the case of voice input, the reception desk can present the voice recognition results to the user and ask for confirmation. This allows the reception desk to accurately receive user requests and prepare to proceed to the next processing step.

[0064] The analysis department uses deep learning to analyze consumer preferences based on information received by the reception department. Consumer preferences include, but are not limited to, past purchase history and survey results. For example, the analysis department can analyze consumer preferences based on past purchase history. The analysis department can also analyze consumer preferences based on survey results. Furthermore, the analysis department can analyze consumer preferences based on social media data. For example, the analysis department inputs past purchase history into a deep learning model to predict consumer preferences. Survey results are used in the analysis as data that reflects consumer preferences. Social media data is used in the analysis as data that reflects consumer interests and concerns. Specifically, the deep learning model learns from past purchase history data and extracts patterns to predict consumer preferences and trends. Survey results are input into the deep learning model as data that reflects consumer preferences and opinions and are used to analyze consumer preferences in more detail. Social media data is input into the deep learning model as data that reflects consumers' real-time interests and concerns and is used to predict consumer preferences. This allows the analytics department to predict consumer preferences with high accuracy and prepare for the next processing step. Furthermore, the analytics department can update consumer preferences in real time and perform analysis based on the latest information. For example, if new purchase history or survey results are added, the deep learning model retrains on this data to keep consumer preferences up-to-date. In addition, social media data is collected in real time, allowing for rapid reflection of changes in consumer interests and preferences. This enables the analytics department to always perform highly accurate analysis based on the latest information and prepare for the next processing step.

[0065] The generation unit generates the optimal design based on the analysis results obtained by the analysis unit. The generation unit generates designs in real time, for example. Real time includes, but is not limited to, a response time of a few seconds. For example, the generation unit generates a design within a few seconds according to the user's request. The generation unit can also generate a design within a few minutes according to the user's request. The generation unit can also generate a design within a few hours according to the user's request. For example, the generation unit uses a deep learning model to generate a design according to the user's request. The deep learning model has learned from a large amount of design data and has advanced design generation capabilities. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's request as a prompt to the generation AI, and the generation AI generates a design. Specifically, the generation unit uses a deep learning model to generate a design according to the user's request. The deep learning model has learned from a large amount of design data and has advanced design generation capabilities. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's request as a prompt to the generation AI, and the generation AI generates a design. Based on the user's request, the generation AI generates prompts to generate the optimal design, and generates a design based on those prompts. The generation AI has learned from a large amount of design data and has advanced design generation capabilities. As a result, the generation unit can quickly generate the optimal design that meets the user's request. Furthermore, the generation unit can present the generated design to the user and collect user feedback. For example, the generation unit can present the generated design to the user and allow the user to input evaluations and comments on the design. As a result, the generation unit can collect user feedback and prepare to proceed to the next process.

[0066] The improvement unit collects user feedback based on the design generated by the generation unit, and the AI ​​performs self-improvement. Self-improvement includes, but is not limited to, the methods for collecting feedback and improvement algorithms. For example, the improvement unit collects user feedback, and the AI ​​performs self-improvement. The improvement unit can also collect user behavior data, and the AI ​​can perform self-improvement. The improvement unit can also collect user evaluation data, and the AI ​​can perform self-improvement. For example, the improvement unit analyzes user feedback using natural language processing technology and extracts areas for improvement. Behavioral data is collected as data reflecting the user's operation history and usage status. Evaluation data is collected as data reflecting the user's satisfaction and evaluation. Specifically, the improvement unit analyzes user feedback using natural language processing technology and extracts areas for improvement. Behavioral data is collected as data reflecting the user's operation history and usage status. Evaluation data is collected as data reflecting the user's satisfaction and evaluation. This allows the improvement unit to perform self-improvement based on user feedback. Furthermore, the improvement unit can provide feedback on the results of its self-improvement to the user, thereby improving user satisfaction. For example, the improvement unit can present the results of its self-improvement to the user, allowing the user to review and evaluate the improvements. This enables the improvement unit to use user feedback to allow the AI ​​to perform self-improvement and prepare to proceed to the next process.

[0067] The analysis unit can analyze consumer behavior using deep learning. Deep learning includes, but is not limited to, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). For example, the analysis unit can analyze consumer behavior using convolutional neural networks. For example, the analysis unit can input consumer purchase history data into a convolutional neural network to predict consumer preferences. The analysis unit can also analyze consumer behavior using recurrent neural networks. For example, the analysis unit can input consumer behavior data into a recurrent neural network to predict consumer behavior patterns. The analysis unit can also cluster consumer preferences using deep learning. For example, the analysis unit can input consumer preference data into a deep learning model to classify consumers into multiple clusters. This improves the accuracy of consumer behavior analysis by using deep learning. Specific implementation methods of deep learning include, but are not limited to, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input consumer behavior data into an AI model and have the AI ​​perform an analysis of consumer behavior patterns.

[0068] The generation unit can generate designs in real time. Real time includes, but is not limited to, a response time of a few seconds. The generation unit can generate a design within a few seconds in response to a user's request. For example, the generation unit can use a deep learning model to generate a design that meets the user's request. The generation unit can also generate a design within a few minutes in response to a user's request. For example, the generation unit can use a generation AI to generate a design that meets the user's request. The generation unit can also generate a design within a few hours in response to a user's request. For example, the generation unit can use a generation AI to generate a design that meets the user's request. This allows for a rapid response to user requests by generating designs in real time. A specific definition of real time includes, but is not limited to, a response time of a few seconds. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit inputs the user's request as a prompt to the generation AI, and the generation AI generates a design.

[0069] The improvement unit allows the AI ​​to perform self-improvement based on user feedback. Self-improvement includes, but is not limited to, methods for collecting feedback and improvement algorithms. For example, the improvement unit can collect user feedback and the AI ​​can perform self-improvement. For example, the improvement unit can analyze user feedback using natural language processing technology and extract areas for improvement. The improvement unit can also collect user behavior data and the AI ​​can perform self-improvement. For example, the improvement unit can collect user operation history and usage status and the AI ​​can perform self-improvement. The improvement unit can also collect user evaluation data and the AI ​​can perform self-improvement. For example, the improvement unit can collect user satisfaction and evaluation data and the AI ​​can perform self-improvement. As a result, design quality is continuously improved by performing self-improvement based on user feedback. Specific methods of self-improvement include, but are not limited to, methods for collecting feedback and improvement algorithms. Some or all of the above-described processes in the improvement unit may be performed using, for example, AI, or not using AI. For example, the improvement department can input user feedback into an AI model and have the AI ​​extract areas for improvement.

[0070] The reception desk can estimate the user's emotions and adjust the timing of request acceptance based on the estimated emotions. For example, if the user is stressed, the reception desk can delay acceptance to allow the user to relax before accepting the request. For example, the reception desk can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The reception desk can also expedite acceptance if the user is in a hurry. For example, the reception desk can record the user's voice and estimate their emotions using voice analysis technology. The reception desk can also accept requests at the normal timing if the user is relaxed. For example, the reception desk can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate acceptance of requests by adjusting the timing 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0071] The reception desk can analyze the user's past request history and select the most suitable reception method. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can analyze the user's past request history and select the most suitable reception method. The reception desk can also suggest the most suitable reception method for a specific time period based on the user's past request history. For example, the reception desk can analyze the user's past request history and select the most efficient reception method. This allows for the selection of the most suitable reception method by analyzing the user's past request history. Past request history includes, but is not limited to, the data format and analysis algorithm of past requests. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past request history into an AI model and have the AI ​​select the most suitable reception method.

[0072] The reception desk can filter requests based on the user's current projects and areas of interest when receiving them. For example, the reception desk can prioritize requests related to the user's current projects. For example, the reception desk can filter requests based on the user's current projects and areas of interest. The reception desk can also filter and receive relevant requests based on the user's areas of interest. For example, the reception desk can receive the most relevant requests according to the progress of the user's current projects. This allows for the priority of receiving highly relevant requests by filtering requests based on the user's current projects and areas of interest. Current projects include, but are not limited to, the type of project and filtering criteria. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input data from the user's current projects into an AI model and have the AI ​​perform the filtering.

[0073] The reception desk can estimate the user's emotions and determine the priority of requests based on those emotions. For example, if the user is stressed, the reception desk may postpone less important requests. For example, the reception desk may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception desk may also prioritize requests that are of high importance if the user is in a hurry. For example, the reception desk may record the user's voice and estimate their emotions using voice analysis technology. The reception desk may also accept requests with normal priority if the user is relaxed. For example, the reception desk may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the reception desk to prioritize requests according to the user's emotions, thereby prioritizing important requests. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0074] The reception desk can prioritize requests based on their relevance, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize requests related to that region. The reception desk can also filter and accept the most relevant requests based on the user's geographical location. For example, if the user is on the move, the reception desk can prioritize requests related to their current location. This allows for prioritizing requests based on the user's geographical location. Geographical location information includes, but is not limited to, methods for prioritizing requests based on the user's location. 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 geographical location into an AI model and have the AI ​​determine the priority of requests.

[0075] The reception desk can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception desk can prioritize requests related to the user's current interests based on their social media activity. For example, the reception desk can analyze the user's social media activity and accept relevant requests. The reception desk can also analyze the content of the user's social media posts and filter and accept relevant requests. For example, the reception desk can refer to the activities of the user's social media followers and friends to accept relevant requests. This allows the reception desk to prioritize requests by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and comments. 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 social media activity data into an AI model and have the AI ​​perform the request filtering.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will use a simple and easy-to-understand presentation. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also use a presentation that includes detailed information if the user is relaxed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. The analysis unit can also use a concise presentation that gets straight to the point if the user is in a hurry. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the analysis to be presented in a way that is appropriate to the user's emotions, thereby providing more accurate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0077] The analytics department can adjust the level of detail of its analysis based on the importance of the consumer. For example, it can provide detailed analysis results to important consumers. For example, it can identify important consumers based on their purchase frequency or customer rank. The analytics department can also provide standard analysis results to general consumers. For example, it can provide standard analysis results based on consumers' purchase history data. The analytics department can also provide concise analysis results to less important consumers. For example, it can provide concise analysis results based on consumers' behavioral data. This allows for the provision of detailed analysis results to important consumers by adjusting the level of detail based on the importance of the consumer. Consumer importance includes, but is not limited to, purchase frequency and customer rank. Some or all of the above processes in the analytics department may be performed using, for example, AI, or not using AI. For example, the analytics department can input consumer importance data into an AI model and have the AI ​​adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analytical algorithms depending on the consumer category during analysis. For example, the analysis unit can perform a detailed purchasing behavior analysis for consumers who purchase expensive goods. For example, the analysis unit can perform a detailed purchasing behavior analysis based on the consumer's purchase history data. The analysis unit can also perform a standard purchasing behavior analysis for consumers who purchase general goods. For example, the analysis unit can perform a standard purchasing behavior analysis based on the consumer's purchase history data. Furthermore, the analysis unit can apply category-specific analytical algorithms to consumers who purchase products in a specific category. For example, the analysis unit can apply category-specific analytical algorithms based on the consumer's purchase history data. By applying different analytical algorithms depending on the consumer category, more appropriate analytical results can be provided. Consumer categories include, but are not limited to, age groups and purchase history. Analytical algorithms include, but are not limited to, clustering algorithms and regression analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis department can input consumer category data into an AI model and have the AI ​​apply the analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, if the user is relaxed, the analysis unit can provide a longer analysis with more detailed explanations. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. Alternatively, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0080] The analysis department can determine the priority of analysis based on the timing of consumer submissions. For example, the analysis department can prioritize the analysis of urgent submissions. For example, the analysis department can identify urgent submissions based on consumer submission timing data. The analysis department can also set a higher priority for consumers whose submission deadlines are approaching. For example, the analysis department can identify consumers whose submission deadlines are approaching based on consumer submission timing data. The analysis department can also perform analysis with normal priority for consumers whose submission deadlines are far off. For example, the analysis department can identify consumers whose submission deadlines are far off based on consumer submission timing data. This allows for priority analysis of urgent submissions by determining the priority of analysis based on consumer submission timing. Consumer submission timing includes, but is not limited to, submission date and time, and submission frequency. Some or all of the above processing in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can input consumer submission timing data into an AI model and have the AI ​​determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on consumer relevance. For example, the analysis unit may perform analysis on important consumers first. For example, the analysis unit can identify important consumers based on consumer relevance data. The analysis unit can also perform analysis on general consumers in a standard order. For example, the analysis unit can identify general consumers based on consumer relevance data. The analysis unit can also postpone analysis on less relevant consumers. For example, the analysis unit can identify less relevant consumers based on consumer relevance data. This allows for prioritizing analysis on important consumers by adjusting the order of analysis based on consumer relevance. Consumer relevance includes, but is not limited to, highly relevant consumers and less relevant consumers. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input consumer relevance data into an AI model and have the AI ​​adjust the order of analysis.

[0082] The generation unit can estimate the user's emotions and adjust the way the generated design is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a design that progresses at a relaxed pace. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Also, if the user is in a hurry, the generation unit can generate a design that emphasizes the shortest route. For example, the generation unit can record the user's voice and estimate their emotions using voice analysis technology. Also, if the user is excited, the generation unit can generate a design with visually stimulating effects. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the generation of more appropriate designs by adjusting the way the design is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0083] The generation unit can adjust the level of detail of the generated designs based on their importance. For example, the generation unit can generate detailed designs for important designs. For example, the generation unit can identify important designs based on design importance data. The generation unit can also generate standard designs for general designs. For example, the generation unit can identify general designs based on design importance data. The generation unit can also generate concise designs for less important designs. For example, the generation unit can identify less important designs based on design importance data. This allows for the generation of detailed designs for important designs by adjusting the level of detail based on design importance. Design importance includes, but is not limited to, evaluation criteria for importance based on user requests. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input design importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the generated designs.

[0084] The generation unit can apply different generation algorithms depending on the design category during generation. For example, the generation unit can apply a detailed design algorithm to designs for expensive products. For example, the generation unit can apply a design algorithm for expensive products based on design category data. The generation unit can also apply a standard design algorithm to designs for general products. For example, the generation unit can apply a design algorithm for general products based on design category data. The generation unit can also apply a design algorithm specific to a particular category of product. For example, the generation unit can apply a design algorithm specific to a particular category based on design category data. By applying different generation algorithms depending on the design category, more appropriate designs can be generated. Design categories include, but are not limited to, web design and graphic design. Generation algorithms include, but are not limited to, genetic algorithms and neural networks. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input design category data into an AI model and have the AI ​​perform the application of the generation algorithm.

[0085] The generation unit can estimate the user's emotions and adjust the length of the generated design based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise design. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also generate a longer design with detailed explanations if the user is relaxed. For example, the generation unit can record the user's voice and estimate their emotions using voice analysis technology. The generation unit can also generate a design with visually stimulating effects if the user is excited. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the generation of more appropriate designs by adjusting the length of the design according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0086] The generation unit can determine the generation priority based on the design submission timing during generation. For example, the generation unit can prioritize the generation of urgent designs. For example, the generation unit can identify urgent designs based on design submission timing data. The generation unit can also set a higher priority for designs with approaching submission deadlines. For example, the generation unit can identify designs with approaching submission deadlines based on design submission timing data. The generation unit can also generate designs with distant submission deadlines with normal priority. For example, the generation unit can identify designs with distant submission deadlines based on design submission timing data. This allows for priority generation of urgent designs by determining the generation priority based on the design submission timing. The design submission timing includes, but is not limited to, the submission date and time, and submission frequency. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input design submission timing data into an AI model and have the AI ​​determine the generation priority.

[0087] The generation unit can adjust the generation order based on the relevance of the designs during generation. For example, the generation unit may generate important designs first. For example, the generation unit can identify important designs based on design relevance data. The generation unit can also generate general designs in a standard order. For example, the generation unit can identify general designs based on design relevance data. The generation unit can also postpone the generation of less relevant designs. For example, the generation unit can identify less relevant designs based on design relevance data. This allows for priority generation of important designs by adjusting the generation order based on design relevance. Design relevance includes, but is not limited to, highly relevant and less relevant designs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input design relevance data into an AI model and have the AI ​​adjust the generation order.

[0088] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated emotions. For example, if the user is stressed, the improvement unit can suggest a simple and easy-to-understand improvement method. For example, the improvement unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The improvement unit can also suggest a detailed improvement method if the user is relaxed. For example, the improvement unit can record the user's voice and estimate the emotion using voice analysis technology. The improvement unit can also suggest a method for quick improvement if the user is in a hurry. For example, the improvement unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows the improvement unit to provide a more appropriate improvement method by adjusting the improvement 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0089] The improvement unit can select the optimal improvement method by analyzing past user feedback during the improvement process. For example, the improvement unit can propose the optimal improvement method based on feedback previously provided by the user. For example, the improvement unit can select the optimal improvement method based on past user feedback data. The improvement unit can also select a method to improve a specific problem from past user feedback. For example, the improvement unit can select a method to improve a specific problem based on past user feedback data. The improvement unit can also analyze past user feedback and select the most effective improvement method. For example, the improvement unit can select the most effective improvement method based on past user feedback data. This allows the optimal improvement method to be selected by analyzing past user feedback. Past feedback includes, but is not limited to, the data format of the feedback and the analysis algorithm. Some or all of the above-described processes in the improvement unit may be performed using, for example, AI, or not using AI. For example, the improvement unit can input past user feedback data into an AI model and have the AI ​​select the improvement method.

[0090] The improvement unit can customize the means of improvement based on the user's current living situation when making improvements. For example, if the user is busy, the improvement unit can suggest improvement measures that can be implemented in a short amount of time. For example, the improvement unit can suggest improvement measures that can be implemented in a short amount of time based on the user's current living situation data. The improvement unit can also suggest detailed improvement measures if the user is relaxed. For example, the improvement unit can suggest detailed improvement measures based on the user's current living situation data. Furthermore, if the user is in a specific living situation, the improvement unit can customize and suggest improvement measures that are best suited to that situation. For example, the improvement unit can customize and suggest improvement measures that are best suited to that situation based on the user's current living situation data. This allows for the provision of more appropriate improvement measures by customizing the means of improvement based on the user's current living situation. Current living situation includes, but is not limited to, living environment and lifestyle. Some or all of the above processing in the improvement unit may be performed using, for example, AI, or not using AI. For example, the improvement unit can input the user's current living situation data into an AI model and have the AI ​​perform the customization of improvement measures.

[0091] The improvement unit can estimate the user's emotions and determine the priority of improvements based on those emotions. For example, if the user is stressed, the improvement unit will postpone improvements of lower importance. For example, the improvement unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The improvement unit can also prioritize high-priority improvements if the user is in a hurry. For example, the improvement unit can record the user's voice and estimate their emotions using voice analysis technology. The improvement unit can also perform improvements in the usual priority order if the user is relaxed. For example, the improvement unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the improvement unit to prioritize important improvements by determining the priority of improvements 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0092] The improvement unit can select the optimal improvement method when making improvements, taking into account the user's geographical location information. For example, if the user is in a specific region, the improvement unit can suggest improvement methods related to that region. For example, the improvement unit can select improvement methods related to a region based on the user's geographical location information. The improvement unit can also select the optimal improvement method based on the user's geographical location information. For example, if the user is on the move, the improvement unit can suggest improvement methods related to the user's current location. This allows the optimal improvement method to be selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, selection criteria for improvement methods based on the user's location information. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the user's geographical location information into an AI model and have the AI ​​perform the selection of improvement methods.

[0093] The improvement unit can analyze the user's social media activity and propose improvement measures during the improvement process. For example, the improvement unit can propose improvement measures related to the user's current interests based on the user's social media activity. For example, the improvement unit can analyze the user's social media activity and propose improvement measures related to the user's current interests. The improvement unit can also analyze the content of the user's social media posts and propose relevant improvement measures. For example, the improvement unit can refer to the activities of the user's social media followers and friends and propose relevant improvement measures. In this way, relevant improvement measures can be proposed by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and comments. Some or all of the above processing in the improvement unit may be performed using, for example, AI, or not using AI. For example, the improvement unit can input the user's social media activity data into an AI model and have the AI ​​execute the proposal of improvement measures.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception desk can analyze a user's past design history and suggest designs based on their preferences. For example, it can analyze the patterns and colors of designs the user has previously selected and suggest similar designs. It can also extract specific design elements from the user's past design history and generate new designs based on them. Furthermore, it can predict the user's preferred design trends based on their past design history and suggest designs based on those trends. This allows for more personalized design suggestions by leveraging the user's past design history.

[0096] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis based on those estimates. For example, if the user is stressed, the analysis unit provides a concise and to-the-point analysis. If the user is relaxed, the analysis unit can also provide an analysis with more detailed information. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually appealing effects. By adjusting the level of detail in the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0097] The generation unit can propose designs based on the user's current project progress. For example, in the early stages of a project, the generation unit can propose designs suitable for idea generation and brainstorming. In the middle stages of the project, it can also propose specific design proposals. Furthermore, in the final stages of the project, it can propose designs suitable for final adjustments and finishing touches. This allows for appropriate design proposals according to the project's progress.

[0098] The improvement unit can estimate the user's emotions and adjust the feedback method for improvement based on those emotions. For example, if the user is stressed, the improvement unit will provide simple and easy-to-understand feedback. If the user is relaxed, it can provide more detailed feedback. Furthermore, if the user is in a hurry, it can suggest ways to improve quickly. In this way, by adjusting the feedback method according to the user's emotions, more appropriate improvement feedback can be provided.

[0099] The reception desk can take the user's geographical location into consideration to provide region-specific design suggestions. For example, if a user is in a specific region, it can suggest designs based on the culture and trends of that region. If a user is traveling, it can also suggest designs related to the region they are visiting. Furthermore, if a user is attending a specific event, it can suggest designs related to that event. In this way, by utilizing the user's geographical location information, it becomes possible to provide more relevant design suggestions.

[0100] The generation unit can estimate the user's emotions and adjust the design's colors based on those emotions. For example, if the user is relaxed, the generation unit will generate a design with calming colors. If the user is excited, the generation unit can also generate a design with vibrant and energetic colors. Furthermore, if the user is stressed, the generation unit can generate a design with calming and soothing colors. By adjusting the design's colors according to the user's emotions, a more appropriate design can be provided.

[0101] The reception desk can analyze users' social media activity and propose designs based on their interests. For example, it can suggest designs related to topics users frequently mention on social media. It can also suggest designs based on the activity of accounts and groups users follow. Furthermore, it can analyze users' social media posts and suggest designs related to their current interests. This allows for more personalized design suggestions by leveraging users' social media activity.

[0102] The generation unit can estimate the user's emotions and adjust the design layout based on those emotions. For example, if the user is relaxed, the generation unit will generate a relaxed layout design. If the user is in a hurry, the generation unit can also generate a layout design that concisely summarizes the information. Furthermore, if the user is excited, the generation unit can generate a visually stimulating layout design. By adjusting the design layout according to the user's emotions, a more appropriate design can be provided.

[0103] The improvement team can analyze past user feedback and propose improvements based on feedback trends. For example, they can improve the system to prevent similar problems from occurring based on issues users have previously pointed out. They can also incorporate design elements that users have previously given high ratings to. Furthermore, they can extract specific areas for improvement from past user feedback and propose improvements based on those. This allows for more effective improvement proposals by utilizing past user feedback.

[0104] The generator can estimate the user's emotions and adjust the design font based on those emotions. For example, if the user is relaxed, the generator will use a soft-looking font. If the user is in a hurry, the generator can use an easy-to-read and highly visible font. Furthermore, if the user is excited, the generator can use a visually impactful font. This allows for a more appropriate design by adjusting the font according to the user's emotions.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives user requests as input. User requests include text input, voice input, and image input. The reception desk receives the text data entered by the user. It can also convert voice data into text data using speech recognition technology. Furthermore, it can analyze image data using image recognition technology and extract requests. Step 2: The analysis department uses deep learning to analyze consumer preferences based on the information received by the reception department. Consumer preferences include past purchase history, survey results, and social media data. The analysis department inputs this data into a deep learning model to predict consumer preferences. Step 3: The generation unit generates the optimal design based on the analysis results obtained by the analysis unit. The generation unit can generate designs in real time and uses deep learning models to generate designs that meet user requirements. Processing in the generation unit may also be performed using generation AI. Step 4: The improvement unit collects user feedback based on the design generated by the generation unit, and the AI ​​performs self-improvement. The improvement unit collects user feedback, behavioral data, and evaluation data, analyzes them using natural language processing technology, and extracts areas for improvement.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and improvement unit, is implemented in 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 user requests as input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes consumer preferences using deep learning. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates designs in real time. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and the AI ​​performs self-improvement based on user feedback. 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.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and improvement unit, is implemented in 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 user requests as input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes consumer preferences using deep learning. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates designs in real time. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and the AI ​​performs self-improvement based on user feedback. 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.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and improvement unit, is implemented in 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 user requests as input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes consumer preferences using deep learning. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates designs in real time. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and the AI ​​performs self-improvement based on user feedback. 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.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and improvement 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 user requests as input. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes consumer preferences using deep learning. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates designs in real time. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and the AI ​​performs self-improvement based on user feedback. 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A reception desk that receives user requests as input, Based on the information received by the aforementioned reception department, an analysis department analyzes consumer preferences. A generation unit that generates an optimal design based on the analysis results obtained by the aforementioned analysis unit, The system includes an improvement unit that collects user feedback based on the design generated by the generation unit and uses AI to perform self-improvement. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyzing consumer behavior using deep learning The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate designs in real time The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned improvement unit is, AI performs self-improvement based on user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of requests based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past request history and select the most suitable method of receiving requests. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving requests, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving requests, we prioritize requests 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 10) The aforementioned reception unit is When receiving a request, the system analyzes the user's social media activity and accepts relevant requests. 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 the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During the analysis, adjust the level of detail based on consumer importance. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the consumer category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when consumers submitted their data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on consumer relevance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the way the generated design is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, adjust the level of detail based on the importance of the design. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, different generation algorithms are applied depending on the design category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the length of the generated design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the generation priority is determined based on the design submission date. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, adjust the generation order based on the relevance of the design. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned improvement unit is, It estimates user sentiment and adjusts improvement methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned improvement unit is, When making improvements, we analyze past user feedback to select the most suitable improvement method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned improvement unit is, When making improvements, customize the methods of improvement based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned improvement unit is, We estimate user emotions and determine improvement priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned improvement unit is, When making improvements, the optimal improvement method will be selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned improvement unit is, During the improvement process, we analyze users' social media activity and propose ways to make improvements. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 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 user requests as input, Based on the information received by the aforementioned reception department, an analysis department analyzes consumer preferences. A generation unit that generates an optimal design based on the analysis results obtained by the aforementioned analysis unit, The system includes an improvement unit that collects user feedback based on the design generated by the generation unit and uses AI to perform self-improvement. A system characterized by the following features.

2. The aforementioned analysis unit is Analyzing consumer behavior using deep learning The system according to feature 1.

3. The generating unit is Generate designs in real time The system according to feature 1.

4. The aforementioned improvement unit is, AI performs self-improvement based on user feedback. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of requests based on those emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past request history and select the most suitable method of receiving requests. The system according to feature 1.

7. The aforementioned reception unit is When receiving requests, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving requests, we prioritize requests that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When receiving a request, the system analyzes the user's social media activity and accepts relevant requests. The system according to feature 1.

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