System

Abstract

The present invention provides a system that detects in advance an idea that is difficult to realize with current generation and AI technology, and provides appropriate feedback to a user.SOLUTION: A system comprising: a user interface means for inputting an idea utilizing generated AI from a user; a communication means for transmitting the input idea to a server; an analysis means for analyzing the idea received by the server; an extraction means for extracting main features of the idea; an evaluation means for evaluating technical feasibility based on the extracted features; and a display means for displaying the received idea to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, many tools using generative AI technology have been developed, but some of these ideas do not function sufficiently with the accuracy of current generative AI. As a result, even when users propose new ideas using generative AI, it is difficult to accurately evaluate in advance whether the ideas are feasible. As a result, technically unrealistic ideas often result in wasted development costs and wasted resources. The purpose of this invention is to provide a system that can detect ideas that are difficult to realize with current generative AI technology in advance and provide appropriate feedback to users. [Means for solving the problem]

[0005] The present invention solves the above problem with a system that includes a user interface means for inputting ideas that utilize generative AI from a user, a communication means for transmitting the input idea to a server, an analysis means for analyzing the idea received by the server using natural language processing technology, an extraction means for extracting key features of the idea from the analyzed data, an evaluation means for evaluating the technical feasibility based on the extracted features, a generation means for generating a feedback message including the evaluation results, a transmission means for transmitting the generated feedback message to the user, and a display means for displaying the received feedback message to the user.This system allows users to check in advance whether their ideas are feasible with current generative AI technology, preventing the waste of development resources on technically unrealistic ideas.

[0006] "User interface means" refers to a means for providing an interface for users to input ideas that utilize generative AI.

[0007] The "communication means" is a means for transmitting the input idea from the user interface means to the server.

[0008] The "analysis means" is a means for analyzing ideas received by the server using natural language processing technology.

[0009] The "extraction means" is a means for extracting the main features of an idea from the data analyzed by the analysis means.

[0010] The "evaluation means" is a means for evaluating technical feasibility based on the features extracted by the extraction means.

[0011] The "generating means" is a means for generating a feedback message including the evaluation result obtained by the evaluating means.

[0012] The "transmission means" is a means for transmitting the generated feedback message to the user.

[0013] The "display means" is a means for displaying the received feedback message to the user. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] This invention relates to a system that uses generative AI to evaluate the technical feasibility of ideas. This system involves a series of processes: idea input from users, idea analysis and evaluation by a server, and feedback of the results.

[0036] The user uses a terminal to input a new idea that utilizes generative AI. For example, a specific example could be, "This idea is a system that automatically generates poetry and then creates art based on that poetry." The idea input through the user interface means is sent to the server via communication means.

[0037] The server analyzes the received ideas using natural language processing technology. This analysis uses methods such as morphological analysis and topic modeling to extract key features contained in the ideas. For example, it can extract that the ideas contain the features of "poetry generation" and "art generation."

[0038] Next, the server evaluates the technical feasibility based on the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generative AI technology. For example, it can be evaluated that generating poetry is possible with current technology, but automatically generating high-quality art from poetry is difficult.

[0039] Based on the evaluation results, the server generates a feedback message, such as "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology."

[0040] The generated feedback message is sent to the user's terminal using the transmission means and displayed to the user by the display means. This allows the user to check the technical feasibility of their idea in advance. This system makes it possible to prevent unnecessary development costs and wasted resources due to ideas that are technically difficult to realize.

[0041] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback to users based on the evaluation.

[0042] The processing flow will be explained below.

[0043] Step 1:

[0044] The user inputs ideas using generative AI through the device.

[0045] The terminal provides an idea input field through a user interface means, and the user inputs "This idea is a system that automatically generates poetry and creates art based on that poetry."

[0046] Step 2:

[0047] The terminal transmits the idea input by the user to the server.

[0048] Using the communication means, the terminal sends the input idea to the server as an HTTP POST request.

[0049] Step 3:

[0050] The server receives the idea sent from the terminal and begins analyzing it using natural language processing technology.

[0051] The server analyzes the idea text using techniques such as morphological analysis and topic modeling to extract important features.

[0052] Step 4:

[0053] The server extracts key features of the idea from the analyzed data.

[0054] The server extracts key features such as "poetry generation" and "art generation."

[0055] Step 5:

[0056] The technical feasibility is evaluated based on the features extracted by the server.

[0057] The server compares each extracted feature with current generative AI technology to assess its feasibility. Specifically, it determines that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[0058] Step 6:

[0059] The server generates a feedback message based on the evaluation results.

[0060] Based on the information obtained by the evaluation means, the server generates a feedback message stating, "Poetry generation is possible with current technology, but automatically generating high-quality art from poetry is technically difficult."

[0061] Step 7:

[0062] The server generates a feedback message and sends it to the user's terminal.

[0063] The server transmits the generated message as an HTTP response to the terminal via the communication means.

[0064] Step 8:

[0065] The terminal displays the received feedback message to the user.

[0066] The device displays the received feedback message on the screen using a display means and conveys it to the user, allowing the user to confirm whether their idea is feasible with current generative AI technology.

[0067] Example 1

[0068] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0069] There is a need to quickly and accurately evaluate the technical feasibility of ideas using generative AI and provide feedback to users. However, with current technology, the evaluation process is often done manually, which is time-consuming and labor-intensive, and the evaluation results lack consistency and accuracy. This leads to the problem of wasting resources and wasting development costs on ideas that are technically difficult to realize.

[0070] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0071] In this invention, the server includes an input means for inputting an idea utilizing a generative AI from a user, a transmission means for transmitting the input idea to the server, an analysis means for analyzing the idea received by the server using natural language processing technology, a feature extraction means for extracting key features of the idea from the analyzed data, an evaluation means for evaluating the technical feasibility based on the extracted features, a generation means for generating a feedback message including the evaluation result, a transmission means for transmitting the generated feedback message to the user, and a display means for displaying the received feedback message to the user. This makes it possible to quickly and accurately evaluate the technical feasibility of an idea and provide the result to the user.

[0072] The "input means" is an interface through which users can input ideas that utilize generative AI.

[0073] The "transmission means" is a means for transmitting the input idea from the terminal to the server.

[0074] The "analysis means" is a means for analyzing ideas received by the server using natural language processing technology.

[0075] The "feature extraction means" is a means for extracting the main features of an idea from the analyzed data.

[0076] The "evaluation means" is a means for evaluating technical feasibility based on the extracted features.

[0077] The "generating means" is a means for generating a feedback message including the evaluation result.

[0078] The "transmitting means" is a means for transmitting the generated feedback message to the user's terminal.

[0079] The "display means" is a means for displaying the received feedback message on the user's terminal.

[0080] "Natural language processing technology" is a technology for analyzing human language and understanding its structure and meaning.

[0081] "Morphological analysis" is a method of dividing a sentence into the smallest meaningful units and analyzing the part of speech of each word.

[0082] "Topic modeling" is a method for automatically extracting topics (themes) from large amounts of text data.

[0083] "Technological feasibility" is an indicator that determines whether a proposed idea can be realized using current technology.

[0084] A "feedback message" is a message that provides the user with information about technical feasibility based on the evaluation results.

[0085] MODE FOR CARRYING OUT THE INVENTION

[0086] This invention relates to a system that uses generative AI to evaluate the technical feasibility of ideas. This system involves a series of processes: idea input from users, idea analysis and evaluation by a server, and feedback of the results.

[0087] The user uses the device to input a new idea that utilizes generative AI. Specifically, the user inputs a prompt statement such as "This is a system that automatically generates poetry and creates art based on that poetry" through the device's user interface. The input idea is sent to the server using the device's transmission means.

[0088] The server analyzes the received ideas using natural language processing techniques, such as morphological analysis and topic modeling. Typically, open-source NLP libraries such as spaCy and NLTK are used for morphological analysis. This allows the server to extract key features, such as "poetry generation" or "art generation."

[0089] Next, the server evaluates the technical feasibility based on the features extracted using the feature extraction method. This evaluation method refers to the latest information and technical papers on current generative AI technology. For example, it may evaluate that existing generative AI models are available for generating poetry, but that it is difficult to generate high-quality art from poetry.

[0090] Based on the evaluation results, the server generates a feedback message such as, "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology."

[0091] The generated feedback message is sent back to the user's terminal using the server's transmission means, and the terminal displays the received feedback message to the user, allowing the user to confirm the technical feasibility of their idea in advance.

[0092] This system makes it possible to prevent unnecessary development costs and wasted resources by first evaluating ideas that are technically difficult to realize. As a concrete example, a user can input a prompt such as "This is a system that automatically generates poetry and creates art based on that poetry," and receive the feedback that "Generating poetry is possible, but generating art is difficult with current technology."

[0093] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback to users based on the evaluation.

[0094] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0095] Program processing steps

[0096] Step 1:

[0097] The user inputs their idea using the user interface on the device, for example, by entering a prompt such as "This is a system that automatically generates poetry and then creates art based on that poetry." This input data is prepared for transmission to the next processing step.

[0098] Step 2:

[0099] The device sends the input idea to the server. Specifically, it sends a prompt to the server using an HTTP request. The input is "This is a system that automatically generates poetry and creates art based on that poetry." The output is text data sent to the server.

[0100] Step 3:

[0101] The server analyzes the received ideas using natural language processing technology. Specifically, it performs morphological analysis, divides the sentence into words, and determines the part of speech of each word. The input is the sent prompt sentence, and the output is the analyzed word list and its part of speech information.

[0102] Step 4:

[0103] The server extracts key features of ideas based on the analysis results. Topic modeling is used to identify key topics, such as "poetry generation" and "art generation." The input is a list of words from the analysis results, and the output is a list of key features.

[0104] Step 5:

[0105] The server evaluates the technical feasibility based on the key features. This evaluation method refers to current generative AI technology to determine the feasibility of each feature. For example, it may evaluate that "generating poetry is possible, but generating art is difficult." The input is a list of features, and the output is the feasibility evaluation result.

[0106] Step 6:

[0107] The server generates a feedback message based on the evaluation results. Specifically, the message generated is, "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology." The input is the evaluation results, and the output is the feedback message.

[0108] Step 7:

[0109] The server sends the generated feedback message to the terminal. Specifically, it sends the feedback message to the terminal using an HTTP response. The input is the generated feedback message, and the output is the message sent to the terminal.

[0110] Step 8:

[0111] The device displays the received feedback message to the user. Specifically, the feedback message is displayed on the user interface. The message displayed is, "Current technology can generate poetry, but it is difficult to automatically generate high-quality art from poetry." The input is the feedback message sent to the device, and the output is the message displayed to the user.

[0112] In this way, specific data processing and calculations are carried out at each step, and users can receive feedback on the technical feasibility of their ideas.

[0113] (Application example 1)

[0114] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0115] Modern virtual stores are powered by many new ideas, but there is a lack of means to assess in advance whether these ideas are actually technically feasible. As a result, a great deal of time and money is often wasted on ideas that are not feasible. Furthermore, there is a growing demand for a system that utilizes generative AI to quickly assess the technical feasibility of innovative ideas and provide appropriate feedback.

[0116] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0117] In this invention, the server includes a user interface means for inputting ideas utilizing a generation AI from a user, a communication means for transmitting the input ideas to the server, an analysis means for analyzing the ideas received by the server using natural language processing technology, an extraction means for extracting key features of the ideas from the analyzed data, an evaluation means for evaluating the technical feasibility based on the extracted features, a generation means for generating a feedback message including the evaluation results, a transmission means for transmitting the generated feedback message to the user, a display means for displaying the received feedback message to the user, and a means for evaluating the technical feasibility of ideas related to the virtual store. This makes it possible to quickly evaluate the technical feasibility of new ideas for the virtual store and prevent unnecessary waste of resources.

[0118] "User interface means" refers to an interface through which a user can input ideas that utilize the generation AI.

[0119] "Communication means" is a means for transmitting the input idea to the server.

[0120] The "analysis means" is a means for analyzing ideas received by the server using natural language processing technology.

[0121] "Extraction means" refers to means for extracting the main features of ideas from the analyzed data.

[0122] The "evaluation means" is a means for evaluating technical feasibility based on the extracted features.

[0123] The "generating means" is a means for generating a feedback message including the evaluation result.

[0124] The "transmission means" is a means for transmitting the generated feedback message to the user.

[0125] The "display means" is a means for displaying the received feedback message to the user.

[0126] The "means for assessing the technical feasibility of ideas related to virtual stores" is a specialized means for assessing the technical feasibility of new ideas in virtual stores.

[0127] MODE FOR CARRYING OUT THE INVENTION

[0128] This invention is a system that utilizes generative AI to evaluate the technical feasibility of new ideas related to virtual stores and provides efficient feedback. The specific configuration and operation of this system will be described below.

[0129] 1. User Interface Methods

[0130] Managers and staff of virtual stores input new ideas related to the virtual store through applications on smartphones or desktop PCs. The user interface means is an interface for inputting ideas using generative AI, and provides easy-to-understand text boxes and selection menus.

[0131] 2. Sending and Receiving Input

[0132] The idea input by the user is transmitted to the server via the communication means, and the server receives this data and analyzes the idea using the analysis means.

[0133] 3. Analysis using natural language processing technology

[0134] The server uses generative AI models such as OpenAI's GPT-3 to analyze the received ideas using natural language processing techniques, such as morphological analysis and topic modeling, to extract key features contained in the ideas.

[0135] 4. Technical feasibility assessment

[0136] Based on the extracted features, the server evaluates the technical feasibility. The evaluation method determines whether each feature is technically feasible in light of the current state of generative AI technology. This evaluation also considers technical resources and specific implementation methods.

[0137] 5. Generating and Sending Feedback Messages

[0138] Based on the evaluation results, the server generates a feedback message. The generation means creates a specific message such as "This idea is feasible with current technology" or "This part is technically difficult to realize." The generated feedback message is sent to the user's terminal using the transmission means.

[0139] 6. Viewing Feedback

[0140] The received feedback messages are displayed on the user's device, allowing the user to quickly check whether their idea is technically feasible and to carry out efficient planning.

[0141] Specific examples

[0142] A virtual store manager inputs an idea for automatically generating new product displays for spring. This input is expressed as a prompt sentence like this:

[0143] text

[0144] Evaluate the technical feasibility of this idea: A system for automatically changing merchandise displays seasonally in a virtual store.

[0145] The server receives this input, analyzes it using the GPT-3 model, evaluates the technical feasibility, and provides specific feedback, such as "Automatic generation is technically possible, but product data needs to be updated."

[0146] Hardware and software used

[0147] The server can be built using a desktop PC or a cloud computing service (such as AWS or Google Cloud). The software used is based on Python programming and uses OpenAI's GPT-3 API for natural language processing. An internet connection is required for data communication.

[0148] As a result, a system is realized that enables virtual store managers to quickly evaluate the technical feasibility of new ideas and prevent unnecessary waste of resources.

[0149] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0150] Step 1:

[0151] A user inputs a new idea related to a virtual store using generative AI into an application on a smartphone or desktop PC. For example, the user inputs "an idea for automatically generating new product displays for spring." This input is entered into a text box and processed using a user interface means.

[0152] Input: User-entered ideas

[0153] Output: Input idea text

[0154] Step 2:

[0155] The terminal transmits the idea input by the user to the server via the communication means, and the input idea is transmitted to the server via the network.

[0156] Input: Entered idea text

[0157] Output: Idea data sent to the server

[0158] Step 3:

[0159] The server analyzes the idea data received via the communication means using an analysis means. Specifically, it uses a generative AI model such as OpenAI's GPT-3 to analyze the idea text through morphological analysis and topic modeling.

[0160] Input: Idea data sent to the server

[0161] Output: Key features of the idea

[0162] Step 4:

[0163] The server uses extraction means to extract key features of ideas from the analyzed data, such as phrases segmented by morphological analysis and topics obtained by topic modeling.

[0164] Input: Parsed idea data

[0165] Output: Extracted key features

[0166] Step 5:

[0167] The server evaluates the technical feasibility based on the extracted features. The evaluation method determines whether each feature is technically feasible in light of the current state of generative AI technology. This evaluation takes into account technical resources and specific implementation methods.

[0168] Input: Extracted key features

[0169] Output: Technical feasibility assessment results

[0170] Step 6:

[0171] The server generates a feedback message based on the evaluation results. A specific feedback message is created by the generation means. The generated message may be something like "This idea is feasible with current technology" or "This part is technically difficult to achieve."

[0172] Input: Technical feasibility assessment results

[0173] Output: Feedback message

[0174] Step 7:

[0175] The server transmits the generated feedback message to the user's terminal via the transmission means, and the feedback message is sent to the user's terminal through the network.

[0176] Input: Feedback message

[0177] Output: Feedback message sent to the terminal

[0178] Step 8:

[0179] The user's terminal displays the received feedback message to the user using a display means, thereby enabling the user to quickly check whether or not their idea is technically feasible.

[0180] Input: Feedback message sent to the device

[0181] Output: The feedback message displayed to the user

[0182] The above steps realize a system that evaluates the technical feasibility of new virtual store ideas and provides efficient feedback.

[0183] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0184] This invention relates to a system that evaluates the technical feasibility of ideas using generative AI, recognizes the user's emotions, and adjusts the content of feedback messages based on those emotions. This system includes a series of processes: idea input from the user, idea analysis and evaluation by the server, emotion recognition, and feedback of the evaluation results.

[0185] The user uses a terminal to input a new idea that utilizes generative AI. For example, a specific example could be, "This idea is a system that automatically generates poetry and then creates art based on that poetry." The idea input through the user interface means is sent to the server via communication means.

[0186] The server analyzes the received ideas using natural language processing techniques, such as morphological analysis and topic modeling, to extract key features from the ideas. For example, it can extract that the ideas contain the features of "poetry generation" and "art generation."

[0187] Next, the server evaluates the technical feasibility based on the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generative AI technology. For example, it may determine that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[0188] Based on the evaluation results, the server generates a feedback message, such as "Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[0189] Furthermore, the present invention includes an emotion recognition means. This emotion recognition means recognizes the user's emotion input through the user interface and analyzes it using natural language processing technology and machine learning algorithms. For example, it can determine the user's emotion from the sentences and input speed when the user inputs ideas, specific keywords, etc.

[0190] Based on the user's perceived emotions, the evaluator can adjust the feedback message accordingly. For example, if the user is very enthusiastic, the evaluator can provide positive feedback such as, "That's a very interesting idea. Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[0191] The generated feedback message is sent to the user's device using a transmission means and displayed to the user using a display means. This allows the user to confirm whether their idea is feasible with current generative AI technology, and also realize that the feedback content takes their feelings into consideration. This system makes it possible to prevent unnecessary development costs and resource wastage due to ideas that are technically difficult to realize, while also maintaining user motivation.

[0192] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback based on the evaluation while taking into consideration the user's feelings.

[0193] The processing flow will be explained below.

[0194] Step 1:

[0195] The user inputs ideas using generative AI through the device.

[0196] The terminal provides an idea input field through a user interface means, and the user inputs "This idea is a system that automatically generates poetry and creates art based on that poetry."

[0197] Step 2:

[0198] The terminal transmits the idea input by the user to the server.

[0199] Using the communication means, the terminal sends the input idea to the server as an HTTP POST request.

[0200] Step 3:

[0201] The server receives the idea sent from the terminal and begins analyzing it using natural language processing technology.

[0202] The server analyzes the idea text using techniques such as morphological analysis and topic modeling to extract important features.

[0203] Step 4:

[0204] The server extracts key features of the idea from the analyzed data.

[0205] The server extracts key features such as "poetry generation" and "art generation."

[0206] Step 5:

[0207] The technical feasibility is evaluated based on the features extracted by the server.

[0208] The server compares each extracted feature with current generative AI technology to assess its feasibility. Specifically, it determines that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[0209] Step 6:

[0210] The server analyzes the user's emotions using an emotion recognition means.

[0211] The server uses emotion recognition to analyze the text entered by the user, the input speed, specific keywords, etc., and determines the user's emotion. For example, if the user enters an idea with enthusiasm, the server recognizes the emotion as "positive."

[0212] Step 7:

[0213] The server generates a feedback message based on the evaluation results and the perceived user sentiment.

[0214] The server bases its message on the basic message, "Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry," and if the user's sentiment is positive, adds a positive comment such as, "That's a very interesting idea."

[0215] Step 8:

[0216] The server generates a feedback message and sends it to the user's terminal.

[0217] The server transmits the generated message as an HTTP response to the terminal via the communication means.

[0218] Step 9:

[0219] The terminal displays the received feedback message to the user.

[0220] The device displays the received feedback message on the screen and conveys it to the user, allowing the user to confirm whether their idea is feasible with current generative AI technology and to feel that the feedback content takes their feelings into consideration.

[0221] This series of steps allows users to confirm the technical feasibility of their ideas in advance and maintain their motivation by receiving emotionally sensitive feedback.

[0222] Example 2

[0223] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0224] In conventional systems, feedback is not provided that takes into account the user's emotions when inputting ideas and evaluating them, making it difficult to maintain user motivation. Also, evaluations of whether an idea is technically feasible may not meet the user's expectations, resulting in incomplete feedback.

[0225] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting a proposal utilizing a generation technology from a user, a communication means for transmitting the input proposal to the processing device, an analysis means for analyzing the proposal received by the processing device using natural language processing technology, an extraction means for extracting main features of the proposal from the analyzed data, an evaluation means for evaluating technical feasibility based on the extracted features, a generation means for generating a response sentence including the evaluation result, a transmission means for transmitting the generated response sentence to the user, a display means for displaying the received response sentence to the user, and further, a recognition means for recognizing the user's emotions and an adjustment means for adjusting the content of the response sentence based on the recognized emotions. This makes it possible to provide feedback that takes the user's emotions into consideration, thereby increasing the user's motivation and accurately evaluating technical feasibility.

[0226] A "user" is an entity that uses this system to input suggestions.

[0227] "Generative technology" is a technology that uses artificial intelligence and machine learning to generate new ideas and data.

[0228] "Suggestion" refers to an idea or concept that a user creates using generative technology and inputs into the system.

[0229] "Input means" refers to an interface through which a user inputs suggestions into the system.

[0230] "Communication means" refers to a means for transmitting a suggestion input by the input means to the server.

[0231] "Processing device" refers to a server or computing unit that receives, analyzes, and evaluates the suggestions.

[0232] "Analysis means" refers to a function for analyzing received proposals using natural language processing technology.

[0233] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0234] "Extraction means" refers to a function for extracting key features of a proposal from the analyzed data.

[0235] "Evaluation means" refers to a function for evaluating the technical feasibility of a proposal based on the extracted features.

[0236] "Generation means" refers to a function for generating a response sentence based on the evaluation result.

[0237] "Transmission means" refers to a function for transmitting the generated response sentence to the user.

[0238] The "display means" refers to an interface for displaying the received response sentence to the user.

[0239] "Recognition means" refers to a function for recognizing the user's emotions.

[0240] "Adjustment means" refers to a function for adjusting the content of a response sentence based on the recognized emotion.

[0241] This invention relates to a system that utilizes generation technology to evaluate the technical feasibility of ideas, recognizes user emotions, and adjusts the content of a response message based on those emotions. This system includes a series of processes: user input of a proposal, analysis and evaluation of the proposal by a server, emotion recognition, and generation and transmission of a response message based on the evaluation results.

[0242] The user inputs a new proposal using the generation technology using a terminal. For example, a specific example could be, "This is a system that automatically generates poetry and then creates art based on that poetry." The proposal input through the user interface means is sent to the server via the communication means.

[0243] The server analyzes the received proposals using natural language processing technology. This analysis involves techniques such as morphological analysis and topic modeling to extract key features contained in the proposals. Natural language processing libraries such as NLTK and spaCy, and morphological analysis tools such as MeCab are used for the analysis. For example, it can be extracted that the proposals contain the features of "poetry generation" and "art generation."

[0244] Next, the server evaluates the technical feasibility of the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generation technology. The evaluation includes consulting academic paper databases and technology review articles. For example, the server determines that while poetry generation is possible with current technology, it is difficult to automatically generate high-quality art from poetry.

[0245] Based on the evaluation results, the server generates a response using a generative AI model such as GPT-3. For example, it could create a specific message such as, "That's a very interesting idea. While generating poetry is possible with current technology, automatically generating high-quality art from poetry is technically difficult."

[0246] Furthermore, the present invention includes an emotion recognition unit that recognizes the user's emotion input through the user interface and analyzes it using natural language processing technology and machine learning algorithms. For example, the emotion of the user can be determined from the sentences, input speed, specific keywords, etc., when the user inputs a suggestion.

[0247] Based on the user's emotions, the evaluator can adjust the content of the response. For example, if the user is very enthusiastic, the evaluator can provide positive feedback such as, "That's a very interesting idea. Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[0248] The generated response sentence is sent to the user's terminal using the transmission means and displayed to the user by the display means. This allows the user to confirm whether their proposal is feasible with current generation technology and to realize that the response sentence takes their feelings into consideration. This system prevents unnecessary development costs and resource wastage due to proposals that are technically difficult to realize, while also maintaining user motivation.

[0249] Specific examples

[0250] Here is an example prompt:

[0251] Example: A prompt that the user types into the terminal:

[0252] "It's a system that automatically generates poetry and then creates art based on that poetry."

[0253] The above is a specific embodiment for carrying out the invention.

[0254] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0255] Step 1: User enters their idea

[0256] The user inputs a new proposal using the generation technology using a terminal. For example, the user inputs "This is a system that automatically generates poetry and creates art based on that poetry" into the input field. The input data is a proposal in text format.

[0257] Step 2: Send your ideas from your device to the server

[0258] The terminal sends the proposal data entered by the user to the server. For transmission, a communication protocol such as an HTTP POST request is used. The input is a text data proposal, which is sent to the server. The output is the proposal data sent to the server.

[0259] Step 3: The server analyzes the idea

[0260] The server analyzes the received proposal data. This analysis uses natural language processing libraries (NLTK and spaCy) to extract key keywords contained in the proposal. For example, keywords such as "poetry generation" and "art generation" are identified. The input is the proposal text, and the output is a list of analyzed keywords.

[0261] Step 4: The server evaluates technical feasibility

[0262] The server evaluates the technical feasibility based on the extracted keywords. For this evaluation, it refers to academic paper databases and technology review articles. For example, the server may determine that generating poetry is possible with current technology, but generating high-quality art is difficult. The input is a list of keywords, and the output is the result of the technical evaluation.

[0263] Step 5: The server recognizes the user's emotion

[0264] The server uses natural language processing technology and machine learning models to recognize the user's emotions. Specifically, it infers emotions from the user's written expressions and input speed. For example, if the user uses a lot of positive expressions, it determines that the user is enthusiastic. The input is the proposed text and input speed data, and the output is the emotion evaluation result.

[0265] Step 6: Server generates feedback message

[0266] The server generates a feedback message based on the evaluation results and the recognized emotions. A generative AI model such as GPT-3 is used for generation. For example, it might generate a message like, "That's a very interesting idea. While generating poetry is possible with current technology, automatically generating high-quality art from poetry is technically difficult." The inputs are the technical evaluation results and the emotion evaluation results, and the output is the feedback message.

[0267] Step 7: The server sends a feedback message to the device

[0268] The server sends the generated feedback message to the terminal using a communication protocol such as HTTP or WebSocket. The input is the feedback message, and the output is the message sent to the user's terminal.

[0269] Step 8: The device displays feedback to the user

[0270] The terminal displays the received feedback message to the user. The user interface displays the message in the form of a pop-up, a dialog box, etc. For example, a message such as "That's a very interesting idea..." may appear on the screen and the user may confirm it. The input is the feedback message, and the output is the feedback displayed to the user.

[0271] The above is a concrete breakdown and explanation of the program processing of this system.

[0272] (Application example 2)

[0273] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0274] Conventional systems using generative artificial intelligence only evaluate the technical feasibility of user ideas and are unable to provide feedback that takes the user's emotions into account. This leads to problems such as reduced user satisfaction and motivation, and a lack of constructive exchange of opinions. To solve this problem, a system that recognizes the user's emotions and provides feedback based on them is needed.

[0275] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0276] In this invention, the server includes a user interface means for inputting ideas that utilize generative artificial intelligence, a communication means for transmitting the input ideas to the server, and an analysis means for analyzing the ideas received by the server using natural language processing technology. This makes it possible to evaluate the technical feasibility of the ideas input by the user, analyze the user's emotions using emotion recognition technology, and generate appropriate feedback messages.

[0277] "Generative AI" is an AI technology that generates new content and information based on large amounts of data.

[0278] "User interface means" refers to means that provides an input interface for a user to access and operate the system.

[0279] A "communication means" is a mechanism for sending and receiving data between a user device and a server.

[0280] "Analysis means" is a means of analyzing received ideas using natural language processing technology, and understanding and classifying their contents.

[0281] An "extraction means" is a mechanism for extracting key features or topics from the analyzed data.

[0282] The "evaluation means" is a mechanism for evaluating technical feasibility based on the extracted features.

[0283] The "generation means" is a process that generates a feedback message based on the evaluation results.

[0284] The "transmission means" is a means for transmitting the generated feedback message to the user.

[0285] The "display means" is an interface for displaying the received feedback message to the user.

[0286] "Emotion recognition means" is a technology for analyzing the content of a user's input and recognizing emotions.

[0287] "Adjustment" is a process for adjusting the content of feedback messages based on perceived emotions.

[0288] "Natural language processing technology" is a general term for algorithms and techniques that enable computers to understand human language.

[0289] A "machine learning algorithm" is an algorithm that learns from data and makes future predictions and decisions.

[0290] The present invention relates to a system that utilizes generative artificial intelligence to evaluate user input ideas and provide feedback based on the evaluation. In particular, the system has a mechanism for recognizing the user's emotions and adjusting the content of the feedback message based on the user's emotions.

[0291] System configuration and program overview

[0292] Hardware

[0293] This system is composed of user terminals and a server as its main hardware components. User terminals mainly include smartphones and PCs, and the server is used for cloud-based processing.

[0294] software

[0295] The main software used in the system includes:

[0296] 1. Generative AI model: Uses OpenAI's API to analyze and evaluate user input ideas.

[0297] 2. Natural Language Processing Library: TextBlob is used to recognize sentiment from user input text.

[0298] 3. Data Communication Library: A communication method for sending and receiving data over the Internet.

[0299] 4. User interface: A means for users to input ideas, provided as a web browser or mobile application.

[0300] The process of idea analysis and emotion recognition

[0301] A user accesses the system using a terminal and inputs a new idea. For example, the user might input, "I think this product is great, but I'm concerned about the short battery life." The terminal then sends this input data to the server using a communication method.

[0302] The server analyzes the received ideas using natural language processing techniques (such as morphological analysis and topic modeling) and extracts key features. Specifically, it extracts positive features such as "the product is great" and negative features such as "the battery life is short" from complex context.

[0303] The server then evaluates the technical feasibility of the extracted features by comparing them with the current state of generative artificial intelligence technology. For example, a "product improvement proposal" is technically feasible, but the "battery life issue" is evaluated as something that needs to be resolved.

[0304] Based on the evaluated information, the server uses the generation means to create a feedback message. At the same time, the server analyzes the user's input text using TextBlob to recognize emotions. Based on the result of emotion recognition, the adjustment means adjusts the feedback message. For example, if the emotion is positive, a positive message such as "Thank you for your suggestion. We will consider improving the product" is generated.

[0305] The generated feedback message is transmitted to the user terminal using the communication means and is displayed to the user by the display means.

[0306] Examples of prompt statements

[0307] Specific examples are shown below.

[0308] Example: "This product is great, but I'm concerned about the short battery life."

[0309] Generated feedback message: "Thank you for your suggestion. We'll take it into consideration for product improvements. We'll also look into addressing the battery life issue."

[0310] Through the above process, not only are user proposals evaluated technically, but feedback that takes into account the user's feelings is also provided, making it possible to improve user satisfaction.

[0311] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0312] Step 1:

[0313] The user inputs an idea.

[0314] Input: The user types their idea in text form into an input field on the device (e.g., "I think this product is great, but I'm concerned about the short battery life.").

[0315] Operation: The user interface means acquires input data and transmits it to the server via the communication means.

[0316] Output: The input idea data is sent to the server.

[0317] Step 2:

[0318] The server analyzes the received ideas.

[0319] Input: User idea data sent to the server.

[0320] How it works: The server uses natural language processing techniques to analyze ideas and extract key features through morphological analysis and topic modeling.

[0321] Output: Feature data: "The product is great" and "The battery life is short."

[0322] Step 3:

[0323] The server evaluates the technical feasibility.

[0324] Input: Extracted feature data.

[0325] How it works: The server compares the current state of generative AI technology with the feature data and evaluates the technical feasibility. For example, it evaluates whether "product improvement is possible" or "battery life improvement is difficult."

[0326] Output: Evaluation data showing that "product improvement is possible" and "battery life is difficult to improve."

[0327] Step 4:

[0328] The server recognizes emotions.

[0329] Input: The idea data originally entered by the user.

[0330] How it works: The server uses natural language processing techniques such as TextBlob to recognize sentiment from idea data, specifically analyzing the positive / negative tone and wording of the text.

[0331] Output: Emotion data such as "has positive emotions."

[0332] Step 5:

[0333] The server generates a feedback message.

[0334] Input: Rating and sentiment data.

[0335] How it works: The server generates an appropriate feedback message based on the rating and emotion data. For example, it creates a message like, "We can improve the product, but the battery life issue needs to be addressed. Thank you."

[0336] Output: The generated feedback message.

[0337] Step 6:

[0338] The server sends a feedback message to the user.

[0339] Input: The generated feedback message.

[0340] Operation: The server sends a feedback message to the user's terminal via a communication means.

[0341] Output: Feedback messages that are displayed on the user's terminal.

[0342] Step 7:

[0343] The user checks the feedback message.

[0344] Input: The feedback message displayed on the user's terminal.

[0345] Action: The user checks the feedback message using the device's display.

[0346] Output: The content of the feedback message is conveyed to the user.

[0347] These steps ensure that users' ideas are properly evaluated and feedback is provided that takes into account their feelings.

[0348] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0349] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0350] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0351] [Second embodiment]

[0352] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0353] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0354] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0355] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0356] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0357] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0358] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0359] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0360] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0361] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0362] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0363] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0364] This invention relates to a system that uses generative AI to evaluate the technical feasibility of ideas. This system involves a series of processes: idea input from users, idea analysis and evaluation by a server, and feedback of the results.

[0365] The user uses a terminal to input a new idea that utilizes generative AI. For example, a specific example could be, "This idea is a system that automatically generates poetry and then creates art based on that poetry." The idea input through the user interface means is sent to the server via communication means.

[0366] The server analyzes the received ideas using natural language processing technology. This analysis uses methods such as morphological analysis and topic modeling to extract key features contained in the ideas. For example, it can extract that the ideas contain the features of "poetry generation" and "art generation."

[0367] Next, the server evaluates the technical feasibility based on the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generative AI technology. For example, it can be evaluated that generating poetry is possible with current technology, but automatically generating high-quality art from poetry is difficult.

[0368] Based on the evaluation results, the server generates a feedback message, such as "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology."

[0369] The generated feedback message is sent to the user's terminal using the transmission means and displayed to the user by the display means. This allows the user to check the technical feasibility of their idea in advance. This system makes it possible to prevent unnecessary development costs and wasted resources due to ideas that are technically difficult to realize.

[0370] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback to users based on the evaluation.

[0371] The processing flow will be explained below.

[0372] Step 1:

[0373] The user inputs ideas using generative AI through the device.

[0374] The terminal provides an idea input field through a user interface means, and the user inputs "This idea is a system that automatically generates poetry and creates art based on that poetry."

[0375] Step 2:

[0376] The terminal transmits the idea input by the user to the server.

[0377] Using the communication means, the terminal sends the input idea to the server as an HTTP POST request.

[0378] Step 3:

[0379] The server receives the idea sent from the terminal and begins analyzing it using natural language processing technology.

[0380] The server analyzes the idea text using techniques such as morphological analysis and topic modeling to extract important features.

[0381] Step 4:

[0382] The server extracts key features of the idea from the analyzed data.

[0383] The server extracts key features such as "poetry generation" and "art generation."

[0384] Step 5:

[0385] The technical feasibility is evaluated based on the features extracted by the server.

[0386] The server compares each extracted feature with current generative AI technology to assess its feasibility. Specifically, it determines that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[0387] Step 6:

[0388] The server generates a feedback message based on the evaluation results.

[0389] Based on the information obtained by the evaluation means, the server generates a feedback message stating, "Poetry generation is possible with current technology, but automatically generating high-quality art from poetry is technically difficult."

[0390] Step 7:

[0391] The server generates a feedback message and sends it to the user's terminal.

[0392] The server transmits the generated message as an HTTP response to the terminal via the communication means.

[0393] Step 8:

[0394] The terminal displays the received feedback message to the user.

[0395] The device displays the received feedback message on the screen using a display means and conveys it to the user, allowing the user to confirm whether their idea is feasible with current generative AI technology.

[0396] Example 1

[0397] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0398] There is a need to quickly and accurately evaluate the technical feasibility of ideas using generative AI and provide feedback to users. However, with current technology, the evaluation process is often done manually, which is time-consuming and labor-intensive, and the evaluation results lack consistency and accuracy. This leads to the problem of wasting resources and wasting development costs on ideas that are technically difficult to realize.

[0399] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0400] In this invention, the server includes an input means for inputting an idea utilizing a generative AI from a user, a transmission means for transmitting the input idea to the server, an analysis means for analyzing the idea received by the server using natural language processing technology, a feature extraction means for extracting key features of the idea from the analyzed data, an evaluation means for evaluating the technical feasibility based on the extracted features, a generation means for generating a feedback message including the evaluation result, a transmission means for transmitting the generated feedback message to the user, and a display means for displaying the received feedback message to the user. This makes it possible to quickly and accurately evaluate the technical feasibility of an idea and provide the result to the user.

[0401] The "input means" is an interface through which users can input ideas that utilize generative AI.

[0402] The "transmission means" is a means for transmitting the input idea from the terminal to the server.

[0403] The "analysis means" is a means for analyzing ideas received by the server using natural language processing technology.

[0404] The "feature extraction means" is a means for extracting the main features of an idea from the analyzed data.

[0405] The "evaluation means" is a means for evaluating technical feasibility based on the extracted features.

[0406] The "generating means" is a means for generating a feedback message including the evaluation result.

[0407] The "transmitting means" is a means for transmitting the generated feedback message to the user's terminal.

[0408] The "display means" is a means for displaying the received feedback message on the user's terminal.

[0409] "Natural language processing technology" is a technology for analyzing human language and understanding its structure and meaning.

[0410] "Morphological analysis" is a method of dividing a sentence into the smallest meaningful units and analyzing the part of speech of each word.

[0411] "Topic modeling" is a method for automatically extracting topics (themes) from large amounts of text data.

[0412] "Technological feasibility" is an indicator that determines whether a proposed idea can be realized using current technology.

[0413] A "feedback message" is a message that provides the user with information about technical feasibility based on the evaluation results.

[0414] MODE FOR CARRYING OUT THE INVENTION

[0415] This invention relates to a system that uses generative AI to evaluate the technical feasibility of ideas. This system involves a series of processes: idea input from users, idea analysis and evaluation by a server, and feedback of the results.

[0416] The user uses the device to input a new idea that utilizes generative AI. Specifically, the user inputs a prompt statement such as "This is a system that automatically generates poetry and creates art based on that poetry" through the device's user interface. The input idea is sent to the server using the device's transmission means.

[0417] The server analyzes the received ideas using natural language processing techniques, such as morphological analysis and topic modeling. Typically, open-source NLP libraries such as spaCy and NLTK are used for morphological analysis. This allows the server to extract key features, such as "poetry generation" or "art generation."

[0418] Next, the server evaluates the technical feasibility based on the features extracted using the feature extraction method. This evaluation method refers to the latest information and technical papers on current generative AI technology. For example, it may evaluate that existing generative AI models are available for generating poetry, but that it is difficult to generate high-quality art from poetry.

[0419] Based on the evaluation results, the server generates a feedback message such as, "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology."

[0420] The generated feedback message is sent back to the user's terminal using the server's transmission means, and the terminal displays the received feedback message to the user, allowing the user to confirm the technical feasibility of their idea in advance.

[0421] This system makes it possible to prevent unnecessary development costs and wasted resources by first evaluating ideas that are technically difficult to realize. As a concrete example, a user can input a prompt such as "This is a system that automatically generates poetry and creates art based on that poetry," and receive the feedback that "Generating poetry is possible, but generating art is difficult with current technology."

[0422] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback to users based on the evaluation.

[0423] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0424] Program processing steps

[0425] Step 1:

[0426] The user inputs their idea using the user interface on the device, for example, by entering a prompt such as "This is a system that automatically generates poetry and then creates art based on that poetry." This input data is prepared for transmission to the next processing step.

[0427] Step 2:

[0428] The device sends the input idea to the server. Specifically, it sends a prompt to the server using an HTTP request. The input is "This is a system that automatically generates poetry and creates art based on that poetry." The output is text data sent to the server.

[0429] Step 3:

[0430] The server analyzes the received ideas using natural language processing technology. Specifically, it performs morphological analysis, divides the sentence into words, and determines the part of speech of each word. The input is the sent prompt sentence, and the output is the analyzed word list and its part of speech information.

[0431] Step 4:

[0432] The server extracts key features of ideas based on the analysis results. Topic modeling is used to identify key topics, such as "poetry generation" and "art generation." The input is a list of words from the analysis results, and the output is a list of key features.

[0433] Step 5:

[0434] The server evaluates the technical feasibility based on the key features. This evaluation method refers to current generative AI technology to determine the feasibility of each feature. For example, it may evaluate that "generating poetry is possible, but generating art is difficult." The input is a list of features, and the output is the feasibility evaluation result.

[0435] Step 6:

[0436] The server generates a feedback message based on the evaluation results. Specifically, the message generated is, "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology." The input is the evaluation results, and the output is the feedback message.

[0437] Step 7:

[0438] The server sends the generated feedback message to the terminal. Specifically, it sends the feedback message to the terminal using an HTTP response. The input is the generated feedback message, and the output is the message sent to the terminal.

[0439] Step 8:

[0440] The device displays the received feedback message to the user. Specifically, the feedback message is displayed on the user interface. The message displayed is, "Current technology can generate poetry, but it is difficult to automatically generate high-quality art from poetry." The input is the feedback message sent to the device, and the output is the message displayed to the user.

[0441] In this way, specific data processing and calculations are carried out at each step, and users can receive feedback on the technical feasibility of their ideas.

[0442] (Application example 1)

[0443] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0444] Modern virtual stores are powered by many new ideas, but there is a lack of means to assess in advance whether these ideas are actually technically feasible. As a result, a great deal of time and money is often wasted on ideas that are not feasible. Furthermore, there is a growing demand for a system that utilizes generative AI to quickly assess the technical feasibility of innovative ideas and provide appropriate feedback.

[0445] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0446] In this invention, the server includes a user interface means for inputting ideas utilizing a generation AI from a user, a communication means for transmitting the input ideas to the server, an analysis means for analyzing the ideas received by the server using natural language processing technology, an extraction means for extracting key features of the ideas from the analyzed data, an evaluation means for evaluating the technical feasibility based on the extracted features, a generation means for generating a feedback message including the evaluation results, a transmission means for transmitting the generated feedback message to the user, a display means for displaying the received feedback message to the user, and a means for evaluating the technical feasibility of ideas related to the virtual store. This makes it possible to quickly evaluate the technical feasibility of new ideas for the virtual store and prevent unnecessary waste of resources.

[0447] "User interface means" refers to an interface through which a user can input ideas that utilize the generation AI.

[0448] "Communication means" is a means for transmitting the input idea to the server.

[0449] The "analysis means" is a means for analyzing ideas received by the server using natural language processing technology.

[0450] "Extraction means" refers to means for extracting the main features of ideas from the analyzed data.

[0451] The "evaluation means" is a means for evaluating technical feasibility based on the extracted features.

[0452] The "generating means" is a means for generating a feedback message including the evaluation result.

[0453] The "transmission means" is a means for transmitting the generated feedback message to the user.

[0454] The "display means" is a means for displaying the received feedback message to the user.

[0455] The "means for assessing the technical feasibility of ideas related to virtual stores" is a specialized means for assessing the technical feasibility of new ideas in virtual stores.

[0456] MODE FOR CARRYING OUT THE INVENTION

[0457] This invention is a system that utilizes generative AI to evaluate the technical feasibility of new ideas related to virtual stores and provides efficient feedback. The specific configuration and operation of this system will be described below.

[0458] 1. User Interface Methods

[0459] Managers and staff of virtual stores input new ideas related to the virtual store through applications on smartphones or desktop PCs. The user interface means is an interface for inputting ideas using generative AI, and provides easy-to-understand text boxes and selection menus.

[0460] 2. Sending and Receiving Input

[0461] The idea input by the user is transmitted to the server via the communication means, and the server receives this data and analyzes the idea using the analysis means.

[0462] 3. Analysis using natural language processing technology

[0463] The server uses generative AI models such as OpenAI's GPT-3 to analyze the received ideas using natural language processing techniques, such as morphological analysis and topic modeling, to extract key features contained in the ideas.

[0464] 4. Technical feasibility assessment

[0465] Based on the extracted features, the server evaluates the technical feasibility. The evaluation method determines whether each feature is technically feasible in light of the current state of generative AI technology. This evaluation also considers technical resources and specific implementation methods.

[0466] 5. Generating and Sending Feedback Messages

[0467] Based on the evaluation results, the server generates a feedback message. The generation means creates a specific message such as "This idea is feasible with current technology" or "This part is technically difficult to realize." The generated feedback message is sent to the user's terminal using the transmission means.

[0468] 6. Viewing Feedback

[0469] The received feedback messages are displayed on the user's device, allowing the user to quickly check whether their idea is technically feasible and to carry out efficient planning.

[0470] Specific examples

[0471] A virtual store manager inputs an idea for automatically generating new product displays for spring. This input is expressed as a prompt sentence like this:

[0472] text

[0473] Evaluate the technical feasibility of this idea: A system for automatically changing merchandise displays seasonally in a virtual store.

[0474] The server receives this input, analyzes it using the GPT-3 model, evaluates the technical feasibility, and provides specific feedback, such as "Automatic generation is technically possible, but product data needs to be updated."

[0475] Hardware and software used

[0476] The server can be built using a desktop PC or a cloud computing service (such as AWS or Google Cloud). The software used is based on Python programming and uses OpenAI's GPT-3 API for natural language processing. An internet connection is required for data communication.

[0477] As a result, a system is realized that enables virtual store managers to quickly evaluate the technical feasibility of new ideas and prevent unnecessary waste of resources.

[0478] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0479] Step 1:

[0480] A user inputs a new idea related to a virtual store using generative AI into an application on a smartphone or desktop PC. For example, the user inputs "an idea for automatically generating new product displays for spring." This input is entered into a text box and processed using a user interface means.

[0481] Input: User-entered ideas

[0482] Output: Input idea text

[0483] Step 2:

[0484] The terminal transmits the idea input by the user to the server via the communication means, and the input idea is transmitted to the server via the network.

[0485] Input: Entered idea text

[0486] Output: Idea data sent to the server

[0487] Step 3:

[0488] The server analyzes the idea data received via the communication means using an analysis means. Specifically, it uses a generative AI model such as OpenAI's GPT-3 to analyze the idea text through morphological analysis and topic modeling.

[0489] Input: Idea data sent to the server

[0490] Output: Key features of the idea

[0491] Step 4:

[0492] The server uses extraction means to extract key features of ideas from the analyzed data, such as phrases segmented by morphological analysis and topics obtained by topic modeling.

[0493] Input: Parsed idea data

[0494] Output: Extracted key features

[0495] Step 5:

[0496] The server evaluates the technical feasibility based on the extracted features. The evaluation method determines whether each feature is technically feasible in light of the current state of generative AI technology. This evaluation takes into account technical resources and specific implementation methods.

[0497] Input: Extracted key features

[0498] Output: Technical feasibility assessment results

[0499] Step 6:

[0500] The server generates a feedback message based on the evaluation results. A specific feedback message is created by the generation means. The generated message may be something like "This idea is feasible with current technology" or "This part is technically difficult to achieve."

[0501] Input: Technical feasibility assessment results

[0502] Output: Feedback message

[0503] Step 7:

[0504] The server transmits the generated feedback message to the user's terminal via the transmission means, and the feedback message is sent to the user's terminal through the network.

[0505] Input: Feedback message

[0506] Output: Feedback message sent to the terminal

[0507] Step 8:

[0508] The user's terminal displays the received feedback message to the user using a display means, thereby enabling the user to quickly check whether or not their idea is technically feasible.

[0509] Input: Feedback message sent to the device

[0510] Output: The feedback message displayed to the user

[0511] The above steps realize a system that evaluates the technical feasibility of new virtual store ideas and provides efficient feedback.

[0512] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0513] This invention relates to a system that evaluates the technical feasibility of ideas using generative AI, recognizes the user's emotions, and adjusts the content of feedback messages based on those emotions. This system includes a series of processes: idea input from the user, idea analysis and evaluation by the server, emotion recognition, and feedback of the evaluation results.

[0514] The user uses a terminal to input a new idea that utilizes generative AI. For example, a specific example could be, "This idea is a system that automatically generates poetry and then creates art based on that poetry." The idea input through the user interface means is sent to the server via communication means.

[0515] The server analyzes the received ideas using natural language processing techniques, such as morphological analysis and topic modeling, to extract key features from the ideas. For example, it can extract that the ideas contain the features of "poetry generation" and "art generation."

[0516] Next, the server evaluates the technical feasibility based on the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generative AI technology. For example, it may determine that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[0517] Based on the evaluation results, the server generates a feedback message, such as "Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[0518] Furthermore, the present invention includes an emotion recognition means. This emotion recognition means recognizes the user's emotion input through the user interface and analyzes it using natural language processing technology and machine learning algorithms. For example, it can determine the user's emotion from the sentences and input speed when the user inputs ideas, specific keywords, etc.

[0519] Based on the user's perceived emotions, the evaluator can adjust the feedback message accordingly. For example, if the user is very enthusiastic, the evaluator can provide positive feedback such as, "That's a very interesting idea. Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[0520] The generated feedback message is sent to the user's device using a transmission means and displayed to the user using a display means. This allows the user to confirm whether their idea is feasible with current generative AI technology, and also realize that the feedback content takes their feelings into consideration. This system makes it possible to prevent unnecessary development costs and resource wastage due to ideas that are technically difficult to realize, while also maintaining user motivation.

[0521] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback based on the evaluation while taking into consideration the user's feelings.

[0522] The processing flow will be explained below.

[0523] Step 1:

[0524] The user inputs ideas using generative AI through the device.

[0525] The terminal provides an idea input field through a user interface means, and the user inputs "This idea is a system that automatically generates poetry and creates art based on that poetry."

[0526] Step 2:

[0527] The terminal transmits the idea input by the user to the server.

[0528] Using the communication means, the terminal sends the input idea to the server as an HTTP POST request.

[0529] Step 3:

[0530] The server receives the idea sent from the terminal and begins analyzing it using natural language processing technology.

[0531] The server analyzes the idea text using techniques such as morphological analysis and topic modeling to extract important features.

[0532] Step 4:

[0533] The server extracts key features of the idea from the analyzed data.

[0534] The server extracts key features such as "poetry generation" and "art generation."

[0535] Step 5:

[0536] The technical feasibility is evaluated based on the features extracted by the server.

[0537] The server compares each extracted feature with current generative AI technology to assess its feasibility. Specifically, it determines that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[0538] Step 6:

[0539] The server analyzes the user's emotions using an emotion recognition means.

[0540] The server uses emotion recognition to analyze the text entered by the user, the input speed, specific keywords, etc., and determines the user's emotion. For example, if the user enters an idea with enthusiasm, the server recognizes the emotion as "positive."

[0541] Step 7:

[0542] The server generates a feedback message based on the evaluation results and the perceived user sentiment.

[0543] The server bases its message on the basic message, "Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry," and if the user's sentiment is positive, adds a positive comment such as, "That's a very interesting idea."

[0544] Step 8:

[0545] The server generates a feedback message and sends it to the user's terminal.

[0546] The server transmits the generated message as an HTTP response to the terminal via the communication means.

[0547] Step 9:

[0548] The terminal displays the received feedback message to the user.

[0549] The device displays the received feedback message on the screen and conveys it to the user, allowing the user to confirm whether their idea is feasible with current generative AI technology and to feel that the feedback content takes their feelings into consideration.

[0550] This series of steps allows users to confirm the technical feasibility of their ideas in advance and maintain their motivation by receiving emotionally sensitive feedback.

[0551] Example 2

[0552] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0553] In conventional systems, feedback is not provided that takes into account the user's emotions when inputting ideas and evaluating them, making it difficult to maintain user motivation. Also, evaluations of whether an idea is technically feasible may not meet the user's expectations, resulting in incomplete feedback.

[0554] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting a proposal utilizing a generation technology from a user, a communication means for transmitting the input proposal to the processing device, an analysis means for analyzing the proposal received by the processing device using natural language processing technology, an extraction means for extracting main features of the proposal from the analyzed data, an evaluation means for evaluating technical feasibility based on the extracted features, a generation means for generating a response sentence including the evaluation result, a transmission means for transmitting the generated response sentence to the user, a display means for displaying the received response sentence to the user, and further, a recognition means for recognizing the user's emotions and an adjustment means for adjusting the content of the response sentence based on the recognized emotions. This makes it possible to provide feedback that takes the user's emotions into consideration, thereby increasing the user's motivation and accurately evaluating technical feasibility.

[0555] A "user" is an entity that uses this system to input suggestions.

[0556] "Generative technology" is a technology that uses artificial intelligence and machine learning to generate new ideas and data.

[0557] "Suggestion" refers to an idea or concept that a user creates using generative technology and inputs into the system.

[0558] "Input means" refers to an interface through which a user inputs suggestions into the system.

[0559] "Communication means" refers to a means for transmitting a suggestion input by the input means to the server.

[0560] "Processing device" refers to a server or computing unit that receives, analyzes, and evaluates the suggestions.

[0561] "Analysis means" refers to a function for analyzing received proposals using natural language processing technology.

[0562] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0563] "Extraction means" refers to a function for extracting key features of a proposal from the analyzed data.

[0564] "Evaluation means" refers to a function for evaluating the technical feasibility of a proposal based on the extracted features.

[0565] "Generation means" refers to a function for generating a response sentence based on the evaluation result.

[0566] "Transmission means" refers to a function for transmitting the generated response sentence to the user.

[0567] The "display means" refers to an interface for displaying the received response sentence to the user.

[0568] "Recognition means" refers to a function for recognizing the user's emotions.

[0569] "Adjustment means" refers to a function for adjusting the content of a response sentence based on the recognized emotion.

[0570] This invention relates to a system that utilizes generation technology to evaluate the technical feasibility of ideas, recognizes user emotions, and adjusts the content of a response message based on those emotions. This system includes a series of processes: user input of a proposal, analysis and evaluation of the proposal by a server, emotion recognition, and generation and transmission of a response message based on the evaluation results.

[0571] The user inputs a new proposal using the generation technology using a terminal. For example, a specific example could be, "This is a system that automatically generates poetry and then creates art based on that poetry." The proposal input through the user interface means is sent to the server via the communication means.

[0572] The server analyzes the received proposals using natural language processing technology. This analysis involves techniques such as morphological analysis and topic modeling to extract key features contained in the proposals. Natural language processing libraries such as NLTK and spaCy, and morphological analysis tools such as MeCab are used for the analysis. For example, it can be extracted that the proposals contain the features of "poetry generation" and "art generation."

[0573] Next, the server evaluates the technical feasibility of the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generation technology. The evaluation includes consulting academic paper databases and technology review articles. For example, the server determines that while poetry generation is possible with current technology, it is difficult to automatically generate high-quality art from poetry.

[0574] Based on the evaluation results, the server generates a response using a generative AI model such as GPT-3. For example, it could create a specific message such as, "That's a very interesting idea. While generating poetry is possible with current technology, automatically generating high-quality art from poetry is technically difficult."

[0575] Furthermore, the present invention includes an emotion recognition unit that recognizes the user's emotion input through the user interface and analyzes it using natural language processing technology and machine learning algorithms. For example, the emotion of the user can be determined from the sentences, input speed, specific keywords, etc., when the user inputs a suggestion.

[0576] Based on the user's emotions, the evaluator can adjust the content of the response. For example, if the user is very enthusiastic, the evaluator can provide positive feedback such as, "That's a very interesting idea. Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[0577] The generated response sentence is sent to the user's terminal using the transmission means and displayed to the user by the display means. This allows the user to confirm whether their proposal is feasible with current generation technology and to realize that the response sentence takes their feelings into consideration. This system prevents unnecessary development costs and resource wastage due to proposals that are technically difficult to realize, while also maintaining user motivation.

[0578] Specific examples

[0579] Here is an example prompt:

[0580] Example: A prompt that the user types into the terminal:

[0581] "It's a system that automatically generates poetry and then creates art based on that poetry."

[0582] The above is a specific embodiment for carrying out the invention.

[0583] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0584] Step 1: User enters their idea

[0585] The user inputs a new proposal using the generation technology using a terminal. For example, the user inputs "This is a system that automatically generates poetry and creates art based on that poetry" into the input field. The input data is a proposal in text format.

[0586] Step 2: Send your ideas from your device to the server

[0587] The terminal sends the proposal data entered by the user to the server. For transmission, a communication protocol such as an HTTP POST request is used. The input is a text data proposal, which is sent to the server. The output is the proposal data sent to the server.

[0588] Step 3: The server analyzes the idea

[0589] The server analyzes the received proposal data. This analysis uses natural language processing libraries (NLTK and spaCy) to extract key keywords contained in the proposal. For example, keywords such as "poetry generation" and "art generation" are identified. The input is the proposal text, and the output is a list of analyzed keywords.

[0590] Step 4: The server evaluates technical feasibility

[0591] The server evaluates the technical feasibility based on the extracted keywords. For this evaluation, it refers to academic paper databases and technology review articles. For example, the server may determine that generating poetry is possible with current technology, but generating high-quality art is difficult. The input is a list of keywords, and the output is the result of the technical evaluation.

[0592] Step 5: The server recognizes the user's emotion

[0593] The server uses natural language processing technology and machine learning models to recognize the user's emotions. Specifically, it infers emotions from the user's written expressions and input speed. For example, if the user uses a lot of positive expressions, it determines that the user is enthusiastic. The input is the proposed text and input speed data, and the output is the emotion evaluation result.

[0594] Step 6: Server generates feedback message

[0595] The server generates a feedback message based on the evaluation results and the recognized emotions. A generative AI model such as GPT-3 is used for generation. For example, it might generate a message like, "That's a very interesting idea. While generating poetry is possible with current technology, automatically generating high-quality art from poetry is technically difficult." The inputs are the technical evaluation results and the emotion evaluation results, and the output is the feedback message.

[0596] Step 7: The server sends a feedback message to the device

[0597] The server sends the generated feedback message to the terminal using a communication protocol such as HTTP or WebSocket. The input is the feedback message, and the output is the message sent to the user's terminal.

[0598] Step 8: The device displays feedback to the user

[0599] The terminal displays the received feedback message to the user. The user interface displays the message in the form of a pop-up, a dialog box, etc. For example, a message such as "That's a very interesting idea..." may appear on the screen and the user may confirm it. The input is the feedback message, and the output is the feedback displayed to the user.

[0600] The above is a concrete breakdown and explanation of the program processing of this system.

[0601] (Application example 2)

[0602] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0603] Conventional systems using generative artificial intelligence only evaluate the technical feasibility of user ideas and are unable to provide feedback that takes the user's emotions into account. This leads to problems such as reduced user satisfaction and motivation, and a lack of constructive exchange of opinions. To solve this problem, a system that recognizes the user's emotions and provides feedback based on them is needed.

[0604] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0605] In this invention, the server includes a user interface means for inputting ideas that utilize generative artificial intelligence, a communication means for transmitting the input ideas to the server, and an analysis means for analyzing the ideas received by the server using natural language processing technology. This makes it possible to evaluate the technical feasibility of the ideas input by the user, analyze the user's emotions using emotion recognition technology, and generate appropriate feedback messages.

[0606] "Generative AI" is an AI technology that generates new content and information based on large amounts of data.

[0607] "User interface means" refers to means that provides an input interface for a user to access and operate the system.

[0608] A "communication means" is a mechanism for sending and receiving data between a user device and a server.

[0609] "Analysis means" is a means of analyzing received ideas using natural language processing technology, and understanding and classifying their contents.

[0610] An "extraction means" is a mechanism for extracting key features or topics from the analyzed data.

[0611] The "evaluation means" is a mechanism for evaluating technical feasibility based on the extracted features.

[0612] The "generation means" is a process that generates a feedback message based on the evaluation results.

[0613] The "transmission means" is a means for transmitting the generated feedback message to the user.

[0614] The "display means" is an interface for displaying the received feedback message to the user.

[0615] "Emotion recognition means" is a technology for analyzing the content of a user's input and recognizing emotions.

[0616] "Adjustment" is a process for adjusting the content of feedback messages based on perceived emotions.

[0617] "Natural language processing technology" is a general term for algorithms and techniques that enable computers to understand human language.

[0618] A "machine learning algorithm" is an algorithm that learns from data and makes future predictions and decisions.

[0619] The present invention relates to a system that utilizes generative artificial intelligence to evaluate user input ideas and provide feedback based on the evaluation. In particular, the system has a mechanism for recognizing the user's emotions and adjusting the content of the feedback message based on the user's emotions.

[0620] System configuration and program overview

[0621] Hardware

[0622] This system is composed of user terminals and a server as its main hardware components. User terminals mainly include smartphones and PCs, and the server is used for cloud-based processing.

[0623] software

[0624] The main software used in the system includes:

[0625] 1. Generative AI model: Uses OpenAI's API to analyze and evaluate user input ideas.

[0626] 2. Natural Language Processing Library: TextBlob is used to recognize sentiment from user input text.

[0627] 3. Data Communication Library: A communication method for sending and receiving data over the Internet.

[0628] 4. User interface: A means for users to input ideas, provided as a web browser or mobile application.

[0629] The process of idea analysis and emotion recognition

[0630] A user accesses the system using a terminal and inputs a new idea. For example, the user might input, "I think this product is great, but I'm concerned about the short battery life." The terminal then sends this input data to the server using a communication method.

[0631] The server analyzes the received ideas using natural language processing techniques (such as morphological analysis and topic modeling) and extracts key features. Specifically, it extracts positive features such as "the product is great" and negative features such as "the battery life is short" from complex context.

[0632] The server then evaluates the technical feasibility of the extracted features by comparing them with the current state of generative artificial intelligence technology. For example, a "product improvement proposal" is technically feasible, but the "battery life issue" is evaluated as something that needs to be resolved.

[0633] Based on the evaluated information, the server uses the generation means to create a feedback message. At the same time, the server analyzes the user's input text using TextBlob to recognize emotions. Based on the result of emotion recognition, the adjustment means adjusts the feedback message. For example, if the emotion is positive, a positive message such as "Thank you for your suggestion. We will consider improving the product" is generated.

[0634] The generated feedback message is transmitted to the user terminal using the communication means and is displayed to the user by the display means.

[0635] Examples of prompt statements

[0636] Specific examples are shown below.

[0637] Example: "This product is great, but I'm concerned about the short battery life."

[0638] Generated feedback message: "Thank you for your suggestion. We'll take it into consideration for product improvements. We'll also look into addressing the battery life issue."

[0639] Through the above process, not only are user proposals evaluated technically, but feedback that takes into account the user's feelings is also provided, making it possible to improve user satisfaction.

[0640] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0641] Step 1:

[0642] The user inputs an idea.

[0643] Input: The user types their idea in text form into an input field on the device (e.g., "I think this product is great, but I'm concerned about the short battery life.").

[0644] Operation: The user interface means acquires input data and transmits it to the server via the communication means.

[0645] Output: The input idea data is sent to the server.

[0646] Step 2:

[0647] The server analyzes the received ideas.

[0648] Input: User idea data sent to the server.

[0649] How it works: The server uses natural language processing techniques to analyze ideas and extract key features through morphological analysis and topic modeling.

[0650] Output: Feature data: "The product is great" and "The battery life is short."

[0651] Step 3:

[0652] The server evaluates the technical feasibility.

[0653] Input: Extracted feature data.

[0654] How it works: The server compares the current state of generative AI technology with the feature data and evaluates the technical feasibility. For example, it evaluates whether "product improvement is possible" or "battery life improvement is difficult."

[0655] Output: Evaluation data showing that "product improvement is possible" and "battery life is difficult to improve."

[0656] Step 4:

[0657] The server recognizes emotions.

[0658] Input: The idea data originally entered by the user.

[0659] How it works: The server uses natural language processing techniques such as TextBlob to recognize sentiment from idea data, specifically analyzing the positive / negative tone and wording of the text.

[0660] Output: Emotion data such as "has positive emotions."

[0661] Step 5:

[0662] The server generates a feedback message.

[0663] Input: Rating and sentiment data.

[0664] How it works: The server generates an appropriate feedback message based on the rating and emotion data. For example, it creates a message like, "We can improve the product, but the battery life issue needs to be addressed. Thank you."

[0665] Output: The generated feedback message.

[0666] Step 6:

[0667] The server sends a feedback message to the user.

[0668] Input: The generated feedback message.

[0669] Operation: The server sends a feedback message to the user's terminal via a communication means.

[0670] Output: Feedback messages that are displayed on the user's terminal.

[0671] Step 7:

[0672] The user checks the feedback message.

[0673] Input: The feedback message displayed on the user's terminal.

[0674] Action: The user checks the feedback message using the device's display.

[0675] Output: The content of the feedback message is conveyed to the user.

[0676] These steps ensure that users' ideas are properly evaluated and feedback is provided that takes into account their feelings.

[0677] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0678] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0679] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0680] [Third embodiment]

[0681] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0682] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0683] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0684] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0685] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0686] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0687] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0688] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0689] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0690] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0691] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0692] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0693] This invention relates to a system that uses generative AI to evaluate the technical feasibility of ideas. This system involves a series of processes: idea input from users, idea analysis and evaluation by a server, and feedback of the results.

[0694] The user uses a terminal to input a new idea that utilizes generative AI. For example, a specific example could be, "This idea is a system that automatically generates poetry and then creates art based on that poetry." The idea input through the user interface means is sent to the server via communication means.

[0695] The server analyzes the received ideas using natural language processing technology. This analysis uses methods such as morphological analysis and topic modeling to extract key features contained in the ideas. For example, it can extract that the ideas contain the features of "poetry generation" and "art generation."

[0696] Next, the server evaluates the technical feasibility based on the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generative AI technology. For example, it can be evaluated that generating poetry is possible with current technology, but automatically generating high-quality art from poetry is difficult.

[0697] Based on the evaluation results, the server generates a feedback message, such as "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology."

[0698] The generated feedback message is sent to the user's terminal using the transmission means and displayed to the user by the display means. This allows the user to check the technical feasibility of their idea in advance. This system makes it possible to prevent unnecessary development costs and wasted resources due to ideas that are technically difficult to realize.

[0699] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback to users based on the evaluation.

[0700] The processing flow will be explained below.

[0701] Step 1:

[0702] The user inputs ideas using generative AI through the device.

[0703] The terminal provides an idea input field through a user interface means, and the user inputs "This idea is a system that automatically generates poetry and creates art based on that poetry."

[0704] Step 2:

[0705] The terminal transmits the idea input by the user to the server.

[0706] Using the communication means, the terminal sends the input idea to the server as an HTTP POST request.

[0707] Step 3:

[0708] The server receives the idea sent from the terminal and begins analyzing it using natural language processing technology.

[0709] The server analyzes the idea text using techniques such as morphological analysis and topic modeling to extract important features.

[0710] Step 4:

[0711] The server extracts key features of the idea from the analyzed data.

[0712] The server extracts key features such as "poetry generation" and "art generation."

[0713] Step 5:

[0714] The technical feasibility is evaluated based on the features extracted by the server.

[0715] The server compares each extracted feature with current generative AI technology to assess its feasibility. Specifically, it determines that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[0716] Step 6:

[0717] The server generates a feedback message based on the evaluation results.

[0718] Based on the information obtained by the evaluation means, the server generates a feedback message stating, "Poetry generation is possible with current technology, but automatically generating high-quality art from poetry is technically difficult."

[0719] Step 7:

[0720] The server generates a feedback message and sends it to the user's terminal.

[0721] The server transmits the generated message as an HTTP response to the terminal via the communication means.

[0722] Step 8:

[0723] The terminal displays the received feedback message to the user.

[0724] The device displays the received feedback message on the screen using a display means and conveys it to the user, allowing the user to confirm whether their idea is feasible with current generative AI technology.

[0725] Example 1

[0726] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0727] There is a need to quickly and accurately evaluate the technical feasibility of ideas using generative AI and provide feedback to users. However, with current technology, the evaluation process is often done manually, which is time-consuming and labor-intensive, and the evaluation results lack consistency and accuracy. This leads to the problem of wasting resources and wasting development costs on ideas that are technically difficult to realize.

[0728] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0729] In this invention, the server includes an input means for inputting an idea utilizing a generative AI from a user, a transmission means for transmitting the input idea to the server, an analysis means for analyzing the idea received by the server using natural language processing technology, a feature extraction means for extracting key features of the idea from the analyzed data, an evaluation means for evaluating the technical feasibility based on the extracted features, a generation means for generating a feedback message including the evaluation result, a transmission means for transmitting the generated feedback message to the user, and a display means for displaying the received feedback message to the user. This makes it possible to quickly and accurately evaluate the technical feasibility of an idea and provide the result to the user.

[0730] The "input means" is an interface through which users can input ideas that utilize generative AI.

[0731] The "transmission means" is a means for transmitting the input idea from the terminal to the server.

[0732] The "analysis means" is a means for analyzing ideas received by the server using natural language processing technology.

[0733] The "feature extraction means" is a means for extracting the main features of an idea from the analyzed data.

[0734] The "evaluation means" is a means for evaluating technical feasibility based on the extracted features.

[0735] The "generating means" is a means for generating a feedback message including the evaluation result.

[0736] The "transmitting means" is a means for transmitting the generated feedback message to the user's terminal.

[0737] The "display means" is a means for displaying the received feedback message on the user's terminal.

[0738] "Natural language processing technology" is a technology for analyzing human language and understanding its structure and meaning.

[0739] "Morphological analysis" is a method of dividing a sentence into the smallest meaningful units and analyzing the part of speech of each word.

[0740] "Topic modeling" is a method for automatically extracting topics (themes) from large amounts of text data.

[0741] "Technological feasibility" is an indicator that determines whether a proposed idea can be realized using current technology.

[0742] A "feedback message" is a message that provides the user with information about technical feasibility based on the evaluation results.

[0743] MODE FOR CARRYING OUT THE INVENTION

[0744] This invention relates to a system that uses generative AI to evaluate the technical feasibility of ideas. This system involves a series of processes: idea input from users, idea analysis and evaluation by a server, and feedback of the results.

[0745] The user uses the device to input a new idea that utilizes generative AI. Specifically, the user inputs a prompt statement such as "This is a system that automatically generates poetry and creates art based on that poetry" through the device's user interface. The input idea is sent to the server using the device's transmission means.

[0746] The server analyzes the received ideas using natural language processing techniques, such as morphological analysis and topic modeling. Typically, open-source NLP libraries such as spaCy and NLTK are used for morphological analysis. This allows the server to extract key features, such as "poetry generation" or "art generation."

[0747] Next, the server evaluates the technical feasibility based on the features extracted using the feature extraction method. This evaluation method refers to the latest information and technical papers on current generative AI technology. For example, it may evaluate that existing generative AI models are available for generating poetry, but that it is difficult to generate high-quality art from poetry.

[0748] Based on the evaluation results, the server generates a feedback message such as, "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology."

[0749] The generated feedback message is sent back to the user's terminal using the server's transmission means, and the terminal displays the received feedback message to the user, allowing the user to confirm the technical feasibility of their idea in advance.

[0750] This system makes it possible to prevent unnecessary development costs and wasted resources by first evaluating ideas that are technically difficult to realize. As a concrete example, a user can input a prompt such as "This is a system that automatically generates poetry and creates art based on that poetry," and receive the feedback that "Generating poetry is possible, but generating art is difficult with current technology."

[0751] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback to users based on the evaluation.

[0752] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0753] Program processing steps

[0754] Step 1:

[0755] The user inputs their idea using the user interface on the device, for example, by entering a prompt such as "This is a system that automatically generates poetry and then creates art based on that poetry." This input data is prepared for transmission to the next processing step.

[0756] Step 2:

[0757] The device sends the input idea to the server. Specifically, it sends a prompt to the server using an HTTP request. The input is "This is a system that automatically generates poetry and creates art based on that poetry." The output is text data sent to the server.

[0758] Step 3:

[0759] The server analyzes the received ideas using natural language processing technology. Specifically, it performs morphological analysis, divides the sentence into words, and determines the part of speech of each word. The input is the sent prompt sentence, and the output is the analyzed word list and its part of speech information.

[0760] Step 4:

[0761] The server extracts key features of ideas based on the analysis results. Topic modeling is used to identify key topics, such as "poetry generation" and "art generation." The input is a list of words from the analysis results, and the output is a list of key features.

[0762] Step 5:

[0763] The server evaluates the technical feasibility based on the key features. This evaluation method refers to current generative AI technology to determine the feasibility of each feature. For example, it may evaluate that "generating poetry is possible, but generating art is difficult." The input is a list of features, and the output is the feasibility evaluation result.

[0764] Step 6:

[0765] The server generates a feedback message based on the evaluation results. Specifically, the message generated is, "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology." The input is the evaluation results, and the output is the feedback message.

[0766] Step 7:

[0767] The server sends the generated feedback message to the terminal. Specifically, it sends the feedback message to the terminal using an HTTP response. The input is the generated feedback message, and the output is the message sent to the terminal.

[0768] Step 8:

[0769] The device displays the received feedback message to the user. Specifically, the feedback message is displayed on the user interface. The message displayed is, "Current technology can generate poetry, but it is difficult to automatically generate high-quality art from poetry." The input is the feedback message sent to the device, and the output is the message displayed to the user.

[0770] In this way, specific data processing and calculations are carried out at each step, and users can receive feedback on the technical feasibility of their ideas.

[0771] (Application example 1)

[0772] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0773] Modern virtual stores are powered by many new ideas, but there is a lack of means to assess in advance whether these ideas are actually technically feasible. As a result, a great deal of time and money is often wasted on ideas that are not feasible. Furthermore, there is a growing demand for a system that utilizes generative AI to quickly assess the technical feasibility of innovative ideas and provide appropriate feedback.

[0774] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0775] In this invention, the server includes a user interface means for inputting ideas utilizing a generation AI from a user, a communication means for transmitting the input ideas to the server, an analysis means for analyzing the ideas received by the server using natural language processing technology, an extraction means for extracting key features of the ideas from the analyzed data, an evaluation means for evaluating the technical feasibility based on the extracted features, a generation means for generating a feedback message including the evaluation results, a transmission means for transmitting the generated feedback message to the user, a display means for displaying the received feedback message to the user, and a means for evaluating the technical feasibility of ideas related to the virtual store. This makes it possible to quickly evaluate the technical feasibility of new ideas for the virtual store and prevent unnecessary waste of resources.

[0776] "User interface means" refers to an interface through which a user can input ideas that utilize the generation AI.

[0777] "Communication means" is a means for transmitting the input idea to the server.

[0778] The "analysis means" is a means for analyzing ideas received by the server using natural language processing technology.

[0779] "Extraction means" refers to means for extracting the main features of ideas from the analyzed data.

[0780] The "evaluation means" is a means for evaluating technical feasibility based on the extracted features.

[0781] The "generating means" is a means for generating a feedback message including the evaluation result.

[0782] The "transmission means" is a means for transmitting the generated feedback message to the user.

[0783] The "display means" is a means for displaying the received feedback message to the user.

[0784] The "means for assessing the technical feasibility of ideas related to virtual stores" is a specialized means for assessing the technical feasibility of new ideas in virtual stores.

[0785] MODE FOR CARRYING OUT THE INVENTION

[0786] This invention is a system that utilizes generative AI to evaluate the technical feasibility of new ideas related to virtual stores and provides efficient feedback. The specific configuration and operation of this system will be described below.

[0787] 1. User Interface Methods

[0788] Managers and staff of virtual stores input new ideas related to the virtual store through applications on smartphones or desktop PCs. The user interface means is an interface for inputting ideas using generative AI, and provides easy-to-understand text boxes and selection menus.

[0789] 2. Sending and Receiving Input

[0790] The idea input by the user is transmitted to the server via the communication means, and the server receives this data and analyzes the idea using the analysis means.

[0791] 3. Analysis using natural language processing technology

[0792] The server uses generative AI models such as OpenAI's GPT-3 to analyze the received ideas using natural language processing techniques, such as morphological analysis and topic modeling, to extract key features contained in the ideas.

[0793] 4. Technical feasibility assessment

[0794] Based on the extracted features, the server evaluates the technical feasibility. The evaluation method determines whether each feature is technically feasible in light of the current state of generative AI technology. This evaluation also considers technical resources and specific implementation methods.

[0795] 5. Generating and Sending Feedback Messages

[0796] Based on the evaluation results, the server generates a feedback message. The generation means creates a specific message such as "This idea is feasible with current technology" or "This part is technically difficult to realize." The generated feedback message is sent to the user's terminal using the transmission means.

[0797] 6. Viewing Feedback

[0798] The received feedback messages are displayed on the user's device, allowing the user to quickly check whether their idea is technically feasible and to carry out efficient planning.

[0799] Specific examples

[0800] A virtual store manager inputs an idea for automatically generating new product displays for spring. This input is expressed as a prompt sentence like this:

[0801] text

[0802] Evaluate the technical feasibility of this idea: A system for automatically changing merchandise displays seasonally in a virtual store.

[0803] The server receives this input, analyzes it using the GPT-3 model, evaluates the technical feasibility, and provides specific feedback, such as "Automatic generation is technically possible, but product data needs to be updated."

[0804] Hardware and software used

[0805] The server can be built using a desktop PC or a cloud computing service (such as AWS or Google Cloud). The software used is based on Python programming and uses OpenAI's GPT-3 API for natural language processing. An internet connection is required for data communication.

[0806] As a result, a system is realized that enables virtual store managers to quickly evaluate the technical feasibility of new ideas and prevent unnecessary waste of resources.

[0807] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0808] Step 1:

[0809] A user inputs a new idea related to a virtual store using generative AI into an application on a smartphone or desktop PC. For example, the user inputs "an idea for automatically generating new product displays for spring." This input is entered into a text box and processed using a user interface means.

[0810] Input: User-entered ideas

[0811] Output: Input idea text

[0812] Step 2:

[0813] The terminal transmits the idea input by the user to the server via the communication means, and the input idea is transmitted to the server via the network.

[0814] Input: Entered idea text

[0815] Output: Idea data sent to the server

[0816] Step 3:

[0817] The server analyzes the idea data received via the communication means using an analysis means. Specifically, it uses a generative AI model such as OpenAI's GPT-3 to analyze the idea text through morphological analysis and topic modeling.

[0818] Input: Idea data sent to the server

[0819] Output: Key features of the idea

[0820] Step 4:

[0821] The server uses extraction means to extract key features of ideas from the analyzed data, such as phrases segmented by morphological analysis and topics obtained by topic modeling.

[0822] Input: Parsed idea data

[0823] Output: Extracted key features

[0824] Step 5:

[0825] The server evaluates the technical feasibility based on the extracted features. The evaluation method determines whether each feature is technically feasible in light of the current state of generative AI technology. This evaluation takes into account technical resources and specific implementation methods.

[0826] Input: Extracted key features

[0827] Output: Technical feasibility assessment results

[0828] Step 6:

[0829] The server generates a feedback message based on the evaluation results. A specific feedback message is created by the generation means. The generated message may be something like "This idea is feasible with current technology" or "This part is technically difficult to achieve."

[0830] Input: Technical feasibility assessment results

[0831] Output: Feedback message

[0832] Step 7:

[0833] The server transmits the generated feedback message to the user's terminal via the transmission means, and the feedback message is sent to the user's terminal through the network.

[0834] Input: Feedback message

[0835] Output: Feedback message sent to the terminal

[0836] Step 8:

[0837] The user's terminal displays the received feedback message to the user using a display means, thereby enabling the user to quickly check whether or not their idea is technically feasible.

[0838] Input: Feedback message sent to the device

[0839] Output: The feedback message displayed to the user

[0840] The above steps realize a system that evaluates the technical feasibility of new virtual store ideas and provides efficient feedback.

[0841] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0842] This invention relates to a system that evaluates the technical feasibility of ideas using generative AI, recognizes the user's emotions, and adjusts the content of feedback messages based on those emotions. This system includes a series of processes: idea input from the user, idea analysis and evaluation by the server, emotion recognition, and feedback of the evaluation results.

[0843] The user uses a terminal to input a new idea that utilizes generative AI. For example, a specific example could be, "This idea is a system that automatically generates poetry and then creates art based on that poetry." The idea input through the user interface means is sent to the server via communication means.

[0844] The server analyzes the received ideas using natural language processing techniques, such as morphological analysis and topic modeling, to extract key features from the ideas. For example, it can extract that the ideas contain the features of "poetry generation" and "art generation."

[0845] Next, the server evaluates the technical feasibility based on the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generative AI technology. For example, it may determine that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[0846] Based on the evaluation results, the server generates a feedback message, such as "Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[0847] Furthermore, the present invention includes an emotion recognition means. This emotion recognition means recognizes the user's emotion input through the user interface and analyzes it using natural language processing technology and machine learning algorithms. For example, it can determine the user's emotion from the sentences and input speed when the user inputs ideas, specific keywords, etc.

[0848] Based on the user's perceived emotions, the evaluator can adjust the feedback message accordingly. For example, if the user is very enthusiastic, the evaluator can provide positive feedback such as, "That's a very interesting idea. Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[0849] The generated feedback message is sent to the user's device using a transmission means and displayed to the user using a display means. This allows the user to confirm whether their idea is feasible with current generative AI technology, and also realize that the feedback content takes their feelings into consideration. This system makes it possible to prevent unnecessary development costs and resource wastage due to ideas that are technically difficult to realize, while also maintaining user motivation.

[0850] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback based on the evaluation while taking into consideration the user's feelings.

[0851] The processing flow will be explained below.

[0852] Step 1:

[0853] The user inputs ideas using generative AI through the device.

[0854] The terminal provides an idea input field through a user interface means, and the user inputs "This idea is a system that automatically generates poetry and creates art based on that poetry."

[0855] Step 2:

[0856] The terminal transmits the idea input by the user to the server.

[0857] Using the communication means, the terminal sends the input idea to the server as an HTTP POST request.

[0858] Step 3:

[0859] The server receives the idea sent from the terminal and begins analyzing it using natural language processing technology.

[0860] The server analyzes the idea text using techniques such as morphological analysis and topic modeling to extract important features.

[0861] Step 4:

[0862] The server extracts key features of the idea from the analyzed data.

[0863] The server extracts key features such as "poetry generation" and "art generation."

[0864] Step 5:

[0865] The technical feasibility is evaluated based on the features extracted by the server.

[0866] The server compares each extracted feature with current generative AI technology to assess its feasibility. Specifically, it determines that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[0867] Step 6:

[0868] The server analyzes the user's emotions using an emotion recognition means.

[0869] The server uses emotion recognition to analyze the text entered by the user, the input speed, specific keywords, etc., and determines the user's emotion. For example, if the user enters an idea with enthusiasm, the server recognizes the emotion as "positive."

[0870] Step 7:

[0871] The server generates a feedback message based on the evaluation results and the perceived user sentiment.

[0872] The server bases its message on the basic message, "Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry," and if the user's sentiment is positive, adds a positive comment such as, "That's a very interesting idea."

[0873] Step 8:

[0874] The server generates a feedback message and sends it to the user's terminal.

[0875] The server transmits the generated message as an HTTP response to the terminal via the communication means.

[0876] Step 9:

[0877] The terminal displays the received feedback message to the user.

[0878] The device displays the received feedback message on the screen and conveys it to the user, allowing the user to confirm whether their idea is feasible with current generative AI technology and to feel that the feedback content takes their feelings into consideration.

[0879] This series of steps allows users to confirm the technical feasibility of their ideas in advance and maintain their motivation by receiving emotionally sensitive feedback.

[0880] Example 2

[0881] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0882] In conventional systems, feedback is not provided that takes into account the user's emotions when inputting ideas and evaluating them, making it difficult to maintain user motivation. Also, evaluations of whether an idea is technically feasible may not meet the user's expectations, resulting in incomplete feedback.

[0883] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting a proposal utilizing a generation technology from a user, a communication means for transmitting the input proposal to the processing device, an analysis means for analyzing the proposal received by the processing device using natural language processing technology, an extraction means for extracting main features of the proposal from the analyzed data, an evaluation means for evaluating technical feasibility based on the extracted features, a generation means for generating a response sentence including the evaluation result, a transmission means for transmitting the generated response sentence to the user, a display means for displaying the received response sentence to the user, and further, a recognition means for recognizing the user's emotions and an adjustment means for adjusting the content of the response sentence based on the recognized emotions. This makes it possible to provide feedback that takes the user's emotions into consideration, thereby increasing the user's motivation and accurately evaluating technical feasibility.

[0884] A "user" is an entity that uses this system to input suggestions.

[0885] "Generative technology" is a technology that uses artificial intelligence and machine learning to generate new ideas and data.

[0886] "Suggestion" refers to an idea or concept that a user creates using generative technology and inputs into the system.

[0887] "Input means" refers to an interface through which a user inputs suggestions into the system.

[0888] "Communication means" refers to a means for transmitting a suggestion input by the input means to the server.

[0889] "Processing device" refers to a server or computing unit that receives, analyzes, and evaluates the suggestions.

[0890] "Analysis means" refers to a function for analyzing received proposals using natural language processing technology.

[0891] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0892] "Extraction means" refers to a function for extracting key features of a proposal from the analyzed data.

[0893] "Evaluation means" refers to a function for evaluating the technical feasibility of a proposal based on the extracted features.

[0894] "Generation means" refers to a function for generating a response sentence based on the evaluation result.

[0895] "Transmission means" refers to a function for transmitting the generated response sentence to the user.

[0896] The "display means" refers to an interface for displaying the received response sentence to the user.

[0897] "Recognition means" refers to a function for recognizing the user's emotions.

[0898] "Adjustment means" refers to a function for adjusting the content of a response sentence based on the recognized emotion.

[0899] This invention relates to a system that utilizes generation technology to evaluate the technical feasibility of ideas, recognizes user emotions, and adjusts the content of a response message based on those emotions. This system includes a series of processes: user input of a proposal, analysis and evaluation of the proposal by a server, emotion recognition, and generation and transmission of a response message based on the evaluation results.

[0900] The user inputs a new proposal using the generation technology using a terminal. For example, a specific example could be, "This is a system that automatically generates poetry and then creates art based on that poetry." The proposal input through the user interface means is sent to the server via the communication means.

[0901] The server analyzes the received proposals using natural language processing technology. This analysis involves techniques such as morphological analysis and topic modeling to extract key features contained in the proposals. Natural language processing libraries such as NLTK and spaCy, and morphological analysis tools such as MeCab are used for the analysis. For example, it can be extracted that the proposals contain the features of "poetry generation" and "art generation."

[0902] Next, the server evaluates the technical feasibility of the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generation technology. The evaluation includes consulting academic paper databases and technology review articles. For example, the server determines that while poetry generation is possible with current technology, it is difficult to automatically generate high-quality art from poetry.

[0903] Based on the evaluation results, the server generates a response using a generative AI model such as GPT-3. For example, it could create a specific message such as, "That's a very interesting idea. While generating poetry is possible with current technology, automatically generating high-quality art from poetry is technically difficult."

[0904] Furthermore, the present invention includes an emotion recognition unit that recognizes the user's emotion input through the user interface and analyzes it using natural language processing technology and machine learning algorithms. For example, the emotion of the user can be determined from the sentences, input speed, specific keywords, etc., when the user inputs a suggestion.

[0905] Based on the user's emotions, the evaluator can adjust the content of the response. For example, if the user is very enthusiastic, the evaluator can provide positive feedback such as, "That's a very interesting idea. Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[0906] The generated response sentence is sent to the user's terminal using the transmission means and displayed to the user by the display means. This allows the user to confirm whether their proposal is feasible with current generation technology and to realize that the response sentence takes their feelings into consideration. This system prevents unnecessary development costs and resource wastage due to proposals that are technically difficult to realize, while also maintaining user motivation.

[0907] Specific examples

[0908] Here is an example prompt:

[0909] Example: A prompt that the user types into the terminal:

[0910] "It's a system that automatically generates poetry and then creates art based on that poetry."

[0911] The above is a specific embodiment for carrying out the invention.

[0912] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0913] Step 1: User enters their idea

[0914] The user inputs a new proposal using the generation technology using a terminal. For example, the user inputs "This is a system that automatically generates poetry and creates art based on that poetry" into the input field. The input data is a proposal in text format.

[0915] Step 2: Send your ideas from your device to the server

[0916] The terminal sends the proposal data entered by the user to the server. For transmission, a communication protocol such as an HTTP POST request is used. The input is a text data proposal, which is sent to the server. The output is the proposal data sent to the server.

[0917] Step 3: The server analyzes the idea

[0918] The server analyzes the received proposal data. This analysis uses natural language processing libraries (NLTK and spaCy) to extract key keywords contained in the proposal. For example, keywords such as "poetry generation" and "art generation" are identified. The input is the proposal text, and the output is a list of analyzed keywords.

[0919] Step 4: The server evaluates technical feasibility

[0920] The server evaluates the technical feasibility based on the extracted keywords. For this evaluation, it refers to academic paper databases and technology review articles. For example, the server may determine that generating poetry is possible with current technology, but generating high-quality art is difficult. The input is a list of keywords, and the output is the result of the technical evaluation.

[0921] Step 5: The server recognizes the user's emotion

[0922] The server uses natural language processing technology and machine learning models to recognize the user's emotions. Specifically, it infers emotions from the user's written expressions and input speed. For example, if the user uses a lot of positive expressions, it determines that the user is enthusiastic. The input is the proposed text and input speed data, and the output is the emotion evaluation result.

[0923] Step 6: Server generates feedback message

[0924] The server generates a feedback message based on the evaluation results and the recognized emotions. A generative AI model such as GPT-3 is used for generation. For example, it might generate a message like, "That's a very interesting idea. While generating poetry is possible with current technology, automatically generating high-quality art from poetry is technically difficult." The inputs are the technical evaluation results and the emotion evaluation results, and the output is the feedback message.

[0925] Step 7: The server sends a feedback message to the device

[0926] The server sends the generated feedback message to the terminal using a communication protocol such as HTTP or WebSocket. The input is the feedback message, and the output is the message sent to the user's terminal.

[0927] Step 8: The device displays feedback to the user

[0928] The terminal displays the received feedback message to the user. The user interface displays the message in the form of a pop-up, a dialog box, etc. For example, a message such as "That's a very interesting idea..." may appear on the screen and the user may confirm it. The input is the feedback message, and the output is the feedback displayed to the user.

[0929] The above is a concrete breakdown and explanation of the program processing of this system.

[0930] (Application example 2)

[0931] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0932] Conventional systems using generative artificial intelligence only evaluate the technical feasibility of user ideas and are unable to provide feedback that takes the user's emotions into account. This leads to problems such as reduced user satisfaction and motivation, and a lack of constructive exchange of opinions. To solve this problem, a system that recognizes the user's emotions and provides feedback based on them is needed.

[0933] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0934] In this invention, the server includes a user interface means for inputting ideas that utilize generative artificial intelligence, a communication means for transmitting the input ideas to the server, and an analysis means for analyzing the ideas received by the server using natural language processing technology. This makes it possible to evaluate the technical feasibility of the ideas input by the user, analyze the user's emotions using emotion recognition technology, and generate appropriate feedback messages.

[0935] "Generative AI" is an AI technology that generates new content and information based on large amounts of data.

[0936] "User interface means" refers to means that provides an input interface for a user to access and operate the system.

[0937] A "communication means" is a mechanism for sending and receiving data between a user device and a server.

[0938] "Analysis means" is a means of analyzing received ideas using natural language processing technology, and understanding and classifying their contents.

[0939] An "extraction means" is a mechanism for extracting key features or topics from the analyzed data.

[0940] The "evaluation means" is a mechanism for evaluating technical feasibility based on the extracted features.

[0941] The "generation means" is a process that generates a feedback message based on the evaluation results.

[0942] The "transmission means" is a means for transmitting the generated feedback message to the user.

[0943] The "display means" is an interface for displaying the received feedback message to the user.

[0944] "Emotion recognition means" is a technology for analyzing the content of a user's input and recognizing emotions.

[0945] "Adjustment" is a process for adjusting the content of feedback messages based on perceived emotions.

[0946] "Natural language processing technology" is a general term for algorithms and techniques that enable computers to understand human language.

[0947] A "machine learning algorithm" is an algorithm that learns from data and makes future predictions and decisions.

[0948] The present invention relates to a system that utilizes generative artificial intelligence to evaluate user input ideas and provide feedback based on the evaluation. In particular, the system has a mechanism for recognizing the user's emotions and adjusting the content of the feedback message based on the user's emotions.

[0949] System configuration and program overview

[0950] Hardware

[0951] This system is composed of user terminals and a server as its main hardware components. User terminals mainly include smartphones and PCs, and the server is used for cloud-based processing.

[0952] software

[0953] The main software used in the system includes:

[0954] 1. Generative AI model: Uses OpenAI's API to analyze and evaluate user input ideas.

[0955] 2. Natural Language Processing Library: TextBlob is used to recognize sentiment from user input text.

[0956] 3. Data Communication Library: A communication method for sending and receiving data over the Internet.

[0957] 4. User interface: A means for users to input ideas, provided as a web browser or mobile application.

[0958] The process of idea analysis and emotion recognition

[0959] A user accesses the system using a terminal and inputs a new idea. For example, the user might input, "I think this product is great, but I'm concerned about the short battery life." The terminal then sends this input data to the server using a communication method.

[0960] The server analyzes the received ideas using natural language processing techniques (such as morphological analysis and topic modeling) and extracts key features. Specifically, it extracts positive features such as "the product is great" and negative features such as "the battery life is short" from complex context.

[0961] The server then evaluates the technical feasibility of the extracted features by comparing them with the current state of generative artificial intelligence technology. For example, a "product improvement proposal" is technically feasible, but the "battery life issue" is evaluated as something that needs to be resolved.

[0962] Based on the evaluated information, the server uses the generation means to create a feedback message. At the same time, the server analyzes the user's input text using TextBlob to recognize emotions. Based on the result of emotion recognition, the adjustment means adjusts the feedback message. For example, if the emotion is positive, a positive message such as "Thank you for your suggestion. We will consider improving the product" is generated.

[0963] The generated feedback message is transmitted to the user terminal using the communication means and is displayed to the user by the display means.

[0964] Examples of prompt statements

[0965] Specific examples are shown below.

[0966] Example: "This product is great, but I'm concerned about the short battery life."

[0967] Generated feedback message: "Thank you for your suggestion. We'll take it into consideration for product improvements. We'll also look into addressing the battery life issue."

[0968] Through the above process, not only are user proposals evaluated technically, but feedback that takes into account the user's feelings is also provided, making it possible to improve user satisfaction.

[0969] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0970] Step 1:

[0971] The user inputs an idea.

[0972] Input: The user types their idea in text form into an input field on the device (e.g., "I think this product is great, but I'm concerned about the short battery life.").

[0973] Operation: The user interface means acquires input data and transmits it to the server via the communication means.

[0974] Output: The input idea data is sent to the server.

[0975] Step 2:

[0976] The server analyzes the received ideas.

[0977] Input: User idea data sent to the server.

[0978] How it works: The server uses natural language processing techniques to analyze ideas and extract key features through morphological analysis and topic modeling.

[0979] Output: Feature data: "The product is great" and "The battery life is short."

[0980] Step 3:

[0981] The server evaluates the technical feasibility.

[0982] Input: Extracted feature data.

[0983] How it works: The server compares the current state of generative AI technology with the feature data and evaluates the technical feasibility. For example, it evaluates whether "product improvement is possible" or "battery life improvement is difficult."

[0984] Output: Evaluation data showing that "product improvement is possible" and "battery life is difficult to improve."

[0985] Step 4:

[0986] The server recognizes emotions.

[0987] Input: The idea data originally entered by the user.

[0988] How it works: The server uses natural language processing techniques such as TextBlob to recognize sentiment from idea data, specifically analyzing the positive / negative tone and wording of the text.

[0989] Output: Emotion data such as "has positive emotions."

[0990] Step 5:

[0991] The server generates a feedback message.

[0992] Input: Rating and sentiment data.

[0993] How it works: The server generates an appropriate feedback message based on the rating and emotion data. For example, it creates a message like, "We can improve the product, but the battery life issue needs to be addressed. Thank you."

[0994] Output: The generated feedback message.

[0995] Step 6:

[0996] The server sends a feedback message to the user.

[0997] Input: The generated feedback message.

[0998] Operation: The server sends a feedback message to the user's terminal via a communication means.

[0999] Output: Feedback messages that are displayed on the user's terminal.

[1000] Step 7:

[1001] The user checks the feedback message.

[1002] Input: The feedback message displayed on the user's terminal.

[1003] Action: The user checks the feedback message using the device's display.

[1004] Output: The content of the feedback message is conveyed to the user.

[1005] These steps ensure that users' ideas are properly evaluated and feedback is provided that takes into account their feelings.

[1006] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1007] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1008] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1009] [Fourth embodiment]

[1010] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1011] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1012] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1013] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1014] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1015] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1016] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1017] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1018] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1019] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1020] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1021] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1022] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1023] This invention relates to a system that uses generative AI to evaluate the technical feasibility of ideas. This system involves a series of processes: idea input from users, idea analysis and evaluation by a server, and feedback of the results.

[1024] The user uses a terminal to input a new idea that utilizes generative AI. For example, a specific example could be, "This idea is a system that automatically generates poetry and then creates art based on that poetry." The idea input through the user interface means is sent to the server via communication means.

[1025] The server analyzes the received ideas using natural language processing technology. This analysis uses methods such as morphological analysis and topic modeling to extract key features contained in the ideas. For example, it can extract that the ideas contain the features of "poetry generation" and "art generation."

[1026] Next, the server evaluates the technical feasibility based on the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generative AI technology. For example, it can be evaluated that generating poetry is possible with current technology, but automatically generating high-quality art from poetry is difficult.

[1027] Based on the evaluation results, the server generates a feedback message, such as "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology."

[1028] The generated feedback message is sent to the user's terminal using the transmission means and displayed to the user by the display means. This allows the user to check the technical feasibility of their idea in advance. This system makes it possible to prevent unnecessary development costs and wasted resources due to ideas that are technically difficult to realize.

[1029] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback to users based on the evaluation.

[1030] The processing flow will be explained below.

[1031] Step 1:

[1032] The user inputs ideas using generative AI through the device.

[1033] The terminal provides an idea input field through a user interface means, and the user inputs "This idea is a system that automatically generates poetry and creates art based on that poetry."

[1034] Step 2:

[1035] The terminal transmits the idea input by the user to the server.

[1036] Using the communication means, the terminal sends the input idea to the server as an HTTP POST request.

[1037] Step 3:

[1038] The server receives the idea sent from the terminal and begins analyzing it using natural language processing technology.

[1039] The server analyzes the idea text using techniques such as morphological analysis and topic modeling to extract important features.

[1040] Step 4:

[1041] The server extracts key features of the idea from the analyzed data.

[1042] The server extracts key features such as "poetry generation" and "art generation."

[1043] Step 5:

[1044] The technical feasibility is evaluated based on the features extracted by the server.

[1045] The server compares each extracted feature with current generative AI technology to assess its feasibility. Specifically, it determines that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[1046] Step 6:

[1047] The server generates a feedback message based on the evaluation results.

[1048] Based on the information obtained by the evaluation means, the server generates a feedback message stating, "Poetry generation is possible with current technology, but automatically generating high-quality art from poetry is technically difficult."

[1049] Step 7:

[1050] The server generates a feedback message and sends it to the user's terminal.

[1051] The server transmits the generated message as an HTTP response to the terminal via the communication means.

[1052] Step 8:

[1053] The terminal displays the received feedback message to the user.

[1054] The device displays the received feedback message on the screen using a display means and conveys it to the user, allowing the user to confirm whether their idea is feasible with current generative AI technology.

[1055] Example 1

[1056] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1057] There is a need to quickly and accurately evaluate the technical feasibility of ideas using generative AI and provide feedback to users. However, with current technology, the evaluation process is often done manually, which is time-consuming and labor-intensive, and the evaluation results lack consistency and accuracy. This leads to the problem of wasting resources and wasting development costs on ideas that are technically difficult to realize.

[1058] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1059] In this invention, the server includes an input means for inputting an idea utilizing a generative AI from a user, a transmission means for transmitting the input idea to the server, an analysis means for analyzing the idea received by the server using natural language processing technology, a feature extraction means for extracting key features of the idea from the analyzed data, an evaluation means for evaluating the technical feasibility based on the extracted features, a generation means for generating a feedback message including the evaluation result, a transmission means for transmitting the generated feedback message to the user, and a display means for displaying the received feedback message to the user. This makes it possible to quickly and accurately evaluate the technical feasibility of an idea and provide the result to the user.

[1060] The "input means" is an interface through which users can input ideas that utilize generative AI.

[1061] The "transmission means" is a means for transmitting the input idea from the terminal to the server.

[1062] The "analysis means" is a means for analyzing ideas received by the server using natural language processing technology.

[1063] The "feature extraction means" is a means for extracting the main features of an idea from the analyzed data.

[1064] The "evaluation means" is a means for evaluating technical feasibility based on the extracted features.

[1065] The "generating means" is a means for generating a feedback message including the evaluation result.

[1066] The "transmitting means" is a means for transmitting the generated feedback message to the user's terminal.

[1067] The "display means" is a means for displaying the received feedback message on the user's terminal.

[1068] "Natural language processing technology" is a technology for analyzing human language and understanding its structure and meaning.

[1069] "Morphological analysis" is a method of dividing a sentence into the smallest meaningful units and analyzing the part of speech of each word.

[1070] "Topic modeling" is a method for automatically extracting topics (themes) from large amounts of text data.

[1071] "Technological feasibility" is an indicator that determines whether a proposed idea can be realized using current technology.

[1072] A "feedback message" is a message that provides the user with information about technical feasibility based on the evaluation results.

[1073] MODE FOR CARRYING OUT THE INVENTION

[1074] This invention relates to a system that uses generative AI to evaluate the technical feasibility of ideas. This system involves a series of processes: idea input from users, idea analysis and evaluation by a server, and feedback of the results.

[1075] The user uses the device to input a new idea that utilizes generative AI. Specifically, the user inputs a prompt statement such as "This is a system that automatically generates poetry and creates art based on that poetry" through the device's user interface. The input idea is sent to the server using the device's transmission means.

[1076] The server analyzes the received ideas using natural language processing techniques, such as morphological analysis and topic modeling. Typically, open-source NLP libraries such as spaCy and NLTK are used for morphological analysis. This allows the server to extract key features, such as "poetry generation" or "art generation."

[1077] Next, the server evaluates the technical feasibility based on the features extracted using the feature extraction method. This evaluation method refers to the latest information and technical papers on current generative AI technology. For example, it may evaluate that existing generative AI models are available for generating poetry, but that it is difficult to generate high-quality art from poetry.

[1078] Based on the evaluation results, the server generates a feedback message such as, "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology."

[1079] The generated feedback message is sent back to the user's terminal using the server's transmission means, and the terminal displays the received feedback message to the user, allowing the user to confirm the technical feasibility of their idea in advance.

[1080] This system makes it possible to prevent unnecessary development costs and wasted resources by first evaluating ideas that are technically difficult to realize. As a concrete example, a user can input a prompt such as "This is a system that automatically generates poetry and creates art based on that poetry," and receive the feedback that "Generating poetry is possible, but generating art is difficult with current technology."

[1081] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback to users based on the evaluation.

[1082] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1083] Program processing steps

[1084] Step 1:

[1085] The user inputs their idea using the user interface on the device, for example, by entering a prompt such as "This is a system that automatically generates poetry and then creates art based on that poetry." This input data is prepared for transmission to the next processing step.

[1086] Step 2:

[1087] The device sends the input idea to the server. Specifically, it sends a prompt to the server using an HTTP request. The input is "This is a system that automatically generates poetry and creates art based on that poetry." The output is text data sent to the server.

[1088] Step 3:

[1089] The server analyzes the received ideas using natural language processing technology. Specifically, it performs morphological analysis, divides the sentence into words, and determines the part of speech of each word. The input is the sent prompt sentence, and the output is the analyzed word list and its part of speech information.

[1090] Step 4:

[1091] The server extracts key features of ideas based on the analysis results. Topic modeling is used to identify key topics, such as "poetry generation" and "art generation." The input is a list of words from the analysis results, and the output is a list of key features.

[1092] Step 5:

[1093] The server evaluates the technical feasibility based on the key features. This evaluation method refers to current generative AI technology to determine the feasibility of each feature. For example, it may evaluate that "generating poetry is possible, but generating art is difficult." The input is a list of features, and the output is the feasibility evaluation result.

[1094] Step 6:

[1095] The server generates a feedback message based on the evaluation results. Specifically, the message generated is, "Poetry generation is possible with current technology, but it is difficult to automatically generate high-quality art from poetry with current technology." The input is the evaluation results, and the output is the feedback message.

[1096] Step 7:

[1097] The server sends the generated feedback message to the terminal. Specifically, it sends the feedback message to the terminal using an HTTP response. The input is the generated feedback message, and the output is the message sent to the terminal.

[1098] Step 8:

[1099] The device displays the received feedback message to the user. Specifically, the feedback message is displayed on the user interface. The message displayed is, "Current technology can generate poetry, but it is difficult to automatically generate high-quality art from poetry." The input is the feedback message sent to the device, and the output is the message displayed to the user.

[1100] In this way, specific data processing and calculations are carried out at each step, and users can receive feedback on the technical feasibility of their ideas.

[1101] (Application example 1)

[1102] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1103] Modern virtual stores are powered by many new ideas, but there is a lack of means to assess in advance whether these ideas are actually technically feasible. As a result, a great deal of time and money is often wasted on ideas that are not feasible. Furthermore, there is a growing demand for a system that utilizes generative AI to quickly assess the technical feasibility of innovative ideas and provide appropriate feedback.

[1104] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1105] In this invention, the server includes a user interface means for inputting ideas utilizing a generation AI from a user, a communication means for transmitting the input ideas to the server, an analysis means for analyzing the ideas received by the server using natural language processing technology, an extraction means for extracting key features of the ideas from the analyzed data, an evaluation means for evaluating the technical feasibility based on the extracted features, a generation means for generating a feedback message including the evaluation results, a transmission means for transmitting the generated feedback message to the user, a display means for displaying the received feedback message to the user, and a means for evaluating the technical feasibility of ideas related to the virtual store. This makes it possible to quickly evaluate the technical feasibility of new ideas for the virtual store and prevent unnecessary waste of resources.

[1106] "User interface means" refers to an interface through which a user can input ideas that utilize the generation AI.

[1107] "Communication means" is a means for transmitting the input idea to the server.

[1108] The "analysis means" is a means for analyzing ideas received by the server using natural language processing technology.

[1109] "Extraction means" refers to means for extracting the main features of ideas from the analyzed data.

[1110] The "evaluation means" is a means for evaluating technical feasibility based on the extracted features.

[1111] The "generating means" is a means for generating a feedback message including the evaluation result.

[1112] The "transmission means" is a means for transmitting the generated feedback message to the user.

[1113] The "display means" is a means for displaying the received feedback message to the user.

[1114] The "means for assessing the technical feasibility of ideas related to virtual stores" is a specialized means for assessing the technical feasibility of new ideas in virtual stores.

[1115] MODE FOR CARRYING OUT THE INVENTION

[1116] This invention is a system that utilizes generative AI to evaluate the technical feasibility of new ideas related to virtual stores and provides efficient feedback. The specific configuration and operation of this system will be described below.

[1117] 1. User Interface Methods

[1118] Managers and staff of virtual stores input new ideas related to the virtual store through applications on smartphones or desktop PCs. The user interface means is an interface for inputting ideas using generative AI, and provides easy-to-understand text boxes and selection menus.

[1119] 2. Sending and Receiving Input

[1120] The idea input by the user is transmitted to the server via the communication means, and the server receives this data and analyzes the idea using the analysis means.

[1121] 3. Analysis using natural language processing technology

[1122] The server uses generative AI models such as OpenAI's GPT-3 to analyze the received ideas using natural language processing techniques, such as morphological analysis and topic modeling, to extract key features contained in the ideas.

[1123] 4. Technical feasibility assessment

[1124] Based on the extracted features, the server evaluates the technical feasibility. The evaluation method determines whether each feature is technically feasible in light of the current state of generative AI technology. This evaluation also considers technical resources and specific implementation methods.

[1125] 5. Generating and Sending Feedback Messages

[1126] Based on the evaluation results, the server generates a feedback message. The generation means creates a specific message such as "This idea is feasible with current technology" or "This part is technically difficult to realize." The generated feedback message is sent to the user's terminal using the transmission means.

[1127] 6. Viewing Feedback

[1128] The received feedback messages are displayed on the user's device, allowing the user to quickly check whether their idea is technically feasible and to carry out efficient planning.

[1129] Specific examples

[1130] A virtual store manager inputs an idea for automatically generating new product displays for spring. This input is expressed as a prompt sentence like this:

[1131] text

[1132] Evaluate the technical feasibility of this idea: A system for automatically changing merchandise displays seasonally in a virtual store.

[1133] The server receives this input, analyzes it using the GPT-3 model, evaluates the technical feasibility, and provides specific feedback, such as "Automatic generation is technically possible, but product data needs to be updated."

[1134] Hardware and software used

[1135] The server can be built using a desktop PC or a cloud computing service (such as AWS or Google Cloud). The software used is based on Python programming and uses OpenAI's GPT-3 API for natural language processing. An internet connection is required for data communication.

[1136] As a result, a system is realized that enables virtual store managers to quickly evaluate the technical feasibility of new ideas and prevent unnecessary waste of resources.

[1137] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1138] Step 1:

[1139] A user inputs a new idea related to a virtual store using generative AI into an application on a smartphone or desktop PC. For example, the user inputs "an idea for automatically generating new product displays for spring." This input is entered into a text box and processed using a user interface means.

[1140] Input: User-entered ideas

[1141] Output: Input idea text

[1142] Step 2:

[1143] The terminal transmits the idea input by the user to the server via the communication means, and the input idea is transmitted to the server via the network.

[1144] Input: Entered idea text

[1145] Output: Idea data sent to the server

[1146] Step 3:

[1147] The server analyzes the idea data received via the communication means using an analysis means. Specifically, it uses a generative AI model such as OpenAI's GPT-3 to analyze the idea text through morphological analysis and topic modeling.

[1148] Input: Idea data sent to the server

[1149] Output: Key features of the idea

[1150] Step 4:

[1151] The server uses extraction means to extract key features of ideas from the analyzed data, such as phrases segmented by morphological analysis and topics obtained by topic modeling.

[1152] Input: Parsed idea data

[1153] Output: Extracted key features

[1154] Step 5:

[1155] The server evaluates the technical feasibility based on the extracted features. The evaluation method determines whether each feature is technically feasible in light of the current state of generative AI technology. This evaluation takes into account technical resources and specific implementation methods.

[1156] Input: Extracted key features

[1157] Output: Technical feasibility assessment results

[1158] Step 6:

[1159] The server generates a feedback message based on the evaluation results. A specific feedback message is created by the generation means. The generated message may be something like "This idea is feasible with current technology" or "This part is technically difficult to achieve."

[1160] Input: Technical feasibility assessment results

[1161] Output: Feedback message

[1162] Step 7:

[1163] The server transmits the generated feedback message to the user's terminal via the transmission means, and the feedback message is sent to the user's terminal through the network.

[1164] Input: Feedback message

[1165] Output: Feedback message sent to the terminal

[1166] Step 8:

[1167] The user's terminal displays the received feedback message to the user using a display means, thereby enabling the user to quickly check whether or not their idea is technically feasible.

[1168] Input: Feedback message sent to the device

[1169] Output: The feedback message displayed to the user

[1170] The above steps realize a system that evaluates the technical feasibility of new virtual store ideas and provides efficient feedback.

[1171] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1172] This invention relates to a system that evaluates the technical feasibility of ideas using generative AI, recognizes the user's emotions, and adjusts the content of feedback messages based on those emotions. This system includes a series of processes: idea input from the user, idea analysis and evaluation by the server, emotion recognition, and feedback of the evaluation results.

[1173] The user uses a terminal to input a new idea that utilizes generative AI. For example, a specific example could be, "This idea is a system that automatically generates poetry and then creates art based on that poetry." The idea input through the user interface means is sent to the server via communication means.

[1174] The server analyzes the received ideas using natural language processing techniques, such as morphological analysis and topic modeling, to extract key features from the ideas. For example, it can extract that the ideas contain the features of "poetry generation" and "art generation."

[1175] Next, the server evaluates the technical feasibility based on the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generative AI technology. For example, it may determine that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[1176] Based on the evaluation results, the server generates a feedback message, such as "Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[1177] Furthermore, the present invention includes an emotion recognition means. This emotion recognition means recognizes the user's emotion input through the user interface and analyzes it using natural language processing technology and machine learning algorithms. For example, it can determine the user's emotion from the sentences and input speed when the user inputs ideas, specific keywords, etc.

[1178] Based on the user's perceived emotions, the evaluator can adjust the feedback message accordingly. For example, if the user is very enthusiastic, the evaluator can provide positive feedback such as, "That's a very interesting idea. Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[1179] The generated feedback message is sent to the user's device using a transmission means and displayed to the user using a display means. This allows the user to confirm whether their idea is feasible with current generative AI technology, and also realize that the feedback content takes their feelings into consideration. This system makes it possible to prevent unnecessary development costs and resource wastage due to ideas that are technically difficult to realize, while also maintaining user motivation.

[1180] As described above, the present invention provides a system that evaluates the technical feasibility of ideas using generative AI and provides appropriate feedback based on the evaluation while taking into consideration the user's feelings.

[1181] The processing flow will be explained below.

[1182] Step 1:

[1183] The user inputs ideas using generative AI through the device.

[1184] The terminal provides an idea input field through a user interface means, and the user inputs "This idea is a system that automatically generates poetry and creates art based on that poetry."

[1185] Step 2:

[1186] The terminal transmits the idea input by the user to the server.

[1187] Using the communication means, the terminal sends the input idea to the server as an HTTP POST request.

[1188] Step 3:

[1189] The server receives the idea sent from the terminal and begins analyzing it using natural language processing technology.

[1190] The server analyzes the idea text using techniques such as morphological analysis and topic modeling to extract important features.

[1191] Step 4:

[1192] The server extracts key features of the idea from the analyzed data.

[1193] The server extracts key features such as "poetry generation" and "art generation."

[1194] Step 5:

[1195] The technical feasibility is evaluated based on the features extracted by the server.

[1196] The server compares each extracted feature with current generative AI technology to assess its feasibility. Specifically, it determines that while current technology can generate poetry, it is difficult to automatically generate high-quality art from poetry.

[1197] Step 6:

[1198] The server analyzes the user's emotions using an emotion recognition means.

[1199] The server uses emotion recognition to analyze the text entered by the user, the input speed, specific keywords, etc., and determines the user's emotion. For example, if the user enters an idea with enthusiasm, the server recognizes the emotion as "positive."

[1200] Step 7:

[1201] The server generates a feedback message based on the evaluation results and the perceived user sentiment.

[1202] The server bases its message on the basic message, "Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry," and if the user's sentiment is positive, adds a positive comment such as, "That's a very interesting idea."

[1203] Step 8:

[1204] The server generates a feedback message and sends it to the user's terminal.

[1205] The server transmits the generated message as an HTTP response to the terminal via the communication means.

[1206] Step 9:

[1207] The terminal displays the received feedback message to the user.

[1208] The device displays the received feedback message on the screen and conveys it to the user, allowing the user to confirm whether their idea is feasible with current generative AI technology and to feel that the feedback content takes their feelings into consideration.

[1209] This series of steps allows users to confirm the technical feasibility of their ideas in advance and maintain their motivation by receiving emotionally sensitive feedback.

[1210] Example 2

[1211] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1212] In conventional systems, feedback is not provided that takes into account the user's emotions when inputting ideas and evaluating them, making it difficult to maintain user motivation. Also, evaluations of whether an idea is technically feasible may not meet the user's expectations, resulting in incomplete feedback.

[1213] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting a proposal utilizing a generation technology from a user, a communication means for transmitting the input proposal to the processing device, an analysis means for analyzing the proposal received by the processing device using natural language processing technology, an extraction means for extracting main features of the proposal from the analyzed data, an evaluation means for evaluating technical feasibility based on the extracted features, a generation means for generating a response sentence including the evaluation result, a transmission means for transmitting the generated response sentence to the user, a display means for displaying the received response sentence to the user, and further, a recognition means for recognizing the user's emotions and an adjustment means for adjusting the content of the response sentence based on the recognized emotions. This makes it possible to provide feedback that takes the user's emotions into consideration, thereby increasing the user's motivation and accurately evaluating technical feasibility.

[1214] A "user" is an entity that uses this system to input suggestions.

[1215] "Generative technology" is a technology that uses artificial intelligence and machine learning to generate new ideas and data.

[1216] "Suggestion" refers to an idea or concept that a user creates using generative technology and inputs into the system.

[1217] "Input means" refers to an interface through which a user inputs suggestions into the system.

[1218] "Communication means" refers to a means for transmitting a suggestion input by the input means to the server.

[1219] "Processing device" refers to a server or computing unit that receives, analyzes, and evaluates the suggestions.

[1220] "Analysis means" refers to a function for analyzing received proposals using natural language processing technology.

[1221] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[1222] "Extraction means" refers to a function for extracting key features of a proposal from the analyzed data.

[1223] "Evaluation means" refers to a function for evaluating the technical feasibility of a proposal based on the extracted features.

[1224] "Generation means" refers to a function for generating a response sentence based on the evaluation result.

[1225] "Transmission means" refers to a function for transmitting the generated response sentence to the user.

[1226] The "display means" refers to an interface for displaying the received response sentence to the user.

[1227] "Recognition means" refers to a function for recognizing the user's emotions.

[1228] "Adjustment means" refers to a function for adjusting the content of a response sentence based on the recognized emotion.

[1229] This invention relates to a system that utilizes generation technology to evaluate the technical feasibility of ideas, recognizes user emotions, and adjusts the content of a response message based on those emotions. This system includes a series of processes: user input of a proposal, analysis and evaluation of the proposal by a server, emotion recognition, and generation and transmission of a response message based on the evaluation results.

[1230] The user inputs a new proposal using the generation technology using a terminal. For example, a specific example could be, "This is a system that automatically generates poetry and then creates art based on that poetry." The proposal input through the user interface means is sent to the server via the communication means.

[1231] The server analyzes the received proposals using natural language processing technology. This analysis involves techniques such as morphological analysis and topic modeling to extract key features contained in the proposals. Natural language processing libraries such as NLTK and spaCy, and morphological analysis tools such as MeCab are used for the analysis. For example, it can be extracted that the proposals contain the features of "poetry generation" and "art generation."

[1232] Next, the server evaluates the technical feasibility of the features obtained by the extraction means. The evaluation means determines whether each feature is technically feasible in light of the current state of generation technology. The evaluation includes consulting academic paper databases and technology review articles. For example, the server determines that while poetry generation is possible with current technology, it is difficult to automatically generate high-quality art from poetry.

[1233] Based on the evaluation results, the server generates a response using a generative AI model such as GPT-3. For example, it could create a specific message such as, "That's a very interesting idea. While generating poetry is possible with current technology, automatically generating high-quality art from poetry is technically difficult."

[1234] Furthermore, the present invention includes an emotion recognition unit that recognizes the user's emotion input through the user interface and analyzes it using natural language processing technology and machine learning algorithms. For example, the emotion of the user can be determined from the sentences, input speed, specific keywords, etc., when the user inputs a suggestion.

[1235] Based on the user's emotions, the evaluator can adjust the content of the response. For example, if the user is very enthusiastic, the evaluator can provide positive feedback such as, "That's a very interesting idea. Poetry generation is possible with current technology, but it is technically difficult to automatically generate high-quality art from poetry."

[1236] The generated response sentence is sent to the user's terminal using the transmission means and displayed to the user by the display means. This allows the user to confirm whether their proposal is feasible with current generation technology and to realize that the response sentence takes their feelings into consideration. This system prevents unnecessary development costs and resource wastage due to proposals that are technically difficult to realize, while also maintaining user motivation.

[1237] Specific examples

[1238] Here is an example prompt:

[1239] Example: A prompt that the user types into the terminal:

[1240] "It's a system that automatically generates poetry and then creates art based on that poetry."

[1241] The above is a specific embodiment for carrying out the invention.

[1242] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1243] Step 1: User enters their idea

[1244] The user inputs a new proposal using the generation technology using a terminal. For example, the user inputs "This is a system that automatically generates poetry and creates art based on that poetry" into the input field. The input data is a proposal in text format.

[1245] Step 2: Send your ideas from your device to the server

[1246] The terminal sends the proposal data entered by the user to the server. For transmission, a communication protocol such as an HTTP POST request is used. The input is a text data proposal, which is sent to the server. The output is the proposal data sent to the server.

[1247] Step 3: The server analyzes the idea

[1248] The server analyzes the received proposal data. This analysis uses natural language processing libraries (NLTK and spaCy) to extract key keywords contained in the proposal. For example, keywords such as "poetry generation" and "art generation" are identified. The input is the proposal text, and the output is a list of analyzed keywords.

[1249] Step 4: The server evaluates technical feasibility

[1250] The server evaluates the technical feasibility based on the extracted keywords. For this evaluation, it refers to academic paper databases and technology review articles. For example, the server may determine that generating poetry is possible with current technology, but generating high-quality art is difficult. The input is a list of keywords, and the output is the result of the technical evaluation.

[1251] Step 5: The server recognizes the user's emotion

[1252] The server uses natural language processing technology and machine learning models to recognize the user's emotions. Specifically, it infers emotions from the user's written expressions and input speed. For example, if the user uses a lot of positive expressions, it determines that the user is enthusiastic. The input is the proposed text and input speed data, and the output is the emotion evaluation result.

[1253] Step 6: Server generates feedback message

[1254] The server generates a feedback message based on the evaluation results and the recognized emotions. A generative AI model such as GPT-3 is used for generation. For example, it might generate a message like, "That's a very interesting idea. While generating poetry is possible with current technology, automatically generating high-quality art from poetry is technically difficult." The inputs are the technical evaluation results and the emotion evaluation results, and the output is the feedback message.

[1255] Step 7: The server sends a feedback message to the device

[1256] The server sends the generated feedback message to the terminal using a communication protocol such as HTTP or WebSocket. The input is the feedback message, and the output is the message sent to the user's terminal.

[1257] Step 8: The device displays feedback to the user

[1258] The terminal displays the received feedback message to the user. The user interface displays the message in the form of a pop-up, a dialog box, etc. For example, a message such as "That's a very interesting idea..." may appear on the screen and the user may confirm it. The input is the feedback message, and the output is the feedback displayed to the user.

[1259] The above is a concrete breakdown and explanation of the program processing of this system.

[1260] (Application example 2)

[1261] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1262] Conventional systems using generative artificial intelligence only evaluate the technical feasibility of user ideas and are unable to provide feedback that takes the user's emotions into account. This leads to problems such as reduced user satisfaction and motivation, and a lack of constructive exchange of opinions. To solve this problem, a system that recognizes the user's emotions and provides feedback based on them is needed.

[1263] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1264] In this invention, the server includes a user interface means for inputting ideas that utilize generative artificial intelligence, a communication means for transmitting the input ideas to the server, and an analysis means for analyzing the ideas received by the server using natural language processing technology. This makes it possible to evaluate the technical feasibility of the ideas input by the user, analyze the user's emotions using emotion recognition technology, and generate appropriate feedback messages.

[1265] "Generative AI" is an AI technology that generates new content and information based on large amounts of data.

[1266] "User interface means" refers to means that provides an input interface for a user to access and operate the system.

[1267] A "communication means" is a mechanism for sending and receiving data between a user device and a server.

[1268] "Analysis means" is a means of analyzing received ideas using natural language processing technology, and understanding and classifying their contents.

[1269] An "extraction means" is a mechanism for extracting key features or topics from the analyzed data.

[1270] The "evaluation means" is a mechanism for evaluating technical feasibility based on the extracted features.

[1271] The "generation means" is a process that generates a feedback message based on the evaluation results.

[1272] The "transmission means" is a means for transmitting the generated feedback message to the user.

[1273] The "display means" is an interface for displaying the received feedback message to the user.

[1274] "Emotion recognition means" is a technology for analyzing the content of a user's input and recognizing emotions.

[1275] "Adjustment" is a process for adjusting the content of feedback messages based on perceived emotions.

[1276] "Natural language processing technology" is a general term for algorithms and techniques that enable computers to understand human language.

[1277] A "machine learning algorithm" is an algorithm that learns from data and makes future predictions and decisions.

[1278] The present invention relates to a system that utilizes generative artificial intelligence to evaluate user input ideas and provide feedback based on the evaluation. In particular, the system has a mechanism for recognizing the user's emotions and adjusting the content of the feedback message based on the user's emotions.

[1279] System configuration and program overview

[1280] Hardware

[1281] This system is composed of user terminals and a server as its main hardware components. User terminals mainly include smartphones and PCs, and the server is used for cloud-based processing.

[1282] software

[1283] The main software used in the system includes:

[1284] 1. Generative AI model: Uses OpenAI's API to analyze and evaluate user input ideas.

[1285] 2. Natural Language Processing Library: TextBlob is used to recognize sentiment from user input text.

[1286] 3. Data Communication Library: A communication method for sending and receiving data over the Internet.

[1287] 4. User interface: A means for users to input ideas, provided as a web browser or mobile application.

[1288] The process of idea analysis and emotion recognition

[1289] A user accesses the system using a terminal and inputs a new idea. For example, the user might input, "I think this product is great, but I'm concerned about the short battery life." The terminal then sends this input data to the server using a communication method.

[1290] The server analyzes the received ideas using natural language processing techniques (such as morphological analysis and topic modeling) and extracts key features. Specifically, it extracts positive features such as "the product is great" and negative features such as "the battery life is short" from complex context.

[1291] The server then evaluates the technical feasibility of the extracted features by comparing them with the current state of generative artificial intelligence technology. For example, a "product improvement proposal" is technically feasible, but the "battery life issue" is evaluated as something that needs to be resolved.

[1292] Based on the evaluated information, the server uses the generation means to create a feedback message. At the same time, the server analyzes the user's input text using TextBlob to recognize emotions. Based on the result of emotion recognition, the adjustment means adjusts the feedback message. For example, if the emotion is positive, a positive message such as "Thank you for your suggestion. We will consider improving the product" is generated.

[1293] The generated feedback message is transmitted to the user terminal using the communication means and is displayed to the user by the display means.

[1294] Examples of prompt statements

[1295] Specific examples are shown below.

[1296] Example: "This product is great, but I'm concerned about the short battery life."

[1297] Generated feedback message: "Thank you for your suggestion. We'll take it into consideration for product improvements. We'll also look into addressing the battery life issue."

[1298] Through the above process, not only are user proposals evaluated technically, but feedback that takes into account the user's feelings is also provided, making it possible to improve user satisfaction.

[1299] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1300] Step 1:

[1301] The user inputs an idea.

[1302] Input: The user types their idea in text form into an input field on the device (e.g., "I think this product is great, but I'm concerned about the short battery life.").

[1303] Operation: The user interface means acquires input data and transmits it to the server via the communication means.

[1304] Output: The input idea data is sent to the server.

[1305] Step 2:

[1306] The server analyzes the received ideas.

[1307] Input: User idea data sent to the server.

[1308] How it works: The server uses natural language processing techniques to analyze ideas and extract key features through morphological analysis and topic modeling.

[1309] Output: Feature data: "The product is great" and "The battery life is short."

[1310] Step 3:

[1311] The server evaluates the technical feasibility.

[1312] Input: Extracted feature data.

[1313] How it works: The server compares the current state of generative AI technology with the feature data and evaluates the technical feasibility. For example, it evaluates whether "product improvement is possible" or "battery life improvement is difficult."

[1314] Output: Evaluation data showing that "product improvement is possible" and "battery life is difficult to improve."

[1315] Step 4:

[1316] The server recognizes emotions.

[1317] Input: The idea data originally entered by the user.

[1318] How it works: The server uses natural language processing techniques such as TextBlob to recognize sentiment from idea data, specifically analyzing the positive / negative tone and wording of the text.

[1319] Output: Emotion data such as "has positive emotions."

[1320] Step 5:

[1321] The server generates a feedback message.

[1322] Input: Rating and sentiment data.

[1323] How it works: The server generates an appropriate feedback message based on the rating and emotion data. For example, it creates a message like, "We can improve the product, but the battery life issue needs to be addressed. Thank you."

[1324] Output: The generated feedback message.

[1325] Step 6:

[1326] The server sends a feedback message to the user.

[1327] Input: The generated feedback message.

[1328] Operation: The server sends a feedback message to the user's terminal via a communication means.

[1329] Output: Feedback messages that are displayed on the user's terminal.

[1330] Step 7:

[1331] The user checks the feedback message.

[1332] Input: The feedback message displayed on the user's terminal.

[1333] Action: The user checks the feedback message using the device's display.

[1334] Output: The content of the feedback message is conveyed to the user.

[1335] These steps ensure that users' ideas are properly evaluated and feedback is provided that takes into account their feelings.

[1336] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1337] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1338] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1339] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1340] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1341] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1342] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1343] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1344] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1345] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1346] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1347] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1348] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1349] 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.

[1350] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1351] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1352] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1353] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1354] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1355] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1356] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1357] The following is further disclosed regarding the above embodiment.

[1358] (Claim 1)

[1359] A user interface means for inputting ideas using the generation AI from a user;

[1360] a communication means for transmitting the input idea to a server;

[1361] an analysis means for analyzing the ideas received by the server using natural language processing technology;

[1362] extraction means for extracting key features of ideas from the analyzed data;

[1363] an evaluation means for evaluating technical feasibility based on the extracted features;

[1364] generating means for generating a feedback message including the evaluation result;

[1365] sending means for sending the generated feedback message to the user;

[1366] The system includes a display means for displaying the received feedback message to a user.

[1367] (Claim 2)

[1368] 10. The system of claim 1, wherein the analyzing means includes means for analyzing ideas by morphological analysis and topic modeling.

[1369] (Claim 3)

[1370] 2. The system of claim 1, wherein the evaluation means includes means for evaluating the technical feasibility of the idea in comparison with the current state of generative AI technology.

[1371] "Example 1"

[1372] (Claim 1)

[1373] An input method for users to input ideas using generative AI;

[1374] a transmitting means for transmitting the input idea to a server;

[1375] an analysis means for analyzing the ideas received by the server using natural language processing technology;

[1376] a feature extraction means for extracting key features of ideas from the analyzed data;

[1377] an evaluation means for evaluating technical feasibility based on the extracted features;

[1378] generating means for generating a feedback message including the evaluation result;

[1379] sending means for sending the generated feedback message to the user;

[1380] and display means for displaying the received feedback message to the user.

[1381] (Claim 2)

[1382] 10. The system of claim 1, wherein the analyzing means includes means for analyzing ideas by morphological analysis and topic modeling.

[1383] (Claim 3)

[1384] 2. The system of claim 1, wherein the evaluation means includes means for evaluating the technical feasibility of the idea in comparison with the current state of generative AI technology.

[1385] "Application Example 1"

[1386] (Claim 1)

[1387] A user interface means for inputting ideas using the generation AI from a user;

[1388] a communication means for transmitting the input idea to a server;

[1389] an analysis means for analyzing the ideas received by the server using natural language processing technology;

[1390] extraction means for extracting key features of ideas from the analyzed data;

[1391] an evaluation means for evaluating technical feasibility based on the extracted features;

[1392] generating means for generating a feedback message including the evaluation result;

[1393] sending means for sending the generated feedback message to the user;

[1394] a display means for displaying the received feedback message to a user;

[1395] a means of assessing the technical feasibility of ideas related to virtual stores;

[1396] A system including:

[1397] (Claim 2)

[1398] 10. The system of claim 1, wherein the analyzing means includes means for analyzing ideas by morphological analysis and topic modeling.

[1399] (Claim 3)

[1400] 2. The system of claim 1, wherein the evaluation means includes means for evaluating the technical feasibility of the idea in comparison with the current state of generative AI technology.

[1401] "Example 2: Combining Emotion Engines"

[1402] (Claim 1)

[1403] an input means for inputting a proposal utilizing the generation technology from a user;

[1404] communication means for transmitting the input proposal to the processing device;

[1405] analysis means for analyzing the proposal received by the processing device using natural language processing techniques;

[1406] extraction means for extracting key features of the proposal from the analyzed data;

[1407] an evaluation means for evaluating technical feasibility based on the extracted features;

[1408] a generating means for generating a response sentence including an evaluation result;

[1409] a transmitting means for transmitting the generated response sentence to a user;

[1410] a display means for displaying the received response to a user;

[1411] Furthermore, a recognition means for recognizing the user's emotion;

[1412] The system includes an adjustment means for adjusting the content of the response sentence based on the recognized emotion.

[1413] (Claim 2)

[1414] 10. The system of claim 1, wherein the analyzing means includes means for analyzing the suggestions using morphological analysis and topic modeling.

[1415] (Claim 3)

[1416] 10. The system of claim 1, wherein the evaluation means includes means for evaluating the technical feasibility of the proposal in comparison with the current state of the art in production technology.

[1417] "Application example 2 when combining emotion engines"

[1418] Claiming a new invention

[1419] (Claim 1)

[1420] a user interface means for inputting ideas using generative artificial intelligence;

[1421] a communication means for transmitting the input idea to a server;

[1422] an analysis means for analyzing the ideas received by the server using natural language processing technology;

[1423] extraction means for extracting key features of ideas from the analyzed data;

[1424] an evaluation means for evaluating technical feasibility based on the extracted features;

[1425] generating means for generating a feedback message including the evaluation result;

[1426] sending means for sending the generated feedback message to the user;

[1427] a display means for displaying the received feedback message to a user;

[1428] emotion recognition means for analyzing user input using emotion recognition technology;

[1429] The system includes an adjustment means for adjusting the content of the feedback message based on the emotion recognized by the emotion recognition means.

[1430] (Claim 2)

[1431] 10. The system of claim 1, wherein the analyzing means includes means for analyzing ideas by morphological analysis and topic modeling.

[1432] (Claim 3)

[1433] 2. The system of claim 1, wherein the evaluation means includes means for evaluating the technical feasibility of the idea in comparison with the current state of generative artificial intelligence technology.

[1434] (Claim 4)

[1435] 2. The system of claim 1, wherein the emotion recognition means includes means for determining emotions from user input using natural language processing techniques and machine learning algorithms.

[1436] (Claim 5)

[1437] 2. The system of claim 1, wherein the adjusting means includes means for adjusting the content of the feedback message to be positive or critical based on the perceived emotion. [Explanation of symbols]

[1438] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A user interface means for inputting ideas using the generation AI from a user; a communication means for transmitting the input idea to a server; an analysis means for analyzing the ideas received by the server using natural language processing technology; extraction means for extracting key features of ideas from the analyzed data; an evaluation means for evaluating technical feasibility based on the extracted features; generating means for generating a feedback message including the evaluation result; sending means for sending the generated feedback message to the user; The system includes a display means for displaying the received feedback message to a user.

2. 2. The system of claim 1, wherein the analyzing means includes means for analyzing ideas by morphological analysis and topic modeling.

3. 2. The system of claim 1, wherein the evaluation means includes means for evaluating the technical feasibility of the idea in comparison with the current state of generative AI technology.

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