system

Abstract

We provide the system. [Solution] A means of collecting past success stories and market trend data through a data collection module, A means of using a generative artificial intelligence model that analyzes the aforementioned data to generate new ideas, A means of identifying partner companies based on their corporate profiles and designing collaboration strategies, A means to automatically analyze market information and generate reports, A means of receiving user feedback and improving the system, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order for a company to create new ideas, the ability to efficiently analyze past success cases and market trends is required. However, many companies do not have sufficient resources and technologies to achieve this, and as a result, they have fallen into a situation where innovation stagnates. In addition, in order to maintain competitiveness in the market, it is necessary to collaborate with appropriate partners, but there are problems that it takes a lot of time to select partners and design effective strategies. Furthermore, due to the inability to conduct rapid market analysis, there is a problem that the strategic decision-making of the company is delayed and opportunity losses occur.

Means for Solving the Problems

[0005] [[ID=4I]] This invention provides companies with a means to efficiently promote innovation by using a generative artificial intelligence model that generates new ideas by collecting past success stories and market trend data using a data collection module and analyzing this data. Furthermore, by introducing a module that identifies optimal partner companies based on company profiles and designs effective collaboration strategies, it enables the rapid establishment of appropriate collaborative systems. In addition, by including an automated market information analysis module with real-time data monitoring capabilities, the system provides a constant grasp of the latest market trends and supports rapid strategic decision-making. This makes it easier for companies to develop innovative projects with limited resources.

[0006] A "data collection module" is a software or hardware component designed to efficiently collect data on past success stories and market trends.

[0007] A "generative artificial intelligence model" is an algorithm that combines machine learning and natural language processing and is used to generate new ideas based on collected data.

[0008] A "profile" is a collection of data that comprehensively shows a company's basic information, characteristics, and strengths, and is used as a criterion for identifying partner companies and designing collaboration strategies.

[0009] "Real-time data monitoring" refers to a function that continuously monitors the latest information on the market and competitors, enabling rapid market analysis.

[0010] The "Market Information Automated Analysis Module" is a system component that analyzes collected market data and automatically generates reports based on the results.

[0011] A "collaboration strategy" is a plan or approach designed to maximize effectiveness when working with specific partners. [Brief explanation of the drawing]

[0012] [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]

[0013] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered 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.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. 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.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention provides a system for companies to efficiently generate new ideas and facilitate strategic collaboration. This system takes the following form.

[0034] First, the server utilizes a data collection module to gather data on past success stories and market trends from publicly available databases on the internet and internal company databases. This data collection is performed periodically by an automated scheduling system.

[0035] Next, the server analyzes the collected data using a generative artificial intelligence model. This model combines natural language processing and machine learning algorithms to extract promising business ideas from the data. This allows for the generation of innovative ideas that might be difficult for in-house experts to obtain.

[0036] Next, the server identifies potential partner companies by referring to their profile information. For each identified partner, it analyzes their past collaboration history and market positioning to design the most effective collaboration strategy. This strategy design is achieved by comparing it with successful patterns accumulated within the system.

[0037] Furthermore, the server uses an automated market information analysis module to monitor market and competitor trends in real time. This module has the ability to instantly generate reports and notify users when new market trends are discovered. This enables companies to make strategic decisions quickly.

[0038] Users can submit feedback on ideas and strategies provided by the server. This feedback is used as data for the system to learn and improve its analytical accuracy. In this way, the entire system is continuously optimized, making the company's planning operations more efficient.

[0039] As a concrete example, consider a manufacturing company exploring new IoT product ideas based on past data. The server analyzes relevant past success stories and proposes features and timing for market launch of the new product. At the same time, it finds partners that match the company's strengths and designs a strategy to enhance the product's market value through collaboration with those partners. In this way, the company can prepare to launch its new product into the market in a short period of time.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server activates a data collection module to gather past success stories and market trend data from external and internal databases. This is done using methods such as web scraping and data acquisition via APIs. The collected data undergoes initial filtering to remove noise.

[0043] Step 2:

[0044] The server inputs the cleansed data into an artificial intelligence model. This model uses natural language processing techniques to analyze the data and extract key trending keywords. Furthermore, it generates new business ideas based on these keywords. This process leverages algorithms learned from existing ideas and trends.

[0045] Step 3:

[0046] The server consults an internal database to identify partner companies that match the company's profile. This profile includes the company's strengths and information on past collaborations. It creates a list of potential partners and designs a collaboration strategy to maximize synergies with each company. This includes an effective action plan to achieve specific business objectives.

[0047] Step 4:

[0048] The server runs an automated market information analysis module, monitoring market segments and competitor trends in real time. The monitoring results are analyzed, and if market opportunities or risks requiring immediate attention are identified, a report is automatically generated. This report is used by users to inform strategic decision-making.

[0049] Step 5:

[0050] Users provide feedback based on proposed ideas and strategies. This feedback is sent to the server and contributes to improving the system's algorithms. This leads to improved accuracy in future proposals and enables the formation of more precise strategies.

[0051] (Example 1)

[0052] 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."

[0053] The process by which companies efficiently generate new business ideas and build strategic partnerships typically requires a tremendous amount of time and effort. Furthermore, it is not easy to grasp market trends in a timely manner and make quick decisions. This creates a risk of missing growth opportunities in today's increasingly competitive business environment.

[0054] 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.

[0055] In this invention, the server includes means for collecting past performance data and market trend data through an information gathering configuration, means for using a generative artificial intelligence algorithm to analyze the data and create new concepts, and means for identifying potential collaboration partners based on the business entity's profile and designing a cooperation strategy. This enables companies to efficiently and quickly generate business ideas, build optimal partnerships, and make decisions that are responsive to market trends.

[0056] An "information gathering configuration" is a system for automatically collecting past performance data and market trend data.

[0057] A "generative artificial intelligence algorithm" is an artificial intelligence technology that analyzes data and creates new concepts, using language processing techniques and machine learning methods.

[0058] "Business entity profile" refers to information such as a company's basic information, business activities, and past collaboration history, and serves as a criterion for identifying potential collaboration partners.

[0059] "Means of designing a cooperation strategy" refers to the process of devising methods for building optimal cooperative relationships with identified potential partners.

[0060] "Market trends" refer to current and projected conditions and changes in a particular industry or sector, and are important factors that influence a company's decision-making process.

[0061] "Real-time data monitoring" refers to the function of observing data in real time and immediately detecting changes.

[0062] "User feedback" refers to feedback and insights provided by users for the purpose of improving the final product or service.

[0063] This invention relates to a system for companies to efficiently generate new business ideas and build collaborative relationships, and is implemented through the collaboration of servers, terminals, and users.

[0064] The server first uses an information gathering configuration to automatically collect performance case studies and market trend data from publicly available databases on the internet and internal company databases. This configuration ensures that the data is always up-to-date, as it is regularly updated by an automated scheduling system. The hardware would consist of a dedicated data server, and the software would likely be a scraping tool based on a programming language such as Python.

[0065] Next, the server analyzes the collected data using a generative AI model. Specifically, a model combining natural language processing technology and machine learning algorithms (e.g., GPT-3®) is used to analyze the data and generate promising new concepts. This model plays a role in extracting important trends and innovative points from vast amounts of data and presenting them as concrete ideas.

[0066] Furthermore, the server identifies potential partners and designs collaboration strategies while referencing the business entity's profile information. The server considers the characteristics and strengths of each company to list the most suitable candidates for collaboration and formulates the optimal strategy based on that list. This promotes collaboration between companies and supports the establishment of cooperative relationships.

[0067] Furthermore, the server constantly monitors market trends and automatically generates reports by analyzing dynamically changing market information in real time. This automatic generation function is a crucial foundation for responding quickly to changes, allowing users to make optimal decisions according to market conditions at any given time.

[0068] Users provide feedback on the ideas and strategies generated by the server. This feedback contributes to the continuous improvement of the system and helps to improve the analytical accuracy of the generated AI model. As a result, users can always proceed with their work based on optimized information.

[0069] For example, when a manufacturing company plans a new IoT product, the server can analyze past cases and suggest product characteristics to consider and the timing of market launch. Furthermore, by identifying partner companies that complement its strengths and designing a collaborative model with those companies, it can increase the probability of product success.

[0070] An example of a prompt message could be: "Generate ideas and partnership strategies for new product planning in the next quarter."

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The server uses an information gathering configuration to collect performance case studies and market trend data from publicly available databases on the internet and internal company databases. In this step, the server periodically accesses various data sources to retrieve new data. The input is a database query, and the output is the collected raw dataset. Specifically, the server executes scripts to perform web scraping and data extraction via APIs.

[0074] Step 2:

[0075] The server inputs the collected data into a generating AI model and performs data analysis. Specifically, it utilizes a model that combines natural language processing technology and machine learning algorithms to organize relevant information and create promising new concepts. The input data consists of collected performance case studies and market trend information, and the output is newly generated business ideas. The server starts the analysis process and lists the generated ideas.

[0076] Step 3:

[0077] The server identifies potential collaborators based on company profiles. Based on the collected and analyzed data, the server lists the most suitable partners, taking into account the company's characteristics and past collaboration history. The input is company profile data, and the output is a list of identified potential collaborators. The server uses an algorithm to perform profile matching and select candidates.

[0078] Step 4:

[0079] The server monitors market trends in real time and analyzes dynamic market information. When market changes are detected, it automatically generates reports immediately. The input is market data collected in real time, and the output is the generated market report. Specifically, the server processes the real-time data stream and triggers report creation when it detects a change.

[0080] Step 5:

[0081] Users provide feedback on the ideas and strategies generated by the server. In this feedback process, user opinions are input into the system and used for subsequent analyses. The input is user feedback data, and the output is insights that lead to system improvements. Users submit their feedback through the provided interface.

[0082] (Application Example 1)

[0083] 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."

[0084] In the field of autonomous driving technology, generating innovative technological ideas and identifying appropriate partners for technological development are crucial. However, there are limited methods for doing this efficiently and effectively. Furthermore, it is difficult to adjust strategies immediately through real-time tracking and analysis of market trends. This invention aims to solve these problems and provide a system that supports new innovation and collaboration in autonomous driving technology.

[0085] 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.

[0086] In this invention, the server includes means for collecting past success data and market trend data through a data acquisition configuration, means for using a generative intelligence model to analyze the data and generate new concepts, and means for identifying collaborating companies based on corporate attribute information and designing a cooperation strategy. This makes it possible to analyze the latest market trends related to autonomous driving technology, provide new technology ideas, and identify appropriate technology development partners.

[0087] A "data acquisition configuration" is a system equipped with functions for effectively collecting past success stories and market trend data.

[0088] A "generative intelligence model" is an artificial intelligence model that analyzes collected data and automatically generates new technological concepts and ideas.

[0089] "Corporate attribute information" refers to data that provides comprehensive information about a company, such as its characteristics, strengths, and business strategy.

[0090] A "collaborating company" refers to a company that may cooperate in the development and market introduction of new technologies.

[0091] A "cooperation strategy" is a plan to optimize technology development and market deployment by collaborating with designated partner companies.

[0092] "Market trends" refer to information regarding changes in demand and supply, as well as competitive activities, within a specific industry or technology field.

[0093] A "technology development partner" is a company or organization that collaborates in the development of new autonomous driving technologies.

[0094] "Real-time data observation" is the process of constantly monitoring current market and technological trends and acquiring information immediately.

[0095] In implementing this system, the server first uses a data acquisition configuration to collect diverse historical success stories and market trend data from a wide range of databases. This includes publicly available company information and relevant industry data. This data is managed on a cloud server and processed efficiently while maintaining security.

[0096] Next, the server utilizes a generative intelligence model to analyze the collected data. In this process, it uses natural language processing libraries such as spaCy and Transformers to analyze text data and machine learning algorithms (e.g., TENSORFLOW® and PyTorch) to generate innovative technical concepts and ideas. This generative intelligence model can identify data relevance and potential market value.

[0097] Furthermore, the server analyzes corporate attribute information to identify suitable collaborating companies. This process takes into account the companies' strengths and technological capabilities to design the optimal collaboration strategy. As a result, the selection of technology development partners becomes faster and more accurate.

[0098] With real-time data monitoring capabilities, market trends are constantly monitored, and the latest information is acquired instantly. This information is promptly fed back to the user via their device, supporting strategic decision-making. For example, a user can input a specific prompt such as, "Find collaboration partners for AI-powered technologies related to electric vehicles," and the system will present data accordingly. Such prompts promote practical and creative technological development.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server collects historical success stories and market trend data from various databases through a data acquisition configuration. Based on the specified query as input, the server searches publicly available databases on the internet and internal company databases. The collected data is stored in cloud storage.

[0102] Step 2:

[0103] The server analyzes the collected data using a generative intelligence model. The input data is parsed using a natural language processing library (e.g., spaCy). Then, machine learning algorithms (e.g., TensorFlow) are used to extract relevant technical ideas and generate new concepts. The resulting ideas are recorded in a database.

[0104] Step 3:

[0105] The server analyzes corporate attribute information to identify suitable collaborating companies. Given corporate profile information as input, it analyzes it and determines the optimal collaboration partner, taking into account technical capabilities and market position. This information is provided to the collaboration strategy design module, which then generates a specific collaboration strategy.

[0106] Step 4:

[0107] The server observes real-time data and constantly monitors market trends. It uses automated sensors and real-time feeds to acquire and analyze the latest trend data. The resulting market reports are then sent to user terminals.

[0108] Step 5:

[0109] The user enters prompts into the system via a terminal and receives suggestions for specific technical development needs. Based on the input prompts, the server selects previously generated ideas and collaboration strategies and presents them to the user. User feedback is collected and used to improve the system.

[0110] 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.

[0111] This invention combines a system for companies to generate new ideas and achieve effective strategic collaboration with an emotion engine that recognizes user emotions. As a result, proposed ideas and strategies are optimally customized for the user, supporting the company's decision-making process.

[0112] First, the server utilizes a data collection module to acquire data on past success stories and market trends. This data is integrated from various data sources and preprocessed. Next, a generative artificial intelligence model analyzes this data and generates new business ideas. This model combines natural language processing and machine learning algorithms to deepen trend insights and drive innovation.

[0113] Next, the process moves to identifying the most suitable partner companies based on the company's profile and designing a collaboration strategy. Here, a strategy is developed to maximize interaction with the identified partners. This strategy is formulated based on the company's strengths and market analysis.

[0114] In addition, this system incorporates an emotion engine. The terminal sends user voice input and operation logs to the emotion engine, which then analyzes the user's emotional state. This engine uses voice analysis and text analysis technologies, and can determine emotions in real time based on user feedback and reactions.

[0115] The server dynamically adjusts the content and visual display of suggestions based on information obtained from the emotion engine. For example, if a user expresses concern about a suggestion, it can prioritize presenting detailed information and alternatives. In this way, suggestions are presented in a manner that matches the user's emotional state, improving their acceptability.

[0116] As a concrete example, consider a scenario where a project manager at a certain company is considering a strategy for launching a new product into the market. The system analyzes past success stories and proposes a new market strategy, but at the same time, an emotion engine reads the user's reaction and, if the user is feeling anxious, delivers a presentation that emphasizes areas for improvement and competitive advantages. In this way, personalized information based on the user's emotions becomes possible, allowing companies to make strategic decisions more smoothly.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The server activates a data collection module, automatically gathering historical success stories and relevant market trend data from external public databases and internal corporate data storage. The collected data is then subjected to noise filtering and data cleansing to prepare it for analysis.

[0120] Step 2:

[0121] The server feeds the prepared data into an artificial intelligence model. This model combines natural language processing and machine learning algorithms to understand the context of the data and generate key trends and new business ideas. The generated ideas are then proposed to meet the specific needs of the company.

[0122] Step 3:

[0123] The server references corporate profile information and creates a list of potential partner companies based on past collaborations and the degree of business area alignment. Furthermore, it develops and proposes effective collaboration strategies for working with these partners. These strategies include specific action plans and target achievement indicators.

[0124] Step 4:

[0125] The emotion engine uses speech recognition and text analysis technologies to monitor the user's emotions in real time. The device continuously collects voice input and feedback from the user and sends it to the emotion engine. This information helps understand the user's emotional response to the proposed idea.

[0126] Step 5:

[0127] Based on the analysis results of the emotion engine, the server dynamically adjusts how and what is presented in the proposed ideas. For example, if a user expresses concern, the server takes an approach to deepen the user's understanding by refining the proposal or presenting alternative strategies.

[0128] Step 6:

[0129] Users provide feedback on the suggestions and strategies presented by the server. This feedback is used as training data for the system and is utilized to improve the quality of future suggestions. Based on the feedback, the entire system is continuously optimized, enabling it to provide more valuable suggestions to companies.

[0130] (Example 2)

[0131] 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".

[0132] In today's business environment, companies need to generate innovative ideas based on advanced data analysis and identify effective collaborators in order to quickly adapt to the market and maintain competitiveness. However, traditional methods have struggled to provide personalized suggestions that take user emotions into account, resulting in decreased acceptance in the decision-making process.

[0133] 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.

[0134] In this invention, the server includes means for collecting past performance information and market trend data through a data processing device, means for using an intelligent processing model that analyzes the data to generate new innovative ideas, and means for determining the user's emotional state using an emotion analysis device. This enables adaptive suggestions based on the user's emotions.

[0135] A "data processing device" is a computer system used to collect and analyze information, possessing the function of acquiring information from various data sources and organizing it into a consistent format.

[0136] "Performance information" refers to various data that shows the effectiveness of a company's operations in the past, including historical data such as success stories and growth patterns.

[0137] "Market trend data" refers to a comprehensive collection of market-related data, including consumer behavior, industry trends, and product demand patterns.

[0138] An "intelligent processing model" refers to an algorithm or system that uses natural language processing and machine learning techniques to generate useful information and innovative ideas from large amounts of data.

[0139] An "emotion analysis device" is a system that determines a user's emotional state in real time, and includes technology that analyzes psychological responses from voice and text data.

[0140] "User emotional state" refers to individual reactions and psychological feelings to suggestions and information presented, including overall satisfaction and anxiety.

[0141] This invention provides a system for supporting corporate decision-making using a data acquisition device, an intelligent processing model, and an emotion analysis device. Specific embodiments are described below.

[0142] The server first uses a data processing unit to collect historical performance information and market trend data from various sources. The software incorporates data integration tools and cleaning algorithms to generate a clean and unified dataset.

[0143] Next, the server leverages an intelligent processing model to analyze the collected data and generate new innovative ideas. This model is based on natural language processing techniques and machine learning algorithms, and processes large-scale data using frameworks such as Hadoop and TensorFlow. The generated ideas include specific market strategies and new product concepts.

[0144] The device determines the user's emotional state in real time through an emotion analysis device. This involves analyzing user input data using speech recognition and text analysis technologies. Speech analysis solutions such as Microsoft® Azure® and Amazon Polly are utilized.

[0145] Based on information obtained from sentiment analysis, the server has the ability to dynamically adjust the content of suggestions and their visual representation. For example, if a user expresses concerns about a new product strategy, the system will prepare a presentation that emphasizes competitive advantages and success stories.

[0146] An example of a prompt is: "Analyze successful case studies of new product launches and propose the optimal strategy. Also, analyze user sentiment towards the proposal and create a customized presentation."

[0147] By using this system, companies can respond quickly and flexibly to market trends and provide users with information optimized for their needs.

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The server uses data processing equipment to collect historical performance data and market trend data. Inputs include data from internal company databases, online news, industry reports, and social media. This data undergoes preprocessing, such as noise reduction and formatting, before being output as an analyzable dataset. This process utilizes data integration software and ETL (extract, transform, load) tools to generate a well-organized dataset.

[0151] Step 2:

[0152] The server inputs pre-processed data into an intelligent processing model to generate innovative ideas. This model utilizes natural language processing and machine learning algorithms. A cleaned dataset is provided as input data, from which ideas such as market strategies and new product concepts are output. Specifically, the machine learning model performs pattern recognition and trend analysis to generate innovative proposals.

[0153] Step 3:

[0154] The device uses an emotion analysis device to collect user voice input and text data to determine their emotional state. Input includes user voice feedback and reaction logs. This data is processed through speech synthesis and text analysis to output information about the user's emotional state. Specifically, speech recognition technology analyzes the user's voice tone and word choice to evaluate their emotions.

[0155] Step 4:

[0156] Based on the emotional information obtained in Step 3, the server adjusts the suggested content and its visual display. The input consists of emotional state information and ideas generated in Step 2, and the output is a presentation adapted to the user's emotions. The server changes the priority of information and adjusts the use of color and information placement in the user interface to make it more acceptable to the user. This increases the acceptability of the suggestions.

[0157] (Application Example 2)

[0158] 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 device 14 will be referred to as the "terminal."

[0159] In recent years, organizations need to generate new ideas and design rapid and accurate collaborative strategies to gain a competitive advantage in the market. However, traditional systems have difficulty quickly analyzing data and reflecting user sentiment, sometimes leading to a decline in the quality of ideas. Furthermore, dynamic proposals that take user sentiment into account are not made, and as a result, proposals and strategies are often not effective. There is a need to provide an effective solution to this problem.

[0160] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting past success references and market trend information through a data collection device; means for using a generative artificial intelligence model that analyzes the information to generate new concepts; means for identifying partner organizations based on the organization's history and designing cooperation strategies; means for receiving user feedback and improving the system; and means for identifying the user's emotions using an emotion recognition device and adjusting suggestions based on those emotions. This makes it possible to generate high-quality concepts based on data and provide dynamic suggestions that reflect the user's emotions.

[0161] A "data collection device" is a device used to acquire past success references and market trend information, and has the function of collecting data from various information sources.

[0162] A "generative artificial intelligence model" is an artificial intelligence model that analyzes collected data and generates new ideas, using natural language processing and machine learning techniques.

[0163] "Identification based on organizational history" refers to a method for identifying appropriate partner organizations based on their organizational history information.

[0164] "Designing a cooperation strategy" is a means of designing a strategy to maximize mutual benefits with partner organizations.

[0165] "User feedback" refers to the reactions and opinions received from users, and is used to improve the system.

[0166] An "emotion identification device" is a device used to identify the emotions of a user, and has the function of analyzing emotional states from voice and actions.

[0167] To realize this invention, the system has the following configuration.

[0168] The server uses data collection devices to comprehensively gather information on past successes and market trends from various databases. This data undergoes initial processing and is then sent to a generative artificial intelligence model that combines natural language processing techniques and machine learning algorithms to generate new ideas. This generation process allows for the prediction of future trends and the proposal of new business strategies based on insights gained from the data.

[0169] The terminal collects user voice input and operation logs through an emotion recognition device and analyzes this data in real time. The emotion recognition device uses Google Cloud's natural language processing API and speech analysis API to analyze the user's emotions and determine how the user is feeling. Based on the analysis results, the server dynamically adjusts the suggestions and presents information optimized for the user's emotions. For example, if the user has doubts about a suggestion, the server will present alternative options and add detailed explanations.

[0170] Users make decisions to accept suggestions through an interface provided by the system. The information selected by the user is collected as feedback, and the server uses this to improve the algorithms and suggestions.

[0171] For example, if a user is considering purchasing a new gadget, the system will suggest the most suitable product based on the user's past purchase history and emotional responses. For instance, if the user appears anxious, the system might suggest a product with superior warranty coverage.

[0172] An example of a prompt message is an instruction input to the AI ​​model that reads, "Check the customer's emotional state each time they view a product, provide detailed information about products that elicit a positive reaction, and suggest alternative products if a negative reaction occurs."

[0173] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0174] Step 1:

[0175] The server uses a data acquisition device to collect past success references and market trend information from the database. The collected information is first pre-processed, including noise reduction and standardization of data formats. This pre-processing transforms the data into a format suitable for analysis. The input is raw data, and the output is cleaned data.

[0176] Step 2:

[0177] The server supplies pre-processed data to an artificial intelligence model that generates new concepts. This model uses a combination of natural language processing and machine learning algorithms to predict market trends and business strategies from the resulting data. The input is cleaned data, and the output is the proposed concepts and strategies.

[0178] Step 3:

[0179] The device uses an emotion recognition device to collect user voice input and operation logs. This data is sent to Google Cloud's natural language processing API and voice analysis API to determine the user's emotional state in real time. The input is user interaction data, and the output is the user's emotional state.

[0180] Step 4:

[0181] The server dynamically adjusts the suggestions based on the processed emotional information. If the emotional state is positive, it presents detailed information; if it is negative, it focuses on displaying alternatives. The input is the user's emotional state and generated ideas, and the output is customized suggestions.

[0182] Step 5:

[0183] The user reviews the suggested information through the terminal and selects the next step. Feedback is collected and used to improve the system. The input is the suggestions from the server, and the output is the user's selection and feedback.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Second Embodiment]

[0188] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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".

[0200] This invention provides a system for companies to efficiently generate new ideas and facilitate strategic collaboration. This system takes the following form.

[0201] First, the server utilizes a data collection module to gather data on past success stories and market trends from publicly available databases on the internet and internal company databases. This data collection is performed periodically by an automated scheduling system.

[0202] Next, the server analyzes the collected data using a generative artificial intelligence model. This model combines natural language processing and machine learning algorithms to extract promising business ideas from the data. This allows for the generation of innovative ideas that might be difficult for in-house experts to obtain.

[0203] Next, the server identifies potential partner companies by referring to their profile information. For each identified partner, it analyzes their past collaboration history and market positioning to design the most effective collaboration strategy. This strategy design is achieved by comparing it with successful patterns accumulated within the system.

[0204] Furthermore, the server uses an automated market information analysis module to monitor market and competitor trends in real time. This module has the ability to instantly generate reports and notify users when new market trends are discovered. This enables companies to make strategic decisions quickly.

[0205] Users can submit feedback on ideas and strategies provided by the server. This feedback is used as data for the system to learn and improve its analytical accuracy. In this way, the entire system is continuously optimized, making the company's planning operations more efficient.

[0206] As a concrete example, consider a manufacturing company exploring new IoT product ideas based on past data. The server analyzes relevant past success stories and proposes features and timing for market launch of the new product. At the same time, it finds partners that match the company's strengths and designs a strategy to enhance the product's market value through collaboration with those partners. In this way, the company can prepare to launch its new product into the market in a short period of time.

[0207] The following describes the processing flow.

[0208] Step 1:

[0209] The server activates a data collection module to gather past success stories and market trend data from external and internal databases. This is done using methods such as web scraping and data acquisition via APIs. The collected data undergoes initial filtering to remove noise.

[0210] Step 2:

[0211] The server inputs the cleansed data into an artificial intelligence model. This model uses natural language processing techniques to analyze the data and extract key trending keywords. Furthermore, it generates new business ideas based on these keywords. This process leverages algorithms learned from existing ideas and trends.

[0212] Step 3:

[0213] The server consults an internal database to identify partner companies that match the company's profile. This profile includes the company's strengths and information on past collaborations. It creates a list of potential partners and designs a collaboration strategy to maximize synergies with each company. This includes an effective action plan to achieve specific business objectives.

[0214] Step 4:

[0215] The server runs an automated market information analysis module, monitoring market segments and competitor trends in real time. The monitoring results are analyzed, and if market opportunities or risks requiring immediate attention are identified, a report is automatically generated. This report is used by users to inform strategic decision-making.

[0216] Step 5:

[0217] Users provide feedback based on proposed ideas and strategies. This feedback is sent to the server and contributes to improving the system's algorithms. This leads to improved accuracy in future proposals and enables the formation of more precise strategies.

[0218] (Example 1)

[0219] 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."

[0220] The process by which companies efficiently generate new business ideas and build strategic partnerships typically requires a tremendous amount of time and effort. Furthermore, it is not easy to grasp market trends in a timely manner and make quick decisions. This creates a risk of missing growth opportunities in today's increasingly competitive business environment.

[0221] 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.

[0222] In this invention, the server includes means for collecting past performance data and market trend data through an information gathering configuration, means for using a generative artificial intelligence algorithm to analyze the data and create new concepts, and means for identifying potential collaboration partners based on the business entity's profile and designing a cooperation strategy. This enables companies to efficiently and quickly generate business ideas, build optimal partnerships, and make decisions that are responsive to market trends.

[0223] An "information gathering configuration" is a system for automatically collecting past performance data and market trend data.

[0224] A "generative artificial intelligence algorithm" is an artificial intelligence technology that analyzes data and creates new concepts, using language processing techniques and machine learning methods.

[0225] "Business entity profile" refers to information such as a company's basic information, business activities, and past collaboration history, and serves as a criterion for identifying potential collaboration partners.

[0226] "Means of designing a cooperation strategy" refers to the process of devising methods for building optimal cooperative relationships with identified potential partners.

[0227] "Market trends" refer to current and projected conditions and changes in a particular industry or sector, and are important factors that influence a company's decision-making process.

[0228] "Real-time data monitoring" refers to the function of observing data in real time and immediately detecting changes.

[0229] "User feedback" refers to feedback and insights provided by users for the purpose of improving the final product or service.

[0230] This invention relates to a system for companies to efficiently generate new business ideas and build collaborative relationships, and is implemented through the collaboration of servers, terminals, and users.

[0231] The server first uses an information gathering configuration to automatically collect performance case studies and market trend data from publicly available databases on the internet and internal company databases. This configuration ensures that the data is always up-to-date, as it is regularly updated by an automated scheduling system. The hardware would consist of a dedicated data server, and the software would likely be a scraping tool based on a programming language such as Python.

[0232] Next, the server analyzes the collected data using a generative AI model. Specifically, a model combining natural language processing technology and machine learning algorithms (e.g., GPT-3) is used to analyze the data and generate promising new concepts. This model plays a role in extracting important trends and innovative points from vast amounts of data and presenting them as concrete ideas.

[0233] Furthermore, the server identifies potential partners and designs collaboration strategies while referencing the business entity's profile information. The server considers the characteristics and strengths of each company to list the most suitable candidates for collaboration and formulates the optimal strategy based on that list. This promotes collaboration between companies and supports the establishment of cooperative relationships.

[0234] Furthermore, the server constantly monitors market trends and automatically generates reports by analyzing dynamically changing market information in real time. This automatic generation function is a crucial foundation for responding quickly to changes, allowing users to make optimal decisions according to market conditions at any given time.

[0235] Users provide feedback on the ideas and strategies generated by the server. This feedback contributes to the continuous improvement of the system and helps to improve the analytical accuracy of the generated AI model. As a result, users can always proceed with their work based on optimized information.

[0236] For example, when a manufacturing company plans a new IoT product, the server can analyze past cases and suggest product characteristics to consider and the timing of market launch. Furthermore, by identifying partner companies that complement its strengths and designing a collaborative model with those companies, it can increase the probability of product success.

[0237] An example of a prompt message could be: "Generate ideas and partnership strategies for new product planning in the next quarter."

[0238] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0239] Step 1:

[0240] The server uses an information gathering configuration to collect performance case studies and market trend data from publicly available databases on the internet and internal company databases. In this step, the server periodically accesses various data sources to retrieve new data. The input is a database query, and the output is the collected raw dataset. Specifically, the server executes scripts to perform web scraping and data extraction via APIs.

[0241] Step 2:

[0242] The server inputs the collected data into a generating AI model and performs data analysis. Specifically, it utilizes a model that combines natural language processing technology and machine learning algorithms to organize relevant information and create promising new concepts. The input data consists of collected performance case studies and market trend information, and the output is newly generated business ideas. The server starts the analysis process and lists the generated ideas.

[0243] Step 3:

[0244] The server identifies potential collaborators based on company profiles. Based on the collected and analyzed data, the server lists the most suitable partners, taking into account the company's characteristics and past collaboration history. The input is company profile data, and the output is a list of identified potential collaborators. The server uses an algorithm to perform profile matching and select candidates.

[0245] Step 4:

[0246] The server monitors market trends in real time and analyzes dynamic market information. When market changes are detected, it automatically generates reports immediately. The input is market data collected in real time, and the output is the generated market report. Specifically, the server processes the real-time data stream and triggers report creation when it detects a change.

[0247] Step 5:

[0248] Users provide feedback on the ideas and strategies generated by the server. In this feedback process, user opinions are input into the system and used for subsequent analyses. The input is user feedback data, and the output is insights that lead to system improvements. Users submit their feedback through the provided interface.

[0249] (Application Example 1)

[0250] 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."

[0251] In the field of autonomous driving technology, generating innovative technological ideas and identifying appropriate partners for technological development are crucial. However, there are limited methods for doing this efficiently and effectively. Furthermore, it is difficult to adjust strategies immediately through real-time tracking and analysis of market trends. This invention aims to solve these problems and provide a system that supports new innovation and collaboration in autonomous driving technology.

[0252] 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.

[0253] In this invention, the server includes means for collecting past success data and market trend data through a data acquisition configuration, means for using a generative intelligence model to analyze the data and generate new concepts, and means for identifying collaborating companies based on corporate attribute information and designing a cooperation strategy. This makes it possible to analyze the latest market trends related to autonomous driving technology, provide new technology ideas, and identify appropriate technology development partners.

[0254] A "data acquisition configuration" is a system equipped with functions for effectively collecting past success stories and market trend data.

[0255] A "generative intelligence model" is an artificial intelligence model that analyzes collected data and automatically generates new technological concepts and ideas.

[0256] "Corporate attribute information" refers to data that provides comprehensive information about a company, such as its characteristics, strengths, and business strategy.

[0257] A "collaborating company" refers to a company that may cooperate in the development and market introduction of new technologies.

[0258] A "cooperation strategy" is a plan to optimize technology development and market deployment by collaborating with designated partner companies.

[0259] "Market trends" refer to information regarding changes in demand and supply, as well as competitive activities, within a specific industry or technology field.

[0260] A "technology development partner" is a company or organization that collaborates in the development of new autonomous driving technologies.

[0261] "Real-time data observation" is the process of constantly monitoring current market and technological trends and acquiring information immediately.

[0262] In implementing this system, the server first uses a data acquisition configuration to collect diverse historical success stories and market trend data from a wide range of databases. This includes publicly available company information and relevant industry data. This data is managed on a cloud server and processed efficiently while maintaining security.

[0263] Next, the server utilizes a generative intelligence model to analyze the collected data. In this process, it uses natural language processing libraries such as spaCy and Transformers to analyze text data and machine learning algorithms (e.g., TensorFlow and PyTorch) to generate innovative technical concepts and ideas. This generative intelligence model can identify data relevance and potential market value.

[0264] Furthermore, the server analyzes corporate attribute information to identify suitable collaborating companies. This process takes into account the companies' strengths and technological capabilities to design the optimal collaboration strategy. As a result, the selection of technology development partners becomes faster and more accurate.

[0265] With real-time data monitoring capabilities, market trends are constantly monitored, and the latest information is acquired instantly. This information is promptly fed back to the user via their device, supporting strategic decision-making. For example, a user can input a specific prompt such as, "Find collaboration partners for AI-powered technologies related to electric vehicles," and the system will present data accordingly. Such prompts promote practical and creative technological development.

[0266] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0267] Step 1:

[0268] The server collects historical success stories and market trend data from various databases through a data acquisition configuration. Based on the specified query as input, the server searches publicly available databases on the internet and internal company databases. The collected data is stored in cloud storage.

[0269] Step 2:

[0270] The server analyzes the collected data using a generative intelligence model. The input data is parsed using a natural language processing library (e.g., spaCy). Then, machine learning algorithms (e.g., TensorFlow) are used to extract relevant technical ideas and generate new concepts. The resulting ideas are recorded in a database.

[0271] Step 3:

[0272] The server analyzes corporate attribute information to identify suitable collaborating companies. Given corporate profile information as input, it analyzes it and determines the optimal collaboration partner, taking into account technical capabilities and market position. This information is provided to the collaboration strategy design module, which then generates a specific collaboration strategy.

[0273] Step 4:

[0274] The server observes real-time data and constantly monitors market trends. It uses automated sensors and real-time feeds to acquire and analyze the latest trend data. The resulting market reports are then sent to user terminals.

[0275] Step 5:

[0276] The user enters prompts into the system via a terminal and receives suggestions for specific technical development needs. Based on the input prompts, the server selects previously generated ideas and collaboration strategies and presents them to the user. User feedback is collected and used to improve the system.

[0277] 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.

[0278] This invention combines a system for companies to generate new ideas and achieve effective strategic collaboration with an emotion engine that recognizes user emotions. As a result, proposed ideas and strategies are optimally customized for the user, supporting the company's decision-making process.

[0279] First, the server utilizes a data collection module to acquire data on past success stories and market trends. This data is integrated from various data sources and preprocessed. Next, a generative artificial intelligence model analyzes this data and generates new business ideas. This model combines natural language processing and machine learning algorithms to deepen trend insights and drive innovation.

[0280] Next, the process moves to identifying the most suitable partner companies based on the company's profile and designing a collaboration strategy. Here, a strategy is developed to maximize interaction with the identified partners. This strategy is formulated based on the company's strengths and market analysis.

[0281] In addition, this system incorporates an emotion engine. The terminal sends user voice input and operation logs to the emotion engine, which then analyzes the user's emotional state. This engine uses voice analysis and text analysis technologies, and can determine emotions in real time based on user feedback and reactions.

[0282] Based on the information obtained from the emotion engine, the server dynamically adjusts the content of the proposal and its visual display. For example, when the user shows concern about the proposal, detailed information or alternatives can be preferentially presented. In this way, the proposal is presented in a form that conforms to the user's emotional state, improving the acceptance of the proposal.

[0283] As a specific example, consider a situation where a project manager of a certain company is considering a strategy for launching a new product. The system analyzes past successful cases and proposes a new market strategy. At this time, the emotion engine reads the user's reaction and, if the user is feeling anxious, presents a presentation that emphasizes improvement points and competitive advantages. In this way, personalized information provision based on the user's emotions becomes possible, and the company's strategic choices can proceed more smoothly.

[0284] The following describes the processing flow.

[0285] Step 1:

[0286] The server activates the data collection module and automatically collects past successful cases and related market trend data from external public databases and the company's internal data storage. The collected data is subjected to noise filtering and data cleansing and is arranged in a form suitable for analysis.

[0287] Step 2:

[0288] The server inputs the arranged data into a generative artificial intelligence model. This model combines natural language processing and machine learning algorithms and has the ability to generate major trends and new business ideas while understanding the context of the data. The generated ideas are proposed according to the specific needs of the company.

[0289] Step 3:

[0290] The server references corporate profile information and creates a list of potential partner companies based on past collaborations and the degree of business area alignment. Furthermore, it develops and proposes effective collaboration strategies for working with these partners. These strategies include specific action plans and target achievement indicators.

[0291] Step 4:

[0292] The emotion engine uses speech recognition and text analysis technologies to monitor the user's emotions in real time. The device continuously collects voice input and feedback from the user and sends it to the emotion engine. This information helps understand the user's emotional response to the proposed idea.

[0293] Step 5:

[0294] Based on the analysis results of the emotion engine, the server dynamically adjusts how and what is presented in the proposed ideas. For example, if a user expresses concern, the server takes an approach to deepen the user's understanding by refining the proposal or presenting alternative strategies.

[0295] Step 6:

[0296] Users provide feedback on the suggestions and strategies presented by the server. This feedback is used as training data for the system and is utilized to improve the quality of future suggestions. Based on the feedback, the entire system is continuously optimized, enabling it to provide more valuable suggestions to companies.

[0297] (Example 2)

[0298] 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".

[0299] In today's business environment, companies need to generate innovative ideas based on advanced data analysis and identify effective collaborators in order to quickly adapt to the market and maintain competitiveness. However, traditional methods have struggled to provide personalized suggestions that take user emotions into account, resulting in decreased acceptance in the decision-making process.

[0300] 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.

[0301] In this invention, the server includes means for collecting past performance information and market trend data through a data processing device, means for using an intelligent processing model that analyzes the data to generate new innovative ideas, and means for determining the user's emotional state using an emotion analysis device. This enables adaptive suggestions based on the user's emotions.

[0302] A "data processing device" is a computer system used to collect and analyze information, possessing the function of acquiring information from various data sources and organizing it into a consistent format.

[0303] "Performance information" refers to various data that shows the effectiveness of a company's operations in the past, including historical data such as success stories and growth patterns.

[0304] "Market trend data" refers to a comprehensive collection of market-related data, including consumer behavior, industry trends, and product demand patterns.

[0305] An "intelligent processing model" refers to an algorithm or system that uses natural language processing and machine learning techniques to generate useful information and innovative ideas from large amounts of data.

[0306] An "emotion analysis device" is a system that determines a user's emotional state in real time, and includes technology that analyzes psychological responses from voice and text data.

[0307] The "user's emotional state" refers to individual reactions and psychological feelings towards proposals and information presentations, including overall satisfaction and uneasiness.

[0308] This invention provides a system for assisting corporate decision-making by using a data collection device, an intelligent processing model, and an emotion analysis device. Specific embodiments will be described below.

[0309] First, the server uses a data processing device to collect past performance information and market trend data from various sources. The software incorporates data integration tools and cleaning algorithms to generate a clean and unified dataset.

[0310] Next, the server utilizes an intelligent processing model to analyze the collected data and generate new innovation proposals. This model is based on natural language processing technology and machine learning algorithms, and uses frameworks such as Hadoop and TensorFlow to process large-scale data. The generated ideas include specific market strategies and new product concepts.

[0311] The terminal determines the user's emotional state in real time through an emotion analysis device. Here, voice recognition technology and text analysis technology are used to analyze the user's input data. Voice analysis solutions such as Microsoft Azure and Amazon Polly are utilized.

[0312] Based on the information obtained from the emotion analysis, the server has the ability to dynamically adjust the proposal content and its visual display. As a specific example, when the user shows uneasiness about a new product strategy, the system prepares a presentation that emphasizes competitive advantages and success stories.

[0313] An example of a prompt sentence is: "Analyze the success stories regarding the market launch of new products, propose the optimal strategy. Also, analyze the user's emotions towards the proposal and create a customized presentation."

[0314] By using this system, companies can respond quickly and flexibly to market trends and provide users with information optimized for their needs.

[0315] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0316] Step 1:

[0317] The server uses data processing equipment to collect historical performance data and market trend data. Inputs include data from internal company databases, online news, industry reports, and social media. This data undergoes preprocessing, such as noise reduction and formatting, before being output as an analyzable dataset. This process utilizes data integration software and ETL (extract, transform, load) tools to generate a well-organized dataset.

[0318] Step 2:

[0319] The server inputs pre-processed data into an intelligent processing model to generate innovative ideas. This model utilizes natural language processing and machine learning algorithms. A cleaned dataset is provided as input data, from which ideas such as market strategies and new product concepts are output. Specifically, the machine learning model performs pattern recognition and trend analysis to generate innovative proposals.

[0320] Step 3:

[0321] The device uses an emotion analysis device to collect user voice input and text data to determine their emotional state. Input includes user voice feedback and reaction logs. This data is processed through speech synthesis and text analysis to output information about the user's emotional state. Specifically, speech recognition technology analyzes the user's voice tone and word choice to evaluate their emotions.

[0322] Step 4:

[0323] Based on the emotional information obtained in Step 3, the server adjusts the suggested content and its visual display. The input consists of emotional state information and ideas generated in Step 2, and the output is a presentation adapted to the user's emotions. The server changes the priority of information and adjusts the use of color and information placement in the user interface to make it more acceptable to the user. This increases the acceptability of the suggestions.

[0324] (Application Example 2)

[0325] 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 as the "terminal".

[0326] In recent years, organizations need to generate new ideas and design rapid and accurate collaborative strategies to gain a competitive advantage in the market. However, traditional systems have difficulty quickly analyzing data and reflecting user sentiment, sometimes leading to a decline in the quality of ideas. Furthermore, dynamic proposals that take user sentiment into account are not made, and as a result, proposals and strategies are often not effective. There is a need to provide an effective solution to this problem.

[0327] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting past success references and market trend information through a data collection device; means for using a generative artificial intelligence model that analyzes the information to generate new concepts; means for identifying partner organizations based on the organization's history and designing cooperation strategies; means for receiving user feedback and improving the system; and means for identifying the user's emotions using an emotion recognition device and adjusting suggestions based on those emotions. This makes it possible to generate high-quality concepts based on data and provide dynamic suggestions that reflect the user's emotions.

[0328] A "data collection device" is a device used to acquire past success references and market trend information, and has the function of collecting data from various information sources.

[0329] A "generative artificial intelligence model" is an artificial intelligence model that analyzes collected data and generates new ideas, using natural language processing and machine learning techniques.

[0330] "Identification based on organizational history" refers to a method for identifying appropriate partner organizations based on their organizational history information.

[0331] "Designing a cooperation strategy" is a means of designing a strategy to maximize mutual benefits with partner organizations.

[0332] "User feedback" refers to the reactions and opinions received from users, and is used to improve the system.

[0333] An "emotion identification device" is a device used to identify the emotions of a user, and has the function of analyzing emotional states from voice and actions.

[0334] To realize this invention, the system has the following configuration.

[0335] The server uses data collection devices to comprehensively gather information on past successes and market trends from various databases. This data undergoes initial processing and is then sent to a generative artificial intelligence model that combines natural language processing techniques and machine learning algorithms to generate new ideas. This generation process allows for the prediction of future trends and the proposal of new business strategies based on insights gained from the data.

[0336] The terminal collects user voice input and operation logs through an emotion recognition device and analyzes this data in real time. The emotion recognition device uses Google Cloud's natural language processing API and speech analysis API to analyze the user's emotions and determine how the user is feeling. Based on the analysis results, the server dynamically adjusts the suggestions and presents information optimized for the user's emotions. For example, if the user has doubts about a suggestion, the server will present alternative options and add detailed explanations.

[0337] Users make decisions to accept suggestions through an interface provided by the system. The information selected by the user is collected as feedback, and the server uses this to improve the algorithms and suggestions.

[0338] For example, if a user is considering purchasing a new gadget, the system will suggest the most suitable product based on the user's past purchase history and emotional responses. For instance, if the user appears anxious, the system might suggest a product with superior warranty coverage.

[0339] An example of a prompt message is an instruction input to the AI ​​model that reads, "Check the customer's emotional state each time they view a product, provide detailed information about products that elicit a positive reaction, and suggest alternative products if a negative reaction occurs."

[0340] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0341] Step 1:

[0342] The server uses a data acquisition device to collect past success references and market trend information from the database. The collected information is first pre-processed, including noise reduction and standardization of data formats. This pre-processing transforms the data into a format suitable for analysis. The input is raw data, and the output is cleaned data.

[0343] Step 2:

[0344] The server supplies pre-processed data to an artificial intelligence model that generates new concepts. This model uses a combination of natural language processing and machine learning algorithms to predict market trends and business strategies from the resulting data. The input is cleaned data, and the output is the proposed concepts and strategies.

[0345] Step 3:

[0346] The device uses an emotion recognition device to collect user voice input and operation logs. This data is sent to Google Cloud's natural language processing API and voice analysis API to determine the user's emotional state in real time. The input is user interaction data, and the output is the user's emotional state.

[0347] Step 4:

[0348] The server dynamically adjusts the suggestions based on the processed emotional information. If the emotional state is positive, it presents detailed information; if it is negative, it focuses on displaying alternatives. The input is the user's emotional state and generated ideas, and the output is customized suggestions.

[0349] Step 5:

[0350] The user reviews the suggested information through the terminal and selects the next step. Feedback is collected and used to improve the system. The input is the suggestions from the server, and the output is the user's selection and feedback.

[0351] 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.

[0352] 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.

[0353] 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.

[0354] [Third Embodiment]

[0355] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0356] 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.

[0357] 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).

[0358] 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.

[0359] 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.

[0360] 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).

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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".

[0367] This invention provides a system for companies to efficiently generate new ideas and facilitate strategic collaboration. This system takes the following form.

[0368] First, the server utilizes a data collection module to gather data on past success stories and market trends from publicly available databases on the internet and internal company databases. This data collection is performed periodically by an automated scheduling system.

[0369] Next, the server analyzes the collected data using a generative artificial intelligence model. This model combines natural language processing and machine learning algorithms to extract promising business ideas from the data. This allows for the generation of innovative ideas that might be difficult for in-house experts to obtain.

[0370] Next, the server identifies potential partner companies by referring to their profile information. For each identified partner, it analyzes their past collaboration history and market positioning to design the most effective collaboration strategy. This strategy design is achieved by comparing it with successful patterns accumulated within the system.

[0371] Furthermore, the server uses an automated market information analysis module to monitor market and competitor trends in real time. This module has the ability to instantly generate reports and notify users when new market trends are discovered. This enables companies to make strategic decisions quickly.

[0372] Users can submit feedback on ideas and strategies provided by the server. This feedback is used as data for the system to learn and improve its analytical accuracy. In this way, the entire system is continuously optimized, making the company's planning operations more efficient.

[0373] As a concrete example, consider a manufacturing company exploring new IoT product ideas based on past data. The server analyzes relevant past success stories and proposes features and timing for market launch of the new product. At the same time, it finds partners that match the company's strengths and designs a strategy to enhance the product's market value through collaboration with those partners. In this way, the company can prepare to launch its new product into the market in a short period of time.

[0374] The following describes the processing flow.

[0375] Step 1:

[0376] The server activates a data collection module to gather past success stories and market trend data from external and internal databases. This is done using methods such as web scraping and data acquisition via APIs. The collected data undergoes initial filtering to remove noise.

[0377] Step 2:

[0378] The server inputs the cleansed data into an artificial intelligence model. This model uses natural language processing techniques to analyze the data and extract key trending keywords. Furthermore, it generates new business ideas based on these keywords. This process leverages algorithms learned from existing ideas and trends.

[0379] Step 3:

[0380] The server consults an internal database to identify partner companies that match the company's profile. This profile includes the company's strengths and information on past collaborations. It creates a list of potential partners and designs a collaboration strategy to maximize synergies with each company. This includes an effective action plan to achieve specific business objectives.

[0381] Step 4:

[0382] The server runs an automated market information analysis module, monitoring market segments and competitor trends in real time. The monitoring results are analyzed, and if market opportunities or risks requiring immediate attention are identified, a report is automatically generated. This report is used by users to inform strategic decision-making.

[0383] Step 5:

[0384] Users provide feedback based on proposed ideas and strategies. This feedback is sent to the server and contributes to improving the system's algorithms. This leads to improved accuracy in future proposals and enables the formation of more precise strategies.

[0385] (Example 1)

[0386] 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."

[0387] The process by which companies efficiently generate new business ideas and build strategic partnerships typically requires a tremendous amount of time and effort. Furthermore, it is not easy to grasp market trends in a timely manner and make quick decisions. This creates a risk of missing growth opportunities in today's increasingly competitive business environment.

[0388] 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.

[0389] In this invention, the server includes means for collecting past performance data and market trend data through an information gathering configuration, means for using a generative artificial intelligence algorithm to analyze the data and create new concepts, and means for identifying potential collaboration partners based on the business entity's profile and designing a cooperation strategy. This enables companies to efficiently and quickly generate business ideas, build optimal partnerships, and make decisions that are responsive to market trends.

[0390] An "information gathering configuration" is a system for automatically collecting past performance data and market trend data.

[0391] A "generative artificial intelligence algorithm" is an artificial intelligence technology that analyzes data and creates new concepts, using language processing techniques and machine learning methods.

[0392] "Business entity profile" refers to information such as a company's basic information, business activities, and past collaboration history, and serves as a criterion for identifying potential collaboration partners.

[0393] "Means of designing a cooperation strategy" refers to the process of devising methods for building optimal cooperative relationships with identified potential partners.

[0394] "Market trends" refer to current and projected conditions and changes in a particular industry or sector, and are important factors that influence a company's decision-making process.

[0395] "Real-time data monitoring" refers to the function of observing data in real time and immediately detecting changes.

[0396] "User feedback" refers to feedback and insights provided by users for the purpose of improving the final product or service.

[0397] This invention relates to a system for companies to efficiently generate new business ideas and build collaborative relationships, and is implemented through the collaboration of servers, terminals, and users.

[0398] The server first uses an information gathering configuration to automatically collect performance case studies and market trend data from publicly available databases on the internet and internal company databases. This configuration ensures that the data is always up-to-date, as it is regularly updated by an automated scheduling system. The hardware would consist of a dedicated data server, and the software would likely be a scraping tool based on a programming language such as Python.

[0399] Next, the server analyzes the collected data using a generative AI model. Specifically, a model combining natural language processing technology and machine learning algorithms (e.g., GPT-3) is used to analyze the data and generate promising new concepts. This model plays a role in extracting important trends and innovative points from vast amounts of data and presenting them as concrete ideas.

[0400] Furthermore, the server identifies potential partners and designs collaboration strategies while referencing the business entity's profile information. The server considers the characteristics and strengths of each company to list the most suitable candidates for collaboration and formulates the optimal strategy based on that list. This promotes collaboration between companies and supports the establishment of cooperative relationships.

[0401] Furthermore, the server constantly monitors market trends and automatically generates reports by analyzing dynamically changing market information in real time. This automatic generation function is a crucial foundation for responding quickly to changes, allowing users to make optimal decisions according to market conditions at any given time.

[0402] Users provide feedback on the ideas and strategies generated by the server. This feedback contributes to the continuous improvement of the system and helps to improve the analytical accuracy of the generated AI model. As a result, users can always proceed with their work based on optimized information.

[0403] For example, when a manufacturing company plans a new IoT product, the server can analyze past cases and suggest product characteristics to consider and the timing of market launch. Furthermore, by identifying partner companies that complement its strengths and designing a collaborative model with those companies, it can increase the probability of product success.

[0404] An example of a prompt message could be: "Generate ideas and partnership strategies for new product planning in the next quarter."

[0405] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0406] Step 1:

[0407] The server uses an information gathering configuration to collect performance case studies and market trend data from publicly available databases on the internet and internal company databases. In this step, the server periodically accesses various data sources to retrieve new data. The input is a database query, and the output is the collected raw dataset. Specifically, the server executes scripts to perform web scraping and data extraction via APIs.

[0408] Step 2:

[0409] The server inputs the collected data into a generating AI model and performs data analysis. Specifically, it utilizes a model that combines natural language processing technology and machine learning algorithms to organize relevant information and create promising new concepts. The input data consists of collected performance case studies and market trend information, and the output is newly generated business ideas. The server starts the analysis process and lists the generated ideas.

[0410] Step 3:

[0411] The server identifies potential collaborators based on company profiles. Based on the collected and analyzed data, the server lists the most suitable partners, taking into account the company's characteristics and past collaboration history. The input is company profile data, and the output is a list of identified potential collaborators. The server uses an algorithm to perform profile matching and select candidates.

[0412] Step 4:

[0413] The server monitors market trends in real time and analyzes dynamic market information. When market changes are detected, it automatically generates reports immediately. The input is market data collected in real time, and the output is the generated market report. Specifically, the server processes the real-time data stream and triggers report creation when it detects a change.

[0414] Step 5:

[0415] Users provide feedback on the ideas and strategies generated by the server. In this feedback process, user opinions are input into the system and used for subsequent analyses. The input is user feedback data, and the output is insights that lead to system improvements. Users submit their feedback through the provided interface.

[0416] (Application Example 1)

[0417] 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."

[0418] In the field of autonomous driving technology, generating innovative technological ideas and identifying appropriate partners for technological development are crucial. However, there are limited methods for doing this efficiently and effectively. Furthermore, it is difficult to adjust strategies immediately through real-time tracking and analysis of market trends. This invention aims to solve these problems and provide a system that supports new innovation and collaboration in autonomous driving technology.

[0419] 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.

[0420] In this invention, the server includes means for collecting past success data and market trend data through a data acquisition configuration, means for using a generative intelligence model to analyze the data and generate new concepts, and means for identifying collaborating companies based on corporate attribute information and designing a cooperation strategy. This makes it possible to analyze the latest market trends related to autonomous driving technology, provide new technology ideas, and identify appropriate technology development partners.

[0421] A "data acquisition configuration" is a system equipped with functions for effectively collecting past success stories and market trend data.

[0422] A "generative intelligence model" is an artificial intelligence model that analyzes collected data and automatically generates new technological concepts and ideas.

[0423] "Corporate attribute information" refers to data that provides comprehensive information about a company, such as its characteristics, strengths, and business strategy.

[0424] A "collaborating company" refers to a company that may cooperate in the development and market introduction of new technologies.

[0425] A "cooperation strategy" is a plan to optimize technology development and market deployment by collaborating with designated partner companies.

[0426] "Market trends" refer to information regarding changes in demand and supply, as well as competitive activities, within a specific industry or technology field.

[0427] A "technology development partner" is a company or organization that collaborates in the development of new autonomous driving technologies.

[0428] "Real-time data observation" is the process of constantly monitoring current market and technological trends and acquiring information immediately.

[0429] In implementing this system, the server first uses a data acquisition configuration to collect diverse historical success stories and market trend data from a wide range of databases. This includes publicly available company information and relevant industry data. This data is managed on a cloud server and processed efficiently while maintaining security.

[0430] Next, the server utilizes a generative intelligence model to analyze the collected data. In this process, it uses natural language processing libraries such as spaCy and Transformers to analyze text data and machine learning algorithms (e.g., TensorFlow and PyTorch) to generate innovative technical concepts and ideas. This generative intelligence model can identify data relevance and potential market value.

[0431] Furthermore, the server analyzes corporate attribute information to identify suitable collaborating companies. This process takes into account the companies' strengths and technological capabilities to design the optimal collaboration strategy. As a result, the selection of technology development partners becomes faster and more accurate.

[0432] With real-time data monitoring capabilities, market trends are constantly monitored, and the latest information is acquired instantly. This information is promptly fed back to the user via their device, supporting strategic decision-making. For example, a user can input a specific prompt such as, "Find collaboration partners for AI-powered technologies related to electric vehicles," and the system will present data accordingly. Such prompts promote practical and creative technological development.

[0433] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0434] Step 1:

[0435] The server collects historical success stories and market trend data from various databases through a data acquisition configuration. Based on the specified query as input, the server searches publicly available databases on the internet and internal company databases. The collected data is stored in cloud storage.

[0436] Step 2:

[0437] The server analyzes the collected data using a generative intelligence model. The input data is parsed using a natural language processing library (e.g., spaCy). Then, machine learning algorithms (e.g., TensorFlow) are used to extract relevant technical ideas and generate new concepts. The resulting ideas are recorded in a database.

[0438] Step 3:

[0439] The server analyzes corporate attribute information to identify suitable collaborating companies. Given corporate profile information as input, it analyzes it and determines the optimal collaboration partner, taking into account technical capabilities and market position. This information is provided to the collaboration strategy design module, which then generates a specific collaboration strategy.

[0440] Step 4:

[0441] The server observes real-time data and constantly monitors market trends. It uses automated sensors and real-time feeds to acquire and analyze the latest trend data. The resulting market reports are then sent to user terminals.

[0442] Step 5:

[0443] The user enters prompts into the system via a terminal and receives suggestions for specific technical development needs. Based on the input prompts, the server selects previously generated ideas and collaboration strategies and presents them to the user. User feedback is collected and used to improve the system.

[0444] 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.

[0445] This invention combines a system for companies to generate new ideas and achieve effective strategic collaboration with an emotion engine that recognizes user emotions. As a result, proposed ideas and strategies are optimally customized for the user, supporting the company's decision-making process.

[0446] First, the server utilizes a data collection module to acquire data on past success stories and market trends. This data is integrated from various data sources and preprocessed. Next, a generative artificial intelligence model analyzes this data and generates new business ideas. This model combines natural language processing and machine learning algorithms to deepen trend insights and drive innovation.

[0447] Next, the process moves to identifying the most suitable partner companies based on the company's profile and designing a collaboration strategy. Here, a strategy is developed to maximize interaction with the identified partners. This strategy is formulated based on the company's strengths and market analysis.

[0448] In addition, this system incorporates an emotion engine. The terminal sends user voice input and operation logs to the emotion engine, which then analyzes the user's emotional state. This engine uses voice analysis and text analysis technologies, and can determine emotions in real time based on user feedback and reactions.

[0449] The server dynamically adjusts the content and visual display of suggestions based on information obtained from the emotion engine. For example, if a user expresses concern about a suggestion, it can prioritize presenting detailed information and alternatives. In this way, suggestions are presented in a manner that matches the user's emotional state, improving their acceptability.

[0450] As a concrete example, consider a scenario where a project manager at a certain company is considering a strategy for launching a new product into the market. The system analyzes past success stories and proposes a new market strategy, but at the same time, an emotion engine reads the user's reaction and, if the user is feeling anxious, delivers a presentation that emphasizes areas for improvement and competitive advantages. In this way, personalized information based on the user's emotions becomes possible, allowing companies to make strategic decisions more smoothly.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] The server activates a data collection module, automatically gathering historical success stories and relevant market trend data from external public databases and internal corporate data storage. The collected data is then subjected to noise filtering and data cleansing to prepare it for analysis.

[0454] Step 2:

[0455] The server feeds the prepared data into an artificial intelligence model. This model combines natural language processing and machine learning algorithms to understand the context of the data and generate key trends and new business ideas. The generated ideas are then proposed to meet the specific needs of the company.

[0456] Step 3:

[0457] The server references corporate profile information and creates a list of potential partner companies based on past collaborations and the degree of business area alignment. Furthermore, it develops and proposes effective collaboration strategies for working with these partners. These strategies include specific action plans and target achievement indicators.

[0458] Step 4:

[0459] The emotion engine uses speech recognition and text analysis technologies to monitor the user's emotions in real time. The device continuously collects voice input and feedback from the user and sends it to the emotion engine. This information helps understand the user's emotional response to the proposed idea.

[0460] Step 5:

[0461] Based on the analysis results of the emotion engine, the server dynamically adjusts how and what is presented in the proposed ideas. For example, if a user expresses concern, the server takes an approach to deepen the user's understanding by refining the proposal or presenting alternative strategies.

[0462] Step 6:

[0463] Users provide feedback on the suggestions and strategies presented by the server. This feedback is used as training data for the system and is utilized to improve the quality of future suggestions. Based on the feedback, the entire system is continuously optimized, enabling it to provide more valuable suggestions to companies.

[0464] (Example 2)

[0465] 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."

[0466] In today's business environment, companies need to generate innovative ideas based on advanced data analysis and identify effective collaborators in order to quickly adapt to the market and maintain competitiveness. However, traditional methods have struggled to provide personalized suggestions that take user emotions into account, resulting in decreased acceptance in the decision-making process.

[0467] 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.

[0468] In this invention, the server includes means for collecting past performance information and market trend data through a data processing device, means for using an intelligent processing model that analyzes the data to generate new innovative ideas, and means for determining the user's emotional state using an emotion analysis device. This enables adaptive suggestions based on the user's emotions.

[0469] A "data processing device" is a computer system used to collect and analyze information, possessing the function of acquiring information from various data sources and organizing it into a consistent format.

[0470] "Performance information" refers to various data that shows the effectiveness of a company's operations in the past, including historical data such as success stories and growth patterns.

[0471] "Market trend data" refers to a comprehensive collection of market-related data, including consumer behavior, industry trends, and product demand patterns.

[0472] An "intelligent processing model" refers to an algorithm or system that uses natural language processing and machine learning techniques to generate useful information and innovative ideas from large amounts of data.

[0473] An "emotion analysis device" is a system that determines a user's emotional state in real time, and includes technology that analyzes psychological responses from voice and text data.

[0474] "User emotional state" refers to individual reactions and psychological feelings to suggestions and information presented, including overall satisfaction and anxiety.

[0475] This invention provides a system for supporting corporate decision-making using a data acquisition device, an intelligent processing model, and an emotion analysis device. Specific embodiments are described below.

[0476] The server first uses a data processing unit to collect historical performance information and market trend data from various sources. The software incorporates data integration tools and cleaning algorithms to generate a clean and unified dataset.

[0477] Next, the server leverages an intelligent processing model to analyze the collected data and generate new innovative ideas. This model is based on natural language processing techniques and machine learning algorithms, and processes large-scale data using frameworks such as Hadoop and TensorFlow. The generated ideas include specific market strategies and new product concepts.

[0478] The device determines the user's emotional state in real time through an emotion analysis device. This involves analyzing user input data using speech recognition and text analysis technologies. Speech analysis solutions such as Microsoft Azure and Amazon Polly are utilized.

[0479] Based on information obtained from sentiment analysis, the server has the ability to dynamically adjust the content of suggestions and their visual representation. For example, if a user expresses concerns about a new product strategy, the system will prepare a presentation that emphasizes competitive advantages and success stories.

[0480] An example of a prompt is: "Analyze successful case studies of new product launches and propose the optimal strategy. Also, analyze user sentiment towards the proposal and create a customized presentation."

[0481] By using this system, companies can respond quickly and flexibly to market trends and provide users with information optimized for their needs.

[0482] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0483] Step 1:

[0484] The server uses data processing equipment to collect historical performance data and market trend data. Inputs include data from internal company databases, online news, industry reports, and social media. This data undergoes preprocessing, such as noise reduction and formatting, before being output as an analyzable dataset. This process utilizes data integration software and ETL (extract, transform, load) tools to generate a well-organized dataset.

[0485] Step 2:

[0486] The server inputs pre-processed data into an intelligent processing model to generate innovative ideas. This model utilizes natural language processing and machine learning algorithms. A cleaned dataset is provided as input data, from which ideas such as market strategies and new product concepts are output. Specifically, the machine learning model performs pattern recognition and trend analysis to generate innovative proposals.

[0487] Step 3:

[0488] The device uses an emotion analysis device to collect user voice input and text data to determine their emotional state. Input includes user voice feedback and reaction logs. This data is processed through speech synthesis and text analysis to output information about the user's emotional state. Specifically, speech recognition technology analyzes the user's voice tone and word choice to evaluate their emotions.

[0489] Step 4:

[0490] Based on the emotional information obtained in Step 3, the server adjusts the suggested content and its visual display. The input consists of emotional state information and ideas generated in Step 2, and the output is a presentation adapted to the user's emotions. The server changes the priority of information and adjusts the use of color and information placement in the user interface to make it more acceptable to the user. This increases the acceptability of the suggestions.

[0491] (Application Example 2)

[0492] 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."

[0493] In recent years, organizations need to generate new ideas and design rapid and accurate collaborative strategies to gain a competitive advantage in the market. However, traditional systems have difficulty quickly analyzing data and reflecting user sentiment, sometimes leading to a decline in the quality of ideas. Furthermore, dynamic proposals that take user sentiment into account are not made, and as a result, proposals and strategies are often not effective. There is a need to provide an effective solution to this problem.

[0494] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting past success references and market trend information through a data collection device; means for using a generative artificial intelligence model that analyzes the information to generate new concepts; means for identifying partner organizations based on the organization's history and designing cooperation strategies; means for receiving user feedback and improving the system; and means for identifying the user's emotions using an emotion recognition device and adjusting suggestions based on those emotions. This makes it possible to generate high-quality concepts based on data and provide dynamic suggestions that reflect the user's emotions.

[0495] A "data collection device" is a device used to acquire past success references and market trend information, and has the function of collecting data from various information sources.

[0496] A "generative artificial intelligence model" is an artificial intelligence model that analyzes collected data and generates new ideas, using natural language processing and machine learning techniques.

[0497] "Identification based on organizational history" refers to a method for identifying appropriate partner organizations based on their organizational history information.

[0498] "Designing a cooperation strategy" is a means of designing a strategy to maximize mutual benefits with partner organizations.

[0499] "User feedback" refers to the reactions and opinions received from users, and is used to improve the system.

[0500] An "emotion identification device" is a device used to identify the emotions of a user, and has the function of analyzing emotional states from voice and actions.

[0501] To realize this invention, the system has the following configuration.

[0502] The server uses data collection devices to comprehensively gather information on past successes and market trends from various databases. This data undergoes initial processing and is then sent to a generative artificial intelligence model that combines natural language processing techniques and machine learning algorithms to generate new ideas. This generation process allows for the prediction of future trends and the proposal of new business strategies based on insights gained from the data.

[0503] The terminal collects user voice input and operation logs through an emotion recognition device and analyzes this data in real time. The emotion recognition device uses Google Cloud's natural language processing API and speech analysis API to analyze the user's emotions and determine how the user is feeling. Based on the analysis results, the server dynamically adjusts the suggestions and presents information optimized for the user's emotions. For example, if the user has doubts about a suggestion, the server will present alternative options and add detailed explanations.

[0504] Users make decisions to accept suggestions through an interface provided by the system. The information selected by the user is collected as feedback, and the server uses this to improve the algorithms and suggestions.

[0505] For example, if a user is considering purchasing a new gadget, the system will suggest the most suitable product based on the user's past purchase history and emotional responses. For instance, if the user appears anxious, the system might suggest a product with superior warranty coverage.

[0506] An example of a prompt message is an instruction input to the AI ​​model that reads, "Check the customer's emotional state each time they view a product, provide detailed information about products that elicit a positive reaction, and suggest alternative products if a negative reaction occurs."

[0507] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0508] Step 1:

[0509] The server uses a data acquisition device to collect past success references and market trend information from the database. The collected information is first pre-processed, including noise reduction and standardization of data formats. This pre-processing transforms the data into a format suitable for analysis. The input is raw data, and the output is cleaned data.

[0510] Step 2:

[0511] The server supplies pre-processed data to an artificial intelligence model that generates new concepts. This model uses a combination of natural language processing and machine learning algorithms to predict market trends and business strategies from the resulting data. The input is cleaned data, and the output is the proposed concepts and strategies.

[0512] Step 3:

[0513] The device uses an emotion recognition device to collect user voice input and operation logs. This data is sent to Google Cloud's natural language processing API and voice analysis API to determine the user's emotional state in real time. The input is user interaction data, and the output is the user's emotional state.

[0514] Step 4:

[0515] The server dynamically adjusts the suggestions based on the processed emotional information. If the emotional state is positive, it presents detailed information; if it is negative, it focuses on displaying alternatives. The input is the user's emotional state and generated ideas, and the output is customized suggestions.

[0516] Step 5:

[0517] The user reviews the suggested information through the terminal and selects the next step. Feedback is collected and used to improve the system. The input is the suggestions from the server, and the output is the user's selection and feedback.

[0518] 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.

[0519] 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.

[0520] 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.

[0521] [Fourth Embodiment]

[0522] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0523] 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.

[0524] 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).

[0525] 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.

[0526] 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.

[0527] 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).

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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.

[0534] 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".

[0535] This invention provides a system for companies to efficiently generate new ideas and facilitate strategic collaboration. This system takes the following form.

[0536] First, the server utilizes a data collection module to gather data on past success stories and market trends from publicly available databases on the internet and internal company databases. This data collection is performed periodically by an automated scheduling system.

[0537] Next, the server analyzes the collected data using a generative artificial intelligence model. This model combines natural language processing and machine learning algorithms to extract promising business ideas from the data. This allows for the generation of innovative ideas that might be difficult for in-house experts to obtain.

[0538] Next, the server identifies potential partner companies by referring to their profile information. For each identified partner, it analyzes their past collaboration history and market positioning to design the most effective collaboration strategy. This strategy design is achieved by comparing it with successful patterns accumulated within the system.

[0539] Furthermore, the server uses an automated market information analysis module to monitor market and competitor trends in real time. This module has the ability to instantly generate reports and notify users when new market trends are discovered. This enables companies to make strategic decisions quickly.

[0540] Users can submit feedback on ideas and strategies provided by the server. This feedback is used as data for the system to learn and improve its analytical accuracy. In this way, the entire system is continuously optimized, making the company's planning operations more efficient.

[0541] As a concrete example, consider a manufacturing company exploring new IoT product ideas based on past data. The server analyzes relevant past success stories and proposes features and timing for market launch of the new product. At the same time, it finds partners that match the company's strengths and designs a strategy to enhance the product's market value through collaboration with those partners. In this way, the company can prepare to launch its new product into the market in a short period of time.

[0542] The following describes the processing flow.

[0543] Step 1:

[0544] The server activates a data collection module to gather past success stories and market trend data from external and internal databases. This is done using methods such as web scraping and data acquisition via APIs. The collected data undergoes initial filtering to remove noise.

[0545] Step 2:

[0546] The server inputs the cleansed data into an artificial intelligence model. This model uses natural language processing techniques to analyze the data and extract key trending keywords. Furthermore, it generates new business ideas based on these keywords. This process leverages algorithms learned from existing ideas and trends.

[0547] Step 3:

[0548] The server consults an internal database to identify partner companies that match the company's profile. This profile includes the company's strengths and information on past collaborations. It creates a list of potential partners and designs a collaboration strategy to maximize synergies with each company. This includes an effective action plan to achieve specific business objectives.

[0549] Step 4:

[0550] The server runs an automated market information analysis module, monitoring market segments and competitor trends in real time. The monitoring results are analyzed, and if market opportunities or risks requiring immediate attention are identified, a report is automatically generated. This report is used by users to inform strategic decision-making.

[0551] Step 5:

[0552] Users provide feedback based on proposed ideas and strategies. This feedback is sent to the server and contributes to improving the system's algorithms. This leads to improved accuracy in future proposals and enables the formation of more precise strategies.

[0553] (Example 1)

[0554] 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".

[0555] The process by which companies efficiently generate new business ideas and build strategic partnerships typically requires a tremendous amount of time and effort. Furthermore, it is not easy to grasp market trends in a timely manner and make quick decisions. This creates a risk of missing growth opportunities in today's increasingly competitive business environment.

[0556] 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.

[0557] In this invention, the server includes means for collecting past performance data and market trend data through an information gathering configuration, means for using a generative artificial intelligence algorithm to analyze the data and create new concepts, and means for identifying potential collaboration partners based on the business entity's profile and designing a cooperation strategy. This enables companies to efficiently and quickly generate business ideas, build optimal partnerships, and make decisions that are responsive to market trends.

[0558] An "information gathering configuration" is a system for automatically collecting past performance data and market trend data.

[0559] A "generative artificial intelligence algorithm" is an artificial intelligence technology that analyzes data and creates new concepts, using language processing techniques and machine learning methods.

[0560] "Business entity profile" refers to information such as a company's basic information, business activities, and past collaboration history, and serves as a criterion for identifying potential collaboration partners.

[0561] "Means of designing a cooperation strategy" refers to the process of devising methods for building optimal cooperative relationships with identified potential partners.

[0562] "Market trends" refer to current and projected conditions and changes in a particular industry or sector, and are important factors that influence a company's decision-making process.

[0563] "Real-time data monitoring" refers to the function of observing data in real time and immediately detecting changes.

[0564] "User feedback" refers to feedback and insights provided by users for the purpose of improving the final product or service.

[0565] This invention relates to a system for companies to efficiently generate new business ideas and build collaborative relationships, and is implemented through the collaboration of servers, terminals, and users.

[0566] The server first uses an information gathering configuration to automatically collect performance case studies and market trend data from publicly available databases on the internet and internal company databases. This configuration ensures that the data is always up-to-date, as it is regularly updated by an automated scheduling system. The hardware would consist of a dedicated data server, and the software would likely be a scraping tool based on a programming language such as Python.

[0567] Next, the server analyzes the collected data using a generative AI model. Specifically, a model combining natural language processing technology and machine learning algorithms (e.g., GPT-3) is used to analyze the data and generate promising new concepts. This model plays a role in extracting important trends and innovative points from vast amounts of data and presenting them as concrete ideas.

[0568] Furthermore, the server identifies potential partners and designs collaboration strategies while referencing the business entity's profile information. The server considers the characteristics and strengths of each company to list the most suitable candidates for collaboration and formulates the optimal strategy based on that list. This promotes collaboration between companies and supports the establishment of cooperative relationships.

[0569] Furthermore, the server constantly monitors market trends and automatically generates reports by analyzing dynamically changing market information in real time. This automatic generation function is a crucial foundation for responding quickly to changes, allowing users to make optimal decisions according to market conditions at any given time.

[0570] Users provide feedback on the ideas and strategies generated by the server. This feedback contributes to the continuous improvement of the system and helps to improve the analytical accuracy of the generated AI model. As a result, users can always proceed with their work based on optimized information.

[0571] For example, when a manufacturing company plans a new IoT product, the server can analyze past cases and suggest product characteristics to consider and the timing of market launch. Furthermore, by identifying partner companies that complement its strengths and designing a collaborative model with those companies, it can increase the probability of product success.

[0572] An example of a prompt message could be: "Generate ideas and partnership strategies for new product planning in the next quarter."

[0573] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0574] Step 1:

[0575] The server uses an information gathering configuration to collect performance case studies and market trend data from publicly available databases on the internet and internal company databases. In this step, the server periodically accesses various data sources to retrieve new data. The input is a database query, and the output is the collected raw dataset. Specifically, the server executes scripts to perform web scraping and data extraction via APIs.

[0576] Step 2:

[0577] The server inputs the collected data into a generating AI model and performs data analysis. Specifically, it utilizes a model that combines natural language processing technology and machine learning algorithms to organize relevant information and create promising new concepts. The input data consists of collected performance case studies and market trend information, and the output is newly generated business ideas. The server starts the analysis process and lists the generated ideas.

[0578] Step 3:

[0579] The server identifies potential collaborators based on company profiles. Based on the collected and analyzed data, the server lists the most suitable partners, taking into account the company's characteristics and past collaboration history. The input is company profile data, and the output is a list of identified potential collaborators. The server uses an algorithm to perform profile matching and select candidates.

[0580] Step 4:

[0581] The server monitors market trends in real time and analyzes dynamic market information. When market changes are detected, it automatically generates reports immediately. The input is market data collected in real time, and the output is the generated market report. Specifically, the server processes the real-time data stream and triggers report creation when it detects a change.

[0582] Step 5:

[0583] Users provide feedback on the ideas and strategies generated by the server. In this feedback process, user opinions are input into the system and used for subsequent analyses. The input is user feedback data, and the output is insights that lead to system improvements. Users submit their feedback through the provided interface.

[0584] (Application Example 1)

[0585] 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".

[0586] In the field of autonomous driving technology, generating innovative technological ideas and identifying appropriate partners for technological development are crucial. However, there are limited methods for doing this efficiently and effectively. Furthermore, it is difficult to adjust strategies immediately through real-time tracking and analysis of market trends. This invention aims to solve these problems and provide a system that supports new innovation and collaboration in autonomous driving technology.

[0587] 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.

[0588] In this invention, the server includes means for collecting past success data and market trend data through a data acquisition configuration, means for using a generative intelligence model to analyze the data and generate new concepts, and means for identifying collaborating companies based on corporate attribute information and designing a cooperation strategy. This makes it possible to analyze the latest market trends related to autonomous driving technology, provide new technology ideas, and identify appropriate technology development partners.

[0589] A "data acquisition configuration" is a system equipped with functions for effectively collecting past success stories and market trend data.

[0590] A "generative intelligence model" is an artificial intelligence model that analyzes collected data and automatically generates new technological concepts and ideas.

[0591] "Corporate attribute information" refers to data that provides comprehensive information about a company, such as its characteristics, strengths, and business strategy.

[0592] A "collaborating company" refers to a company that may cooperate in the development and market introduction of new technologies.

[0593] A "cooperation strategy" is a plan to optimize technology development and market deployment by collaborating with designated partner companies.

[0594] "Market trends" refer to information regarding changes in demand and supply, as well as competitive activities, within a specific industry or technology field.

[0595] A "technology development partner" is a company or organization that collaborates in the development of new autonomous driving technologies.

[0596] "Real-time data observation" is the process of constantly monitoring current market and technological trends and acquiring information immediately.

[0597] In implementing this system, the server first uses a data acquisition configuration to collect diverse historical success stories and market trend data from a wide range of databases. This includes publicly available company information and relevant industry data. This data is managed on a cloud server and processed efficiently while maintaining security.

[0598] Next, the server utilizes a generative intelligence model to analyze the collected data. In this process, it uses natural language processing libraries such as spaCy and Transformers to analyze text data and machine learning algorithms (e.g., TensorFlow and PyTorch) to generate innovative technical concepts and ideas. This generative intelligence model can identify data relevance and potential market value.

[0599] Furthermore, the server analyzes corporate attribute information to identify suitable collaborating companies. This process takes into account the companies' strengths and technological capabilities to design the optimal collaboration strategy. As a result, the selection of technology development partners becomes faster and more accurate.

[0600] With real-time data monitoring capabilities, market trends are constantly monitored, and the latest information is acquired instantly. This information is promptly fed back to the user via their device, supporting strategic decision-making. For example, a user can input a specific prompt such as, "Find collaboration partners for AI-powered technologies related to electric vehicles," and the system will present data accordingly. Such prompts promote practical and creative technological development.

[0601] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0602] Step 1:

[0603] The server collects historical success stories and market trend data from various databases through a data acquisition configuration. Based on the specified query as input, the server searches publicly available databases on the internet and internal company databases. The collected data is stored in cloud storage.

[0604] Step 2:

[0605] The server analyzes the collected data using a generative intelligence model. The input data is parsed using a natural language processing library (e.g., spaCy). Then, machine learning algorithms (e.g., TensorFlow) are used to extract relevant technical ideas and generate new concepts. The resulting ideas are recorded in a database.

[0606] Step 3:

[0607] The server analyzes corporate attribute information to identify suitable collaborating companies. Given corporate profile information as input, it analyzes it and determines the optimal collaboration partner, taking into account technical capabilities and market position. This information is provided to the collaboration strategy design module, which then generates a specific collaboration strategy.

[0608] Step 4:

[0609] The server observes real-time data and constantly monitors market trends. It uses automated sensors and real-time feeds to acquire and analyze the latest trend data. The resulting market reports are then sent to user terminals.

[0610] Step 5:

[0611] The user enters prompts into the system via a terminal and receives suggestions for specific technical development needs. Based on the input prompts, the server selects previously generated ideas and collaboration strategies and presents them to the user. User feedback is collected and used to improve the system.

[0612] 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.

[0613] This invention combines a system for companies to generate new ideas and achieve effective strategic collaboration with an emotion engine that recognizes user emotions. As a result, proposed ideas and strategies are optimally customized for the user, supporting the company's decision-making process.

[0614] First, the server utilizes a data collection module to acquire data on past success stories and market trends. This data is integrated from various data sources and preprocessed. Next, a generative artificial intelligence model analyzes this data and generates new business ideas. This model combines natural language processing and machine learning algorithms to deepen trend insights and drive innovation.

[0615] Next, the process moves to identifying the most suitable partner companies based on the company's profile and designing a collaboration strategy. Here, a strategy is developed to maximize interaction with the identified partners. This strategy is formulated based on the company's strengths and market analysis.

[0616] In addition, this system incorporates an emotion engine. The terminal sends user voice input and operation logs to the emotion engine, which then analyzes the user's emotional state. This engine uses voice analysis and text analysis technologies, and can determine emotions in real time based on user feedback and reactions.

[0617] The server dynamically adjusts the content and visual display of suggestions based on information obtained from the emotion engine. For example, if a user expresses concern about a suggestion, it can prioritize presenting detailed information and alternatives. In this way, suggestions are presented in a manner that matches the user's emotional state, improving their acceptability.

[0618] As a concrete example, consider a scenario where a project manager at a certain company is considering a strategy for launching a new product into the market. The system analyzes past success stories and proposes a new market strategy, but at the same time, an emotion engine reads the user's reaction and, if the user is feeling anxious, delivers a presentation that emphasizes areas for improvement and competitive advantages. In this way, personalized information based on the user's emotions becomes possible, allowing companies to make strategic decisions more smoothly.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] The server activates a data collection module, automatically gathering historical success stories and relevant market trend data from external public databases and internal corporate data storage. The collected data is then subjected to noise filtering and data cleansing to prepare it for analysis.

[0622] Step 2:

[0623] The server feeds the prepared data into an artificial intelligence model. This model combines natural language processing and machine learning algorithms to understand the context of the data and generate key trends and new business ideas. The generated ideas are then proposed to meet the specific needs of the company.

[0624] Step 3:

[0625] The server references corporate profile information and creates a list of potential partner companies based on past collaborations and the degree of business area alignment. Furthermore, it develops and proposes effective collaboration strategies for working with these partners. These strategies include specific action plans and target achievement indicators.

[0626] Step 4:

[0627] The emotion engine uses speech recognition and text analysis technologies to monitor the user's emotions in real time. The device continuously collects voice input and feedback from the user and sends it to the emotion engine. This information helps understand the user's emotional response to the proposed idea.

[0628] Step 5:

[0629] Based on the analysis results of the emotion engine, the server dynamically adjusts how and what is presented in the proposed ideas. For example, if a user expresses concern, the server takes an approach to deepen the user's understanding by refining the proposal or presenting alternative strategies.

[0630] Step 6:

[0631] Users provide feedback on the suggestions and strategies presented by the server. This feedback is used as training data for the system and is utilized to improve the quality of future suggestions. Based on the feedback, the entire system is continuously optimized, enabling it to provide more valuable suggestions to companies.

[0632] (Example 2)

[0633] 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".

[0634] In today's business environment, companies need to generate innovative ideas based on advanced data analysis and identify effective collaborators in order to quickly adapt to the market and maintain competitiveness. However, traditional methods have struggled to provide personalized suggestions that take user emotions into account, resulting in decreased acceptance in the decision-making process.

[0635] 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.

[0636] In this invention, the server includes means for collecting past performance information and market trend data through a data processing device, means for using an intelligent processing model that analyzes the data to generate new innovative ideas, and means for determining the user's emotional state using an emotion analysis device. This enables adaptive suggestions based on the user's emotions.

[0637] A "data processing device" is a computer system used to collect and analyze information, possessing the function of acquiring information from various data sources and organizing it into a consistent format.

[0638] "Performance information" refers to various data that shows the effectiveness of a company's operations in the past, including historical data such as success stories and growth patterns.

[0639] "Market trend data" refers to a comprehensive collection of market-related data, including consumer behavior, industry trends, and product demand patterns.

[0640] An "intelligent processing model" refers to an algorithm or system that uses natural language processing and machine learning techniques to generate useful information and innovative ideas from large amounts of data.

[0641] An "emotion analysis device" is a system that determines a user's emotional state in real time, and includes technology that analyzes psychological responses from voice and text data.

[0642] "User emotional state" refers to individual reactions and psychological feelings to suggestions and information presented, including overall satisfaction and anxiety.

[0643] This invention provides a system for supporting corporate decision-making using a data acquisition device, an intelligent processing model, and an emotion analysis device. Specific embodiments are described below.

[0644] The server first uses a data processing unit to collect historical performance information and market trend data from various sources. The software incorporates data integration tools and cleaning algorithms to generate a clean and unified dataset.

[0645] Next, the server leverages an intelligent processing model to analyze the collected data and generate new innovative ideas. This model is based on natural language processing techniques and machine learning algorithms, and processes large-scale data using frameworks such as Hadoop and TensorFlow. The generated ideas include specific market strategies and new product concepts.

[0646] The device determines the user's emotional state in real time through an emotion analysis device. This involves analyzing user input data using speech recognition and text analysis technologies. Speech analysis solutions such as Microsoft Azure and Amazon Polly are utilized.

[0647] Based on information obtained from sentiment analysis, the server has the ability to dynamically adjust the content of suggestions and their visual representation. For example, if a user expresses concerns about a new product strategy, the system will prepare a presentation that emphasizes competitive advantages and success stories.

[0648] An example of a prompt is: "Analyze successful case studies of new product launches and propose the optimal strategy. Also, analyze user sentiment towards the proposal and create a customized presentation."

[0649] By using this system, companies can respond quickly and flexibly to market trends and provide users with information optimized for their needs.

[0650] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0651] Step 1:

[0652] The server uses data processing equipment to collect historical performance data and market trend data. Inputs include data from internal company databases, online news, industry reports, and social media. This data undergoes preprocessing, such as noise reduction and formatting, before being output as an analyzable dataset. This process utilizes data integration software and ETL (extract, transform, load) tools to generate a well-organized dataset.

[0653] Step 2:

[0654] The server inputs pre-processed data into an intelligent processing model to generate innovative ideas. This model utilizes natural language processing and machine learning algorithms. A cleaned dataset is provided as input data, from which ideas such as market strategies and new product concepts are output. Specifically, the machine learning model performs pattern recognition and trend analysis to generate innovative proposals.

[0655] Step 3:

[0656] The device uses an emotion analysis device to collect user voice input and text data to determine their emotional state. Input includes user voice feedback and reaction logs. This data is processed through speech synthesis and text analysis to output information about the user's emotional state. Specifically, speech recognition technology analyzes the user's voice tone and word choice to evaluate their emotions.

[0657] Step 4:

[0658] Based on the emotional information obtained in Step 3, the server adjusts the suggested content and its visual display. The input consists of emotional state information and ideas generated in Step 2, and the output is a presentation adapted to the user's emotions. The server changes the priority of information and adjusts the use of color and information placement in the user interface to make it more acceptable to the user. This increases the acceptability of the suggestions.

[0659] (Application Example 2)

[0660] 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".

[0661] In recent years, organizations need to generate new ideas and design rapid and accurate collaborative strategies to gain a competitive advantage in the market. However, traditional systems have difficulty quickly analyzing data and reflecting user sentiment, sometimes leading to a decline in the quality of ideas. Furthermore, dynamic proposals that take user sentiment into account are not made, and as a result, proposals and strategies are often not effective. There is a need to provide an effective solution to this problem.

[0662] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting past success references and market trend information through a data collection device; means for using a generative artificial intelligence model that analyzes the information to generate new concepts; means for identifying partner organizations based on the organization's history and designing cooperation strategies; means for receiving user feedback and improving the system; and means for identifying the user's emotions using an emotion recognition device and adjusting suggestions based on those emotions. This makes it possible to generate high-quality concepts based on data and provide dynamic suggestions that reflect the user's emotions.

[0663] A "data collection device" is a device used to acquire past success references and market trend information, and has the function of collecting data from various information sources.

[0664] A "generative artificial intelligence model" is an artificial intelligence model that analyzes collected data and generates new ideas, using natural language processing and machine learning techniques.

[0665] "Identification based on organizational history" refers to a method for identifying appropriate partner organizations based on their organizational history information.

[0666] "Designing a cooperation strategy" is a means of designing a strategy to maximize mutual benefits with partner organizations.

[0667] "User feedback" refers to the reactions and opinions received from users, and is used to improve the system.

[0668] An "emotion identification device" is a device used to identify the emotions of a user, and has the function of analyzing emotional states from voice and actions.

[0669] To realize this invention, the system has the following configuration.

[0670] The server uses data collection devices to comprehensively gather information on past successes and market trends from various databases. This data undergoes initial processing and is then sent to a generative artificial intelligence model that combines natural language processing techniques and machine learning algorithms to generate new ideas. This generation process allows for the prediction of future trends and the proposal of new business strategies based on insights gained from the data.

[0671] The terminal collects user voice input and operation logs through an emotion recognition device and analyzes this data in real time. The emotion recognition device uses Google Cloud's natural language processing API and speech analysis API to analyze the user's emotions and determine how the user is feeling. Based on the analysis results, the server dynamically adjusts the suggestions and presents information optimized for the user's emotions. For example, if the user has doubts about a suggestion, the server will present alternative options and add detailed explanations.

[0672] Users make decisions to accept suggestions through an interface provided by the system. The information selected by the user is collected as feedback, and the server uses this to improve the algorithms and suggestions.

[0673] For example, if a user is considering purchasing a new gadget, the system will suggest the most suitable product based on the user's past purchase history and emotional responses. For instance, if the user appears anxious, the system might suggest a product with superior warranty coverage.

[0674] An example of a prompt message is an instruction input to the AI ​​model that reads, "Check the customer's emotional state each time they view a product, provide detailed information about products that elicit a positive reaction, and suggest alternative products if a negative reaction occurs."

[0675] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0676] Step 1:

[0677] The server uses a data acquisition device to collect past success references and market trend information from the database. The collected information is first pre-processed, including noise reduction and standardization of data formats. This pre-processing transforms the data into a format suitable for analysis. The input is raw data, and the output is cleaned data.

[0678] Step 2:

[0679] The server supplies pre-processed data to an artificial intelligence model that generates new concepts. This model uses a combination of natural language processing and machine learning algorithms to predict market trends and business strategies from the resulting data. The input is cleaned data, and the output is the proposed concepts and strategies.

[0680] Step 3:

[0681] The device uses an emotion recognition device to collect user voice input and operation logs. This data is sent to Google Cloud's natural language processing API and voice analysis API to determine the user's emotional state in real time. The input is user interaction data, and the output is the user's emotional state.

[0682] Step 4:

[0683] The server dynamically adjusts the suggestions based on the processed emotional information. If the emotional state is positive, it presents detailed information; if it is negative, it focuses on displaying alternatives. The input is the user's emotional state and generated ideas, and the output is customized suggestions.

[0684] Step 5:

[0685] The user reviews the suggested information through the terminal and selects the next step. Feedback is collected and used to improve the system. The input is the suggestions from the server, and the output is the user's selection and feedback.

[0686] 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.

[0687] 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.

[0688] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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."

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0707] The following is further disclosed regarding the embodiments described above.

[0708] (Claim 1)

[0709] A means of collecting past success stories and market trend data through a data collection module,

[0710] A means of using a generative artificial intelligence model that analyzes the aforementioned data to generate new ideas,

[0711] A means of identifying partner companies based on their corporate profiles and designing collaboration strategies,

[0712] A means to automatically analyze market information and generate reports,

[0713] A means of receiving user feedback and improving the system,

[0714] A system that includes this.

[0715] (Claim 2)

[0716] The system according to claim 1, wherein a generative artificial intelligence model extracts ideas from successful cases using natural language processing and machine learning algorithms.

[0717] (Claim 3)

[0718] The system according to claim 1, comprising a module for monitoring real-time data in the automated analysis of market information.

[0719] "Example 1"

[0720] (Claim 1)

[0721] A means of collecting past performance examples and market trend data through information gathering configuration,

[0722] A means of using a generative artificial intelligence algorithm to analyze the aforementioned data and create a new concept,

[0723] A means of identifying potential collaborators based on the business entity's profile and designing a cooperation strategy,

[0724] A means to automatically analyze market trends and generate reports,

[0725] A means of optimizing the system by receiving feedback from users,

[0726] A system that includes this.

[0727] (Claim 2)

[0728] The system according to claim 1, wherein a generative artificial intelligence algorithm extracts concepts from performance examples using language processing techniques and machine learning methods.

[0729] (Claim 3)

[0730] The system according to claim 1, which has a function for monitoring real-time data in market trend analysis.

[0731] "Application Example 1"

[0732] (Claim 1)

[0733] A means of collecting past success stories and market trend data through a data acquisition configuration,

[0734] A means of using a generative intelligence model that analyzes the aforementioned data to generate a new concept,

[0735] A means of identifying collaborating companies based on corporate attribute information and designing cooperation strategies,

[0736] A means of instantly analyzing market information and generating reports,

[0737] A means of obtaining user feedback and implementing system improvements,

[0738] A means of providing new technology ideas related to autonomous driving technology and identifying technology development partners,

[0739] A means of proposing innovative strategies specific to an industry,

[0740] A system that includes this.

[0741] (Claim 2)

[0742] The system according to claim 1, wherein a generative intelligence model extracts concepts from success stories using natural language processing and machine learning algorithms and analyzes the latest market trends related to autonomous driving technology.

[0743] (Claim 3)

[0744] The system according to claim 1, which has a structure for observing real-time data in the immediate analysis of market information, and for identifying technology development partners for autonomous driving technology.

[0745] "Example 2 of combining an emotion engine"

[0746] (Claim 1)

[0747] A means of collecting historical performance information and market trend data through a data processing device,

[0748] A means of using an intelligent processing model that analyzes the aforementioned data to generate new innovative proposals,

[0749] A means of identifying collaborators and designing joint strategies based on organizational characteristics,

[0750] A means for determining the emotional state of a user using an emotion analysis device,

[0751] A means of adaptively adjusting the content presented based on the user's emotional information,

[0752] ...

[0753] A system that includes this.

[0754] (Claim 2)

[0755] The system according to claim 1, wherein an intelligent processing model derives innovative ideas from performance information using language analysis technology and a predictive model.

[0756] (Claim 3)

[0757] The system according to claim 1, comprising a device that performs automatic analysis of market trends using real-time data.

[0758] "Application example 2 when combining with an emotional engine"

[0759] (Claim 1)

[0760] A means for collecting past success references and market trend information through a data collection device,

[0761] A means of using a generative artificial intelligence model that analyzes the aforementioned information to generate new concepts,

[0762] A means of identifying partner organizations based on their organizational history and designing cooperation strategies,

[0763] A means of automatically analyzing market information and generating reports,

[0764] A means of receiving user feedback and improving the system,

[0765] A means for identifying the user's emotions using an emotion recognition device and adjusting suggestions based on those emotions,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, wherein a generative artificial intelligence model extracts ideas from successful references using natural language processing and machine learning techniques.

[0769] (Claim 3)

[0770] The system according to claim 1, comprising a device for monitoring real-time information in the automated analysis of market information. [Explanation of symbols]

[0771] 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. A means of collecting past success stories and market trend data through a data collection module, A means of using a generative artificial intelligence model that analyzes the aforementioned data to generate new ideas, A means of identifying partner companies based on their corporate profiles and designing collaboration strategies, A means to automatically analyze market information and generate reports, A means of receiving user feedback and improving the system, A system that includes this.

2. The system according to claim 1, wherein a generative artificial intelligence model extracts ideas from successful cases using natural language processing and machine learning algorithms.

3. The system according to claim 1, comprising a module for monitoring real-time data in the automated analysis of market information.

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