system

Abstract

The system according to this embodiment aims to enable users to select an appropriate business framework and utilize it effectively. [Solution] The system according to the embodiment comprises an analysis unit, a selection unit, an analysis unit, and a provision unit. The analysis unit analyzes keywords entered by the user. The selection unit selects an appropriate business framework based on the keywords analyzed by the analysis unit. The analysis unit performs analysis based on the business framework selected by the selection unit. The provision unit provides the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to select and effectively utilize an appropriate business framework.

[0005] The system according to the embodiment aims to enable a user to select and effectively utilize an appropriate business framework.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a selection unit, an analysis unit, and a provision unit. The analysis unit analyzes keywords entered by the user. The selection unit selects an appropriate business framework based on the keywords analyzed by the analysis unit. The analysis unit performs analysis based on the business framework selected by the selection unit. The provision unit provides the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to select an appropriate business framework and utilize it effectively. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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 2, RAM 30, and 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 Business Framework Thinking Support AI Agent according to an embodiment of the present invention is an AI agent utilization tool that supports business people in making decisions using business frameworks. This Business Framework Thinking Support AI Agent analyzes keywords entered by the user, selects an appropriate business framework, and provides analysis results based on that framework. For example, if the user enters the keyword "new market entry," the Business Framework Thinking Support AI Agent selects PEST analysis and provides analysis results for each element of political, economic, social, and technological. The Business Framework Thinking Support AI Agent can also use SWOT analysis to provide analysis results for each element of strengths, weaknesses, opportunities, and threats. Furthermore, the Business Framework Thinking Support AI Agent can also use Five Forces analysis to provide analysis results of the competitive environment. This allows users to make decisions from multiple perspectives and increase the probability of business success. In this way, the Business Framework Thinking Support AI Agent can support decision-making by selecting an appropriate business framework based on keywords entered by the user and providing analysis results.

[0029] The business framework thinking support AI agent according to this embodiment comprises an analysis unit, a selection unit, an analysis unit, and a provision unit. The analysis unit analyzes keywords entered by the user. The analysis unit analyzes the meaning of the keywords using, for example, natural language processing technology. The analysis unit can also refer to relevant background information to understand the context of the keywords. For example, the analysis unit collects news articles and academic papers from the internet and analyzes the meaning of the keywords. The selection unit selects an appropriate business framework based on the keywords analyzed by the analysis unit. The selection unit selects a business framework based on, for example, industry standards or user needs. The selection unit can also optimize the selection algorithm by referring to past selection history. For example, the selection unit proposes an optimal selection algorithm based on business frameworks previously selected by the user. The analysis unit performs analysis based on the business framework selected by the selection unit. The analysis unit performs analysis using, for example, business frameworks such as PEST analysis, SWOT analysis, and Five Forces analysis. The analysis unit can also apply different analysis methods depending on the user's industry and job type. For example, the analysis unit considers the characteristics of the industry to which the user belongs and applies industry-specific analysis methods. The delivery unit provides the analysis results obtained by the analysis unit. The delivery unit displays the analysis results, for example, through a web application or a mobile application. The delivery unit can also estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions of the user. For example, if the delivery unit is feeling stressed, it will select a simpler presentation and avoid complex procedures. In this way, the business framework thinking support AI agent according to the embodiment can support decision-making by selecting an appropriate business framework based on keywords entered by the user and providing analysis results.

[0030] The analysis unit analyzes keywords entered by the user. For example, the analysis unit uses natural language processing (NLP) techniques to analyze the meaning of keywords. Specifically, it combines and uses techniques such as morphological analysis, syntactic analysis, and semantic analysis. Morphological analysis divides keywords into individual words and identifies the part of speech of each word. Syntactic analysis analyzes the relationships between words and understands the structure of sentences. Semantic analysis understands the meaning of words and sentences and provides appropriate interpretations based on the context. The analysis unit can also refer to relevant background information to understand the context of the keywords. For example, it collects news articles and academic papers from the internet and analyzes the meaning of keywords. This allows the analysis unit to accurately understand the meaning of keywords and perform appropriate analysis based on the context. Furthermore, the analysis unit can refer to the user's past input and search history to grasp the user's intent and interests. This allows the analysis unit to provide appropriate analysis results that meet the user's needs.

[0031] The selection unit selects an appropriate business framework based on keywords analyzed by the analysis unit. For example, the selection unit selects a business framework based on industry standards or user needs. Specifically, the selection unit refers to a database of business frameworks that differ by industry and selects a framework suitable for the user's industry. Furthermore, the selection unit can propose a combination of multiple business frameworks depending on the user's needs and objectives. For example, if the user is considering launching a new business, the selection unit can propose a combination of SWOT analysis and a business model canvas. In addition, the selection unit can optimize its selection algorithm by referring to past selection history. For example, the selection unit can propose an optimal selection algorithm based on business frameworks previously selected by the user. This allows the selection unit to quickly select an appropriate business framework that meets the user's needs.

[0032] The analysis department conducts analysis based on business frameworks selected by the selection department. For example, the analysis department uses business frameworks such as PEST analysis, SWOT analysis, and Five Forces analysis. Specifically, PEST analysis analyzes the external environment from four perspectives: political, economic, social, and technological factors. SWOT analysis analyzes the internal and external environments from four perspectives: strengths, weaknesses, opportunities, and threats. Five Forces analysis analyzes the five forces of competition (threat of new entrants, threat of substitutes, bargaining power of buyers, bargaining power of suppliers, and competition within the industry). Furthermore, the analysis department can apply different analytical methods depending on the user's industry and job type. For example, the analysis department considers the characteristics of the industry to which the user belongs and applies industry-specific analytical methods. This allows the analysis department to provide appropriate analytical results that meet the user's needs. In addition, the analysis department can use AI to rapidly process large amounts of data and provide highly accurate analytical results. For example, AI can learn from past data and trends to make future predictions. This allows the analysis department to provide useful information to support user decision-making.

[0033] The service provider provides the analysis results obtained by the analysis provider. The service provider displays the analysis results, for example, through web applications or mobile applications. Specifically, the service provider provides a user-friendly interface and displays the analysis results in a visually easy-to-understand manner. For example, it visualizes data using graphs and charts so that users can intuitively understand it. The service provider can also estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the service provider is feeling stressed, it will choose a simpler presentation and avoid complex procedures. This allows the service provider to provide appropriate information according to the user's situation. Furthermore, the service provider can collect feedback from users and continuously improve the quality of the information it provides. For example, it can analyze how users reacted to the information provided and reflect this in future information provision. This allows the service provider to provide high-quality information that meets the user's needs.

[0034] The analysis unit can analyze the user's past input history and select the optimal analysis algorithm. For example, the analysis unit can select the optimal analysis algorithm based on keywords that the user has frequently used in the past. The analysis unit can also extract specific patterns from the user's past input history and optimize the analysis algorithm based on them. Furthermore, the analysis unit can analyze the user's past input history and propose the most effective analysis algorithm. In this way, the optimal analysis algorithm can be selected by analyzing the user's past input history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past input history data into a generating AI and have the generating AI perform the selection of the optimal analysis algorithm.

[0035] The analysis unit can improve the accuracy of keyword analysis based on the user's industry and occupation. For example, the analysis unit considers the characteristics of the industry to which the user belongs and applies an industry-specific analysis algorithm. The analysis unit can also select an occupation-specific analysis method according to the user's occupation. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to data related to the user's industry and occupation. By improving the accuracy of the analysis based on the user's industry and occupation, it can provide more accurate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the user's industry and occupation into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0036] The analysis unit can prioritize providing highly relevant analysis results by considering the user's geographical location information when analyzing keywords. For example, the analysis unit can prioritize analyzing region-specific business information based on the user's current location. The analysis unit can also provide analysis results that reflect regional market trends by considering the user's geographical location information. Furthermore, the analysis unit can provide analysis results that consider the regional competitive situation based on the user's location information. In this way, by considering the user's geographical location information, highly relevant analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant analysis results.

[0037] The analysis unit can analyze a user's social media activity during keyword analysis and provide relevant analysis results. For example, the analysis unit can analyze a user's social media posts and propose relevant business frameworks. The analysis unit can also extract topics of interest from the user's social media activity and provide analysis results based on those topics. Furthermore, the analysis unit can analyze a user's social media network and provide relevant business information. Thus, by analyzing a user's social media activity, relevant analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user social media activity data into a generating AI and have the generating AI perform the provision of relevant analysis results.

[0038] The selection unit can optimize its selection algorithm by referring to past selection history during the selection process. For example, the selection unit can propose the optimal selection algorithm based on business frameworks previously selected by the user. Furthermore, the selection unit can extract specific patterns from past selection history and optimize the selection algorithm based on those patterns. In addition, the selection unit can analyze the user's past selection history and propose the most effective selection algorithm. This allows for the optimization of the selection algorithm by referring to past selection history. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input past selection history data into a generating AI and have the generating AI perform the optimization of the selection algorithm.

[0039] The selection unit can apply different selection algorithms depending on the user's industry and job type during the selection process. For example, the selection unit can consider the characteristics of the industry to which the user belongs and apply an industry-specific selection algorithm. Furthermore, the selection unit can select a job-specific selection method depending on the user's job type. In addition, the selection unit can optimize the selection algorithm by referring to data related to the user's industry and job type. This allows for the selection of a more appropriate framework by applying the selection algorithm according to the user's industry and job type. Some or all of the above processes in the selection unit may be performed using AI, or not. For example, the selection unit can input data related to the user's industry and job type into a generating AI and have the generating AI execute the application of the selection algorithm.

[0040] The selection unit can select the optimal business framework by considering the user's geographical location information during the selection process. For example, the selection unit can select a region-specific business framework based on the user's current location. Furthermore, the selection unit can also select a business framework that reflects regional market trends by considering the user's geographical location information. In addition, the selection unit can select a business framework that considers the regional competitive landscape based on the user's location information. This allows for the selection of the optimal business framework by considering the user's geographical location information. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of the optimal business framework.

[0041] The selection unit can analyze the user's social media activity and select relevant business frameworks during the selection process. For example, the selection unit can analyze the user's social media posts and propose relevant business frameworks. It can also extract topics of interest from the user's social media activity and select business frameworks based on those topics. Furthermore, the selection unit can analyze the user's social media network and select relevant business frameworks. Thus, relevant business frameworks can be selected by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the selection of relevant business frameworks.

[0042] The analysis unit can optimize its analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can propose the optimal analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can extract specific patterns from past analysis data and optimize the analysis algorithm based on those patterns. In addition, the analysis unit can analyze the user's past analysis data and propose the most effective analysis algorithm. This allows for the optimization of the analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0043] The analysis unit can apply different analytical methods depending on the user's industry and occupation during analysis. For example, the analysis unit can consider the characteristics of the industry to which the user belongs and apply industry-specific analytical methods. Furthermore, the analysis unit can select occupation-specific analytical methods depending on the user's occupation. In addition, the analysis unit can optimize analytical methods by referring to data related to the user's industry and occupation. This allows for the provision of more appropriate analytical results by applying analytical methods according to the user's industry and occupation. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data related to the user's industry and occupation into a generating AI and have the generating AI execute the application of analytical methods.

[0044] The analysis unit can provide optimal analysis results by considering the user's geographical location information during analysis. For example, the analysis unit can prioritize analyzing region-specific business information based on the user's current location. Furthermore, the analysis unit can also provide analysis results that reflect regional market trends, taking into account the user's geographical location information. In addition, the analysis unit can provide analysis results that consider the regional competitive landscape based on the user's location information. This allows for the provision of optimal analysis results by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal analysis results.

[0045] The analysis unit can analyze a user's social media activity and provide relevant analysis results during the analysis process. For example, the analysis unit can analyze a user's social media posts and propose relevant business frameworks. It can also extract topics of interest from the user's social media activity and provide analysis results based on those topics. Furthermore, the analysis unit can analyze a user's social media network and provide relevant business information. Thus, by analyzing a user's social media activity, relevant analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user social media activity data into a generating AI and have the generating AI perform the task of providing relevant analysis results.

[0046] The service provider can optimize its service algorithm by referring to past service history at the time of service provision. For example, the service provider can propose the optimal service algorithm based on analysis results previously provided to the user. The service provider can also extract specific patterns from past service history and optimize the service algorithm based on those patterns. Furthermore, the service provider can analyze the user's past service history and propose the most effective service algorithm. In this way, the service algorithm can be optimized by referring to past service history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past service history data into a generating AI and have the generating AI perform the optimization of the service algorithm.

[0047] The delivery unit can apply different delivery methods depending on the user's industry and occupation at the time of delivery. For example, the delivery unit can consider the characteristics of the industry to which the user belongs and apply an industry-specific delivery method. Furthermore, the delivery unit can select an occupation-specific delivery method depending on the user's occupation. In addition, the delivery unit can optimize the delivery method by referring to data related to the user's industry and occupation. This allows for the provision of more appropriate delivery results by applying a delivery method according to the user's industry and occupation. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input data related to the user's industry and occupation into a generating AI and have the generating AI execute the application of the delivery method.

[0048] The service provider can provide optimal service results by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing region-specific business information based on the user's current location. The service provider can also provide service results that reflect regional market trends by considering the user's geographical location information. Furthermore, the service provider can provide service results that take into account the regional competitive situation based on the user's location information. In this way, optimal service results can be provided by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI execute the provision of optimal service results.

[0049] The service provider can analyze the user's social media activity and provide relevant results at the time of delivery. For example, the service provider can analyze the user's social media posts and propose relevant business frameworks. It can also extract topics of interest from the user's social media activity and provide results based on those topics. Furthermore, the service provider can analyze the user's social media network and provide relevant business information. Thus, by analyzing the user's social media activity, relevant results can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant results.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The analytics unit can perform real-time analysis of user-input keywords, reflecting market trends. For example, it can collect the latest news articles and social media trends and incorporate them into keyword analysis. It can also refer to the latest reports on specific industries of interest to the user and reflect them in the analysis results. Furthermore, the analytics unit can prioritize analysis of highly relevant information based on the user's past search history. This ensures that users always receive analysis results based on the latest information, enabling them to make more accurate decisions.

[0052] The selection unit can analyze a user's past selection history and propose the optimal business framework. For example, it can extract patterns of business frameworks previously selected by the user and propose the most suitable framework based on that. It can also refer to the user's selection history according to their industry and job type and propose industry-specific frameworks. Furthermore, based on the user's selection history, the selection unit can optimize its selection algorithm and propose a more effective framework. This allows users to utilize the optimal business framework based on their past selection history, improving the accuracy of their decision-making.

[0053] The analysis department can apply different analytical methods depending on the user's industry and job type. For example, it can consider the characteristics of the industry to which the user belongs and apply industry-specific analytical methods. It can also select job-specific analytical methods depending on the user's job type. Furthermore, the analysis department can optimize analytical methods by referring to data related to the user's industry and job type. This allows for the application of the most appropriate analytical method for the user's industry and job type, providing more accurate analytical results.

[0054] The service provider can deliver optimal results by considering the user's geographical location. For example, it can prioritize providing region-specific business information based on the user's current location. It can also provide results that reflect regional market trends, taking the user's geographical location into account. Furthermore, it can provide results that consider the regional competitive landscape based on the user's location. This enables the provision of optimal results that take the user's geographical location into account, thereby supporting the user's decision-making.

[0055] The service provider can analyze users' social media activity and provide relevant results. For example, it can analyze users' social media posts and propose relevant business frameworks. It can also extract topics of interest from users' social media activity and provide results based on those topics. Furthermore, it can analyze users' social media networks and provide relevant business information. This allows the service provider to deliver optimal results based on the analysis of users' social media activity, thereby supporting users' decision-making.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The analysis unit analyzes the keywords entered by the user. The analysis unit analyzes the meaning of the keywords, for example, using natural language processing technology. The analysis unit can also refer to relevant background information to understand the context of the keywords. For example, the analysis unit collects news articles and academic papers from the internet and analyzes the meaning of the keywords. Step 2: The selection unit selects an appropriate business framework based on the keywords analyzed by the analysis unit. The selection unit selects a business framework based, for example, industry standards or user needs. The selection unit can also optimize its selection algorithm by referring to past selection history. For example, the selection unit proposes an optimal selection algorithm based on business frameworks previously selected by the user. Step 3: The analysis department conducts analysis based on the business framework selected by the selection department. The analysis department uses business frameworks such as PEST analysis, SWOT analysis, and Five Forces analysis. The analysis department can also apply different analytical methods depending on the user's industry and job type. For example, the analysis department considers the characteristics of the industry to which the user belongs and applies industry-specific analytical methods. Step 4: The service provider provides the analysis results obtained by the analysis provider. The service provider displays the analysis results, for example, through a web application or mobile application. The service provider can also estimate the user's emotions and adjust how the analysis results are presented based on the estimated emotions of the user. For example, if the service provider is feeling stressed, it will choose a simpler presentation and avoid complex procedures.

[0058] (Example of form 2) The Business Framework Thinking Support AI Agent according to an embodiment of the present invention is an AI agent utilization tool that supports business people in making decisions using business frameworks. This Business Framework Thinking Support AI Agent analyzes keywords entered by the user, selects an appropriate business framework, and provides analysis results based on that framework. For example, if the user enters the keyword "new market entry," the Business Framework Thinking Support AI Agent selects PEST analysis and provides analysis results for each element of political, economic, social, and technological. The Business Framework Thinking Support AI Agent can also use SWOT analysis to provide analysis results for each element of strengths, weaknesses, opportunities, and threats. Furthermore, the Business Framework Thinking Support AI Agent can also use Five Forces analysis to provide analysis results of the competitive environment. This allows users to make decisions from multiple perspectives and increase the probability of business success. In this way, the Business Framework Thinking Support AI Agent can support decision-making by selecting an appropriate business framework based on keywords entered by the user and providing analysis results.

[0059] The business framework thinking support AI agent according to this embodiment comprises an analysis unit, a selection unit, an analysis unit, and a provision unit. The analysis unit analyzes keywords entered by the user. The analysis unit analyzes the meaning of the keywords using, for example, natural language processing technology. The analysis unit can also refer to relevant background information to understand the context of the keywords. For example, the analysis unit collects news articles and academic papers from the internet and analyzes the meaning of the keywords. The selection unit selects an appropriate business framework based on the keywords analyzed by the analysis unit. The selection unit selects a business framework based on, for example, industry standards or user needs. The selection unit can also optimize the selection algorithm by referring to past selection history. For example, the selection unit proposes an optimal selection algorithm based on business frameworks previously selected by the user. The analysis unit performs analysis based on the business framework selected by the selection unit. The analysis unit performs analysis using, for example, business frameworks such as PEST analysis, SWOT analysis, and Five Forces analysis. The analysis unit can also apply different analysis methods depending on the user's industry and job type. For example, the analysis unit considers the characteristics of the industry to which the user belongs and applies industry-specific analysis methods. The delivery unit provides the analysis results obtained by the analysis unit. The delivery unit displays the analysis results, for example, through a web application or a mobile application. The delivery unit can also estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions of the user. For example, if the delivery unit is feeling stressed, it will select a simpler presentation and avoid complex procedures. In this way, the business framework thinking support AI agent according to the embodiment can support decision-making by selecting an appropriate business framework based on keywords entered by the user and providing analysis results.

[0060] The analysis unit analyzes keywords entered by the user. For example, the analysis unit uses natural language processing (NLP) techniques to analyze the meaning of keywords. Specifically, it combines and uses techniques such as morphological analysis, syntactic analysis, and semantic analysis. Morphological analysis divides keywords into individual words and identifies the part of speech of each word. Syntactic analysis analyzes the relationships between words and understands the structure of sentences. Semantic analysis understands the meaning of words and sentences and provides appropriate interpretations based on the context. The analysis unit can also refer to relevant background information to understand the context of the keywords. For example, it collects news articles and academic papers from the internet and analyzes the meaning of keywords. This allows the analysis unit to accurately understand the meaning of keywords and perform appropriate analysis based on the context. Furthermore, the analysis unit can refer to the user's past input and search history to grasp the user's intent and interests. This allows the analysis unit to provide appropriate analysis results that meet the user's needs.

[0061] The selection unit selects an appropriate business framework based on keywords analyzed by the analysis unit. For example, the selection unit selects a business framework based on industry standards or user needs. Specifically, the selection unit refers to a database of business frameworks that differ by industry and selects a framework suitable for the user's industry. Furthermore, the selection unit can propose a combination of multiple business frameworks depending on the user's needs and objectives. For example, if the user is considering launching a new business, the selection unit can propose a combination of SWOT analysis and a business model canvas. In addition, the selection unit can optimize its selection algorithm by referring to past selection history. For example, the selection unit can propose an optimal selection algorithm based on business frameworks previously selected by the user. This allows the selection unit to quickly select an appropriate business framework that meets the user's needs.

[0062] The analysis department conducts analysis based on business frameworks selected by the selection department. For example, the analysis department uses business frameworks such as PEST analysis, SWOT analysis, and Five Forces analysis. Specifically, PEST analysis analyzes the external environment from four perspectives: political, economic, social, and technological factors. SWOT analysis analyzes the internal and external environments from four perspectives: strengths, weaknesses, opportunities, and threats. Five Forces analysis analyzes the five forces of competition (threat of new entrants, threat of substitutes, bargaining power of buyers, bargaining power of suppliers, and competition within the industry). Furthermore, the analysis department can apply different analytical methods depending on the user's industry and job type. For example, the analysis department considers the characteristics of the industry to which the user belongs and applies industry-specific analytical methods. This allows the analysis department to provide appropriate analytical results that meet the user's needs. In addition, the analysis department can use AI to rapidly process large amounts of data and provide highly accurate analytical results. For example, AI can learn from past data and trends to make future predictions. This allows the analysis department to provide useful information to support user decision-making.

[0063] The service provider provides the analysis results obtained by the analysis provider. The service provider displays the analysis results, for example, through web applications or mobile applications. Specifically, the service provider provides a user-friendly interface and displays the analysis results in a visually easy-to-understand manner. For example, it visualizes data using graphs and charts so that users can intuitively understand it. The service provider can also estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the service provider is feeling stressed, it will choose a simpler presentation and avoid complex procedures. This allows the service provider to provide appropriate information according to the user's situation. Furthermore, the service provider can collect feedback from users and continuously improve the quality of the information it provides. For example, it can analyze how users reacted to the information provided and reflect this in future information provision. This allows the service provider to provide high-quality information that meets the user's needs.

[0064] The analysis unit can estimate the user's emotions and adjust the keyword analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can select a simple analysis method and avoid complex procedures. If the user is relaxed, the analysis unit can select a detailed analysis method to provide deeper insights. Furthermore, if the user is in a hurry, the analysis unit can select a rapid analysis method to provide results in a short time. This allows for more appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The analysis unit can analyze the user's past input history and select the optimal analysis algorithm. For example, the analysis unit can select the optimal analysis algorithm based on keywords that the user has frequently used in the past. The analysis unit can also extract specific patterns from the user's past input history and optimize the analysis algorithm based on them. Furthermore, the analysis unit can analyze the user's past input history and propose the most effective analysis algorithm. In this way, the optimal analysis algorithm can be selected by analyzing the user's past input history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past input history data into a generating AI and have the generating AI perform the selection of the optimal analysis algorithm.

[0066] The analysis unit can improve the accuracy of keyword analysis based on the user's industry and occupation. For example, the analysis unit considers the characteristics of the industry to which the user belongs and applies an industry-specific analysis algorithm. The analysis unit can also select an occupation-specific analysis method according to the user's occupation. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to data related to the user's industry and occupation. By improving the accuracy of the analysis based on the user's industry and occupation, it can provide more accurate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the user's industry and occupation into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0067] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can prioritize providing reassuring analysis results. It can also prioritize providing stimulating analysis results if the user is excited. Furthermore, if the user is calm, the analysis unit can prioritize providing logical and detailed analysis results. This allows for the provision of more appropriate analysis results by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0068] The analysis unit can prioritize providing highly relevant analysis results by considering the user's geographical location information when analyzing keywords. For example, the analysis unit can prioritize analyzing region-specific business information based on the user's current location. The analysis unit can also provide analysis results that reflect regional market trends by considering the user's geographical location information. Furthermore, the analysis unit can provide analysis results that consider the regional competitive situation based on the user's location information. In this way, by considering the user's geographical location information, highly relevant analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant analysis results.

[0069] The analysis unit can analyze a user's social media activity during keyword analysis and provide relevant analysis results. For example, the analysis unit can analyze a user's social media posts and propose relevant business frameworks. The analysis unit can also extract topics of interest from the user's social media activity and provide analysis results based on those topics. Furthermore, the analysis unit can analyze a user's social media network and provide relevant business information. Thus, by analyzing a user's social media activity, relevant analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user social media activity data into a generating AI and have the generating AI perform the provision of relevant analysis results.

[0070] The selection unit can estimate the user's emotions and adjust the selection criteria for business frameworks based on the estimated user emotions. For example, if the user is stressed, the selection unit may select a simple business framework. If the user is relaxed, the selection unit may select a more detailed business framework. Furthermore, if the user is in a hurry, the selection unit may select a business framework that can deliver results quickly. This allows for the selection of a more appropriate framework by adjusting the selection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the selection unit may be performed using AI or not using AI. For example, the selection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The selection unit can optimize its selection algorithm by referring to past selection history during the selection process. For example, the selection unit can propose the optimal selection algorithm based on business frameworks previously selected by the user. Furthermore, the selection unit can extract specific patterns from past selection history and optimize the selection algorithm based on those patterns. In addition, the selection unit can analyze the user's past selection history and propose the most effective selection algorithm. This allows for the optimization of the selection algorithm by referring to past selection history. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input past selection history data into a generating AI and have the generating AI perform the optimization of the selection algorithm.

[0072] The selection unit can apply different selection algorithms depending on the user's industry and job type during the selection process. For example, the selection unit can consider the characteristics of the industry to which the user belongs and apply an industry-specific selection algorithm. Furthermore, the selection unit can select a job-specific selection method depending on the user's job type. In addition, the selection unit can optimize the selection algorithm by referring to data related to the user's industry and job type. This allows for the selection of a more appropriate framework by applying the selection algorithm according to the user's industry and job type. Some or all of the above processes in the selection unit may be performed using AI, or not. For example, the selection unit can input data related to the user's industry and job type into a generating AI and have the generating AI execute the application of the selection algorithm.

[0073] The selection unit can estimate the user's emotions and adjust the display method of the selection results based on the estimated user emotions. For example, if the user is feeling anxious, the selection unit can provide a display method that provides a sense of security. It can also provide a stimulating display method if the user is excited. Furthermore, if the user is calm, the selection unit can provide a logical and detailed display method. This allows for a more appropriate display method to be provided by adjusting the display method of the selection results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using AI, or not. For example, the selection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The selection unit can select the optimal business framework by considering the user's geographical location information during the selection process. For example, the selection unit can select a region-specific business framework based on the user's current location. Furthermore, the selection unit can also select a business framework that reflects regional market trends by considering the user's geographical location information. In addition, the selection unit can select a business framework that considers the regional competitive landscape based on the user's location information. This allows for the selection of the optimal business framework by considering the user's geographical location information. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of the optimal business framework.

[0075] The selection unit can analyze the user's social media activity and select relevant business frameworks during the selection process. For example, the selection unit can analyze the user's social media posts and propose relevant business frameworks. It can also extract topics of interest from the user's social media activity and select business frameworks based on those topics. Furthermore, the selection unit can analyze the user's social media network and select relevant business frameworks. Thus, relevant business frameworks can be selected by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the selection of relevant business frameworks.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can select a simple analysis method and avoid complex procedures. If the user is relaxed, the analysis unit can select a detailed analysis method to provide deeper insights. Furthermore, if the user is in a hurry, the analysis unit can select a rapid analysis method to provide results in a short time. This allows for more appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The analysis unit can optimize its analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can propose the optimal analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can extract specific patterns from past analysis data and optimize the analysis algorithm based on those patterns. In addition, the analysis unit can analyze the user's past analysis data and propose the most effective analysis algorithm. This allows for the optimization of the analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0078] The analysis unit can apply different analytical methods depending on the user's industry and occupation during analysis. For example, the analysis unit can consider the characteristics of the industry to which the user belongs and apply industry-specific analytical methods. Furthermore, the analysis unit can select occupation-specific analytical methods depending on the user's occupation. In addition, the analysis unit can optimize analytical methods by referring to data related to the user's industry and occupation. This allows for the provision of more appropriate analytical results by applying analytical methods according to the user's industry and occupation. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data related to the user's industry and occupation into a generating AI and have the generating AI execute the application of analytical methods.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a display method that provides reassurance. It can also provide a stimulating display method if the user is excited. Furthermore, if the user is calm, the analysis unit can provide a logical and detailed display method. This allows for a more appropriate display method by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can provide optimal analysis results by considering the user's geographical location information during analysis. For example, the analysis unit can prioritize analyzing region-specific business information based on the user's current location. Furthermore, the analysis unit can also provide analysis results that reflect regional market trends, taking into account the user's geographical location information. In addition, the analysis unit can provide analysis results that consider the regional competitive landscape based on the user's location information. This allows for the provision of optimal analysis results by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal analysis results.

[0081] The analysis unit can analyze a user's social media activity and provide relevant analysis results during the analysis process. For example, the analysis unit can analyze a user's social media posts and propose relevant business frameworks. It can also extract topics of interest from the user's social media activity and provide analysis results based on those topics. Furthermore, the analysis unit can analyze a user's social media network and provide relevant business information. Thus, by analyzing a user's social media activity, relevant analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user social media activity data into a generating AI and have the generating AI perform the task of providing relevant analysis results.

[0082] The service provider can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is stressed, the service provider can choose a simple presentation and avoid complex procedures. If the user is relaxed, the service provider can choose a detailed presentation to provide deeper insights. Furthermore, if the user is in a hurry, the service provider can choose a rapid presentation to deliver results quickly. This allows for the provision of more appropriate presentations by adjusting the presentation of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The service provider can optimize its service algorithm by referring to past service history at the time of service provision. For example, the service provider can propose the optimal service algorithm based on analysis results previously provided to the user. The service provider can also extract specific patterns from past service history and optimize the service algorithm based on those patterns. Furthermore, the service provider can analyze the user's past service history and propose the most effective service algorithm. In this way, the service algorithm can be optimized by referring to past service history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past service history data into a generating AI and have the generating AI perform the optimization of the service algorithm.

[0084] The delivery unit can apply different delivery methods depending on the user's industry and occupation at the time of delivery. For example, the delivery unit can consider the characteristics of the industry to which the user belongs and apply an industry-specific delivery method. Furthermore, the delivery unit can select an occupation-specific delivery method depending on the user's occupation. In addition, the delivery unit can optimize the delivery method by referring to data related to the user's industry and occupation. This allows for the provision of more appropriate delivery results by applying a delivery method according to the user's industry and occupation. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input data related to the user's industry and occupation into a generating AI and have the generating AI execute the application of the delivery method.

[0085] The service provider can estimate the user's emotions and adjust the display method of the results based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide a reassuring display method. It can also provide a stimulating display method if the user is excited. Furthermore, if the user is calm, the service provider can provide a logical and detailed display method. This allows for a more appropriate display method to be provided by adjusting the display method of the results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The service provider can provide optimal service results by considering the user's geographical location information at the time of delivery. For example, the service provider can prioritize providing region-specific business information based on the user's current location. The service provider can also provide service results that reflect regional market trends by considering the user's geographical location information. Furthermore, the service provider can provide service results that take into account the regional competitive situation based on the user's location information. In this way, optimal service results can be provided by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI execute the provision of optimal service results.

[0087] The service provider can analyze the user's social media activity and provide relevant results at the time of delivery. For example, the service provider can analyze the user's social media posts and propose relevant business frameworks. It can also extract topics of interest from the user's social media activity and provide results based on those topics. Furthermore, the service provider can analyze the user's social media network and provide relevant business information. Thus, by analyzing the user's social media activity, relevant results can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant results.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The analytics unit can perform real-time analysis of user-input keywords, reflecting market trends. For example, it can collect the latest news articles and social media trends and incorporate them into keyword analysis. It can also refer to the latest reports on specific industries of interest to the user and reflect them in the analysis results. Furthermore, the analytics unit can prioritize analysis of highly relevant information based on the user's past search history. This ensures that users always receive analysis results based on the latest information, enabling them to make more accurate decisions.

[0090] The analysis unit can estimate the user's emotions and adjust how the analysis results are presented based on those estimated emotions. For example, if the user is feeling anxious, the analysis unit can prioritize presenting positive analysis results that provide a sense of security. Similarly, if the user is excited, the analysis unit can emphasize and present stimulating analysis results. Furthermore, if the user is calm, the analysis unit can provide logical and detailed analysis results. This allows for the provision of optimal analysis results tailored to the user's emotions, more effectively supporting their decision-making.

[0091] The selection unit can analyze a user's past selection history and propose the optimal business framework. For example, it can extract patterns of business frameworks previously selected by the user and propose the most suitable framework based on that. It can also refer to the user's selection history according to their industry and job type and propose industry-specific frameworks. Furthermore, based on the user's selection history, the selection unit can optimize its selection algorithm and propose a more effective framework. This allows users to utilize the optimal business framework based on their past selection history, improving the accuracy of their decision-making.

[0092] The selection process can estimate the user's emotions and adjust the selection criteria for business frameworks based on those estimated emotions. For example, if the user is stressed, the selection process can select a simple and easy-to-understand business framework. If the user is relaxed, the selection process can select a business framework that provides detailed and in-depth insights. Furthermore, if the user is in a hurry, the selection process can select a business framework that delivers results quickly. This allows for the selection of the optimal business framework tailored to the user's emotions, thereby improving the efficiency of decision-making.

[0093] The analysis department can apply different analytical methods depending on the user's industry and job type. For example, it can consider the characteristics of the industry to which the user belongs and apply industry-specific analytical methods. It can also select job-specific analytical methods depending on the user's job type. Furthermore, the analysis department can optimize analytical methods by referring to data related to the user's industry and job type. This allows for the application of the most appropriate analytical method for the user's industry and job type, providing more accurate analytical results.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a display method that provides reassurance. If the user is excited, the analysis unit can provide a stimulating display method. Furthermore, if the user is calm, the analysis unit can provide a logical and detailed display method. This allows for the provision of the most appropriate display method according to the user's emotions, thereby deepening the user's understanding.

[0095] The service provider can deliver optimal results by considering the user's geographical location. For example, it can prioritize providing region-specific business information based on the user's current location. It can also provide results that reflect regional market trends, taking the user's geographical location into account. Furthermore, it can provide results that consider the regional competitive landscape based on the user's location. This enables the provision of optimal results that take the user's geographical location into account, thereby supporting the user's decision-making.

[0096] The service provider can estimate the user's emotions and adjust the presentation of the analysis results based on those estimated emotions. For example, if the user is stressed, the service provider can choose a simple presentation and avoid complex procedures. If the user is relaxed, the service provider can choose a detailed presentation to provide deeper insights. Furthermore, if the user is in a hurry, the service provider can choose a rapid presentation to deliver results quickly. This allows for the provision of the most appropriate presentation method tailored to the user's emotions, leading to a deeper understanding of the user.

[0097] The service provider can analyze users' social media activity and provide relevant results. For example, it can analyze users' social media posts and propose relevant business frameworks. It can also extract topics of interest from users' social media activity and provide results based on those topics. Furthermore, it can analyze users' social media networks and provide relevant business information. This allows the service provider to deliver optimal results based on the analysis of users' social media activity, thereby supporting users' decision-making.

[0098] The service provider can estimate the user's emotions and adjust the display method of the results based on those estimated emotions. For example, if the user is feeling anxious, the service provider can provide a reassuring display method. If the user is excited, the service provider can provide a stimulating display method. Furthermore, if the user is calm, the service provider can provide a logical and detailed display method. This allows for the provision of the most appropriate display method according to the user's emotions, thereby deepening the user's understanding.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The analysis unit analyzes the keywords entered by the user. The analysis unit analyzes the meaning of the keywords, for example, using natural language processing technology. The analysis unit can also refer to relevant background information to understand the context of the keywords. For example, the analysis unit collects news articles and academic papers from the internet and analyzes the meaning of the keywords. Step 2: The selection unit selects an appropriate business framework based on the keywords analyzed by the analysis unit. The selection unit selects a business framework based, for example, industry standards or user needs. The selection unit can also optimize its selection algorithm by referring to past selection history. For example, the selection unit proposes an optimal selection algorithm based on business frameworks previously selected by the user. Step 3: The analysis department conducts analysis based on the business framework selected by the selection department. The analysis department uses business frameworks such as PEST analysis, SWOT analysis, and Five Forces analysis. The analysis department can also apply different analytical methods depending on the user's industry and job type. For example, the analysis department considers the characteristics of the industry to which the user belongs and applies industry-specific analytical methods. Step 4: The service provider provides the analysis results obtained by the analysis provider. The service provider displays the analysis results, for example, through a web application or mobile application. The service provider can also estimate the user's emotions and adjust how the analysis results are presented based on the estimated emotions of the user. For example, if the service provider is feeling stressed, it will choose a simpler presentation and avoid complex procedures.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the analysis unit, selection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes keywords entered by the user. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects an appropriate business framework based on the analyzed keywords. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs analysis based on the selected business framework. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the analysis results to the user. 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.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the analysis unit, selection unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes keywords entered by the user. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects an appropriate business framework based on the analyzed keywords. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs analysis based on the selected business framework. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the analysis results to the user. 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.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the analysis unit, selection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes keywords entered by the user. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects an appropriate business framework based on the analyzed keywords. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs analysis based on the selected business framework. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the analysis results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the analysis unit, selection unit, analysis unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes keywords entered by the user. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects an appropriate business framework based on the analyzed keywords. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs analysis based on the selected business framework. The provision unit is implemented by the control unit 46A of the robot 414 and provides the analysis results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) An analysis unit that analyzes keywords entered by the user, A selection unit selects an appropriate business framework based on the keywords analyzed by the aforementioned analysis unit, An analysis unit that performs analysis based on the business framework selected by the aforementioned selection unit, The system comprises a providing unit that provides the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We estimate the user's emotions and adjust the keyword analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system analyzes the user's past input history and selects the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, When analyzing keywords, improve the accuracy of the analysis based on the user's industry and job title. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, When analyzing keywords, the system prioritizes providing highly relevant analysis results by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, When analyzing keywords, we analyze users' social media activity and provide relevant analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned selection unit is We estimate user sentiment and adjust the selection criteria for business frameworks based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned selection unit is During the selection process, the selection algorithm is optimized by referring to past selection history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned selection unit is During the selection process, different selection algorithms are applied depending on the user's industry and job type. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned selection unit is The system estimates the user's emotions and adjusts how the selection results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned selection unit is When selecting a business framework, the optimal framework will be chosen considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned selection unit is During the selection process, we analyze users' social media activity and select relevant business frameworks. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analytical methods are applied depending on the user's industry and job type. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, the system takes into account the user's geographical location to provide optimal analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During the analysis, we analyze users' social media activity and provide relevant analytical results. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We estimate the user's emotions and adjust how the analysis results are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing a service, the service algorithm is optimized by referring to past service history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, different delivery methods will be applied depending on the user's industry and job type. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we take the user's geographical location into consideration to provide the optimal delivery result. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, At the time of delivery, we analyze the user's social media activity and provide relevant delivery results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 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. An analysis unit that analyzes keywords entered by the user, A selection unit selects an appropriate business framework based on the keywords analyzed by the aforementioned analysis unit, An analysis unit that performs analysis based on the business framework selected by the aforementioned selection unit, The system comprises a providing unit that provides the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, We estimate the user's emotions and adjust the keyword analysis method based on the estimated user emotions. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes the user's past input history and selects the optimal analysis algorithm. The system according to feature 1.

4. The aforementioned analysis unit, When analyzing keywords, improve the accuracy of the analysis based on the user's industry and job title. The system according to feature 1.

5. The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system according to feature 1.

6. The aforementioned analysis unit, When analyzing keywords, the system prioritizes providing highly relevant analysis results by considering the user's geographical location. The system according to feature 1.

7. The aforementioned analysis unit, When analyzing keywords, we analyze users' social media activity and provide relevant analysis results. The system according to feature 1.

8. The aforementioned selection unit is We estimate user sentiment and adjust the selection criteria for business frameworks based on the estimated user sentiment. The system according to feature 1.

9. The aforementioned selection unit is During the selection process, the selection algorithm is optimized by referring to past selection history. The system according to feature 1.

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