system

Abstract

We provide the system. [Solution] Communication means for obtaining various data from external databases, A generation means for generating new ideas based on acquired data, An evaluation method for evaluating and ranking the generated ideas, A display means for visually displaying the evaluated ideas, Analytical tools for analyzing the differentiating factors from competitors, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional idea generation processes require a great deal of time and effort because they involve huge data collection and market analysis. Also, skills are required to conduct competitive analysis and formulate an idea differentiation strategy, and in many cases, efficient idea creation has been inhibited. Due to such problems, it has been difficult to quickly seize new business opportunities.

Means for Solving the Problems

[0005] This invention provides a system that combines communication means for automatically acquiring necessary data from an external database, generation means for generating new ideas based on the acquired data, evaluation means for evaluating and ranking the generated ideas, display means for visually displaying them, and analysis means for conducting competitive analysis and clarifying differentiating factors. This makes it possible to provide users with a process for efficiently and quickly generating new ideas that are relevant to the market and selecting the most suitable one from among them.

[0006] A "communication means" is a component that has the function of automatically retrieving necessary data from an external database.

[0007] A "generation means" is a component that has the function of generating new ideas based on acquired data.

[0008] An "evaluation tool" is a component that has the function of evaluating generated ideas and ranking them based on established criteria.

[0009] A "display means" is a component that has the function of visually presenting an evaluated idea to the user.

[0010] "Analysis tools" are components that perform competitive analysis on generated ideas and clarify the elements for differentiation. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention provides a realization of efficient novel idea generation using an information processing system. This system supports the rapid creation of new business ideas by appropriately coordinating communication means, generation means, evaluation means, display means, and analysis means.

[0033] System configuration and operation

[0034] Data acquisition and generation

[0035] First, the server accesses an external database using communication methods to collect data necessary for idea generation, such as industry trends and competitor information. Based on this data, the server generates new ideas using generation methods. By utilizing multiple AI algorithms and generating diverse ideas based on the collected data, it is possible to present a wide range of possibilities.

[0036] Idea evaluation and display

[0037] The generated ideas are automatically evaluated and ranked by an evaluation system based on criteria tailored to the user's needs. These evaluation criteria include, for example, market value, innovativeness, and feasibility. The ranked ideas are presented to the user visually through a display on their device, allowing them to quickly understand the key features and evaluation of each idea.

[0038] Specific example

[0039] For example, if a user seeks innovative product ideas for the food industry, they input criteria targeting the food industry into the system. In response, the server collects data on the latest food trends and consumer preferences from an external database. Next, a generation tool is used to create new recipes and product concepts, which are then evaluated by an evaluation tool. Finally, through a display tool, the user can select compelling ideas and further refine their differentiating factors using an analysis tool.

[0040] Thus, the system of the present invention can significantly reduce the effort required for users to generate ideas and provide powerful support for making quick decisions.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user enters the subject area or specific conditions for which they want to generate ideas. These conditions include the target industry, purpose, and target market.

[0044] Step 2:

[0045] The server connects to an external database and retrieves relevant data based on the user's input. This includes industry trends, market size, and competitor information. The collected data is stored in an internal database.

[0046] Step 3:

[0047] The server generates new ideas using generation methods based on information stored in the database. Multiple AI models are operated simultaneously to propose multiple ideas from different perspectives.

[0048] Step 4:

[0049] The server evaluates each generated idea using evaluation tools. It scores and ranks each idea based on criteria such as market value, feasibility, and innovativeness.

[0050] Step 5:

[0051] The device visually presents ideas to the user based on the evaluation results. Key features and evaluation points of the ideas are displayed in graphic or short video format.

[0052] Step 6:

[0053] The user can select the most interesting idea from the displayed group of ideas. Based on this selection, the server analyzes additional differentiating factors and presents the results to the user.

[0054] Step 7:

[0055] The device suggests actionable plans and next steps based on the ideas selected by the user. This helps to initiate a concrete development process.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In today's information-saturated society, it is difficult to quickly generate novel and competitively advantageous ideas. Furthermore, the lack of efficient means to evaluate and visually present these generated ideas leads to delays in the decision-making process.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes communication means for acquiring various types of information from external information sources, generation means for generating new concepts based on the acquired information, means for using a generation AI model, evaluation means for evaluating and ranking the generated concepts, and means for considering market value and innovativeness as specific evaluation criteria. This enables the rapid generation of novel ideas and efficient decision-making through their evaluation and presentation.

[0061] "Communication means" refers to a device or system that has the function of acquiring various types of information from external sources.

[0062] A "generation method" is a device or system that has the function of creating new concepts based on acquired information. By using a generation AI model, it is possible to generate new ideas from data.

[0063] An "evaluation tool" is a device or system that has the function of evaluating and ranking generated concepts based on various criteria. It is possible to perform evaluations that take into account specific criteria such as market value or innovativeness.

[0064] A "presentation means" is a device or system that has the function of visually presenting an evaluated concept to the user.

[0065] "Analysis means" refers to a device or system that has the function of analyzing the differentiating factors of a generated concept from those of competitors and extracting competitive advantages.

[0066] An "input / output device" is a device equipped with an interface for inputting conceptual conditions generated by the user and receiving the results as output.

[0067] An "activity plan" is a plan that presents specific implementation procedures and measures related to the concepts selected by the user.

[0068] This invention is an information processing system that integrates information gathering from external sources, generation of novel concepts using a generative AI model, automatic evaluation of these concepts, visual presentation, and analysis of differentiating factors. Specific embodiments are described in detail below.

[0069] The server connects to external information sources using communication methods and retrieves the necessary information. This information includes data on industry trends and consumer preferences. Specifically, the server has the capability to collect the latest data using REST APIs and SQL queries.

[0070] Next, the server uses a generation method to generate new concepts based on the acquired information. In this process, a generative AI model is used, and new ideas are generated by inputting prompts such as "Generate innovative recipes for the food industry." This generation process uses natural language processing technology to extract highly relevant keywords from the information and generate diverse ideas.

[0071] The generated ideas are evaluated based on multiple criteria using an evaluation system. Market value and innovativeness are particularly considered. The server automatically evaluates and ranks the ideas based on these criteria. The evaluation information is then linked with a presentation system and visually displayed on the terminal.

[0072] Users can use the information displayed on their device to verify the details of an idea. The device visualizes the information using ranking tables, graphs, and other visual aids. Finally, the user's selected idea is analyzed using analytical tools to identify its differentiating factors, which are then presented as a concrete action plan.

[0073] Through this system, users can efficiently generate new ideas from vast amounts of information and make rapid decisions.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server accesses external information sources using communication methods and collects the necessary information. The input includes requests regarding industry trends and consumer preferences, and based on this, it executes REST APIs or SQL queries to retrieve data. The output is a raw dataset necessary for idea generation. Specifically, the server retrieves the latest food trend information from a trend database.

[0077] Step 2:

[0078] The server generates new concepts using a generative AI model based on the data acquired in Step 1. The input consists of collected data and a prompt (e.g., "Generate innovative recipes for the food industry"). The AI ​​analyzes the data, extracts relevant keywords, and then generates a variety of ideas. The output is a list of newly generated ideas. Specifically, the server uses natural language processing techniques to analyze the data and create new recipe ideas.

[0079] Step 3:

[0080] The server automatically evaluates the generated ideas using evaluation tools. The input is the list of ideas generated in step 2, to which predefined evaluation criteria such as market value, innovativeness, and feasibility are applied. The output is a ranking list of the evaluated ideas. Specifically, the server applies a scoring algorithm to evaluate each idea and assign a ranking.

[0081] Step 4:

[0082] The terminal visually presents evaluated ideas received from the server. The input is a ranking list of evaluated ideas, and the terminal uses this list to provide information to the user. The output is visual information that the user can view on the screen. Specifically, the terminal displays idea information in the form of ranking tables or graphs.

[0083] Step 5:

[0084] The user selects an idea from those presented on the device and obtains further details. The input is the user's idea selection, and based on the selected idea, differentiating factors are analyzed through analytical tools. The output is detailed information presented to the user as a concrete activity plan and implementation procedure. Specifically, the user uses a filtering function to prioritize and review ideas with high market value.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] In the advertising industry, there is a need to quickly generate new campaigns and advertising ideas, and to effectively and efficiently evaluate and propose them. However, traditional methods require significant time and resources for information gathering and analysis, hindering rapid decision-making. This challenge needs to be addressed.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes communication means for acquiring data from an external information storage system, generation means for generating new concepts based on the acquired data, and means for supporting and suggesting improvements to user behavior based on the generated advertising proposals. This enables the rapid generation and evaluation of new ideas based on the latest advertising trends.

[0090] "Communication means" refers to a method or process used to retrieve data from an external information storage system.

[0091] A "generative means" is a method or process for creating new concepts or ideas based on acquired data.

[0092] "Evaluation means" refers to a method or process for evaluating generated concepts and ranking them in terms of superiority or inferiority.

[0093] "Display means" refers to a method or process for visually presenting an evaluated concept to a user.

[0094] "Analytical tools" refer to methods or processes for investigating and understanding the factors that differentiate a company from its competitors in detail.

[0095] A "generative AI model" is a program or algorithm that uses artificial intelligence technology to generate new ideas or advertising proposals.

[0096] "Means for making improvement suggestions" refers to a method or process for making useful improvement suggestions to users based on the generated advertising drafts.

[0097] "Means to support user behavior" refers to support measures or processes that guide users to make appropriate choices and actions.

[0098] This system is designed to help generate, evaluate, and improve new ideas and campaign proposals in the advertising industry. The core of the system consists of communication, generation, evaluation, and improvement suggestion mechanisms. The system's implementation utilizes servers, terminals, and a generation AI model.

[0099] The server uses communication methods to access external information storage systems via the internet and collects various data such as advertising industry trend information and competitor data. Then, using generation methods, it generates new advertising concepts and campaign proposals based on this collected data. The generation AI model uses an AI framework such as TENSORFLOW® to generate diverse ideas based on the generated prompt messages.

[0100] The generated ideas are analyzed using evaluation tools, and their value and effectiveness are quantified. The evaluation uses an algorithm running in Python, and the results are displayed visually on the terminal. This display utilizes a framework such as Bootstrap to create an intuitive and user-friendly interface.

[0101] When a user selects an idea from the presented options and requests specific improvement suggestions, the server uses a generative AI model to generate further improvement suggestions tailored to the user's needs. This process supports users in quickly and efficiently selecting and applying new advertising ideas.

[0102] As a concrete example, let's consider a scenario where a user requests a "new advertising concept focused on environmental protection for women in their 20s." In this case, an example prompt might be, "Generate a new campaign proposal for the target audience (e.g., women in their 20s) based on the latest advertising trends." Based on the generated campaign proposal, the user can then select the optimal writing strategy and visual concept to effectively implement the advertising campaign.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server uses communication methods to access an external information storage system via the internet. Here, it collects data on the latest trends and competitors in the advertising industry. The input is an access request, and the output is advertising trend data and competitor data. The server retrieves this data and prepares for the next processing step.

[0106] Step 2:

[0107] The server uses a generation mechanism to create new advertising concepts and campaign proposals based on the collected data. The input is the advertising trend data and competitor data obtained in step 1, and the output is the generated advertising ideas. Here, a generation AI model is used to create multiple prompt sentences based on the input data and generate ideas accordingly.

[0108] Step 3:

[0109] The server uses evaluation tools to analyze the generated advertising ideas. The input is the advertising ideas generated in step 2, and the output is the evaluation results and the ranking of the ideas. An evaluation algorithm implemented in Python or similar is used to output numerical information that evaluates the market value, innovativeness, and potential of the ideas.

[0110] Step 4:

[0111] The terminal visually presents the evaluated ideas to the user through a display mechanism. The input is the evaluation results and idea rankings obtained in step 3, and the output is the display on the user interface. The Bootstrap framework is used to provide an intuitive and user-friendly UI.

[0112] Step 5:

[0113] If the user requests improvement suggestions based on the ideas they have presented, the server will further utilize its generative AI model to create improvement plans tailored to the user. The input is the ideas selected by the user, and the output is improvement suggestions. Specific improvement ideas are generated based on the generation prompt.

[0114] Step 6:

[0115] The server presents the generated improvement suggestions to the user and assists in developing the next action plan and implementation plan. The input is the improvement suggestions generated in step 5, and the output is a draft implementation plan. Specifically, it provides information to help the user make the best choice.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] This invention is an embodiment of a system that recognizes the user's emotions and incorporates the results of the emotion analysis into the idea generation process to create novel ideas optimized for the user. In addition to conventional communication means, generation means, evaluation means, display means, and analysis means, this system combines an emotion engine to propose ideas according to the user's emotional state.

[0118] System configuration and operation

[0119] Recognition and analysis of emotions

[0120] First, when the user inputs the conditions for idea generation through the interface, the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state. This allows the system to understand what emotions the user is experiencing.

[0121] Data acquisition and generation

[0122] Next, the server retrieves relevant information from an external database via communication. Based on this information, the generation mechanism generates ideas while considering the analysis results of the emotion engine. For example, if the user is relaxed, it can be configured to present more innovative and bold ideas.

[0123] Idea evaluation and emotional feedback

[0124] Each generated idea is evaluated using evaluation tools, including whether it resonates with the user's emotions. The emotion engine then analyzes the user's facial expressions and reactions to determine which idea is most favorably received. This feedback is incorporated into the scoring, and the ideas are ranked accordingly.

[0125] Specific example

[0126] For example, if a user is looking for a concept for a new marketing campaign, they first input their expectations and requirements. The emotion engine then analyzes the user's facial expressions to gauge their level of seriousness and excitement. Taking these results into account, the server generates ideas based on newly acquired market data, tailored to the user's expectations, and identifies the most appealing idea. Next, these ideas are visually displayed on the device, allowing the user to select the idea that resonates most strongly. Based on the user's selection, a more detailed plan is then proposed.

[0127] Thus, the system of the present invention accurately incorporates the user's emotions, enabling more personalized idea generation and supporting the decision-making process.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user uses the system interface to input the conditions and goals of the idea they want to generate. This information is sent to the server as initial setup for idea generation.

[0131] Step 2:

[0132] The device activates an emotion engine and captures the user's facial expressions and voice data in real time. This allows for the analysis of the user's emotional state (e.g., excitement, relaxation, tension, etc.).

[0133] Step 3:

[0134] The server connects to an external database using communication methods and retrieves the latest industry information and market data related to the conditions specified by the user. The retrieved data is stored in an internal database.

[0135] Step 4:

[0136] The server generates multiple new ideas using generation methods based on the stored data and the analysis results of the emotion engine. In this step, the user's emotional state is taken into consideration to provide more appropriate suggestions.

[0137] Step 5:

[0138] The server evaluates each generated idea using an evaluation tool and assigns a score. The evaluation criteria also include elements that are appropriate to the user's emotions. A list of ranked ideas is then generated.

[0139] Step 6:

[0140] The device provides the user with a visual list of ideas. The ideas are displayed through graphics and short videos, designed to further complement the user's emotional response.

[0141] Step 7:

[0142] The user selects the most appealing idea from those presented. Based on the selection, the server proposes relevant implementation plans and differentiation strategies, assisting the user in taking the next steps.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] In recent years, in this age of information overload, it has become increasingly difficult for users to generate accurate and effective ideas. Furthermore, conventional idea generation systems do not take user emotions into consideration, resulting in a lack of personalized suggestions and an inability to fully meet user needs.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes communication means for acquiring various data from an external information infrastructure, generation means for generating new ideas based on the acquired information and the user's emotional state, and recognition means for detecting and quantifying the user's emotional state. This makes it possible to generate and propose personalized ideas that respond to the user's emotions.

[0148] "Communication means" refers to methods and devices for acquiring various types of data from external information infrastructure.

[0149] "Generation means" refers to methods or devices for generating new ideas based on acquired information and the user's emotional state.

[0150] "Recognition means" refers to methods or devices that have the function of detecting and quantifying a user's emotional state.

[0151] "Evaluation means" refers to methods and devices for evaluating and ranking generated ideas based on whether they align with user sentiment.

[0152] "Display means" refers to methods or devices for visually presenting evaluated ideas to users.

[0153] This invention relates to a system that analyzes a user's emotions and generates personalized ideas accordingly. Specific embodiments for carrying out the invention are described below.

[0154] This system operates through the interaction of a server, a terminal, and a user. First, the user inputs the conditions for idea generation via the terminal. During this process, the camera and microphone equipped on the terminal collect the user's facial expressions and voice, which are then analyzed by an emotion engine. The emotion engine can use commercially available emotion analysis software or a custom-developed algorithm. This analysis quantifies the user's emotional state and transmits it to the server.

[0155] The server acquires necessary data from an external information infrastructure via communication channels. This data includes market trends and customer feedback. Using the acquired data and user sentiment data as input, a generative AI model within the server operates to generate new ideas. Generative AI models typically employ natural language processing techniques or machine learning algorithms.

[0156] The generated ideas are evaluated to determine how well they resonate with the user's emotions. The evaluation results are displayed step-by-step on the device's screen, allowing the user to visually confirm them. The user can then select the idea they resonate with the most, and a specific implementation plan corresponding to that idea is proposed.

[0157] For example, if a user is looking for a new marketing campaign concept, they input the conditions, and the emotion engine analyzes their facial expressions and tone of voice. Taking these results into account, the server uses a generative AI model to generate and evaluate new ideas. The generated ideas are displayed on the terminal and optimized based on the user's selection. An example prompt here is, "Generate new marketing campaign ideas. Use a model that considers the user's emotional state and provides suggestions that are easy to engage with in a relaxed mood."

[0158] This configuration allows the system to generate effective ideas that reflect the user's emotions, enabling it to offer more compelling suggestions to the user.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The user inputs the conditions for idea generation via a device. The device uses its camera and microphone to record the user's facial expressions and voice, and sends this information to the emotion engine. The input data consists of the user's idea generation requirements and raw audio / video data, and the device prepares it for analysis by the emotion engine.

[0162] Step 2:

[0163] The device uses an emotion engine to analyze the user's facial expressions and voice, and quantifies their emotional state. This data is sent to a server. The data processing performed here involves associating facial expressions and voice tone with emotions, and the output is the user's specific emotional score.

[0164] Step 3:

[0165] The server retrieves data from an external information infrastructure using communication methods. The retrieved data includes market trends and related information, and is filtered to show the most relevant data based on the user's intent. The input is a large-scale external database, and the output is organized data that matches the user's criteria.

[0166] Step 4:

[0167] The server inputs the collected data and the user's sentiment score into a generating AI model. Following prompts, it generates ideas, producing multiple ideas corresponding to the user's sentiment as output. The data calculations here are performed by the AI ​​model's generation process.

[0168] Step 5:

[0169] The server evaluates the generated ideas using evaluation tools and verifies their compatibility with the user's emotional state. Based on the evaluation results, it ranks the ideas and selects the optimal one. The input is the group of generated ideas, and the output is the ranked ideas.

[0170] Step 6:

[0171] The terminal visually displays ranked ideas, allowing the user to select the most appealing one. Based on the user's selection, a concrete implementation plan is presented, which the user can then review. The displayed information is a list of evaluated ideas, and the output is the user's selected idea and the plan based on it.

[0172] (Application Example 2)

[0173] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0174] Currently, it is difficult to suggest products or services that take into account the emotional state of individual users. Therefore, there is a possibility that suggestions may differ from what users truly desire, and improving the quality of the individual purchasing experience is crucial. Furthermore, a lack of means to emotionally rank ideas or recommendations and present more suitable options during the user's acceptance process is a significant challenge.

[0175] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0176] In this invention, the server includes emotion analysis means for recognizing emotions from the user's facial expressions and voice, generation means for generating new ideas based on the emotion analysis results and the acquired information, and evaluation means for evaluating the generated ideas and ranking them considering the user's emotions and suitability. This enables personalized product or service suggestions optimized for the user's emotions, providing a high-quality purchasing experience.

[0177] "Emotion analysis means" refers to a technology that analyzes the user's facial expressions and voice data to recognize their emotional state in real time.

[0178] "Communication methods" refer to technologies used to obtain various types of information from external sources, and play a role in securing data via the internet and databases.

[0179] A "generation method" is a technology that automatically generates new ideas based on acquired information and emotion analysis results.

[0180] An "evaluation tool" is a technology that ranks generated ideas based on user sentiment and relevance to identify superior options.

[0181] "Display means" refers to technologies for visually and clearly displaying evaluated ideas to users.

[0182] "Analytical methods" refer to techniques for thoroughly examining and identifying the differentiating factors from those of competitors.

[0183] In the system that realizes this invention, the server, terminal, and user each play important roles. The server uses emotion analysis means to analyze the user's facial expressions and voice, and recognizes the user's emotions through the results. Face recognition APIs and voice recognition APIs are used in this process. Specifically, Google® Cloud Vision API and Microsoft® Azure® Face API could be used.

[0184] The server acquires information from external sources (communication means) and generates new ideas considering the sentiment analysis results (generation means). This generation process utilizes a generation AI model to form personalized ideas based on the user's emotions.

[0185] The generated ideas are evaluated on a server and ranked based on user sentiment and relevance (evaluation method). After evaluation, the ideas are sent to the terminal and visualized and displayed on the user's device (display method). The user can review the visually presented ideas and select them as needed.

[0186] As a concrete example, when a user opens an e-commerce application on their smartphone, an emotion analysis system recognizes their face and voice on the spot, and if they are relaxed, recommends relaxation products suitable for the user. The recommended products are then ranked highly by an evaluation system and displayed to the user through their device.

[0187] An example of a prompt message would be: "Recognize the user's emotions and suggest the most suitable products to help them relax. For example, include relaxation items or stress-relief services."

[0188] In this way, we can quickly provide users with optimized suggestions, making it possible to create a more attractive purchasing experience for them.

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The user launches a smartphone application and positions the device so that their face and voice can be read. The input consists of the user's facial expression data and voice data. The output is raw data converted into a format suitable for analysis. The device's camera and microphone are used in this process.

[0192] Step 2:

[0193] The server processes raw data acquired using emotion analysis methods to recognize the user's current emotional state. Specifically, it utilizes face recognition APIs and speech recognition APIs to infer emotions from recognized facial features and voice tone. The input is raw data, and the output is the recognized emotion label.

[0194] Step 3:

[0195] The server uses communication methods to retrieve relevant information from external sources based on recognized emotion labels and builds a dataset for generating ideas. Specifically, it utilizes a generative AI model to extract product-related information from the cloud. The input is emotion labels, and the output is a dataset of extracted relevant information.

[0196] Step 4:

[0197] The server processes the dataset using a generation method and generates new ideas (product and service suggestions) corresponding to emotion labels. A generative AI model is used to create ideas optimized for the user's feelings. The input is a dataset of related information, and the output is the generated new ideas.

[0198] Step 5:

[0199] The server ranks the generated ideas using an evaluation system. Here, the degree of emotional resonance is used as the criterion, and prompt messages are used to establish the evaluation criteria. The input is new ideas, and the output is a list of ranked ideas.

[0200] Step 6:

[0201] The server sends the evaluated ideas to the terminal, which then displays the ideas visually. The user reviews the displayed ideas and makes selections as needed. The input is a list of ranked ideas, and the output is the visual content presented to the user.

[0202] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0209] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0213] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0218] This invention provides a realization of efficient novel idea generation using an information processing system. This system supports the rapid creation of new business ideas by appropriately coordinating communication means, generation means, evaluation means, display means, and analysis means.

[0219] System configuration and operation

[0220] Data acquisition and generation

[0221] First, the server accesses an external database using communication methods to collect data necessary for idea generation, such as industry trends and competitor information. Based on this data, the server generates new ideas using generation methods. By utilizing multiple AI algorithms and generating diverse ideas based on the collected data, it is possible to present a wide range of possibilities.

[0222] Idea evaluation and display

[0223] The generated ideas are automatically evaluated and ranked by an evaluation system based on criteria tailored to the user's needs. These evaluation criteria include, for example, market value, innovativeness, and feasibility. The ranked ideas are presented to the user visually through a display on their device, allowing them to quickly understand the key features and evaluation of each idea.

[0224] Specific example

[0225] For example, if a user seeks innovative product ideas for the food industry, they input criteria targeting the food industry into the system. In response, the server collects data on the latest food trends and consumer preferences from an external database. Next, a generation tool is used to create new recipes and product concepts, which are then evaluated by an evaluation tool. Finally, through a display tool, the user can select compelling ideas and further refine their differentiating factors using an analysis tool.

[0226] Thus, the system of the present invention can significantly reduce the effort required for users to generate ideas and provide powerful support for making quick decisions.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The user enters the subject area or specific conditions for which they want to generate ideas. These conditions include the target industry, purpose, and target market.

[0230] Step 2:

[0231] The server connects to an external database and retrieves relevant data based on the user's input. This includes industry trends, market size, and competitor information. The collected data is stored in an internal database.

[0232] Step 3:

[0233] The server generates new ideas using generation methods based on information stored in the database. Multiple AI models are operated simultaneously to propose multiple ideas from different perspectives.

[0234] Step 4:

[0235] The server evaluates each generated idea using evaluation tools. It scores and ranks each idea based on criteria such as market value, feasibility, and innovativeness.

[0236] Step 5:

[0237] The device visually presents ideas to the user based on the evaluation results. Key features and evaluation points of the ideas are displayed in graphic or short video format.

[0238] Step 6:

[0239] The user can select the most interesting idea from the displayed group of ideas. Based on this selection, the server analyzes additional differentiating factors and presents the results to the user.

[0240] Step 7:

[0241] The device suggests actionable plans and next steps based on the ideas selected by the user. This helps to initiate a concrete development process.

[0242] (Example 1)

[0243] Next, we will describe Example 1. 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."

[0244] In today's information-saturated society, it is difficult to quickly generate novel and competitively advantageous ideas. Furthermore, the lack of efficient means to evaluate and visually present these generated ideas leads to delays in the decision-making process.

[0245] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0246] In this invention, the server includes communication means for acquiring various types of information from external information sources, generation means for generating new concepts based on the acquired information, means for using a generation AI model, evaluation means for evaluating and ranking the generated concepts, and means for considering market value and innovativeness as specific evaluation criteria. This enables the rapid generation of novel ideas and efficient decision-making through their evaluation and presentation.

[0247] "Communication means" refers to a device or system that has the function of acquiring various types of information from external sources.

[0248] A "generation method" is a device or system that has the function of creating new concepts based on acquired information. By using a generation AI model, it is possible to generate new ideas from data.

[0249] An "evaluation tool" is a device or system that has the function of evaluating and ranking generated concepts based on various criteria. It is possible to perform evaluations that take into account specific criteria such as market value or innovativeness.

[0250] A "presentation means" is a device or system that has the function of visually presenting an evaluated concept to the user.

[0251] "Analysis means" refers to a device or system that has the function of analyzing the differentiating factors of a generated concept from those of competitors and extracting competitive advantages.

[0252] An "input / output device" is a device equipped with an interface for inputting conceptual conditions generated by the user and receiving the results as output.

[0253] An "activity plan" is a plan that presents specific implementation procedures and measures related to the concepts selected by the user.

[0254] This invention is an information processing system that integrates information gathering from external sources, generation of novel concepts using a generative AI model, automatic evaluation of these concepts, visual presentation, and analysis of differentiating factors. Specific embodiments are described in detail below.

[0255] The server connects to external information sources using communication methods and retrieves the necessary information. This information includes data on industry trends and consumer preferences. Specifically, the server has the capability to collect the latest data using REST APIs and SQL queries.

[0256] Next, the server uses a generation method to generate new concepts based on the acquired information. In this process, a generative AI model is used, and new ideas are generated by inputting prompts such as "Generate innovative recipes for the food industry." This generation process uses natural language processing technology to extract highly relevant keywords from the information and generate diverse ideas.

[0257] The generated ideas are evaluated based on multiple criteria using an evaluation system. Market value and innovativeness are particularly considered. The server automatically evaluates and ranks the ideas based on these criteria. The evaluation information is then linked with a presentation system and visually displayed on the terminal.

[0258] Users can use the information displayed on their device to verify the details of an idea. The device visualizes the information using ranking tables, graphs, and other visual aids. Finally, the user's selected idea is analyzed using analytical tools to identify its differentiating factors, which are then presented as a concrete action plan.

[0259] Through this system, users can efficiently generate new ideas from vast amounts of information and make rapid decisions.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] The server accesses external information sources using communication methods and collects the necessary information. The input includes requests regarding industry trends and consumer preferences, and based on this, it executes REST APIs or SQL queries to retrieve data. The output is a raw dataset necessary for idea generation. Specifically, the server retrieves the latest food trend information from a trend database.

[0263] Step 2:

[0264] The server generates new concepts using a generative AI model based on the data acquired in Step 1. The input consists of collected data and a prompt (e.g., "Generate innovative recipes for the food industry"). The AI ​​analyzes the data, extracts relevant keywords, and then generates a variety of ideas. The output is a list of newly generated ideas. Specifically, the server uses natural language processing techniques to analyze the data and create new recipe ideas.

[0265] Step 3:

[0266] The server automatically evaluates the generated ideas using evaluation tools. The input is the list of ideas generated in step 2, to which predefined evaluation criteria such as market value, innovativeness, and feasibility are applied. The output is a ranking list of the evaluated ideas. Specifically, the server applies a scoring algorithm to evaluate each idea and assign a ranking.

[0267] Step 4:

[0268] The terminal visually presents evaluated ideas received from the server. The input is a ranking list of evaluated ideas, and the terminal uses this list to provide information to the user. The output is visual information that the user can view on the screen. Specifically, the terminal displays idea information in the form of ranking tables or graphs.

[0269] Step 5:

[0270] The user selects an idea from those presented on the device and obtains further details. The input is the user's idea selection, and based on the selected idea, differentiating factors are analyzed through analytical tools. The output is detailed information presented to the user as a concrete activity plan and implementation procedure. Specifically, the user uses a filtering function to prioritize and review ideas with high market value.

[0271] (Application Example 1)

[0272] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] In the advertising industry, there is a need to quickly generate new campaigns and advertising ideas, and to effectively and efficiently evaluate and propose them. However, traditional methods require significant time and resources for information gathering and analysis, hindering rapid decision-making. This challenge needs to be addressed.

[0274] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0275] In this invention, the server includes communication means for acquiring data from an external information storage system, generation means for generating new concepts based on the acquired data, and means for supporting and suggesting improvements to user behavior based on the generated advertising proposals. This enables the rapid generation and evaluation of new ideas based on the latest advertising trends.

[0276] "Communication means" refers to a method or process used to retrieve data from an external information storage system.

[0277] A "generative means" is a method or process for creating new concepts or ideas based on acquired data.

[0278] "Evaluation means" refers to a method or process for evaluating generated concepts and ranking them in terms of superiority or inferiority.

[0279] "Display means" refers to a method or process for visually presenting an evaluated concept to a user.

[0280] "Analytical tools" refer to methods or processes for investigating and understanding the factors that differentiate a company from its competitors in detail.

[0281] The "generative AI model" is a program or algorithm for generating new ideas and advertising proposals using artificial intelligence technology.

[0282] The "means for making improvement suggestions" is a method or process for making beneficial improvement suggestions to users based on the generated advertising proposals.

[0283] The "means for assisting user behavior" is a support means or process for guiding users to make appropriate choices and actions.

[0284] This system is designed to assist in the generation, evaluation, and improvement of new ideas and campaign proposals in the advertising industry. The core part of the system consists of communication means, generation means, evaluation means, and means for making improvement suggestions. For the implementation of the system, a server, terminals, and a generative AI model are utilized.

[0285] The server uses the communication means to access an external information storage system via the Internet and collect various data such as trend information and competitive data in the advertising industry. Then, based on the collected data, the generation means generates new advertising concepts and campaign proposals. The generative AI model uses an AI framework such as TensorFlow to create diverse ideas based on the generated prompt text.

[0286] The generated ideas are analyzed by the evaluation means to quantify their value and effectiveness. An algorithm operating in Python is used for the evaluation, and the evaluation results are visually displayed on the terminal. This display uses a framework such as Bootstrap to provide an intuitive and user-friendly user interface.

[0287] When a user selects an idea from the presented options and requests specific improvement suggestions, the server uses a generative AI model to generate further improvement suggestions tailored to the user's needs. This process supports users in quickly and efficiently selecting and applying new advertising ideas.

[0288] As a concrete example, let's consider a scenario where a user requests a "new advertising concept focused on environmental protection for women in their 20s." In this case, an example prompt might be, "Generate a new campaign proposal for the target audience (e.g., women in their 20s) based on the latest advertising trends." Based on the generated campaign proposal, the user can then select the optimal writing strategy and visual concept to effectively implement the advertising campaign.

[0289] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0290] Step 1:

[0291] The server uses communication methods to access an external information storage system via the internet. Here, it collects data on the latest trends and competitors in the advertising industry. The input is an access request, and the output is advertising trend data and competitor data. The server retrieves this data and prepares for the next processing step.

[0292] Step 2:

[0293] The server uses a generation mechanism to create new advertising concepts and campaign proposals based on the collected data. The input is the advertising trend data and competitor data obtained in step 1, and the output is the generated advertising ideas. Here, a generation AI model is used to create multiple prompt sentences based on the input data and generate ideas accordingly.

[0294] Step 3:

[0295] The server uses evaluation tools to analyze the generated advertising ideas. The input is the advertising ideas generated in step 2, and the output is the evaluation results and the ranking of the ideas. An evaluation algorithm implemented in Python or similar is used to output numerical information that evaluates the market value, innovativeness, and potential of the ideas.

[0296] Step 4:

[0297] The terminal visually presents the evaluated ideas to the user through a display mechanism. The input is the evaluation results and idea rankings obtained in step 3, and the output is the display on the user interface. The Bootstrap framework is used to provide an intuitive and user-friendly UI.

[0298] Step 5:

[0299] If the user requests improvement suggestions based on the ideas they have presented, the server will further utilize its generative AI model to create improvement plans tailored to the user. The input is the ideas selected by the user, and the output is improvement suggestions. Specific improvement ideas are generated based on the generation prompt.

[0300] Step 6:

[0301] The server presents the generated improvement suggestions to the user and assists in developing the next action plan and implementation plan. The input is the improvement suggestions generated in step 5, and the output is a draft implementation plan. Specifically, it provides information to help the user make the best choice.

[0302] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0303] The present invention is an embodiment of a system that creates new ideas optimized for a user by recognizing the user's emotions and incorporating the results of emotion analysis into the idea generation process. In addition to conventional communication means, generation means, evaluation means, display means, and analysis means, this system combines an emotion engine to propose ideas according to the user's emotional state.

[0304] Configuration and Operation of the System

[0305] Recognition and Analysis of Emotions

[0306] First, when the user inputs the conditions for idea generation through the interface, the emotion engine analyzes the user's expression and voice to recognize the current emotional state. This enables the system to understand what emotions the user has.

[0307] Data Acquisition and Generation

[0308] Next, the server acquires relevant information from an external database via the communication means. Based on this information, the generation means generates ideas while considering the analysis results of the emotion engine. For example, when the user is relaxed, it can be set to present more innovative and bold ideas.

[0309] Evaluation of Ideas and Emotion Feedback

[0310] Each generated idea is evaluated based on criteria including whether it conforms to the user's emotions using the evaluation means. The emotion engine analyzes the user's expression and reaction again to determine which idea is most favorably received. This feedback is reflected in the scoring, and the ideas are ranked.

[0311] Specific Examples

[0312] For example, if a user is looking for a concept for a new marketing campaign, they first input their expectations and requirements. The emotion engine then analyzes the user's facial expressions to gauge their level of seriousness and excitement. Taking these results into account, the server generates ideas based on newly acquired market data, tailored to the user's expectations, and identifies the most appealing idea. Next, these ideas are visually displayed on the device, allowing the user to select the idea that resonates most strongly. Based on the user's selection, a more detailed plan is then proposed.

[0313] Thus, the system of the present invention accurately incorporates the user's emotions, enabling more personalized idea generation and supporting the decision-making process.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] The user uses the system interface to input the conditions and goals of the idea they want to generate. This information is sent to the server as initial setup for idea generation.

[0317] Step 2:

[0318] The device activates an emotion engine and captures the user's facial expressions and voice data in real time. This allows for the analysis of the user's emotional state (e.g., excitement, relaxation, tension, etc.).

[0319] Step 3:

[0320] The server connects to an external database using communication methods and retrieves the latest industry information and market data related to the conditions specified by the user. The retrieved data is stored in an internal database.

[0321] Step 4:

[0322] The server generates multiple new ideas using generation methods based on the stored data and the analysis results of the emotion engine. In this step, the user's emotional state is taken into consideration to provide more appropriate suggestions.

[0323] Step 5:

[0324] The server evaluates each generated idea using an evaluation tool and assigns a score. The evaluation criteria also include elements that are appropriate to the user's emotions. A list of ranked ideas is then generated.

[0325] Step 6:

[0326] The device provides the user with a visual list of ideas. The ideas are displayed through graphics and short videos, designed to further complement the user's emotional response.

[0327] Step 7:

[0328] The user selects the most appealing idea from those presented. Based on the selection, the server proposes relevant implementation plans and differentiation strategies, assisting the user in taking the next steps.

[0329] (Example 2)

[0330] Next, we will describe Example 2. 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".

[0331] In recent years, in this age of information overload, it has become increasingly difficult for users to generate accurate and effective ideas. Furthermore, conventional idea generation systems do not take user emotions into consideration, resulting in a lack of personalized suggestions and an inability to fully meet user needs.

[0332] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0333] In this invention, the server includes communication means for acquiring various data from an external information infrastructure, generation means for generating new ideas based on the acquired information and the user's emotional state, and recognition means for detecting and quantifying the user's emotional state. This makes it possible to generate and propose personalized ideas that respond to the user's emotions.

[0334] "Communication means" refers to methods and devices for acquiring various types of data from external information infrastructure.

[0335] "Generation means" refers to methods or devices for generating new ideas based on acquired information and the user's emotional state.

[0336] "Recognition means" refers to methods or devices that have the function of detecting and quantifying a user's emotional state.

[0337] "Evaluation means" refers to methods and devices for evaluating and ranking generated ideas based on whether they align with user sentiment.

[0338] "Display means" refers to methods or devices for visually presenting evaluated ideas to users.

[0339] This invention relates to a system that analyzes a user's emotions and generates personalized ideas accordingly. Specific embodiments for carrying out the invention are described below.

[0340] This system operates through the interaction of a server, a terminal, and a user. First, the user inputs the conditions for idea generation via the terminal. During this process, the camera and microphone equipped on the terminal collect the user's facial expressions and voice, which are then analyzed by an emotion engine. The emotion engine can use commercially available emotion analysis software or a custom-developed algorithm. This analysis quantifies the user's emotional state and transmits it to the server.

[0341] The server acquires necessary data from an external information infrastructure via communication channels. This data includes market trends and customer feedback. Using the acquired data and user sentiment data as input, a generative AI model within the server operates to generate new ideas. Generative AI models typically employ natural language processing techniques or machine learning algorithms.

[0342] The generated ideas are evaluated to determine how well they resonate with the user's emotions. The evaluation results are displayed step-by-step on the device's screen, allowing the user to visually confirm them. The user can then select the idea they resonate with the most, and a specific implementation plan corresponding to that idea is proposed.

[0343] For example, if a user is looking for a new marketing campaign concept, they input the conditions, and the emotion engine analyzes their facial expressions and tone of voice. Taking these results into account, the server uses a generative AI model to generate and evaluate new ideas. The generated ideas are displayed on the terminal and optimized based on the user's selection. An example prompt here is, "Generate new marketing campaign ideas. Use a model that considers the user's emotional state and provides suggestions that are easy to engage with in a relaxed mood."

[0344] This configuration allows the system to generate effective ideas that reflect the user's emotions, enabling it to offer more compelling suggestions to the user.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The user inputs the conditions for idea generation via a device. The device uses its camera and microphone to record the user's facial expressions and voice, and sends this information to the emotion engine. The input data consists of the user's idea generation requirements and raw audio / video data, and the device prepares it for analysis by the emotion engine.

[0348] Step 2:

[0349] The device uses an emotion engine to analyze the user's facial expressions and voice, and quantifies their emotional state. This data is sent to a server. The data processing performed here involves associating facial expressions and voice tone with emotions, and the output is the user's specific emotional score.

[0350] Step 3:

[0351] The server retrieves data from an external information infrastructure using communication methods. The retrieved data includes market trends and related information, and is filtered to show the most relevant data based on the user's intent. The input is a large-scale external database, and the output is organized data that matches the user's criteria.

[0352] Step 4:

[0353] The server inputs the collected data and the user's sentiment score into a generating AI model. Following prompts, it generates ideas, producing multiple ideas corresponding to the user's sentiment as output. The data calculations here are performed by the AI ​​model's generation process.

[0354] Step 5:

[0355] The server evaluates the generated ideas using evaluation tools and verifies their compatibility with the user's emotional state. Based on the evaluation results, it ranks the ideas and selects the optimal one. The input is the group of generated ideas, and the output is the ranked ideas.

[0356] Step 6:

[0357] The terminal visually displays ranked ideas, allowing the user to select the most appealing one. Based on the user's selection, a concrete implementation plan is presented, which the user can then review. The displayed information is a list of evaluated ideas, and the output is the user's selected idea and the plan based on it.

[0358] (Application Example 2)

[0359] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0360] Currently, it is difficult to suggest products or services that take into account the emotional state of individual users. Therefore, there is a possibility that suggestions may differ from what users truly desire, and improving the quality of the individual purchasing experience is crucial. Furthermore, a lack of means to emotionally rank ideas or recommendations and present more suitable options during the user's acceptance process is a significant challenge.

[0361] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0362] In this invention, the server includes emotion analysis means for recognizing emotions from the user's facial expressions and voice, generation means for generating new ideas based on the emotion analysis results and the acquired information, and evaluation means for evaluating the generated ideas and ranking them considering the user's emotions and suitability. This enables personalized product or service suggestions optimized for the user's emotions, providing a high-quality purchasing experience.

[0363] "Emotion analysis means" refers to a technology that analyzes the user's facial expressions and voice data to recognize their emotional state in real time.

[0364] "Communication methods" refer to technologies used to obtain various types of information from external sources, and play a role in securing data via the internet and databases.

[0365] A "generation method" is a technology that automatically generates new ideas based on acquired information and emotion analysis results.

[0366] An "evaluation tool" is a technology that ranks generated ideas based on user sentiment and relevance to identify superior options.

[0367] "Display means" refers to technologies for visually and clearly displaying evaluated ideas to users.

[0368] "Analytical methods" refer to techniques for thoroughly examining and identifying the differentiating factors from those of competitors.

[0369] In the system that realizes this invention, the server, terminal, and user each play important roles. The server uses emotion analysis means to analyze the user's facial expressions and voice, and recognizes the user's emotions through the results. Face recognition APIs and voice recognition APIs are used in this process. Specifically, the use of Google Cloud Vision API and Microsoft Azure Face API is conceivable.

[0370] The server acquires information from external sources (communication means) and generates new ideas considering the sentiment analysis results (generation means). This generation process utilizes a generation AI model to form personalized ideas based on the user's emotions.

[0371] The generated ideas are evaluated on a server and ranked based on user sentiment and relevance (evaluation method). After evaluation, the ideas are sent to the terminal and visualized and displayed on the user's device (display method). The user can review the visually presented ideas and select them as needed.

[0372] As a concrete example, when a user opens an e-commerce application on their smartphone, an emotion analysis system recognizes their face and voice on the spot, and if they are relaxed, recommends relaxation products suitable for the user. The recommended products are then ranked highly by an evaluation system and displayed to the user through their device.

[0373] An example of a prompt message would be: "Recognize the user's emotions and suggest the most suitable products to help them relax. For example, include relaxation items or stress-relief services."

[0374] In this way, we can quickly provide users with optimized suggestions, making it possible to create a more attractive purchasing experience for them.

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The user launches a smartphone application and positions the device so that their face and voice can be read. The input consists of the user's facial expression data and voice data. The output is raw data converted into a format suitable for analysis. The device's camera and microphone are used in this process.

[0378] Step 2:

[0379] The server processes raw data acquired using emotion analysis methods to recognize the user's current emotional state. Specifically, it utilizes face recognition APIs and speech recognition APIs to infer emotions from recognized facial features and voice tone. The input is raw data, and the output is the recognized emotion label.

[0380] Step 3:

[0381] The server uses communication methods to retrieve relevant information from external sources based on recognized emotion labels and builds a dataset for generating ideas. Specifically, it utilizes a generative AI model to extract product-related information from the cloud. The input is emotion labels, and the output is a dataset of extracted relevant information.

[0382] Step 4:

[0383] The server processes the dataset using a generation method and generates new ideas (product and service suggestions) corresponding to emotion labels. A generative AI model is used to create ideas optimized for the user's feelings. The input is a dataset of related information, and the output is the generated new ideas.

[0384] Step 5:

[0385] The server ranks the generated ideas using an evaluation system. Here, the degree of emotional resonance is used as the criterion, and prompt messages are used to establish the evaluation criteria. The input is new ideas, and the output is a list of ranked ideas.

[0386] Step 6:

[0387] The server sends the evaluated ideas to the terminal, which then displays the ideas visually. The user reviews the displayed ideas and makes selections as needed. The input is a list of ranked ideas, and the output is the visual content presented to the user.

[0388] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0389] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0390] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0391] [Third Embodiment]

[0392] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0393] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0394] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0395] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0396] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0397] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0398] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0399] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0400] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0401] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0402] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0403] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0404] This invention provides a realization of efficient novel idea generation using an information processing system. This system supports the rapid creation of new business ideas by appropriately coordinating communication means, generation means, evaluation means, display means, and analysis means.

[0405] System configuration and operation

[0406] Data acquisition and generation

[0407] First, the server accesses an external database using communication methods to collect data necessary for idea generation, such as industry trends and competitor information. Based on this data, the server generates new ideas using generation methods. By utilizing multiple AI algorithms and generating diverse ideas based on the collected data, it is possible to present a wide range of possibilities.

[0408] Idea evaluation and display

[0409] The generated ideas are automatically evaluated and ranked by an evaluation system based on criteria tailored to the user's needs. These evaluation criteria include, for example, market value, innovativeness, and feasibility. The ranked ideas are presented to the user visually through a display on their device, allowing them to quickly understand the key features and evaluation of each idea.

[0410] Specific example

[0411] For example, if a user seeks innovative product ideas for the food industry, they input criteria targeting the food industry into the system. In response, the server collects data on the latest food trends and consumer preferences from an external database. Next, a generation tool is used to create new recipes and product concepts, which are then evaluated by an evaluation tool. Finally, through a display tool, the user can select compelling ideas and further refine their differentiating factors using an analysis tool.

[0412] Thus, the system of the present invention can significantly reduce the effort required for users to generate ideas and provide powerful support for making quick decisions.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] The user enters the subject area or specific conditions for which they want to generate ideas. These conditions include the target industry, purpose, and target market.

[0416] Step 2:

[0417] The server connects to an external database and retrieves relevant data based on the user's input. This includes industry trends, market size, and competitor information. The collected data is stored in an internal database.

[0418] Step 3:

[0419] The server generates new ideas using generation methods based on information stored in the database. Multiple AI models are operated simultaneously to propose multiple ideas from different perspectives.

[0420] Step 4:

[0421] The server evaluates each generated idea using evaluation tools. It scores and ranks each idea based on criteria such as market value, feasibility, and innovativeness.

[0422] Step 5:

[0423] The device visually presents ideas to the user based on the evaluation results. Key features and evaluation points of the ideas are displayed in graphic or short video format.

[0424] Step 6:

[0425] The user can select the most interesting idea from the displayed group of ideas. Based on this selection, the server analyzes additional differentiating factors and presents the results to the user.

[0426] Step 7:

[0427] The device suggests actionable plans and next steps based on the ideas selected by the user. This helps to initiate a concrete development process.

[0428] (Example 1)

[0429] Next, we will describe Example 1. 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."

[0430] In today's information-saturated society, it is difficult to quickly generate novel and competitively advantageous ideas. Furthermore, the lack of efficient means to evaluate and visually present these generated ideas leads to delays in the decision-making process.

[0431] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0432] In this invention, the server includes communication means for acquiring various types of information from external information sources, generation means for generating new concepts based on the acquired information, means for using a generation AI model, evaluation means for evaluating and ranking the generated concepts, and means for considering market value and innovativeness as specific evaluation criteria. This enables the rapid generation of novel ideas and efficient decision-making through their evaluation and presentation.

[0433] "Communication means" refers to a device or system that has the function of acquiring various types of information from external sources.

[0434] A "generation method" is a device or system that has the function of creating new concepts based on acquired information. By using a generation AI model, it is possible to generate new ideas from data.

[0435] An "evaluation tool" is a device or system that has the function of evaluating and ranking generated concepts based on various criteria. It is possible to perform evaluations that take into account specific criteria such as market value or innovativeness.

[0436] A "presentation means" is a device or system that has the function of visually presenting an evaluated concept to the user.

[0437] "Analysis means" refers to a device or system that has the function of analyzing the differentiating factors of a generated concept from those of competitors and extracting competitive advantages.

[0438] An "input / output device" is a device equipped with an interface for inputting conceptual conditions generated by the user and receiving the results as output.

[0439] An "activity plan" is a plan that presents specific implementation procedures and measures related to the concepts selected by the user.

[0440] This invention is an information processing system that integrates information gathering from external sources, generation of novel concepts using a generative AI model, automatic evaluation of these concepts, visual presentation, and analysis of differentiating factors. Specific embodiments are described in detail below.

[0441] The server connects to external information sources using communication methods and retrieves the necessary information. This information includes data on industry trends and consumer preferences. Specifically, the server has the capability to collect the latest data using REST APIs and SQL queries.

[0442] Next, the server uses a generation method to generate new concepts based on the acquired information. In this process, a generative AI model is used, and new ideas are generated by inputting prompts such as "Generate innovative recipes for the food industry." This generation process uses natural language processing technology to extract highly relevant keywords from the information and generate diverse ideas.

[0443] The generated ideas are evaluated based on multiple criteria using an evaluation system. Market value and innovativeness are particularly considered. The server automatically evaluates and ranks the ideas based on these criteria. The evaluation information is then linked with a presentation system and visually displayed on the terminal.

[0444] Users can use the information displayed on their device to verify the details of an idea. The device visualizes the information using ranking tables, graphs, and other visual aids. Finally, the user's selected idea is analyzed using analytical tools to identify its differentiating factors, which are then presented as a concrete action plan.

[0445] Through this system, users can efficiently generate new ideas from vast amounts of information and make rapid decisions.

[0446] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0447] Step 1:

[0448] The server accesses external information sources using communication methods and collects the necessary information. The input includes requests regarding industry trends and consumer preferences, and based on this, it executes REST APIs or SQL queries to retrieve data. The output is a raw dataset necessary for idea generation. Specifically, the server retrieves the latest food trend information from a trend database.

[0449] Step 2:

[0450] The server generates new concepts using a generative AI model based on the data acquired in Step 1. The input consists of collected data and a prompt (e.g., "Generate innovative recipes for the food industry"). The AI ​​analyzes the data, extracts relevant keywords, and then generates a variety of ideas. The output is a list of newly generated ideas. Specifically, the server uses natural language processing techniques to analyze the data and create new recipe ideas.

[0451] Step 3:

[0452] The server automatically evaluates the generated ideas using evaluation tools. The input is the list of ideas generated in step 2, to which predefined evaluation criteria such as market value, innovativeness, and feasibility are applied. The output is a ranking list of the evaluated ideas. Specifically, the server applies a scoring algorithm to evaluate each idea and assign a ranking.

[0453] Step 4:

[0454] The terminal visually presents evaluated ideas received from the server. The input is a ranking list of evaluated ideas, and the terminal uses this list to provide information to the user. The output is visual information that the user can view on the screen. Specifically, the terminal displays idea information in the form of ranking tables or graphs.

[0455] Step 5:

[0456] The user selects an idea from those presented on the device and obtains further details. The input is the user's idea selection, and based on the selected idea, differentiating factors are analyzed through analytical tools. The output is detailed information presented to the user as a concrete activity plan and implementation procedure. Specifically, the user uses a filtering function to prioritize and review ideas with high market value.

[0457] (Application Example 1)

[0458] Next, we will explain Application Example 1. In the following explanation, 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."

[0459] In the advertising industry, there is a need to quickly generate new campaigns and advertising ideas, and to effectively and efficiently evaluate and propose them. However, traditional methods require significant time and resources for information gathering and analysis, hindering rapid decision-making. This challenge needs to be addressed.

[0460] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0461] In this invention, the server includes communication means for acquiring data from an external information storage system, generation means for generating new concepts based on the acquired data, and means for supporting and suggesting improvements to user behavior based on the generated advertising proposals. This enables the rapid generation and evaluation of new ideas based on the latest advertising trends.

[0462] "Communication means" refers to a method or process used to retrieve data from an external information storage system.

[0463] A "generative means" is a method or process for creating new concepts or ideas based on acquired data.

[0464] "Evaluation means" refers to a method or process for evaluating generated concepts and ranking them in terms of superiority or inferiority.

[0465] "Display means" refers to a method or process for visually presenting an evaluated concept to a user.

[0466] "Analytical tools" refer to methods or processes for investigating and understanding the factors that differentiate a company from its competitors in detail.

[0467] A "generative AI model" is a program or algorithm that uses artificial intelligence technology to generate new ideas or advertising proposals.

[0468] "Means for making improvement suggestions" refers to a method or process for making useful improvement suggestions to users based on the generated advertising drafts.

[0469] "Means to support user behavior" refers to support measures or processes that guide users to make appropriate choices and actions.

[0470] This system is designed to help generate, evaluate, and improve new ideas and campaign proposals in the advertising industry. The core of the system consists of communication, generation, evaluation, and improvement suggestion mechanisms. The system's implementation utilizes servers, terminals, and a generation AI model.

[0471] The server uses communication methods to access external information storage systems via the internet and collects various data, such as advertising industry trend information and competitor data. Then, using generation methods, it generates new advertising concepts and campaign proposals based on this collected data. The generation AI model uses an AI framework such as TensorFlow to generate diverse ideas based on the generated prompt statements.

[0472] The generated ideas are analyzed using evaluation tools, and their value and effectiveness are quantified. The evaluation uses an algorithm running in Python, and the results are displayed visually on the terminal. This display utilizes a framework such as Bootstrap to create an intuitive and user-friendly interface.

[0473] When a user selects an idea from the presented options and requests specific improvement suggestions, the server uses a generative AI model to generate further improvement suggestions tailored to the user's needs. This process supports users in quickly and efficiently selecting and applying new advertising ideas.

[0474] As a concrete example, let's consider a scenario where a user requests a "new advertising concept focused on environmental protection for women in their 20s." In this case, an example prompt might be, "Generate a new campaign proposal for the target audience (e.g., women in their 20s) based on the latest advertising trends." Based on the generated campaign proposal, the user can then select the optimal writing strategy and visual concept to effectively implement the advertising campaign.

[0475] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0476] Step 1:

[0477] The server uses communication methods to access an external information storage system via the internet. Here, it collects data on the latest trends and competitors in the advertising industry. The input is an access request, and the output is advertising trend data and competitor data. The server retrieves this data and prepares for the next processing step.

[0478] Step 2:

[0479] The server uses a generation mechanism to create new advertising concepts and campaign proposals based on the collected data. The input is the advertising trend data and competitor data obtained in step 1, and the output is the generated advertising ideas. Here, a generation AI model is used to create multiple prompt sentences based on the input data and generate ideas accordingly.

[0480] Step 3:

[0481] The server uses evaluation tools to analyze the generated advertising ideas. The input is the advertising ideas generated in step 2, and the output is the evaluation results and the ranking of the ideas. An evaluation algorithm implemented in Python or similar is used to output numerical information that evaluates the market value, innovativeness, and potential of the ideas.

[0482] Step 4:

[0483] The terminal visually presents the evaluated ideas to the user through a display mechanism. The input is the evaluation results and idea rankings obtained in step 3, and the output is the display on the user interface. The Bootstrap framework is used to provide an intuitive and user-friendly UI.

[0484] Step 5:

[0485] If the user requests improvement suggestions based on the ideas they have presented, the server will further utilize its generative AI model to create improvement plans tailored to the user. The input is the ideas selected by the user, and the output is improvement suggestions. Specific improvement ideas are generated based on the generation prompt.

[0486] Step 6:

[0487] The server presents the generated improvement suggestions to the user and assists in developing the next action plan and implementation plan. The input is the improvement suggestions generated in step 5, and the output is a draft implementation plan. Specifically, it provides information to help the user make the best choice.

[0488] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0489] This invention is an embodiment of a system that recognizes the user's emotions and incorporates the results of the emotion analysis into the idea generation process to create novel ideas optimized for the user. In addition to conventional communication means, generation means, evaluation means, display means, and analysis means, this system combines an emotion engine to propose ideas according to the user's emotional state.

[0490] System configuration and operation

[0491] Recognition and analysis of emotions

[0492] First, when the user inputs the conditions for idea generation through the interface, the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state. This allows the system to understand what emotions the user is experiencing.

[0493] Data acquisition and generation

[0494] Next, the server retrieves relevant information from an external database via communication. Based on this information, the generation mechanism generates ideas while considering the analysis results of the emotion engine. For example, if the user is relaxed, it can be configured to present more innovative and bold ideas.

[0495] Idea evaluation and emotional feedback

[0496] Each generated idea is evaluated using evaluation tools, including whether it resonates with the user's emotions. The emotion engine then analyzes the user's facial expressions and reactions to determine which idea is most favorably received. This feedback is incorporated into the scoring, and the ideas are ranked accordingly.

[0497] Specific example

[0498] For example, if a user is looking for a concept for a new marketing campaign, they first input their expectations and requirements. The emotion engine then analyzes the user's facial expressions to gauge their level of seriousness and excitement. Taking these results into account, the server generates ideas based on newly acquired market data, tailored to the user's expectations, and identifies the most appealing idea. Next, these ideas are visually displayed on the device, allowing the user to select the idea that resonates most strongly. Based on the user's selection, a more detailed plan is then proposed.

[0499] Thus, the system of the present invention accurately incorporates the user's emotions, enabling more personalized idea generation and supporting the decision-making process.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] The user uses the system interface to input the conditions and goals of the idea they want to generate. This information is sent to the server as initial setup for idea generation.

[0503] Step 2:

[0504] The device activates an emotion engine and captures the user's facial expressions and voice data in real time. This allows for the analysis of the user's emotional state (e.g., excitement, relaxation, tension, etc.).

[0505] Step 3:

[0506] The server connects to an external database using communication methods and retrieves the latest industry information and market data related to the conditions specified by the user. The retrieved data is stored in an internal database.

[0507] Step 4:

[0508] The server generates multiple new ideas using generation methods based on the stored data and the analysis results of the emotion engine. In this step, the user's emotional state is taken into consideration to provide more appropriate suggestions.

[0509] Step 5:

[0510] The server evaluates each generated idea using an evaluation tool and assigns a score. The evaluation criteria also include elements that are appropriate to the user's emotions. A list of ranked ideas is then generated.

[0511] Step 6:

[0512] The device provides the user with a visual list of ideas. The ideas are displayed through graphics and short videos, designed to further complement the user's emotional response.

[0513] Step 7:

[0514] The user selects the most appealing idea from those presented. Based on the selection, the server proposes relevant implementation plans and differentiation strategies, assisting the user in taking the next steps.

[0515] (Example 2)

[0516] Next, we will describe Example 2. 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."

[0517] In recent years, in this age of information overload, it has become increasingly difficult for users to generate accurate and effective ideas. Furthermore, conventional idea generation systems do not take user emotions into consideration, resulting in a lack of personalized suggestions and an inability to fully meet user needs.

[0518] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0519] In this invention, the server includes communication means for acquiring various data from an external information infrastructure, generation means for generating new ideas based on the acquired information and the user's emotional state, and recognition means for detecting and quantifying the user's emotional state. This makes it possible to generate and propose personalized ideas that respond to the user's emotions.

[0520] "Communication means" refers to methods and devices for acquiring various types of data from external information infrastructure.

[0521] "Generation means" refers to methods or devices for generating new ideas based on acquired information and the user's emotional state.

[0522] "Recognition means" refers to methods or devices that have the function of detecting and quantifying a user's emotional state.

[0523] "Evaluation means" refers to methods and devices for evaluating and ranking generated ideas based on whether they align with user sentiment.

[0524] "Display means" refers to methods or devices for visually presenting evaluated ideas to users.

[0525] This invention relates to a system that analyzes a user's emotions and generates personalized ideas accordingly. Specific embodiments for carrying out the invention are described below.

[0526] This system operates through the interaction of a server, a terminal, and a user. First, the user inputs the conditions for idea generation via the terminal. During this process, the camera and microphone equipped on the terminal collect the user's facial expressions and voice, which are then analyzed by an emotion engine. The emotion engine can use commercially available emotion analysis software or a custom-developed algorithm. This analysis quantifies the user's emotional state and transmits it to the server.

[0527] The server acquires necessary data from an external information infrastructure via communication channels. This data includes market trends and customer feedback. Using the acquired data and user sentiment data as input, a generative AI model within the server operates to generate new ideas. Generative AI models typically employ natural language processing techniques or machine learning algorithms.

[0528] The generated ideas are evaluated to determine how well they resonate with the user's emotions. The evaluation results are displayed step-by-step on the device's screen, allowing the user to visually confirm them. The user can then select the idea they resonate with the most, and a specific implementation plan corresponding to that idea is proposed.

[0529] For example, if a user is looking for a new marketing campaign concept, they input the conditions, and the emotion engine analyzes their facial expressions and tone of voice. Taking these results into account, the server uses a generative AI model to generate and evaluate new ideas. The generated ideas are displayed on the terminal and optimized based on the user's selection. An example prompt here is, "Generate new marketing campaign ideas. Use a model that considers the user's emotional state and provides suggestions that are easy to engage with in a relaxed mood."

[0530] This configuration allows the system to generate effective ideas that reflect the user's emotions, enabling it to offer more compelling suggestions to the user.

[0531] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0532] Step 1:

[0533] The user inputs the conditions for idea generation via a device. The device uses its camera and microphone to record the user's facial expressions and voice, and sends this information to the emotion engine. The input data consists of the user's idea generation requirements and raw audio / video data, and the device prepares it for analysis by the emotion engine.

[0534] Step 2:

[0535] The device uses an emotion engine to analyze the user's facial expressions and voice, and quantifies their emotional state. This data is sent to a server. The data processing performed here involves associating facial expressions and voice tone with emotions, and the output is the user's specific emotional score.

[0536] Step 3:

[0537] The server retrieves data from an external information infrastructure using communication methods. The retrieved data includes market trends and related information, and is filtered to show the most relevant data based on the user's intent. The input is a large-scale external database, and the output is organized data that matches the user's criteria.

[0538] Step 4:

[0539] The server inputs the collected data and the user's sentiment score into a generating AI model. Following prompts, it generates ideas, producing multiple ideas corresponding to the user's sentiment as output. The data calculations here are performed by the AI ​​model's generation process.

[0540] Step 5:

[0541] The server evaluates the generated ideas using evaluation tools and verifies their compatibility with the user's emotional state. Based on the evaluation results, it ranks the ideas and selects the optimal one. The input is the group of generated ideas, and the output is the ranked ideas.

[0542] Step 6:

[0543] The terminal visually displays ranked ideas, allowing the user to select the most appealing one. Based on the user's selection, a concrete implementation plan is presented, which the user can then review. The displayed information is a list of evaluated ideas, and the output is the user's selected idea and the plan based on it.

[0544] (Application Example 2)

[0545] Next, we will explain application example 2. In the following explanation, 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."

[0546] Currently, it is difficult to suggest products or services that take into account the emotional state of individual users. Therefore, there is a possibility that suggestions may differ from what users truly desire, and improving the quality of the individual purchasing experience is crucial. Furthermore, a lack of means to emotionally rank ideas or recommendations and present more suitable options during the user's acceptance process is a significant challenge.

[0547] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0548] In this invention, the server includes emotion analysis means for recognizing emotions from the user's facial expressions and voice, generation means for generating new ideas based on the emotion analysis results and the acquired information, and evaluation means for evaluating the generated ideas and ranking them considering the user's emotions and suitability. This enables personalized product or service suggestions optimized for the user's emotions, providing a high-quality purchasing experience.

[0549] "Emotion analysis means" refers to a technology that analyzes the user's facial expressions and voice data to recognize their emotional state in real time.

[0550] "Communication methods" refer to technologies used to obtain various types of information from external sources, and play a role in securing data via the internet and databases.

[0551] A "generation method" is a technology that automatically generates new ideas based on acquired information and emotion analysis results.

[0552] An "evaluation tool" is a technology that ranks generated ideas based on user sentiment and relevance to identify superior options.

[0553] "Display means" refers to technologies for visually and clearly displaying evaluated ideas to users.

[0554] "Analytical methods" refer to techniques for thoroughly examining and identifying the differentiating factors from those of competitors.

[0555] In the system that realizes this invention, the server, terminal, and user each play important roles. The server uses emotion analysis means to analyze the user's facial expressions and voice, and recognizes the user's emotions through the results. Face recognition APIs and voice recognition APIs are used in this process. Specifically, the use of Google Cloud Vision API and Microsoft Azure Face API is conceivable.

[0556] The server acquires information from external sources (communication means) and generates new ideas considering the sentiment analysis results (generation means). This generation process utilizes a generation AI model to form personalized ideas based on the user's emotions.

[0557] The generated ideas are evaluated on a server and ranked based on user sentiment and relevance (evaluation method). After evaluation, the ideas are sent to the terminal and visualized and displayed on the user's device (display method). The user can review the visually presented ideas and select them as needed.

[0558] As a concrete example, when a user opens an e-commerce application on their smartphone, an emotion analysis system recognizes their face and voice on the spot, and if they are relaxed, recommends relaxation products suitable for the user. The recommended products are then ranked highly by an evaluation system and displayed to the user through their device.

[0559] An example of a prompt message would be: "Recognize the user's emotions and suggest the most suitable products to help them relax. For example, include relaxation items or stress-relief services."

[0560] In this way, we can quickly provide users with optimized suggestions, making it possible to create a more attractive purchasing experience for them.

[0561] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0562] Step 1:

[0563] The user launches a smartphone application and positions the device so that their face and voice can be read. The input consists of the user's facial expression data and voice data. The output is raw data converted into a format suitable for analysis. The device's camera and microphone are used in this process.

[0564] Step 2:

[0565] The server processes raw data acquired using emotion analysis methods to recognize the user's current emotional state. Specifically, it utilizes face recognition APIs and speech recognition APIs to infer emotions from recognized facial features and voice tone. The input is raw data, and the output is the recognized emotion label.

[0566] Step 3:

[0567] The server uses communication methods to retrieve relevant information from external sources based on recognized emotion labels and builds a dataset for generating ideas. Specifically, it utilizes a generative AI model to extract product-related information from the cloud. The input is emotion labels, and the output is a dataset of extracted relevant information.

[0568] Step 4:

[0569] The server processes the dataset using a generation method and generates new ideas (product and service suggestions) corresponding to emotion labels. A generative AI model is used to create ideas optimized for the user's feelings. The input is a dataset of related information, and the output is the generated new ideas.

[0570] Step 5:

[0571] The server ranks the generated ideas using an evaluation system. Here, the degree of emotional resonance is used as the criterion, and prompt messages are used to establish the evaluation criteria. The input is new ideas, and the output is a list of ranked ideas.

[0572] Step 6:

[0573] The server sends the evaluated ideas to the terminal, which then displays the ideas visually. The user reviews the displayed ideas and makes selections as needed. The input is a list of ranked ideas, and the output is the visual content presented to the user.

[0574] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0575] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0576] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0577] [Fourth Embodiment]

[0578] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0579] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0580] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0581] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0582] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0583] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0584] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0585] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0586] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0587] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0588] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0589] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0590] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0591] This invention provides a realization of efficient novel idea generation using an information processing system. This system supports the rapid creation of new business ideas by appropriately coordinating communication means, generation means, evaluation means, display means, and analysis means.

[0592] System configuration and operation

[0593] Data acquisition and generation

[0594] First, the server accesses an external database using communication methods to collect data necessary for idea generation, such as industry trends and competitor information. Based on this data, the server generates new ideas using generation methods. By utilizing multiple AI algorithms and generating diverse ideas based on the collected data, it is possible to present a wide range of possibilities.

[0595] Idea evaluation and display

[0596] The generated ideas are automatically evaluated and ranked by an evaluation system based on criteria tailored to the user's needs. These evaluation criteria include, for example, market value, innovativeness, and feasibility. The ranked ideas are presented to the user visually through a display on their device, allowing them to quickly understand the key features and evaluation of each idea.

[0597] Specific example

[0598] For example, if a user seeks innovative product ideas for the food industry, they input criteria targeting the food industry into the system. In response, the server collects data on the latest food trends and consumer preferences from an external database. Next, a generation tool is used to create new recipes and product concepts, which are then evaluated by an evaluation tool. Finally, through a display tool, the user can select compelling ideas and further refine their differentiating factors using an analysis tool.

[0599] Thus, the system of the present invention can significantly reduce the effort required for users to generate ideas and provide powerful support for making quick decisions.

[0600] The following describes the processing flow.

[0601] Step 1:

[0602] The user enters the subject area or specific conditions for which they want to generate ideas. These conditions include the target industry, purpose, and target market.

[0603] Step 2:

[0604] The server connects to an external database and retrieves relevant data based on the user's input. This includes industry trends, market size, and competitor information. The collected data is stored in an internal database.

[0605] Step 3:

[0606] The server generates new ideas using generation methods based on information stored in the database. Multiple AI models are operated simultaneously to propose multiple ideas from different perspectives.

[0607] Step 4:

[0608] The server evaluates each generated idea using evaluation tools. It scores and ranks each idea based on criteria such as market value, feasibility, and innovativeness.

[0609] Step 5:

[0610] The device visually presents ideas to the user based on the evaluation results. Key features and evaluation points of the ideas are displayed in graphic or short video format.

[0611] Step 6:

[0612] The user can select the most interesting idea from the displayed group of ideas. Based on this selection, the server analyzes additional differentiating factors and presents the results to the user.

[0613] Step 7:

[0614] The device suggests actionable plans and next steps based on the ideas selected by the user. This helps to initiate a concrete development process.

[0615] (Example 1)

[0616] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0617] In today's information-saturated society, it is difficult to quickly generate novel and competitively advantageous ideas. Furthermore, the lack of efficient means to evaluate and visually present these generated ideas leads to delays in the decision-making process.

[0618] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0619] In this invention, the server includes communication means for acquiring various types of information from external information sources, generation means for generating new concepts based on the acquired information, means for using a generation AI model, evaluation means for evaluating and ranking the generated concepts, and means for considering market value and innovativeness as specific evaluation criteria. This enables the rapid generation of novel ideas and efficient decision-making through their evaluation and presentation.

[0620] "Communication means" refers to a device or system that has the function of acquiring various types of information from external sources.

[0621] A "generation method" is a device or system that has the function of creating new concepts based on acquired information. By using a generation AI model, it is possible to generate new ideas from data.

[0622] An "evaluation tool" is a device or system that has the function of evaluating and ranking generated concepts based on various criteria. It is possible to perform evaluations that take into account specific criteria such as market value or innovativeness.

[0623] A "presentation means" is a device or system that has the function of visually presenting an evaluated concept to the user.

[0624] "Analysis means" refers to a device or system that has the function of analyzing the differentiating factors of a generated concept from those of competitors and extracting competitive advantages.

[0625] An "input / output device" is a device equipped with an interface for inputting conceptual conditions generated by the user and receiving the results as output.

[0626] An "activity plan" is a plan that presents specific implementation procedures and measures related to the concepts selected by the user.

[0627] This invention is an information processing system that integrates information gathering from external sources, generation of novel concepts using a generative AI model, automatic evaluation of these concepts, visual presentation, and analysis of differentiating factors. Specific embodiments are described in detail below.

[0628] The server connects to external information sources using communication methods and retrieves the necessary information. This information includes data on industry trends and consumer preferences. Specifically, the server has the capability to collect the latest data using REST APIs and SQL queries.

[0629] Next, the server uses a generation method to generate new concepts based on the acquired information. In this process, a generative AI model is used, and new ideas are generated by inputting prompts such as "Generate innovative recipes for the food industry." This generation process uses natural language processing technology to extract highly relevant keywords from the information and generate diverse ideas.

[0630] The generated ideas are evaluated based on multiple criteria using an evaluation system. Market value and innovativeness are particularly considered. The server automatically evaluates and ranks the ideas based on these criteria. The evaluation information is then linked with a presentation system and visually displayed on the terminal.

[0631] Users can use the information displayed on their device to verify the details of an idea. The device visualizes the information using ranking tables, graphs, and other visual aids. Finally, the user's selected idea is analyzed using analytical tools to identify its differentiating factors, which are then presented as a concrete action plan.

[0632] Through this system, users can efficiently generate new ideas from vast amounts of information and make rapid decisions.

[0633] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0634] Step 1:

[0635] The server accesses external information sources using communication methods and collects the necessary information. The input includes requests regarding industry trends and consumer preferences, and based on this, it executes REST APIs or SQL queries to retrieve data. The output is a raw dataset necessary for idea generation. Specifically, the server retrieves the latest food trend information from a trend database.

[0636] Step 2:

[0637] The server generates new concepts using a generative AI model based on the data acquired in Step 1. The input consists of collected data and a prompt (e.g., "Generate innovative recipes for the food industry"). The AI ​​analyzes the data, extracts relevant keywords, and then generates a variety of ideas. The output is a list of newly generated ideas. Specifically, the server uses natural language processing techniques to analyze the data and create new recipe ideas.

[0638] Step 3:

[0639] The server automatically evaluates the generated ideas using evaluation tools. The input is the list of ideas generated in step 2, to which predefined evaluation criteria such as market value, innovativeness, and feasibility are applied. The output is a ranking list of the evaluated ideas. Specifically, the server applies a scoring algorithm to evaluate each idea and assign a ranking.

[0640] Step 4:

[0641] The terminal visually presents evaluated ideas received from the server. The input is a ranking list of evaluated ideas, and the terminal uses this list to provide information to the user. The output is visual information that the user can view on the screen. Specifically, the terminal displays idea information in the form of ranking tables or graphs.

[0642] Step 5:

[0643] The user selects an idea from those presented on the device and obtains further details. The input is the user's idea selection, and based on the selected idea, differentiating factors are analyzed through analytical tools. The output is detailed information presented to the user as a concrete activity plan and implementation procedure. Specifically, the user uses a filtering function to prioritize and review ideas with high market value.

[0644] (Application Example 1)

[0645] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0646] In the advertising industry, there is a need to quickly generate new campaigns and advertising ideas, and to effectively and efficiently evaluate and propose them. However, traditional methods require significant time and resources for information gathering and analysis, hindering rapid decision-making. This challenge needs to be addressed.

[0647] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0648] In this invention, the server includes communication means for acquiring data from an external information storage system, generation means for generating new concepts based on the acquired data, and means for supporting and suggesting improvements to user behavior based on the generated advertising proposals. This enables the rapid generation and evaluation of new ideas based on the latest advertising trends.

[0649] "Communication means" refers to a method or process used to retrieve data from an external information storage system.

[0650] A "generative means" is a method or process for creating new concepts or ideas based on acquired data.

[0651] "Evaluation means" refers to a method or process for evaluating generated concepts and ranking them in terms of superiority or inferiority.

[0652] "Display means" refers to a method or process for visually presenting an evaluated concept to a user.

[0653] "Analytical tools" refer to methods or processes for investigating and understanding the factors that differentiate a company from its competitors in detail.

[0654] A "generative AI model" is a program or algorithm that uses artificial intelligence technology to generate new ideas or advertising proposals.

[0655] "Means for making improvement suggestions" refers to a method or process for making useful improvement suggestions to users based on the generated advertising drafts.

[0656] "Means to support user behavior" refers to support measures or processes that guide users to make appropriate choices and actions.

[0657] This system is designed to help generate, evaluate, and improve new ideas and campaign proposals in the advertising industry. The core of the system consists of communication, generation, evaluation, and improvement suggestion mechanisms. The system's implementation utilizes servers, terminals, and a generation AI model.

[0658] The server uses communication methods to access external information storage systems via the internet and collects various data, such as advertising industry trend information and competitor data. Then, using generation methods, it generates new advertising concepts and campaign proposals based on this collected data. The generation AI model uses an AI framework such as TensorFlow to generate diverse ideas based on the generated prompt statements.

[0659] The generated ideas are analyzed using evaluation tools, and their value and effectiveness are quantified. The evaluation uses an algorithm running in Python, and the results are displayed visually on the terminal. This display utilizes a framework such as Bootstrap to create an intuitive and user-friendly interface.

[0660] When a user selects an idea from the presented options and requests specific improvement suggestions, the server uses a generative AI model to generate further improvement suggestions tailored to the user's needs. This process supports users in quickly and efficiently selecting and applying new advertising ideas.

[0661] As a concrete example, let's consider a scenario where a user requests a "new advertising concept focused on environmental protection for women in their 20s." In this case, an example prompt might be, "Generate a new campaign proposal for the target audience (e.g., women in their 20s) based on the latest advertising trends." Based on the generated campaign proposal, the user can then select the optimal writing strategy and visual concept to effectively implement the advertising campaign.

[0662] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0663] Step 1:

[0664] The server uses communication methods to access an external information storage system via the internet. Here, it collects data on the latest trends and competitors in the advertising industry. The input is an access request, and the output is advertising trend data and competitor data. The server retrieves this data and prepares for the next processing step.

[0665] Step 2:

[0666] The server uses a generation mechanism to create new advertising concepts and campaign proposals based on the collected data. The input is the advertising trend data and competitor data obtained in step 1, and the output is the generated advertising ideas. Here, a generation AI model is used to create multiple prompt sentences based on the input data and generate ideas accordingly.

[0667] Step 3:

[0668] The server uses evaluation tools to analyze the generated advertising ideas. The input is the advertising ideas generated in step 2, and the output is the evaluation results and the ranking of the ideas. An evaluation algorithm implemented in Python or similar is used to output numerical information that evaluates the market value, innovativeness, and potential of the ideas.

[0669] Step 4:

[0670] The terminal visually presents the evaluated ideas to the user through a display mechanism. The input is the evaluation results and idea rankings obtained in step 3, and the output is the display on the user interface. The Bootstrap framework is used to provide an intuitive and user-friendly UI.

[0671] Step 5:

[0672] If the user requests improvement suggestions based on the ideas they have presented, the server will further utilize its generative AI model to create improvement plans tailored to the user. The input is the ideas selected by the user, and the output is improvement suggestions. Specific improvement ideas are generated based on the generation prompt.

[0673] Step 6:

[0674] The server presents the generated improvement suggestions to the user and assists in developing the next action plan and implementation plan. The input is the improvement suggestions generated in step 5, and the output is a draft implementation plan. Specifically, it provides information to help the user make the best choice.

[0675] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0676] This invention is an embodiment of a system that recognizes the user's emotions and incorporates the results of the emotion analysis into the idea generation process to create novel ideas optimized for the user. In addition to conventional communication means, generation means, evaluation means, display means, and analysis means, this system combines an emotion engine to propose ideas according to the user's emotional state.

[0677] System configuration and operation

[0678] Recognition and analysis of emotions

[0679] First, when the user inputs the conditions for idea generation through the interface, the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state. This allows the system to understand what emotions the user is experiencing.

[0680] Data acquisition and generation

[0681] Next, the server retrieves relevant information from an external database via communication. Based on this information, the generation mechanism generates ideas while considering the analysis results of the emotion engine. For example, if the user is relaxed, it can be configured to present more innovative and bold ideas.

[0682] Idea evaluation and emotional feedback

[0683] Each generated idea is evaluated using evaluation tools, including whether it resonates with the user's emotions. The emotion engine then analyzes the user's facial expressions and reactions to determine which idea is most favorably received. This feedback is incorporated into the scoring, and the ideas are ranked accordingly.

[0684] Specific example

[0685] For example, if a user is looking for a concept for a new marketing campaign, they first input their expectations and requirements. The emotion engine then analyzes the user's facial expressions to gauge their level of seriousness and excitement. Taking these results into account, the server generates ideas based on newly acquired market data, tailored to the user's expectations, and identifies the most appealing idea. Next, these ideas are visually displayed on the device, allowing the user to select the idea that resonates most strongly. Based on the user's selection, a more detailed plan is then proposed.

[0686] Thus, the system of the present invention accurately incorporates the user's emotions, enabling more personalized idea generation and supporting the decision-making process.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The user uses the system interface to input the conditions and goals of the idea they want to generate. This information is sent to the server as initial setup for idea generation.

[0690] Step 2:

[0691] The device activates an emotion engine and captures the user's facial expressions and voice data in real time. This allows for the analysis of the user's emotional state (e.g., excitement, relaxation, tension, etc.).

[0692] Step 3:

[0693] The server connects to an external database using communication methods and retrieves the latest industry information and market data related to the conditions specified by the user. The retrieved data is stored in an internal database.

[0694] Step 4:

[0695] The server generates multiple new ideas using generation methods based on the stored data and the analysis results of the emotion engine. In this step, the user's emotional state is taken into consideration to provide more appropriate suggestions.

[0696] Step 5:

[0697] The server evaluates each generated idea using an evaluation tool and assigns a score. The evaluation criteria also include elements that are appropriate to the user's emotions. A list of ranked ideas is then generated.

[0698] Step 6:

[0699] The device provides the user with a visual list of ideas. The ideas are displayed through graphics and short videos, designed to further complement the user's emotional response.

[0700] Step 7:

[0701] The user selects the most appealing idea from those presented. Based on the selection, the server proposes relevant implementation plans and differentiation strategies, assisting the user in taking the next steps.

[0702] (Example 2)

[0703] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0704] In recent years, in this age of information overload, it has become increasingly difficult for users to generate accurate and effective ideas. Furthermore, conventional idea generation systems do not take user emotions into consideration, resulting in a lack of personalized suggestions and an inability to fully meet user needs.

[0705] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0706] In this invention, the server includes communication means for acquiring various data from an external information infrastructure, generation means for generating new ideas based on the acquired information and the user's emotional state, and recognition means for detecting and quantifying the user's emotional state. This makes it possible to generate and propose personalized ideas that respond to the user's emotions.

[0707] "Communication means" refers to methods and devices for acquiring various types of data from external information infrastructure.

[0708] "Generation means" refers to methods or devices for generating new ideas based on acquired information and the user's emotional state.

[0709] "Recognition means" refers to methods or devices that have the function of detecting and quantifying a user's emotional state.

[0710] "Evaluation means" refers to methods and devices for evaluating and ranking generated ideas based on whether they align with user sentiment.

[0711] "Display means" refers to methods or devices for visually presenting evaluated ideas to users.

[0712] This invention relates to a system that analyzes a user's emotions and generates personalized ideas accordingly. Specific embodiments for carrying out the invention are described below.

[0713] This system operates through the interaction of a server, a terminal, and a user. First, the user inputs the conditions for idea generation via the terminal. During this process, the camera and microphone equipped on the terminal collect the user's facial expressions and voice, which are then analyzed by an emotion engine. The emotion engine can use commercially available emotion analysis software or a custom-developed algorithm. This analysis quantifies the user's emotional state and transmits it to the server.

[0714] The server acquires necessary data from an external information infrastructure via communication channels. This data includes market trends and customer feedback. Using the acquired data and user sentiment data as input, a generative AI model within the server operates to generate new ideas. Generative AI models typically employ natural language processing techniques or machine learning algorithms.

[0715] The generated ideas are evaluated to determine how well they resonate with the user's emotions. The evaluation results are displayed step-by-step on the device's screen, allowing the user to visually confirm them. The user can then select the idea they resonate with the most, and a specific implementation plan corresponding to that idea is proposed.

[0716] For example, if a user is looking for a new marketing campaign concept, they input the conditions, and the emotion engine analyzes their facial expressions and tone of voice. Taking these results into account, the server uses a generative AI model to generate and evaluate new ideas. The generated ideas are displayed on the terminal and optimized based on the user's selection. An example prompt here is, "Generate new marketing campaign ideas. Use a model that considers the user's emotional state and provides suggestions that are easy to engage with in a relaxed mood."

[0717] This configuration allows the system to generate effective ideas that reflect the user's emotions, enabling it to offer more compelling suggestions to the user.

[0718] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0719] Step 1:

[0720] The user inputs the conditions for idea generation via a device. The device uses its camera and microphone to record the user's facial expressions and voice, and sends this information to the emotion engine. The input data consists of the user's idea generation requirements and raw audio / video data, and the device prepares it for analysis by the emotion engine.

[0721] Step 2:

[0722] The device uses an emotion engine to analyze the user's facial expressions and voice, and quantifies their emotional state. This data is sent to a server. The data processing performed here involves associating facial expressions and voice tone with emotions, and the output is the user's specific emotional score.

[0723] Step 3:

[0724] The server retrieves data from an external information infrastructure using communication methods. The retrieved data includes market trends and related information, and is filtered to show the most relevant data based on the user's intent. The input is a large-scale external database, and the output is organized data that matches the user's criteria.

[0725] Step 4:

[0726] The server inputs the collected data and the user's sentiment score into a generating AI model. Following prompts, it generates ideas, producing multiple ideas corresponding to the user's sentiment as output. The data calculations here are performed by the AI ​​model's generation process.

[0727] Step 5:

[0728] The server evaluates the generated ideas using evaluation tools and verifies their compatibility with the user's emotional state. Based on the evaluation results, it ranks the ideas and selects the optimal one. The input is the group of generated ideas, and the output is the ranked ideas.

[0729] Step 6:

[0730] The terminal visually displays ranked ideas, allowing the user to select the most appealing one. Based on the user's selection, a concrete implementation plan is presented, which the user can then review. The displayed information is a list of evaluated ideas, and the output is the user's selected idea and the plan based on it.

[0731] (Application Example 2)

[0732] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0733] Currently, it is difficult to suggest products or services that take into account the emotional state of individual users. Therefore, there is a possibility that suggestions may differ from what users truly desire, and improving the quality of the individual purchasing experience is crucial. Furthermore, a lack of means to emotionally rank ideas or recommendations and present more suitable options during the user's acceptance process is a significant challenge.

[0734] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0735] In this invention, the server includes emotion analysis means for recognizing emotions from the user's facial expressions and voice, generation means for generating new ideas based on the emotion analysis results and the acquired information, and evaluation means for evaluating the generated ideas and ranking them considering the user's emotions and suitability. This enables personalized product or service suggestions optimized for the user's emotions, providing a high-quality purchasing experience.

[0736] "Emotion analysis means" refers to a technology that analyzes the user's facial expressions and voice data to recognize their emotional state in real time.

[0737] "Communication methods" refer to technologies used to obtain various types of information from external sources, and play a role in securing data via the internet and databases.

[0738] A "generation method" is a technology that automatically generates new ideas based on acquired information and emotion analysis results.

[0739] An "evaluation tool" is a technology that ranks generated ideas based on user sentiment and relevance to identify superior options.

[0740] "Display means" refers to technologies for visually and clearly displaying evaluated ideas to users.

[0741] "Analytical methods" refer to techniques for thoroughly examining and identifying the differentiating factors from those of competitors.

[0742] In the system that realizes this invention, the server, terminal, and user each play important roles. The server uses emotion analysis means to analyze the user's facial expressions and voice, and recognizes the user's emotions through the results. Face recognition APIs and voice recognition APIs are used in this process. Specifically, the use of Google Cloud Vision API and Microsoft Azure Face API is conceivable.

[0743] The server acquires information from external sources (communication means) and generates new ideas considering the sentiment analysis results (generation means). This generation process utilizes a generation AI model to form personalized ideas based on the user's emotions.

[0744] The generated ideas are evaluated on a server and ranked based on user sentiment and relevance (evaluation method). After evaluation, the ideas are sent to the terminal and visualized and displayed on the user's device (display method). The user can review the visually presented ideas and select them as needed.

[0745] As a concrete example, when a user opens an e-commerce application on their smartphone, an emotion analysis system recognizes their face and voice on the spot, and if they are relaxed, recommends relaxation products suitable for the user. The recommended products are then ranked highly by an evaluation system and displayed to the user through their device.

[0746] An example of a prompt message would be: "Recognize the user's emotions and suggest the most suitable products to help them relax. For example, include relaxation items or stress-relief services."

[0747] In this way, we can quickly provide users with optimized suggestions, making it possible to create a more attractive purchasing experience for them.

[0748] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0749] Step 1:

[0750] The user launches a smartphone application and positions the device so that their face and voice can be read. The input consists of the user's facial expression data and voice data. The output is raw data converted into a format suitable for analysis. The device's camera and microphone are used in this process.

[0751] Step 2:

[0752] The server processes raw data acquired using emotion analysis methods to recognize the user's current emotional state. Specifically, it utilizes face recognition APIs and speech recognition APIs to infer emotions from recognized facial features and voice tone. The input is raw data, and the output is the recognized emotion label.

[0753] Step 3:

[0754] The server uses communication methods to retrieve relevant information from external sources based on recognized emotion labels and builds a dataset for generating ideas. Specifically, it utilizes a generative AI model to extract product-related information from the cloud. The input is emotion labels, and the output is a dataset of extracted relevant information.

[0755] Step 4:

[0756] The server processes the dataset using a generation method and generates new ideas (product and service suggestions) corresponding to emotion labels. A generative AI model is used to create ideas optimized for the user's feelings. The input is a dataset of related information, and the output is the generated new ideas.

[0757] Step 5:

[0758] The server ranks the generated ideas using an evaluation system. Here, the degree of emotional resonance is used as the criterion, and prompt messages are used to establish the evaluation criteria. The input is new ideas, and the output is a list of ranked ideas.

[0759] Step 6:

[0760] The server sends the evaluated ideas to the terminal, which then displays the ideas visually. The user reviews the displayed ideas and makes selections as needed. The input is a list of ranked ideas, and the output is the visual content presented to the user.

[0761] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0762] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0763] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0764] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0765] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0766] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0767] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0768] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0769] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0770] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0771] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0772] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0773] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0774] 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.

[0775] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0776] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0777] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0778] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0779] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0780] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0781] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0782] The following is further disclosed regarding the embodiments described above.

[0783] (Claim 1)

[0784] Communication means for obtaining various data from external databases,

[0785] A generation means for generating new ideas based on acquired data,

[0786] An evaluation method for evaluating and ranking the generated ideas,

[0787] A display means for visually displaying the evaluated ideas,

[0788] Analytical tools for analyzing the differentiating factors from competitors,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, comprising an interface that allows the user to input conditions for an idea they generate.

[0792] (Claim 3)

[0793] The system according to claim 1, which includes means for presenting an implementation plan when a visually displayed idea is selected by the user.

[0794]

[0795] "Example 1"

[0796] (Claim 1)

[0797] Communication means for obtaining various types of information from external sources,

[0798] A means for generating new concepts based on acquired information, a means for using a generative AI model,

[0799] Evaluation means for assessing and ranking the generated concepts, and means for considering market value and innovativeness as specific evaluation criteria,

[0800] A means of visually presenting the evaluated concept,

[0801] Analytical means for analyzing the differentiating factors from competitors,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, comprising an input / output device capable of inputting conditions for a concept generated by a user.

[0805] (Claim 3)

[0806] The system according to claim 1, which includes means for presenting a related activity plan once a visually presented concept is selected by the user.

[0807] "Application Example 1"

[0808] (Claim 1)

[0809] A means of communication for obtaining data from an external information storage system,

[0810] A generation means for generating new concepts based on acquired data,

[0811] An evaluation method for evaluating and ranking the generated concepts,

[0812] A display means for visually displaying the evaluated concept,

[0813] Based on data obtained from the industry information storage system, an analytical method is provided to analyze the factors that differentiate us from competitors.

[0814] A means of analyzing concepts generated using a generative AI model and generating new advertising proposals,

[0815] Based on the generated ad drafts, a means to support and suggest improvements to user behavior,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, comprising an input / output device capable of inputting conditions for a concept generated by the user.

[0819] (Claim 3)

[0820] The system according to claim 1, which includes means for presenting an implementation plan when a visually displayed concept is selected by the user.

[0821] "Example 2 of combining an emotion engine"

[0822] (Claim 1)

[0823] Communication means for acquiring various types of data from an external information infrastructure,

[0824] A generation means for generating new ideas based on acquired information and the user's emotional state,

[0825] A recognition method for detecting and quantifying the user's emotional state,

[0826] An evaluation method for evaluating and ranking generated ideas based on whether they align with user sentiment,

[0827] A display means for visually displaying the evaluated ideas,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, comprising an interface for analyzing the user's facial expressions and voice to understand their emotional state.

[0831] (Claim 3)

[0832] The system according to claim 1, which includes means for presenting a relevant specific plan once a visually displayed idea has been selected by the user.

[0833] "Application example 2 of combining emotional engines"

[0834] (Claim 1)

[0835] An emotion analysis method for recognizing emotions from the user's facial expressions and voice,

[0836] Communication means for obtaining various types of information from external sources,

[0837] A means for generating new ideas by considering the sentiment analysis results based on the acquired information,

[0838] An evaluation method for assessing generated ideas and ranking them while considering user sentiment and relevance,

[0839] A display means for highly visualizing and displaying evaluated ideas,

[0840] Analytical tools for analyzing the differentiating factors from competitors in detail,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, which includes an interface that allows the user to input conditions for ideas they generate, and which makes product or service suggestions based on the user's emotional state.

[0844] (Claim 3)

[0845] The system according to claim 1, which includes means for presenting an emotion-based, relevant implementation plan once a visually displayed idea has been selected by the user. [Explanation of Symbols]

[0846] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Communication means for obtaining various data from external databases, A generation means for generating new ideas based on acquired data, An evaluation method for evaluating and ranking the generated ideas, A display means for visually displaying the evaluated ideas, Analytical tools for analyzing the differentiating factors from competitors, A system that includes this.

2. The system according to claim 1, comprising an interface that allows the user to input conditions for an idea they generate.

3. The system according to claim 1, which includes means for presenting an implementation plan when a visually displayed idea is selected by the user.

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