system

Abstract

The system according to this embodiment aims to efficiently manage processes ranging from planning new businesses and products to market research, competitor analysis, prototype development planning, and market testing. [Solution] The system according to the embodiment comprises a dialogue unit, a research unit, a planning unit, and an analysis unit. The dialogue unit plans new businesses and new products while engaging in dialogue with planners. The research unit conducts market research and competitor analysis based on the plans formulated by the dialogue unit. The planning unit formulates a prototype development plan based on the results obtained by the research unit. The analysis unit analyzes consumer feedback in real time.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process from the planning of a new business or new product to market research, competitive analysis, prototype development plan, and market test is complex and difficult to proceed efficiently.

[0005] The system according to the embodiment aims to efficiently proceed with the process from the planning of a new business or new product to market research, competitive analysis, prototype development plan, and market test.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a dialogue unit, a research unit, a planning unit, and an analysis unit. The dialogue unit plans new businesses and new products while engaging in dialogue with planners. The research unit conducts market research and competitive analysis based on the plans formulated by the dialogue unit. The planning unit formulates prototype development plans based on the results obtained by the research unit. The analysis unit analyzes consumer feedback in real time. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage processes from planning new businesses and products to market research, competitor analysis, prototype development planning, and market testing. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The AI ​​integrated planning agent system according to an embodiment of the present invention is a system that utilizes an AI agent to automate processes from planning new businesses and new products to market research, competitor analysis, prototype development planning, and market testing. First, a conversational AI converses with a planner to formulate plans for new businesses and new products. In this process, the AI ​​analyzes market needs based on the planner's ideas, generating ideas, creating concepts, and developing plans. Next, the AI ​​automatically conducts market research and competitor analysis, providing feedback to the planner. This allows the planner to strategically advance their plans while understanding the market situation. Furthermore, the AI ​​formulates a prototype development plan and provides prototype products or services to consumers. Consumer feedback is acquired via cameras and microphones, and generated AI analyzes it in real time. Based on this feedback, the AI ​​runs a PDCA cycle to improve the product. This enables a rapid and efficient development pipeline. The AI ​​integrated planning agent system targets new business departments, product development personnel, and industries seeking to streamline their production processes. It is particularly suitable for companies with global presence and a strong desire to promote digital transformation. This system addresses the challenges of traditional manual market research and competitor analysis, which are time-consuming and inaccurate, enabling a seamless and efficient process from planning to market testing. With the advancement of AI technology expanding the range of tasks that can be automated, the number of potential customers seeking to improve corporate efficiency is increasing, making this a timely market entry. The goal is to realize a future where companies with a competitive advantage in the global market can leverage AI to seamlessly manage everything from planning to market deployment. This allows the AI-powered integrated planning agent system to automate and efficiently advance processes from planning and market research to competitor analysis, prototype development planning, and feedback analysis.

[0029] The AI ​​integrated planning agent system according to this embodiment comprises a dialogue unit, a research unit, a planning unit, and an analysis unit. The dialogue unit plans new businesses and new products while engaging in dialogue with planners. For example, the dialogue unit analyzes market needs, generates ideas, creates concepts, and creates plans while engaging in dialogue with planners. For example, the dialogue unit can use AI to analyze market needs based on the planner's ideas. The dialogue unit can also use AI to support idea generation. Furthermore, the dialogue unit can use AI to create concepts and plans. The research unit conducts market research and competitor analysis based on the plans formulated by the dialogue unit. For example, the research unit automatically conducts market research and provides feedback to planners. For example, the research unit can use AI to conduct market research. Furthermore, the research unit can use AI to conduct competitor analysis. Furthermore, the research unit can use AI to provide feedback on the results of market research and competitor analysis to planners. The planning unit formulates prototype development plans based on the results obtained by the research unit. For example, the planning unit formulates prototype development plans and provides prototype products and services to consumers. The planning department can, for example, use AI to formulate prototype development plans. The planning department can also use AI to provide prototype products and services to consumers. Furthermore, the planning department can use AI to manage the progress of prototype development plans. The analysis department analyzes consumer feedback in real time. For example, the analysis department analyzes consumer feedback acquired through cameras and microphones in real time. The analysis department can analyze consumer feedback in real time using, for example, generative AI. Furthermore, the analysis department can use generative AI to implement a PDCA cycle based on the feedback. In addition, the analysis department can use generative AI to improve products. As a result, the AI ​​integrated planning agent system according to this embodiment can automate and efficiently advance processes from planning to market research, competitor analysis, prototype development planning, and feedback analysis.

[0030] The Dialogue Department develops new business and product plans while engaging in dialogue with project planners. For example, the Dialogue Department analyzes market needs, generates ideas, creates concepts, and develops plans while interacting with project planners. Specifically, the Dialogue Department uses natural language processing technology to analyze project planners' statements and extract important keywords and trends. This allows for an accurate understanding of market needs based on the project planners' ideas. The Dialogue Department can also support idea generation using generative AI. For example, it can generate related ideas based on keywords provided by the project planners and propose them to them. Furthermore, the Dialogue Department can use generative AI to create concepts and plans. Specifically, the generative AI uses market data and trend information to condense the project planners' ideas into concrete concepts and proposals. In this process, the generative AI can refer to past success and failure cases to propose the most suitable plan. This allows the Dialogue Department to efficiently materialize project planners' ideas and develop high-quality plans.

[0031] The Research Department conducts market research and competitive analysis based on plans formulated by the Dialogue Department. For example, the Research Department can automatically conduct market research and provide feedback to planners. Specifically, the Research Department uses web scraping technology to collect vast amounts of data from the internet and analyzes this data using AI. For example, it collects consumer opinions and evaluations on specific markets and extracts important insights using text mining technology. The Research Department can also use AI to conduct competitive analysis. Specifically, it collects information on competitors' products and services, and the AI ​​analyzes this information to identify the strengths and weaknesses of competitors. Furthermore, the Research Department can use AI to provide feedback on the results of market research and competitive analysis to planners. For example, it can visualize research results in graphs and charts so that planners can understand them intuitively. This allows the Research Department to provide planners with information to accurately grasp market conditions and competitor trends and optimize their plans.

[0032] The Planning Department formulates prototype development plans based on the results obtained by the Research Department. For example, the Planning Department formulates prototype development plans and provides prototype products and services to consumers. Specifically, the Planning Department can use AI to formulate prototype development plans. For example, the AI ​​can propose optimal prototype specifications and development schedules based on market research results and competitor analysis results. The Planning Department can also use AI to provide prototype products and services to consumers. Specifically, the AI ​​can collect consumer feedback in real time and identify areas for improvement in the prototype. Furthermore, the Planning Department can use AI to manage the progress of the prototype development plan. For example, the AI ​​can monitor the progress of the development process and detect schedule delays and resource shortages early. This allows the Planning Department to proceed with prototype development efficiently and effectively.

[0033] The analysis unit analyzes consumer feedback in real time. For example, it analyzes consumer feedback obtained through cameras and microphones in real time. Specifically, the analysis unit can use generative AI to analyze consumer feedback in real time. For example, the generative AI can analyze consumers' facial expressions and tone of voice to evaluate their emotions and satisfaction. The analysis unit can also use generative AI to implement the PDCA cycle based on the feedback. Specifically, the generative AI identifies areas for product improvement based on the feedback and reflects them in the next development cycle. Furthermore, the analysis unit can also use generative AI to improve the product. For example, the generative AI can suggest improvements to the product's design and functionality based on consumer feedback. This allows the analysis unit to quickly reflect consumer needs and opinions, improving product quality and satisfaction.

[0034] The Dialogue Department can analyze market needs, generate ideas, create concepts, and develop plans while engaging in dialogue with planners. For example, the Dialogue Department can analyze market needs while engaging in dialogue with planners. The Dialogue Department can analyze market needs using AI, for example. The Dialogue Department can also support idea generation using AI. Furthermore, the Dialogue Department can create concepts and plans using AI. This enables the Dialogue Department to analyze market needs while engaging in dialogue with planners, leading to effective idea generation and plan development. Specific methods and criteria for analyzing market needs include, for example, consumer research, trend analysis, and evaluation of competing products. Specific methods and criteria for idea generation include, for example, brainstorming, mind mapping, and design thinking. Specific methods and criteria for concept creation include, for example, concept boards, storyboards, and prototype creation.

[0035] The research department can automatically conduct market research and competitive analysis and provide feedback to planners. For example, the research department can automatically conduct market research. For example, the research department can use AI to conduct market research. Furthermore, the research department can use AI to conduct competitive analysis. In addition, the research department can use AI to provide feedback on the results of market research and competitive analysis to planners. This allows planners to strategically advance their plans by enabling the research department to automatically conduct market research and competitive analysis. Specific methods and criteria for market research include, for example, surveys, interviews, and data analysis. Specific methods and criteria for competitive analysis include, for example, SWOT analysis and Porter's Five Forces analysis.

[0036] The planning department can formulate prototype development plans and provide prototype products and services to consumers. For example, the planning department can formulate prototype development plans. The planning department can, for example, use AI to formulate prototype development plans. Furthermore, the planning department can use AI to provide prototype products and services to consumers. In addition, the planning department can use AI to manage the progress of the prototype development plan. This allows the planning department to conduct rapid market testing by formulating prototype development plans and providing prototype products and services to consumers. Specific details and criteria of the prototype development plan include, for example, development schedule, resource allocation, and technical requirements. Specific details and criteria of the prototype product or service include, for example, product functions, service delivery methods, and usability testing.

[0037] The analysis unit can analyze consumer feedback acquired through cameras and microphones in real time and implement the PDCA cycle. For example, the analysis unit can analyze consumer feedback acquired through cameras and microphones in real time. The analysis unit can also analyze consumer feedback in real time using, for example, generative AI. Furthermore, the analysis unit can use generative AI to implement the PDCA cycle based on the feedback. This allows the analysis unit to rapidly improve products by analyzing consumer feedback in real time and implementing the PDCA cycle. Specific methods for acquiring and analyzing feedback include, for example, consumer surveys, review analysis, and real-time data collection. Specific implementation methods and criteria for the PDCA cycle include, for example, details of each step: planning, execution, evaluation, and improvement.

[0038] The analysis unit can use generative AI to analyze market feedback in real time and improve products. For example, the analysis unit can use generative AI to analyze market feedback in real time. The analysis unit can also use generative AI to improve products. This allows the analysis unit to rapidly improve products by analyzing market feedback in real time using generative AI. Specific types and implementation methods of generative AI include, for example, natural language generation, image generation, and data generation. Specific methods for obtaining and analyzing market feedback include, for example, consumer surveys, review analysis, and real-time data collection.

[0039] The dialogue unit can analyze the planner's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can analyze the planner's past dialogue history. For example, the dialogue unit can use AI to analyze the planner's past dialogue history. For example, the dialogue unit selects the optimal dialogue method based on the planner's past dialogue history. For example, the dialogue unit conducts the dialogue in a similar style based on the dialogue style the planner has preferred in the past. For example, the dialogue unit avoids topics the planner has avoided in the past and prioritizes topics they have preferred. For example, the dialogue unit selects the most effective dialogue method from the planner's past dialogue history and conducts the dialogue. In this way, by analyzing the planner's past dialogue history, the dialogue unit can select the optimal dialogue method and enable effective dialogue. Specific methods for acquiring and analyzing dialogue history include, for example, past dialogue logs, dialogue content, and dialogue results. Specific selection criteria and methods for the optimal dialogue method include, for example, dialogue style, topic selection, and depth of questions.

[0040] The dialogue unit can filter the dialogue content based on the planner's current projects and areas of interest during the conversation. For example, the dialogue unit can filter the dialogue content based on the planner's current projects and areas of interest during the conversation. For example, the dialogue unit can use AI to filter the dialogue content based on the planner's current projects and areas of interest. For example, the dialogue unit can prioritize providing information related to the planner's current projects. For example, the dialogue unit can suggest relevant ideas and concepts based on the planner's areas of interest. For example, the dialogue unit can provide competitive information and market trends related to the planner's current projects. In this way, the dialogue unit can provide highly relevant information by filtering the dialogue content based on the planner's current projects and areas of interest. Specific methods and criteria for identifying current projects and areas of interest include, for example, project progress, topics in the area of ​​interest, and relevant keywords. Specific methods and criteria for filtering the dialogue content include, for example, selecting highly relevant information and excluding unnecessary information.

[0041] The dialogue unit can prioritize providing highly relevant information during a conversation, taking into account the geographical location of the planner. For example, the dialogue unit can prioritize providing highly relevant information during a conversation, taking into account the geographical location of the planner. For example, the dialogue unit can use AI to prioritize providing highly relevant information, taking into account the geographical location of the planner. For example, if the planner is in a specific region, the dialogue unit can provide market information and competitor information related to that region. For example, if the planner is working on a project in a specific region, the dialogue unit can provide success stories and trends related to that region. For example, if the planner is interested in a specific region, the dialogue unit can propose new ideas and concepts related to that region. In this way, the dialogue unit can provide region-specific information by providing highly relevant information, taking into account the geographical location of the planner. Specific methods for acquiring and using geographical location information include, for example, GPS data, location information services, and geographical relevance. Specific selection criteria and methods for providing highly relevant information include, for example, region-specific information and information based on user interests.

[0042] The dialogue unit can analyze the project planner's social media activity during the dialogue and provide relevant information. For example, the dialogue unit can analyze the project planner's social media activity during the dialogue. For example, the dialogue unit can use AI to analyze the project planner's social media activity. For example, the dialogue unit can provide relevant information based on the project planner's social media activity. For example, the dialogue unit can suggest relevant ideas and concepts based on topics the project planner has shown interest in on social media. For example, the dialogue unit can provide opinions and trends from industry leaders that the project planner follows on social media. For example, the dialogue unit can analyze the project planner's current areas of interest and trends from their social media activity and provide relevant information. In this way, the dialogue unit improves the quality of the dialogue by providing relevant information through analysis of the project planner's social media activity. Specific methods and criteria for analyzing social media activity include, for example, analysis of post content, analysis of followers, and evaluation of engagement. Specific selection criteria and methods for providing relevant information include, for example, information based on user interests and trend information.

[0043] The research department can optimize its research algorithm by referring to past research data during a research project. For example, the research department can refer to past research data during a research project. For example, the research department can refer to past research data using AI. For example, the research department optimizes its research algorithm based on past research data. For example, the research department selects the most effective research method based on past research data. For example, the research department analyzes trends related to specific markets and competitors from past research data and optimizes its research algorithm. For example, the research department applies the most appropriate research method according to the research category by referring to past research data. This allows the research department to optimize its research algorithm and improve the accuracy of the research by referring to past research data. Specific methods for acquiring and using past research data include, for example, acquiring it from a database and analyzing past research results. Specific methods and criteria for optimizing the research algorithm include, for example, adjusting machine learning algorithms and optimizing parameters.

[0044] The research department can apply different research methods depending on the category of the research subject. For example, the research department can apply different research methods depending on the category of the research subject. For example, the research department can use AI to apply different research methods depending on the category of the research subject. For example, in consumer market research, the research department conducts questionnaire surveys and interviews. For example, in competitive analysis, the research department collects and analyzes reviews of competitors' products and services. For example, in new market research, the research department observes local market trends and consumer purchasing behavior. In this way, the research department improves the accuracy of the research by applying different research methods depending on the category of the research subject. Specific classification methods and criteria for the categories of research subject include, for example, product categories, service categories, and consumer categories. Specific application methods and criteria for research methods include, for example, quantitative research, qualitative research, and mixed research.

[0045] The research department can conduct research while considering the geographical distribution of the research subjects. For example, the research department can conduct research while considering the geographical distribution of the research subjects. For example, the research department can use AI to conduct research while considering the geographical distribution of the research subjects. For example, the research department can conduct market research in a specific region and analyze consumer trends in that region. For example, the research department can apply region-specific research methods while considering geographically dispersed research subjects. For example, the research department can select the optimal research method according to the category of research subjects based on geographical distribution. In this way, the research department can provide region-specific information by conducting research while considering the geographical distribution of the research subjects. Specific methods and criteria for research include, for example, questionnaire surveys, interviews, and data analysis.

[0046] The research department can improve the accuracy of its research by referring to relevant literature during the research process. For example, the research department can refer to relevant literature during the research process. For example, the research department can use AI to refer to relevant literature. For example, the research department can improve the accuracy of its research based on relevant literature. For example, the research department can improve the reliability of its research results by referring to relevant academic papers and industry reports. For example, the research department can improve the accuracy of its research methodology by referring to past research results and case studies. For example, the research department can apply the most appropriate research methodology to the category of research subject based on relevant literature. In this way, the research department improves the accuracy of its research by referring to relevant literature. Specific methods and uses of referencing relevant literature include, for example, referring to academic papers and using industry reports. Specific methods and criteria for improving the accuracy of research include, for example, improving the reliability of data and improving research methodology.

[0047] The planning department can optimize its planning algorithm by referring to past development plan data when formulating a plan. For example, the planning department can refer to past development plan data when formulating a plan. For example, the planning department can refer to past development plan data using AI. For example, the planning department can optimize its planning algorithm based on past development plan data. For example, the planning department can select the most effective planning method based on past development plan data. For example, the planning department can analyze trends related to a specific project from past development plan data and optimize its planning algorithm. For example, the planning department can apply the optimal planning method according to the category of the development target by referring to past development plan data. In this way, the planning department can optimize its planning algorithm by referring to past development plan data and improve the accuracy of the development plan. Specific methods for obtaining and using past development plan data include, for example, obtaining it from a database and analyzing past planning results. Specific methods and criteria for optimizing the planning algorithm include, for example, adjusting the machine learning algorithm and optimizing parameters.

[0048] The planning department can apply different planning methodologies depending on the category of the development target when formulating a plan. For example, the planning department can apply different planning methodologies depending on the category of the development target when formulating a plan. For example, the planning department can use AI to apply different planning methodologies depending on the category of the development target. For example, the planning department can apply agile methodologies in software development. For example, the planning department can apply waterfall methodologies in hardware development. For example, the planning department can apply design thinking in service development. In this way, the planning department can improve the accuracy of development plans by applying different planning methodologies depending on the category of the development target. Specific classification methods and criteria for the categories of development targets include, for example, product categories, service categories, and consumer categories. Specific application methods and criteria for planning methodologies include, for example, agile development, waterfall development, and lean development.

[0049] The planning department can create plans while considering the geographical distribution of the development targets. For example, the planning department can create plans while considering the geographical distribution of the development targets. For example, the planning department can use AI to create plans while considering the geographical distribution of the development targets. For example, the planning department can create development plans for each region while considering geographically dispersed development targets. For example, the planning department can apply the optimal planning method according to the category of the development target based on the geographical distribution. In this way, the planning department can create region-specific development plans by creating plans while considering the geographical distribution of the development targets. Specific methods for obtaining and using geographical distribution include, for example, collecting data by region and using geographic information systems.

[0050] The planning department can improve the accuracy of its plans by referring to relevant literature during the planning stage. For example, the planning department can refer to relevant literature during the planning stage. For example, the planning department can use AI to refer to relevant literature. For example, the planning department can improve the accuracy of its plans based on relevant literature. For example, the planning department can improve the reliability of its plans by referring to relevant academic papers and industry reports. For example, the planning department can improve the accuracy of its planning methodologies by referring to past development plans and case studies. For example, the planning department can apply the most appropriate planning methodology based on relevant literature, according to the category of the development target. In this way, the planning department improves the accuracy of its plans by referring to relevant literature. Specific methods of referring to and using relevant literature include, for example, referring to academic papers and using industry reports.

[0051] The analysis unit can optimize its analysis algorithm by referring to past feedback data during analysis. For example, the analysis unit can refer to past feedback data during analysis. For example, the analysis unit can refer to past feedback data using AI. For example, the analysis unit optimizes its analysis algorithm based on past feedback data. For example, the analysis unit selects the most effective analysis method based on past feedback data. For example, the analysis unit analyzes trends related to specific products or services from past feedback data and optimizes its analysis algorithm. For example, the analysis unit applies the most appropriate analysis method according to the category of feedback target by referring to past feedback data. In this way, the analysis unit optimizes its analysis algorithm by referring to past feedback data and improves the accuracy of feedback analysis. Specific methods for obtaining and using past feedback data include, for example, obtaining it from a database and analyzing past feedback results.

[0052] The analysis unit can apply different analytical methods depending on the category of feedback being received during analysis. For example, the analysis unit can apply different analytical methods depending on the category of feedback being received during analysis. For example, the analysis unit can use AI to apply different analytical methods depending on the category of feedback being received. For example, in consumer market feedback, the analysis unit analyzes survey results and reviews. For example, in competitive analysis, the analysis unit collects and analyzes feedback on competitors' products and services. For example, in new market feedback, the analysis unit observes the opinions and purchasing behavior of local consumers. By doing so, the analysis unit improves the accuracy of feedback analysis by applying different analytical methods depending on the category of feedback being received. Specific classification methods and criteria for the categories of feedback being received include, for example, product categories, service categories, and consumer categories.

[0053] The analysis unit can perform analysis while considering the geographical distribution of the feedback target. For example, the analysis unit can perform analysis while considering the geographical distribution of the feedback target. For example, the analysis unit can use AI to perform analysis while considering the geographical distribution of the feedback target. For example, the analysis unit can prioritize the analysis of feedback in a specific region and analyze consumer trends in that region. For example, the analysis unit can apply region-specific analysis methods while considering geographically dispersed feedback. For example, the analysis unit can select the optimal analysis method according to the category of the feedback target based on geographical distribution. As a result, the analysis unit can perform region-specific feedback analysis by considering the geographical distribution of the feedback target. Specific methods for obtaining and using geographical distribution include, for example, collecting data by region and using geographic information systems.

[0054] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can refer to relevant literature during the analysis. For example, the analysis unit can use AI to refer to relevant literature. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature. For example, the analysis unit can improve the reliability of its analysis results by referring to relevant academic papers and industry reports. For example, the analysis unit can improve the accuracy of its analysis methods by referring to past feedback results and case studies. For example, the analysis unit can apply the most appropriate analysis method based on the category of feedback subject, using relevant literature. In this way, the analysis unit improves the accuracy of its analysis by referring to relevant literature. Specific methods for referring to and using relevant literature include, for example, referring to academic papers and using industry reports.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The research department can optimize its research algorithms by referring to past research data during the research process. For example, it can select the most effective research method based on past research data. It can analyze trends related to specific markets and competitors from past research data and optimize the research algorithm. It can apply the most appropriate research method according to the category being researched by referring to past research data. In this way, the research department can optimize its research algorithms and improve the accuracy of the research by referring to past research data.

[0057] The planning department can optimize its planning algorithm by referring to past development plan data during the planning stage. For example, it can select the most effective planning method based on past development plan data. It can analyze trends related to a specific project from past development plan data and optimize the planning algorithm. It can apply the most suitable planning method according to the category of the development target by referring to past development plan data. In this way, the planning department can optimize its planning algorithm and improve the accuracy of development plans by referring to past development plan data.

[0058] The analysis unit can optimize its analysis algorithm by referring to past feedback data during analysis. For example, it can select the most effective analysis method based on past feedback data. It can analyze trends related to specific products or services from past feedback data and optimize the analysis algorithm. It can apply the most appropriate analysis method according to the category of feedback target by referring to past feedback data. In this way, the analysis unit can optimize its analysis algorithm by referring to past feedback data and improve the accuracy of feedback analysis.

[0059] The dialogue department can analyze the planner's past dialogue history and select the most suitable dialogue method. For example, it can conduct the dialogue in a similar style based on the planner's preferred dialogue style in the past. It can avoid topics the planner has avoided in the past and prioritize topics they have preferred. It can select the most effective dialogue method from the planner's past dialogue history and proceed with the dialogue. In this way, the dialogue department can select the most suitable dialogue method and conduct an effective dialogue by analyzing the planner's past dialogue history.

[0060] The research department can conduct research while considering the geographical distribution of the research subjects. For example, it can conduct market research in a specific region and analyze consumer trends in that region. It can apply region-specific research methods, taking into account the geographically dispersed research subjects. Based on the geographical distribution, it can select the most appropriate research method according to the category of research subjects. As a result, the research department can provide region-specific information by conducting research while considering the geographical distribution of the research subjects.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The dialogue department develops new business and product plans while engaging in dialogue with the planner. For example, the dialogue department analyzes market needs, generates ideas, creates concepts, and develops plans while engaging in dialogue with the planner. The dialogue department can use AI to analyze market needs based on the planner's ideas, support idea generation, and create concepts and plans. Step 2: The research department conducts market research and competitive analysis based on the plan formulated by the dialogue department. For example, the research department can automatically conduct market research and provide feedback to the planner. The research department can use AI to conduct market research and competitive analysis and provide feedback on the results to the planner. Step 3: The Planning Department formulates a prototype development plan based on the results obtained by the Research Department. The Planning Department, for example, formulates a prototype development plan and provides prototype products or services to consumers. The Planning Department can use AI to formulate prototype development plans, provide prototype products or services to consumers, and manage the progress of the prototype development plan. Step 4: The analysis unit analyzes consumer feedback in real time. The analysis unit analyzes consumer feedback obtained, for example, through cameras and microphones, in real time. The analysis unit uses generative AI to analyze consumer feedback in real time, and based on that feedback, it can run the PDCA cycle and improve the product.

[0063] (Example of form 2) The AI ​​integrated planning agent system according to an embodiment of the present invention is a system that utilizes an AI agent to automate processes from planning new businesses and new products to market research, competitor analysis, prototype development planning, and market testing. First, a conversational AI converses with a planner to formulate plans for new businesses and new products. In this process, the AI ​​analyzes market needs based on the planner's ideas, generating ideas, creating concepts, and developing plans. Next, the AI ​​automatically conducts market research and competitor analysis, providing feedback to the planner. This allows the planner to strategically advance their plans while understanding the market situation. Furthermore, the AI ​​formulates a prototype development plan and provides prototype products or services to consumers. Consumer feedback is acquired via cameras and microphones, and generated AI analyzes it in real time. Based on this feedback, the AI ​​runs a PDCA cycle to improve the product. This enables a rapid and efficient development pipeline. The AI ​​integrated planning agent system targets new business departments, product development personnel, and industries seeking to streamline their production processes. It is particularly suitable for companies with global presence and a strong desire to promote digital transformation. This system addresses the challenges of traditional manual market research and competitor analysis, which are time-consuming and inaccurate, enabling a seamless and efficient process from planning to market testing. With the advancement of AI technology expanding the range of tasks that can be automated, the number of potential customers seeking to improve corporate efficiency is increasing, making this a timely market entry. The goal is to realize a future where companies with a competitive advantage in the global market can leverage AI to seamlessly manage everything from planning to market deployment. This allows the AI-powered integrated planning agent system to automate and efficiently advance processes from planning and market research to competitor analysis, prototype development planning, and feedback analysis.

[0064] The AI ​​integrated planning agent system according to this embodiment comprises a dialogue unit, a research unit, a planning unit, and an analysis unit. The dialogue unit plans new businesses and new products while engaging in dialogue with planners. For example, the dialogue unit analyzes market needs, generates ideas, creates concepts, and creates plans while engaging in dialogue with planners. For example, the dialogue unit can use AI to analyze market needs based on the planner's ideas. The dialogue unit can also use AI to support idea generation. Furthermore, the dialogue unit can use AI to create concepts and plans. The research unit conducts market research and competitor analysis based on the plans formulated by the dialogue unit. For example, the research unit automatically conducts market research and provides feedback to planners. For example, the research unit can use AI to conduct market research. Furthermore, the research unit can use AI to conduct competitor analysis. Furthermore, the research unit can use AI to provide feedback on the results of market research and competitor analysis to planners. The planning unit formulates prototype development plans based on the results obtained by the research unit. For example, the planning unit formulates prototype development plans and provides prototype products and services to consumers. The planning department can, for example, use AI to formulate prototype development plans. The planning department can also use AI to provide prototype products and services to consumers. Furthermore, the planning department can use AI to manage the progress of prototype development plans. The analysis department analyzes consumer feedback in real time. For example, the analysis department analyzes consumer feedback acquired through cameras and microphones in real time. The analysis department can analyze consumer feedback in real time using, for example, generative AI. Furthermore, the analysis department can use generative AI to implement a PDCA cycle based on the feedback. In addition, the analysis department can use generative AI to improve products. As a result, the AI ​​integrated planning agent system according to this embodiment can automate and efficiently advance processes from planning to market research, competitor analysis, prototype development planning, and feedback analysis.

[0065] The Dialogue Department develops new business and product plans while engaging in dialogue with project planners. For example, the Dialogue Department analyzes market needs, generates ideas, creates concepts, and develops plans while interacting with project planners. Specifically, the Dialogue Department uses natural language processing technology to analyze project planners' statements and extract important keywords and trends. This allows for an accurate understanding of market needs based on the project planners' ideas. The Dialogue Department can also support idea generation using generative AI. For example, it can generate related ideas based on keywords provided by the project planners and propose them to them. Furthermore, the Dialogue Department can use generative AI to create concepts and plans. Specifically, the generative AI uses market data and trend information to condense the project planners' ideas into concrete concepts and proposals. In this process, the generative AI can refer to past success and failure cases to propose the most suitable plan. This allows the Dialogue Department to efficiently materialize project planners' ideas and develop high-quality plans.

[0066] The Research Department conducts market research and competitive analysis based on plans formulated by the Dialogue Department. For example, the Research Department can automatically conduct market research and provide feedback to planners. Specifically, the Research Department uses web scraping technology to collect vast amounts of data from the internet and analyzes this data using AI. For example, it collects consumer opinions and evaluations on specific markets and extracts important insights using text mining technology. The Research Department can also use AI to conduct competitive analysis. Specifically, it collects information on competitors' products and services, and the AI ​​analyzes this information to identify the strengths and weaknesses of competitors. Furthermore, the Research Department can use AI to provide feedback on the results of market research and competitive analysis to planners. For example, it can visualize research results in graphs and charts so that planners can understand them intuitively. This allows the Research Department to provide planners with information to accurately grasp market conditions and competitor trends and optimize their plans.

[0067] The Planning Department formulates prototype development plans based on the results obtained by the Research Department. For example, the Planning Department formulates prototype development plans and provides prototype products and services to consumers. Specifically, the Planning Department can use AI to formulate prototype development plans. For example, the AI ​​can propose optimal prototype specifications and development schedules based on market research results and competitor analysis results. The Planning Department can also use AI to provide prototype products and services to consumers. Specifically, the AI ​​can collect consumer feedback in real time and identify areas for improvement in the prototype. Furthermore, the Planning Department can use AI to manage the progress of the prototype development plan. For example, the AI ​​can monitor the progress of the development process and detect schedule delays and resource shortages early. This allows the Planning Department to proceed with prototype development efficiently and effectively.

[0068] The analysis unit analyzes consumer feedback in real time. For example, it analyzes consumer feedback obtained through cameras and microphones in real time. Specifically, the analysis unit can use generative AI to analyze consumer feedback in real time. For example, the generative AI can analyze consumers' facial expressions and tone of voice to evaluate their emotions and satisfaction. The analysis unit can also use generative AI to implement the PDCA cycle based on the feedback. Specifically, the generative AI identifies areas for product improvement based on the feedback and reflects them in the next development cycle. Furthermore, the analysis unit can also use generative AI to improve the product. For example, the generative AI can suggest improvements to the product's design and functionality based on consumer feedback. This allows the analysis unit to quickly reflect consumer needs and opinions, improving product quality and satisfaction.

[0069] The Dialogue Department can analyze market needs, generate ideas, create concepts, and develop plans while engaging in dialogue with planners. For example, the Dialogue Department can analyze market needs while engaging in dialogue with planners. The Dialogue Department can analyze market needs using AI, for example. The Dialogue Department can also support idea generation using AI. Furthermore, the Dialogue Department can create concepts and plans using AI. This enables the Dialogue Department to analyze market needs while engaging in dialogue with planners, leading to effective idea generation and plan development. Specific methods and criteria for analyzing market needs include, for example, consumer research, trend analysis, and evaluation of competing products. Specific methods and criteria for idea generation include, for example, brainstorming, mind mapping, and design thinking. Specific methods and criteria for concept creation include, for example, concept boards, storyboards, and prototype creation.

[0070] The research department can automatically conduct market research and competitive analysis and provide feedback to planners. For example, the research department can automatically conduct market research. For example, the research department can use AI to conduct market research. Furthermore, the research department can use AI to conduct competitive analysis. In addition, the research department can use AI to provide feedback on the results of market research and competitive analysis to planners. This allows planners to strategically advance their plans by enabling the research department to automatically conduct market research and competitive analysis. Specific methods and criteria for market research include, for example, surveys, interviews, and data analysis. Specific methods and criteria for competitive analysis include, for example, SWOT analysis and Porter's Five Forces analysis.

[0071] The planning department can formulate prototype development plans and provide prototype products and services to consumers. For example, the planning department can formulate prototype development plans. The planning department can, for example, use AI to formulate prototype development plans. Furthermore, the planning department can use AI to provide prototype products and services to consumers. In addition, the planning department can use AI to manage the progress of the prototype development plan. This allows the planning department to conduct rapid market testing by formulating prototype development plans and providing prototype products and services to consumers. Specific details and criteria of the prototype development plan include, for example, development schedule, resource allocation, and technical requirements. Specific details and criteria of the prototype product or service include, for example, product functions, service delivery methods, and usability testing.

[0072] The analysis unit can analyze consumer feedback acquired through cameras and microphones in real time and implement the PDCA cycle. For example, the analysis unit can analyze consumer feedback acquired through cameras and microphones in real time. The analysis unit can also analyze consumer feedback in real time using, for example, generative AI. Furthermore, the analysis unit can use generative AI to implement the PDCA cycle based on the feedback. This allows the analysis unit to rapidly improve products by analyzing consumer feedback in real time and implementing the PDCA cycle. Specific methods for acquiring and analyzing feedback include, for example, consumer surveys, review analysis, and real-time data collection. Specific implementation methods and criteria for the PDCA cycle include, for example, details of each step: planning, execution, evaluation, and improvement.

[0073] The analysis unit can use generative AI to analyze market feedback in real time and improve products. For example, the analysis unit can use generative AI to analyze market feedback in real time. The analysis unit can also use generative AI to improve products. This allows the analysis unit to rapidly improve products by analyzing market feedback in real time using generative AI. Specific types and implementation methods of generative AI include, for example, natural language generation, image generation, and data generation. Specific methods for obtaining and analyzing market feedback include, for example, consumer surveys, review analysis, and real-time data collection.

[0074] The dialogue unit can estimate the emotions of the planner and adjust the pace and content of the dialogue based on the estimated emotions. For example, the dialogue unit can estimate the emotions of the planner. The dialogue unit can estimate emotions using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) or multimodal generation AI. For example, the dialogue unit estimates the emotions of the planner and adjusts the pace of the dialogue based on the estimated emotions. For example, if the planner is excited, the dialogue unit will speed up the pace of the dialogue to quickly generate ideas. For example, if the planner is calm, the dialogue unit will slow down the pace of the dialogue to provide detailed explanations and discussions. For example, if the planner is stressed, the dialogue unit will make the content of the dialogue concise and provide topics that help them relax. In this way, the dialogue unit can enable more effective dialogue by adjusting the pace and content of the dialogue according to the emotions of the planner. Specific methods and criteria for estimating emotions include, for example, facial recognition, speech analysis, and text analysis. Specific methods and criteria for adjusting the pace and content of a dialogue include, for example, the tempo of the conversation, the selection of topics, and the depth of questions.

[0075] The dialogue unit can analyze the planner's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can analyze the planner's past dialogue history. For example, the dialogue unit can use AI to analyze the planner's past dialogue history. For example, the dialogue unit selects the optimal dialogue method based on the planner's past dialogue history. For example, the dialogue unit conducts the dialogue in a similar style based on the dialogue style the planner has preferred in the past. For example, the dialogue unit avoids topics the planner has avoided in the past and prioritizes topics they have preferred. For example, the dialogue unit selects the most effective dialogue method from the planner's past dialogue history and conducts the dialogue. In this way, by analyzing the planner's past dialogue history, the dialogue unit can select the optimal dialogue method and enable effective dialogue. Specific methods for acquiring and analyzing dialogue history include, for example, past dialogue logs, dialogue content, and dialogue results. Specific selection criteria and methods for the optimal dialogue method include, for example, dialogue style, topic selection, and depth of questions.

[0076] The dialogue unit can filter the dialogue content based on the planner's current projects and areas of interest during the conversation. For example, the dialogue unit can filter the dialogue content based on the planner's current projects and areas of interest during the conversation. For example, the dialogue unit can use AI to filter the dialogue content based on the planner's current projects and areas of interest. For example, the dialogue unit can prioritize providing information related to the planner's current projects. For example, the dialogue unit can suggest relevant ideas and concepts based on the planner's areas of interest. For example, the dialogue unit can provide competitive information and market trends related to the planner's current projects. In this way, the dialogue unit can provide highly relevant information by filtering the dialogue content based on the planner's current projects and areas of interest. Specific methods and criteria for identifying current projects and areas of interest include, for example, project progress, topics in the area of ​​interest, and relevant keywords. Specific methods and criteria for filtering the dialogue content include, for example, selecting highly relevant information and excluding unnecessary information.

[0077] The dialogue unit can estimate the emotions of the planner and determine the priority of the dialogue based on the estimated emotions. For example, the dialogue unit can estimate the emotions of the planner. The dialogue unit can estimate emotions using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) or multimodal generation AI. The dialogue unit can estimate the emotions of the planner and determine the priority of the dialogue based on the estimated emotions. For example, if the planner is excited, the dialogue unit will prioritize important ideas and concepts. For example, if the planner is calm, the dialogue unit will prioritize detailed discussions and explanations. For example, if the planner is stressed, the dialogue unit will prioritize providing relaxing topics. In this way, the dialogue unit can prioritize providing important information by determining the priority of the dialogue according to the planner's emotions. Specific methods and criteria for estimating emotions include, for example, facial recognition, speech analysis, and text analysis. Specific criteria and methods for determining the prioritization of dialogue include, for example, prioritizing the provision of highly important information and prioritizing the provision of highly urgent information.

[0078] The dialogue unit can prioritize providing highly relevant information during a conversation, taking into account the geographical location of the planner. For example, the dialogue unit can prioritize providing highly relevant information during a conversation, taking into account the geographical location of the planner. For example, the dialogue unit can use AI to prioritize providing highly relevant information, taking into account the geographical location of the planner. For example, if the planner is in a specific region, the dialogue unit can provide market information and competitor information related to that region. For example, if the planner is working on a project in a specific region, the dialogue unit can provide success stories and trends related to that region. For example, if the planner is interested in a specific region, the dialogue unit can propose new ideas and concepts related to that region. In this way, the dialogue unit can provide region-specific information by providing highly relevant information, taking into account the geographical location of the planner. Specific methods for acquiring and using geographical location information include, for example, GPS data, location information services, and geographical relevance. Specific selection criteria and methods for providing highly relevant information include, for example, region-specific information and information based on user interests.

[0079] The dialogue unit can analyze the project planner's social media activity during the dialogue and provide relevant information. For example, the dialogue unit can analyze the project planner's social media activity during the dialogue. For example, the dialogue unit can use AI to analyze the project planner's social media activity. For example, the dialogue unit can provide relevant information based on the project planner's social media activity. For example, the dialogue unit can suggest relevant ideas and concepts based on topics the project planner has shown interest in on social media. For example, the dialogue unit can provide opinions and trends from industry leaders that the project planner follows on social media. For example, the dialogue unit can analyze the project planner's current areas of interest and trends from their social media activity and provide relevant information. In this way, the dialogue unit improves the quality of the dialogue by providing relevant information through analysis of the project planner's social media activity. Specific methods and criteria for analyzing social media activity include, for example, analysis of post content, analysis of followers, and evaluation of engagement. Specific selection criteria and methods for providing relevant information include, for example, information based on user interests and trend information.

[0080] The research department can estimate the emotions of the planner and adjust how the survey results are displayed based on the estimated emotions. For example, the research department can estimate the emotions of the planner. The research department can estimate emotions using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) or multimodal generation AI. For example, the research department can estimate the emotions of the planner and adjust how the survey results are displayed based on the estimated emotions. For example, if the planner is excited, the research department can display the survey results in visually appealing graphs and charts. For example, if the planner is calm, the research department can display detailed data and analysis results in text format. For example, if the planner is stressed, the research department can display concise and to-the-point survey results. This allows the research department to provide more effective information by adjusting how the survey results are displayed according to the emotions of the planner. Specific methods and criteria for estimating emotions include, for example, facial recognition, voice analysis, and text analysis. Specific methods and criteria for adjusting how survey results are displayed include, for example, graphs, text, and interactive displays.

[0081] The research department can optimize its research algorithm by referring to past research data during a research project. For example, the research department can refer to past research data during a research project. For example, the research department can refer to past research data using AI. For example, the research department optimizes its research algorithm based on past research data. For example, the research department selects the most effective research method based on past research data. For example, the research department analyzes trends related to specific markets and competitors from past research data and optimizes its research algorithm. For example, the research department applies the most appropriate research method according to the research category by referring to past research data. This allows the research department to optimize its research algorithm and improve the accuracy of the research by referring to past research data. Specific methods for acquiring and using past research data include, for example, acquiring it from a database and analyzing past research results. Specific methods and criteria for optimizing the research algorithm include, for example, adjusting machine learning algorithms and optimizing parameters.

[0082] The research department can apply different research methods depending on the category of the research subject. For example, the research department can apply different research methods depending on the category of the research subject. For example, the research department can use AI to apply different research methods depending on the category of the research subject. For example, in consumer market research, the research department conducts questionnaire surveys and interviews. For example, in competitive analysis, the research department collects and analyzes reviews of competitors' products and services. For example, in new market research, the research department observes local market trends and consumer purchasing behavior. In this way, the research department improves the accuracy of the research by applying different research methods depending on the category of the research subject. Specific classification methods and criteria for the categories of research subject include, for example, product categories, service categories, and consumer categories. Specific application methods and criteria for research methods include, for example, quantitative research, qualitative research, and mixed research.

[0083] The research department can estimate the emotions of the planner and prioritize the survey results based on the estimated emotions. For example, the research department can estimate the emotions of the planner. The research department can estimate emotions using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) or multimodal generation AI. For example, the research department can estimate the emotions of the planner and prioritize the survey results based on the estimated emotions. For example, if the planner is excited, the research department will prioritize displaying important survey results. For example, if the planner is calm, the research department will sequentially display detailed survey results. For example, if the planner is stressed, the research department will prioritize displaying concise and to-the-point survey results. In this way, the research department can prioritize providing important information by prioritizing the survey results according to the emotions of the planner. Specific methods and criteria for estimating emotions include, for example, facial recognition, voice analysis, and text analysis. Specific criteria and methods for prioritizing survey results include, for example, prioritizing the provision of highly important information and prioritizing the provision of highly urgent information.

[0084] The research department can conduct research while considering the geographical distribution of the research subjects. For example, the research department can conduct research while considering the geographical distribution of the research subjects. For example, the research department can use AI to conduct research while considering the geographical distribution of the research subjects. For example, the research department can conduct market research in a specific region and analyze consumer trends in that region. For example, the research department can apply region-specific research methods while considering geographically dispersed research subjects. For example, the research department can select the optimal research method according to the category of research subjects based on geographical distribution. In this way, the research department can provide region-specific information by conducting research while considering the geographical distribution of the research subjects. Specific methods and criteria for research include, for example, questionnaire surveys, interviews, and data analysis.

[0085] The research department can improve the accuracy of its research by referring to relevant literature during the research process. For example, the research department can refer to relevant literature during the research process. For example, the research department can use AI to refer to relevant literature. For example, the research department can improve the accuracy of its research based on relevant literature. For example, the research department can improve the reliability of its research results by referring to relevant academic papers and industry reports. For example, the research department can improve the accuracy of its research methodology by referring to past research results and case studies. For example, the research department can apply the most appropriate research methodology to the category of research subject based on relevant literature. In this way, the research department improves the accuracy of its research by referring to relevant literature. Specific methods and uses of referencing relevant literature include, for example, referring to academic papers and using industry reports. Specific methods and criteria for improving the accuracy of research include, for example, improving the reliability of data and improving research methodology.

[0086] The planning department can estimate the emotions of the planner and adjust the content of the development plan based on the estimated emotions. For example, the planning department can estimate the emotions of the planner. The planning department can estimate emotions using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) or multimodal generation AI. For example, the planning department can estimate the emotions of the planner and adjust the content of the development plan based on the estimated emotions. For example, if the planner is excited, the planning department will formulate an aggressive development plan. For example, if the planner is calm, the planning department will formulate a detailed development plan. For example, if the planner is stressed, the planning department will formulate a concise and actionable development plan. In this way, the planning department can formulate a more effective development plan by adjusting the content of the development plan according to the emotions of the planner. Specific methods and criteria for estimating emotions include, for example, facial recognition, speech analysis, and text analysis. Specific methods and criteria for adjusting the content of the development plan include, for example, adjusting the development schedule and changing resource allocation.

[0087] The planning department can optimize its planning algorithm by referring to past development plan data when formulating a plan. For example, the planning department can refer to past development plan data when formulating a plan. For example, the planning department can refer to past development plan data using AI. For example, the planning department can optimize its planning algorithm based on past development plan data. For example, the planning department can select the most effective planning method based on past development plan data. For example, the planning department can analyze trends related to a specific project from past development plan data and optimize its planning algorithm. For example, the planning department can apply the optimal planning method according to the category of the development target by referring to past development plan data. In this way, the planning department can optimize its planning algorithm by referring to past development plan data and improve the accuracy of the development plan. Specific methods for obtaining and using past development plan data include, for example, obtaining it from a database and analyzing past planning results. Specific methods and criteria for optimizing the planning algorithm include, for example, adjusting the machine learning algorithm and optimizing parameters.

[0088] The planning department can apply different planning methodologies depending on the category of the development target when formulating a plan. For example, the planning department can apply different planning methodologies depending on the category of the development target when formulating a plan. For example, the planning department can use AI to apply different planning methodologies depending on the category of the development target. For example, the planning department can apply agile methodologies in software development. For example, the planning department can apply waterfall methodologies in hardware development. For example, the planning department can apply design thinking in service development. In this way, the planning department can improve the accuracy of development plans by applying different planning methodologies depending on the category of the development target. Specific classification methods and criteria for the categories of development targets include, for example, product categories, service categories, and consumer categories. Specific application methods and criteria for planning methodologies include, for example, agile development, waterfall development, and lean development.

[0089] The planning department can estimate the emotions of the planner and determine the priorities of the development plan based on the estimated emotions. The planning department can estimate emotions using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) or multimodal generation AI. The planning department can estimate the emotions of the planner and determine the priorities of the development plan based on the estimated emotions. For example, if the planner is excited, the planning department will prioritize planning important development items. For example, if the planner is calm, the planning department will plan detailed development items sequentially. For example, if the planner is stressed, the planning department will prioritize planning concise and actionable development items. In this way, the planning department can prioritize important development items by determining the priorities of the development plan according to the emotions of the planner. Specific methods and criteria for estimating emotions include, for example, facial recognition, speech analysis, and text analysis. Specific criteria and methods for determining the priorities of a development plan include, for example, prioritizing the delivery of high-priority development items and prioritizing the delivery of urgent development items.

[0090] The planning department can create plans while considering the geographical distribution of the development targets. For example, the planning department can create plans while considering the geographical distribution of the development targets. For example, the planning department can use AI to create plans while considering the geographical distribution of the development targets. For example, the planning department can create development plans for each region while considering geographically dispersed development targets. For example, the planning department can apply the optimal planning method according to the category of the development target based on the geographical distribution. In this way, the planning department can create region-specific development plans by creating plans while considering the geographical distribution of the development targets. Specific methods for obtaining and using geographical distribution include, for example, collecting data by region and using geographic information systems.

[0091] The planning department can improve the accuracy of its plans by referring to relevant literature during the planning stage. For example, the planning department can refer to relevant literature during the planning stage. For example, the planning department can use AI to refer to relevant literature. For example, the planning department can improve the accuracy of its plans based on relevant literature. For example, the planning department can improve the reliability of its plans by referring to relevant academic papers and industry reports. For example, the planning department can improve the accuracy of its planning methodologies by referring to past development plans and case studies. For example, the planning department can apply the most appropriate planning methodology based on relevant literature, according to the category of the development target. In this way, the planning department improves the accuracy of its plans by referring to relevant literature. Specific methods of referring to and using relevant literature include, for example, referring to academic papers and using industry reports.

[0092] The analysis unit can estimate the consumer's emotions and adjust the feedback analysis method based on the estimated emotions. For example, the analysis unit estimates the consumer's emotions. The analysis unit can estimate emotions using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) or multimodal generation AI. The analysis unit estimates the consumer's emotions and adjusts the feedback analysis method based on the estimated emotions. For example, if the consumer is excited, the analysis unit will emphasize and analyze positive feedback. For example, if the consumer is calm, the analysis unit will analyze detailed feedback. For example, if the consumer is dissatisfied, the analysis unit will focus on analyzing negative feedback. This allows the analysis unit to perform more effective feedback analysis by adjusting the feedback analysis method according to the consumer's emotions. Specific methods and criteria for estimating emotions include, for example, facial recognition, voice analysis, and text analysis.

[0093] The analysis unit can optimize its analysis algorithm by referring to past feedback data during analysis. For example, the analysis unit can refer to past feedback data during analysis. For example, the analysis unit can refer to past feedback data using AI. For example, the analysis unit optimizes its analysis algorithm based on past feedback data. For example, the analysis unit selects the most effective analysis method based on past feedback data. For example, the analysis unit analyzes trends related to specific products or services from past feedback data and optimizes its analysis algorithm. For example, the analysis unit applies the most appropriate analysis method according to the category of feedback target by referring to past feedback data. In this way, the analysis unit optimizes its analysis algorithm by referring to past feedback data and improves the accuracy of feedback analysis. Specific methods for obtaining and using past feedback data include, for example, obtaining it from a database and analyzing past feedback results.

[0094] The analysis unit can apply different analytical methods depending on the category of feedback being received during analysis. For example, the analysis unit can apply different analytical methods depending on the category of feedback being received during analysis. For example, the analysis unit can use AI to apply different analytical methods depending on the category of feedback being received. For example, in consumer market feedback, the analysis unit analyzes survey results and reviews. For example, in competitive analysis, the analysis unit collects and analyzes feedback on competitors' products and services. For example, in new market feedback, the analysis unit observes the opinions and purchasing behavior of local consumers. By doing so, the analysis unit improves the accuracy of feedback analysis by applying different analytical methods depending on the category of feedback being received. Specific classification methods and criteria for the categories of feedback being received include, for example, product categories, service categories, and consumer categories.

[0095] The analysis unit can estimate the consumer's emotions and prioritize feedback based on the estimated emotions. For example, the analysis unit estimates the consumer's emotions. The analysis unit can estimate emotions using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) or multimodal generation AI. The analysis unit estimates the consumer's emotions and prioritizes feedback based on the estimated emotions. For example, if the consumer is excited, the analysis unit prioritizes analyzing positive feedback. For example, if the consumer is calm, the analysis unit sequentially analyzes detailed feedback. For example, if the consumer is dissatisfied, the analysis unit prioritizes analyzing negative feedback. In this way, the analysis unit can prioritize important feedback by prioritizing feedback according to the consumer's emotions. Specific methods and criteria for estimating emotions include, for example, facial recognition, voice analysis, and text analysis.

[0096] The analysis unit can perform analysis while considering the geographical distribution of the feedback target. For example, the analysis unit can perform analysis while considering the geographical distribution of the feedback target. For example, the analysis unit can use AI to perform analysis while considering the geographical distribution of the feedback target. For example, the analysis unit can prioritize the analysis of feedback in a specific region and analyze consumer trends in that region. For example, the analysis unit can apply region-specific analysis methods while considering geographically dispersed feedback. For example, the analysis unit can select the optimal analysis method according to the category of the feedback target based on geographical distribution. As a result, the analysis unit can perform region-specific feedback analysis by considering the geographical distribution of the feedback target. Specific methods for obtaining and using geographical distribution include, for example, collecting data by region and using geographic information systems.

[0097] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can refer to relevant literature during the analysis. For example, the analysis unit can use AI to refer to relevant literature. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature. For example, the analysis unit can improve the reliability of its analysis results by referring to relevant academic papers and industry reports. For example, the analysis unit can improve the accuracy of its analysis methods by referring to past feedback results and case studies. For example, the analysis unit can apply the most appropriate analysis method based on the category of feedback subject, using relevant literature. In this way, the analysis unit improves the accuracy of its analysis by referring to relevant literature. Specific methods for referring to and using relevant literature include, for example, referring to academic papers and using industry reports.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The dialogue unit can estimate the emotions of the planner and adjust the pace and content of the dialogue based on those estimates. For example, if the planner is excited, the dialogue can be sped up to facilitate rapid idea generation. If the planner is calm, the dialogue can be slowed down to allow for detailed explanations and discussions. If the planner is stressed, the dialogue can be kept concise and topics that promote relaxation can be provided. In this way, the dialogue unit can enable more effective dialogue by adjusting the pace and content of the dialogue according to the planner's emotions.

[0100] The research department can estimate the emotions of the planner when visually displaying survey results and adjust the display method based on those emotions. For example, if the planner is excited, the survey results can be displayed in visually appealing graphs and charts. If the planner is calm, detailed data and analysis results can be displayed in text format. If the planner is stressed, the survey results can be displayed in a concise and to-the-point manner. In this way, the research department can provide more effective information by adjusting the display method of survey results according to the emotions of the planner.

[0101] The planning department can estimate the emotions of the planners and adjust the content of the development plan based on those estimates. For example, if the planner is excited, an aggressive development plan can be formulated. If the planner is calm, a detailed development plan can be formulated. If the planner is stressed, a concise and actionable development plan can be formulated. In this way, the planning department can formulate more effective development plans by adjusting the content of the development plan according to the emotions of the planners.

[0102] The analysis unit can estimate the consumer's emotions and adjust the feedback analysis method based on those estimated emotions. For example, if the consumer is excited, it can emphasize and analyze positive feedback. If the consumer is calm, it can analyze detailed feedback. If the consumer is dissatisfied, it can focus on analyzing negative feedback. In this way, the analysis unit can adjust the feedback analysis method according to the consumer's emotions, enabling more effective feedback analysis.

[0103] The dialogue unit can estimate the organizer's emotions and determine the priority of the dialogue based on those emotions. For example, if the organizer is excited, important ideas and concepts can be prioritized. If the organizer is calm, detailed discussions and explanations can be prioritized. If the organizer is stressed, relaxing topics can be prioritized. In this way, the dialogue unit can prioritize the delivery of important information by determining the priority of the dialogue according to the organizer's emotions.

[0104] The research department can optimize its research algorithms by referring to past research data during the research process. For example, it can select the most effective research method based on past research data. It can analyze trends related to specific markets and competitors from past research data and optimize the research algorithm. It can apply the most appropriate research method according to the category being researched by referring to past research data. In this way, the research department can optimize its research algorithms and improve the accuracy of the research by referring to past research data.

[0105] The planning department can optimize its planning algorithm by referring to past development plan data during the planning stage. For example, it can select the most effective planning method based on past development plan data. It can analyze trends related to a specific project from past development plan data and optimize the planning algorithm. It can apply the most suitable planning method according to the category of the development target by referring to past development plan data. In this way, the planning department can optimize its planning algorithm and improve the accuracy of development plans by referring to past development plan data.

[0106] The analysis unit can optimize its analysis algorithm by referring to past feedback data during analysis. For example, it can select the most effective analysis method based on past feedback data. It can analyze trends related to specific products or services from past feedback data and optimize the analysis algorithm. It can apply the most appropriate analysis method according to the category of feedback target by referring to past feedback data. In this way, the analysis unit can optimize its analysis algorithm by referring to past feedback data and improve the accuracy of feedback analysis.

[0107] The dialogue department can analyze the planner's past dialogue history and select the most suitable dialogue method. For example, it can conduct the dialogue in a similar style based on the planner's preferred dialogue style in the past. It can avoid topics the planner has avoided in the past and prioritize topics they have preferred. It can select the most effective dialogue method from the planner's past dialogue history and proceed with the dialogue. In this way, the dialogue department can select the most suitable dialogue method and conduct an effective dialogue by analyzing the planner's past dialogue history.

[0108] The research department can conduct research while considering the geographical distribution of the research subjects. For example, it can conduct market research in a specific region and analyze consumer trends in that region. It can apply region-specific research methods, taking into account the geographically dispersed research subjects. Based on the geographical distribution, it can select the most appropriate research method according to the category of research subjects. As a result, the research department can provide region-specific information by conducting research while considering the geographical distribution of the research subjects.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The dialogue department develops new business and product plans while engaging in dialogue with the planner. For example, the dialogue department analyzes market needs, generates ideas, creates concepts, and develops plans while engaging in dialogue with the planner. The dialogue department can use AI to analyze market needs based on the planner's ideas, support idea generation, and create concepts and plans. Step 2: The research department conducts market research and competitive analysis based on the plan formulated by the dialogue department. For example, the research department can automatically conduct market research and provide feedback to the planner. The research department can use AI to conduct market research and competitive analysis and provide feedback on the results to the planner. Step 3: The Planning Department formulates a prototype development plan based on the results obtained by the Research Department. The Planning Department, for example, formulates a prototype development plan and provides prototype products or services to consumers. The Planning Department can use AI to formulate prototype development plans, provide prototype products or services to consumers, and manage the progress of the prototype development plan. Step 4: The analysis unit analyzes consumer feedback in real time. The analysis unit analyzes consumer feedback obtained, for example, through cameras and microphones, in real time. The analysis unit uses generative AI to analyze consumer feedback in real time, and based on that feedback, it can run the PDCA cycle and improve the product.

[0111] 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the dialogue unit, research unit, planning unit, and analysis unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the smart device 14 and plans new businesses and new products while interacting with the planner. The research unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and conducts market research and competitive analysis and provides feedback to the planner. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and plans prototype development plans and provides prototype products and services to consumers. The analysis unit acquires feedback from consumers using, for example, the camera 42 and microphone 38B of the smart device 14 and analyzes it in real time by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] 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.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] 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.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the dialogue unit, research unit, planning unit, and analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the smart glasses 214 and plans new businesses and new products while interacting with the planner. The research unit is implemented by the specific processing unit 290 of the data processing unit 12 and conducts market research and competitive analysis and provides feedback to the planner. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and plans prototype development plans and provides prototype products and services to consumers. The analysis unit acquires feedback from consumers using the camera 42 and microphone 238 of the smart glasses 214 and analyzes it in real time by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] 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.

[0138] 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.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the dialogue unit, research unit, planning unit, and analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the headset terminal 314 and plans new businesses and new products while interacting with the planner. The research unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and conducts market research and competitive analysis and provides feedback to the planner. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and plans prototype development plans and provides prototype products and services to consumers. The analysis unit acquires feedback from consumers using, for example, the camera 42 and microphone 238 of the headset terminal 314 and analyzes it in real time by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] 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.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] 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.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the dialogue unit, research unit, planning unit, and analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the robot 414 and plans new businesses and new products while interacting with the planner. The research unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and conducts market research and competitive analysis and provides feedback to the planner. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and plans prototype development plans and provides prototype products and services to consumers. The analysis unit acquires feedback from consumers using, for example, the camera 42 and microphone 238 of the robot 414 and analyzes it in real time by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0164] 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.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] 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.

[0167] 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.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] 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."

[0170] 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.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] 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.

[0182] (Note 1) The Dialogue Department, which plans new businesses and new products while engaging in dialogue with planners, Based on the plans formulated by the aforementioned dialogue department, the research department conducts market research and competitive analysis, Based on the results obtained by the aforementioned research department, the planning department formulates a prototype development plan. It includes an analysis unit that analyzes consumer feedback in real time. A system characterized by the following features. (Note 2) The aforementioned dialogue unit, We analyze market needs in dialogue with the planners, generate ideas, create concepts, and develop plans. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned investigation department, It automatically performs market research and competitor analysis and provides feedback to planners. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned planning department, We develop prototype development plans and provide prototype products and services to consumers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We analyze consumer feedback obtained through cameras and microphones in real time and run the PDCA cycle. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We use generative AI to analyze market feedback in real time and improve our products. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned dialogue unit, The system estimates the emotions of the planner and adjusts the pace and content of the dialogue based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned dialogue unit, Analyze the planner's past dialogue history and select the most suitable dialogue method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned dialogue unit, During the conversation, the content is filtered based on the organizer's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned dialogue unit, Estimate the emotions of the planner and determine the priority of the dialogue based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned dialogue unit, During the conversation, the organizer's geographical location will be taken into consideration to prioritize providing highly relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned dialogue unit, During the dialogue, we will analyze the organizer's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned investigation department, We estimate the emotions of the planners and adjust the way the survey results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned investigation department, During the survey, we optimize the survey algorithm by referring to past survey data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned investigation department, During the survey, different research methods will be applied depending on the category of the subject being surveyed. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned investigation department, The project planner's emotions are estimated, and the priority of the survey results is determined based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned investigation department, When conducting a survey, the geographical distribution of the subjects of the survey should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned investigation department, During the research, refer to relevant literature to improve the accuracy of the research. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned planning department, Estimate the emotions of the planners and adjust the content of the development plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned planning department, During the planning stage, the planning algorithm is optimized by referring to past development plan data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planning department, When planning, different planning methods are applied depending on the category of the development target. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planning department, Estimate the emotions of the planners and determine the priorities of the development plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planning department, When planning, the geographical distribution of the development targets should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned planning department, When planning, refer to relevant literature to improve the accuracy of the plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, We estimate consumer sentiment and adjust the feedback analysis method based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the category of feedback being received. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, It estimates consumer sentiment and prioritizes feedback based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, During the analysis, the geographical distribution of the feedback target will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Dialogue Department, which plans new businesses and new products while engaging in dialogue with planners, Based on the plans formulated by the aforementioned dialogue department, the research department conducts market research and competitive analysis, Based on the results obtained by the aforementioned research department, the planning department formulates a prototype development plan. It includes an analysis unit that analyzes consumer feedback in real time. A system characterized by the following features.

2. The aforementioned dialogue unit, We analyze market needs in dialogue with the planners, generate ideas, create concepts, and develop plans. The system according to feature 1.

3. The aforementioned investigation department, It automatically performs market research and competitor analysis and provides feedback to planners. The system according to feature 1.

4. The aforementioned planning department, We develop prototype development plans and provide prototype products and services to consumers. The system according to feature 1.

5. The aforementioned analysis unit, We analyze consumer feedback obtained through cameras and microphones in real time and run the PDCA cycle. The system according to feature 1.

6. The aforementioned analysis unit, We use generative AI to analyze market feedback in real time and improve our products. The system according to feature 1.

7. The aforementioned dialogue unit, The system estimates the emotions of the planner and adjusts the pace and content of the dialogue based on those estimated emotions. The system according to feature 1.

8. The aforementioned dialogue unit, Analyze the planner's past dialogue history and select the most suitable dialogue method. The system according to feature 1.

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