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 a reception unit, a selection unit, an analysis unit, and a provision unit. The reception unit receives keywords entered by the user. The selection unit selects an appropriate business framework based on the keywords received by the reception unit. The analysis unit performs a detailed analysis based on the framework selected by the selection unit. The provision unit provides the user with 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 method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a business person 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 a reception unit, a selection unit, an analysis unit, and a provision unit. The reception unit receives keywords entered by the user. The selection unit selects an appropriate business framework based on the keywords received by the reception unit. The analysis unit performs a detailed analysis based on the framework selected by the selection unit. The provision unit provides the user with 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The business framework thinking support system according to an embodiment of the present invention is a system that supports business people in making decisions using business frameworks. This system supports thinking in accordance with frameworks such as PEST analysis, SWOT analysis, Five Forces analysis, Business Model Canvas, PDCA cycle, and OODA loop, based on keywords entered by the user. Specifically, it consists of the following steps. First, it receives keywords entered by the user. Next, it selects an appropriate business framework based on the received keywords. Based on the selected framework, it performs a specific analysis and provides the results to the user. For example, in the case of PEST analysis, it analyzes each element of political, economic, social, and technological, and in the case of SWOT analysis, it identifies strengths, weaknesses, opportunities, and threats. Furthermore, it proposes a specific action plan based on the results of applying each framework. This mechanism enables the user to make effective decisions. First, it receives keywords entered by the user. At this time, the user only needs to enter keywords related to business. For example, they enter keywords such as "market environment" or "competition". This information is input into the AI. Next, the AI ​​analyzes the entered keywords and selects an appropriate business framework. For example, if the keyword is "market environment," a PEST analysis is selected; if the keyword is "competition," a Five Forces analysis is selected. This selection is performed automatically by AI. Based on the selected framework, a specific analysis is performed. For example, a PEST analysis analyzes political, economic, social, and technological elements, while a SWOT analysis identifies strengths, weaknesses, opportunities, and threats. This analysis is performed by AI. The analysis results are provided to the user. For example, a function to visually display the analysis results or a function to propose specific action plans could be considered. This allows the user to intuitively understand the analysis results and make effective decisions. Furthermore, specific action plans are proposed based on the results of applying each framework. For example, based on the results of a SWOT analysis, strategies to leverage strengths and strategies to overcome weaknesses are proposed. These proposals are made by AI. This mechanism enables the user to make effective decisions.For example, it can be used in various business scenarios, such as formulating business strategies, conducting market analysis, and developing competitive strategies. This allows the business framework thinking support system to select appropriate business frameworks based on keywords entered by the user and provide concrete analysis results, thereby supporting effective decision-making.

[0029] The business framework thinking support system according to this embodiment comprises a reception unit, a selection unit, an analysis unit, and a provision unit. The reception unit receives keywords entered by the user. Keywords entered by the user include, for example, "market environment" and "competition," but are not limited to such examples. The reception unit receives the keywords entered by the user in text format, for example. The reception unit can also receive keywords using voice input. For example, it uses speech recognition technology to convert the keywords spoken by the user into text data. The selection unit selects an appropriate business framework based on the keywords received by the reception unit. For example, if the keyword is "market environment," the selection unit selects a PEST analysis. The selection unit can also select a Five Forces analysis if the keyword is "competition." For example, the selection unit selects the correspondence between keywords and business frameworks based on a database. The analysis unit performs a specific analysis based on the framework selected by the selection unit. For example, if it is a PEST analysis, the analysis unit analyzes each element: political, economic, social, and technological. Furthermore, the analysis unit can also identify strengths, weaknesses, opportunities, and threats in a SWOT analysis. For example, the analysis unit uses natural language processing technology to collect and analyze information related to keywords. The provision unit provides the user with the analysis results obtained by the analysis unit. The provision unit displays the analysis results visually, for example, using graphs and charts to make the results intuitively understandable. The provision unit can also propose concrete action plans. For example, based on the results of the SWOT analysis, it can propose strategies to leverage strengths and strategies to overcome weaknesses. In this way, the business framework thinking support system according to the embodiment can support effective decision-making by selecting an appropriate business framework based on keywords entered by the user and providing concrete analysis results.

[0030] The selection team can choose a PEST analysis if the keyword is "market environment," or a Five Forces analysis if the keyword is "competition." For example, if the keyword is "market environment," the selection team will choose a PEST analysis. PEST analysis is a framework that analyzes political, economic, social, and technological elements. For example, political elements include government policies, regulations, and laws. Economic elements include economic growth rate, inflation rate, and unemployment rate. Social elements include demographics, culture, and education levels. Technological elements include the speed of technological innovation and the adoption status of new technologies. The selection team can also choose a Five Forces analysis if the keyword is "competition." Five Forces analysis is a framework that analyzes competitive factors. For example, competitive factors include the strength of existing competitors, the threat of new entrants, the threat of substitutes, the bargaining power of suppliers, and the bargaining power of customers. By selecting the appropriate business framework according to the keyword, the selection team can improve the accuracy of the analysis.

[0031] The analysis department can analyze political, economic, social, and technological elements in a PEST analysis, and identify strengths, weaknesses, opportunities, and threats in a SWOT analysis. For example, when conducting a PEST analysis, the analysis department analyzes political, economic, social, and technological elements. For example, political elements include government policies, regulations, and laws. Economic elements include economic growth rates, inflation rates, and unemployment rates. Social elements include demographics, culture, and education levels. Technological elements include the speed of technological innovation and the adoption of new technologies. The analysis department can also identify strengths, weaknesses, opportunities, and threats when conducting a SWOT analysis. For example, strengths include identifying a company's resources, capabilities, and competitive advantages. Weaknesses include identifying a company's shortcomings, resource shortages, and competitive disadvantages. Opportunities include identifying market growth, technological innovation, and the development of new markets. Threats include identifying increased competition, regulatory changes, and economic uncertainty. This allows the analytics department to provide users with useful information by conducting detailed analyses based on each business framework.

[0032] The service provider can visually display the analysis results. For example, the service provider can visually display the analysis results. Methods of visual display include graphs, charts, and infographics. For example, the service provider can display the results of a PEST analysis as a bar graph or pie chart. The service provider can also display the results of a SWOT analysis in a matrix format. For example, it can display strengths, weaknesses, opportunities, and threats divided into four quadrants. Furthermore, the service provider can display the results of a Five Forces analysis as a radar chart. In this way, the service provider can make it easier for users to intuitively understand the analysis results by visually displaying them.

[0033] The service provider can propose concrete action plans. For example, the service provider can propose concrete action plans based on the results of a SWOT analysis. For instance, as a strategy to leverage strengths, they can propose measures to strengthen the company's competitive advantage. As a strategy to overcome weaknesses, they can propose measures to compensate for resource shortages. As a strategy to capitalize on opportunities, they can propose measures to develop new markets and promote technological innovation. As a strategy to address threats, they can propose measures to cope with intensifying competition. In this way, by proposing concrete action plans, the service provider can make it easier for users to make effective decisions.

[0034] The service provider can display analysis results in a way that is easy to understand intuitively. For example, the service provider can display analysis results in a way that is easy to understand intuitively. Intuitive display methods include the design of the user interface and the arrangement of information. For example, the service provider can display analysis results in a card format, summarizing the key points on each card. The service provider can also display analysis results in a timeline format, organizing information chronologically. Furthermore, the service provider can display analysis results in a dashboard format, allowing users to view multiple pieces of information on a single screen. As a result, the service provider enables users to make quick decisions through an intuitively understandable display method.

[0035] The reception desk can analyze a user's past keyword input history and select the optimal reception method. For example, the reception desk analyzes a user's past keyword input history, which includes input frequency and trends in input content. For instance, the reception desk can automatically display keywords that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest keywords that the user will use at specific times based on their past input history. In this way, by analyzing past input history, the reception desk can provide the user with the most suitable reception method and improve input efficiency.

[0036] The reception system can filter keywords based on the user's current projects and areas of interest. For example, the reception system can filter keywords based on the user's current projects and areas of interest. Current projects include the project's objectives, progress, and members involved. For example, the reception system will prioritize receiving keywords related to the user's current projects. The reception system can also filter and receive highly relevant keywords based on the user's areas of interest. Furthermore, the reception system can suggest relevant keywords based on areas the user has shown interest in in the past. This allows the reception system to prioritize receiving highly relevant keywords by filtering based on the user's current projects and areas of interest.

[0037] The reception system can prioritize receiving keywords that are highly relevant to the user's geographical location, taking this information into consideration when receiving keywords. For example, the reception system can consider the user's geographical location when receiving keywords. This geographical location information includes GPS data, IP addresses, and location services. For example, if the user is in a specific region, the reception system will prioritize receiving keywords related to that region. Furthermore, if the user is traveling, the reception system can prioritize receiving keywords related to their travel destination. Additionally, if the user is at home, the reception system can prioritize receiving keywords related to their home location. In this way, the reception system can prioritize receiving region-related keywords by considering geographical location information.

[0038] The reception desk can analyze a user's social media activity when receiving keywords and accept relevant keywords. For example, the reception desk analyzes a user's social media activity, which includes the content of posts, the number of likes, and the number of followers. For example, the reception desk prioritizes receiving keywords that the user frequently uses on social media. The reception desk can also accept keywords related to topics the user is interested in, based on their social media activity. Furthermore, the reception desk can accept keywords related to accounts the user follows on social media. In this way, by analyzing social media activity, the reception desk can prioritize receiving keywords based on the user's interests.

[0039] The selection unit can improve the accuracy of framework selection by considering the interrelationships of keywords during the selection process. For example, the selection unit analyzes the interrelationships of keywords. Keyword interrelationships include co-occurrence networks and relevance scores. For example, the selection unit analyzes the interrelationships between keywords and selects the framework with the highest relevance. The selection unit can also combine multiple frameworks based on the interrelationships of keywords. Furthermore, the selection unit can select the optimal framework by considering the interrelationships of keywords. In this way, the selection unit can improve the accuracy of framework selection by considering the interrelationships of keywords.

[0040] The selection team can choose a framework by considering the attribute information of the keyword submitter during the selection process. For example, the selection team considers the attribute information of the keyword submitter. This attribute information includes age, gender, occupation, and field of expertise. For example, the selection team can select an appropriate framework based on the keyword submitter's occupation and position. The selection team can also select a highly relevant framework based on the keyword submitter's industry and field. Furthermore, the selection team can select the optimal framework based on the keyword submitter's experience and skills. In this way, the selection team can select an appropriate framework by considering the submitter's attribute information.

[0041] The selection unit can choose a framework while considering the geographical distribution of keywords. For example, the selection unit analyzes the geographical distribution of keywords. This geographical distribution includes the frequency of keywords in each region and region-specific topics. For example, the selection unit analyzes the geographical distribution of keywords and selects an appropriate framework for each region. The selection unit can also select region-specific frameworks based on the geographical distribution of keywords. Furthermore, the selection unit can select the optimal framework by considering the geographical distribution of keywords. In this way, the selection unit can select a framework suitable for each region by considering geographical distribution.

[0042] The selection unit can improve the accuracy of framework selection by referring to related literature for keywords during the selection process. For example, the selection unit can refer to related literature for keywords. Related literature includes academic papers, industry reports, and patent documents. For example, the selection unit can refer to literature related to keywords and select the most suitable framework. The selection unit can also combine multiple frameworks based on the related literature for keywords. Furthermore, the selection unit can select the most suitable framework by considering the related literature for keywords. In this way, the selection unit can improve the accuracy of framework selection by referring to related literature.

[0043] The analysis department can adjust the level of detail in its analysis based on the importance of the frameworks. For example, the analysis department can adjust the level of detail based on the importance of the frameworks. Framework importance includes factors such as business impact and feasibility. For instance, the analysis department can perform detailed analyses on highly important frameworks, while performing simplified analyses on less important frameworks. Furthermore, the analysis department can adjust the level of detail based on the importance of the frameworks. This allows the analysis department to perform efficient analyses by adjusting the level of detail according to the importance of the frameworks.

[0044] The analysis department can apply different analytical algorithms depending on the framework category during analysis. For example, the analysis department can apply different analytical algorithms depending on the framework category. Framework categories include strategic, marketing, and operational frameworks. For instance, in a PEST analysis, the analysis department can apply different algorithms to each element: political, economic, social, and technological. Similarly, in a SWOT analysis, the analysis department can apply different algorithms to each element: strengths, weaknesses, opportunities, and threats. Furthermore, in a Five Forces analysis, the analysis department can apply different algorithms to each competitive factor. This allows the analysis department to provide highly accurate analytical results by applying the optimal analytical algorithm according to the framework category.

[0045] The analysis department can prioritize analyses based on the submission date of the frameworks. For example, the analysis department might prioritize the analysis based on the submission date, time, and frequency. For instance, the analysis department might prioritize analyzing the most recent frameworks. It could also postpone analyzing older frameworks. Furthermore, the analysis department can adjust the analysis priority based on the submission date. This allows the analysis department to prioritize the analysis of the most up-to-date information by prioritizing analyses based on submission date.

[0046] The analysis department can adjust the order of analysis based on the relevance of the frameworks during the analysis process. For example, the analysis department can adjust the order of analysis based on the relevance of the frameworks. This relevance includes factors such as keyword matching and past performance. For instance, the analysis department can prioritize analyzing frameworks with high relevance. It can also postpone analyzing frameworks with low relevance. Furthermore, the analysis department can adjust the order of analysis based on the relevance of the frameworks. This allows the analysis department to perform more efficient analysis by adjusting the order of analysis based on relevance.

[0047] The service provider can select the optimal display method by referring to the user's past operation history at the time of delivery. For example, the service provider can refer to the user's past operation history. Past operation history includes click history, browsing history, and operation frequency. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. The service provider can also suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can provide a customized display method based on the display methods the user has used in the past. In this way, the service provider can provide the user with the optimal display method by referring to past operation history.

[0048] The service provider can customize the content provided based on the user's current project status at the time of delivery. For example, the service provider can customize the content based on the user's current project status. This current project status includes the project's progress, the members involved, and the project's objectives. For instance, the service provider can prioritize providing analysis results relevant to the user's current project. Furthermore, the service provider can provide highly relevant information based on the user's project status. In addition, the service provider can customize the content according to the user's project progress. This allows the service provider to provide highly relevant information to the user by customizing the content based on the current project status.

[0049] The service provider can provide the most suitable action plan at the time of delivery, taking into account the user's geographical location information. For example, the service provider can provide an action plan that takes into account the user's geographical location information. This geographical location information includes GPS data, IP addresses, and location services. For example, if the service provider is in a specific region, it can prioritize providing an action plan related to that region. Furthermore, if the service provider is traveling, it can prioritize providing an action plan related to the travel destination. Additionally, if the service provider is at home, it can prioritize providing an action plan related to the user's home. In this way, by considering geographical location information, the service provider can prioritize providing action plans relevant to a specific region.

[0050] The service provider can analyze the user's social media activity and propose an action plan at the time of delivery. For example, the service provider can analyze the user's social media activity, which includes the content of posts, the number of likes, and the number of followers. For example, the service provider can provide an action plan related to keywords that the user frequently uses on social media. The service provider can also provide an action plan related to topics that the user has shown interest in, based on their social media activity. Furthermore, the service provider can provide an action plan related to accounts that the user follows on social media. In this way, the service provider can provide an action plan based on the user's interests by analyzing their social media activity.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can analyze a user's past input history and automatically suggest frequently used keywords. For example, if a user has frequently entered keywords such as "market environment" or "competition" in the past, these keywords will be displayed preferentially. The reception desk can also predict and suggest highly relevant keywords based on the user's input history. Furthermore, the reception desk can predict and suggest keywords used during specific time periods based on the user's input history. In this way, the reception desk can leverage the user's past input history to support efficient keyword input.

[0053] The selection team can choose an appropriate business framework based on the user's current project status. For example, if the user is launching a new business, they might select the Business Model Canvas. Alternatively, if the user is improving an existing business, they might select the PDCA cycle. Furthermore, if the user is developing a competitive strategy, they might select the Five Forces analysis. This allows the selection team to choose the optimal business framework according to the user's project situation.

[0054] The service provider can provide highly relevant analysis results by taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize providing market analysis results relevant to that region. Furthermore, if the user is traveling, it can provide business information relevant to their travel destination. Additionally, if the user is at home, it can provide business information relevant to their home location. In this way, the service provider can provide information that is highly relevant to the user by considering their geographical location.

[0055] The service provider can analyze users' social media activity and provide analysis results related to topics they are interested in. For example, if a user frequently posts on social media about topics such as "market environment" or "competition," the service provider will prioritize providing analysis results related to these topics. It can also provide business information related to accounts the user follows. Furthermore, it can predict new topics of interest based on the user's social media activity and provide relevant analysis results. This allows the service provider to provide information based on the user's interests.

[0056] The selection process can combine multiple business frameworks, taking into account the interrelationships of keywords. For example, if the keywords "market environment" and "competition" are entered simultaneously, the selection will combine PEST analysis and Five Forces analysis. Similarly, if the keywords "new business" and "risk management" are entered, the selection can combine the Business Model Canvas and SWOT analysis. Furthermore, if the keywords "growth strategy" and "customer satisfaction" are entered, the selection can combine the PDCA cycle and OODA loop. This allows the selection process to choose a more accurate business framework by considering the interrelationships of keywords.

[0057] The service provider can select the optimal display method by referring to the user's past operation history. For example, it can prioritize providing the display method the user has preferred in the past. It can also suggest the optimal display method based on the user's past operation history. Furthermore, it can provide a customized display method based on the display methods the user has used in the past. In this way, the service provider can provide the user with the optimal display method by referring to past operation history.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives keywords entered by the user. Keywords entered by the user may include, but are not limited to, "market environment" or "competition." The reception desk may, for example, receive keywords entered by the user in text format. The reception desk can also receive keywords using voice input. For example, it may use speech recognition technology to convert keywords spoken by the user into text data. Step 2: The selection department selects an appropriate business framework based on the keywords received by the reception department. For example, if the keyword is "market environment," the selection department might select PEST analysis. Alternatively, if the keyword is "competition," the selection department might select Five Forces analysis. For example, the selection department selects the corresponding business framework based on a database. Step 3: The analysis department conducts a specific analysis based on the framework selected by the selection department. For example, in a PEST analysis, the analysis department analyzes each element: political, economic, social, and technological. Alternatively, in a SWOT analysis, the analysis department can identify strengths, weaknesses, opportunities, and threats. For example, the analysis department uses natural language processing technology to collect and analyze information related to keywords. Step 4: The service provider provides the user with the analysis results obtained by the analysis provider. The service provider may, for example, display the analysis results visually. For example, it may use graphs and charts to display the analysis results in an intuitive way. The service provider can also propose concrete action plans. For example, based on the results of the SWOT analysis, it may propose strategies to leverage strengths and strategies to overcome weaknesses.

[0060] (Example of form 2) The business framework thinking support system according to an embodiment of the present invention is a system that supports business people in making decisions using business frameworks. This system supports thinking in accordance with frameworks such as PEST analysis, SWOT analysis, Five Forces analysis, Business Model Canvas, PDCA cycle, and OODA loop, based on keywords entered by the user. Specifically, it consists of the following steps. First, it receives keywords entered by the user. Next, it selects an appropriate business framework based on the received keywords. Based on the selected framework, it performs a specific analysis and provides the results to the user. For example, in the case of PEST analysis, it analyzes each element of political, economic, social, and technological, and in the case of SWOT analysis, it identifies strengths, weaknesses, opportunities, and threats. Furthermore, it proposes a specific action plan based on the results of applying each framework. This mechanism enables the user to make effective decisions. First, it receives keywords entered by the user. At this time, the user only needs to enter keywords related to business. For example, they enter keywords such as "market environment" or "competition". This information is input into the AI. Next, the AI ​​analyzes the entered keywords and selects an appropriate business framework. For example, if the keyword is "market environment," a PEST analysis is selected; if the keyword is "competition," a Five Forces analysis is selected. This selection is performed automatically by AI. Based on the selected framework, a specific analysis is performed. For example, a PEST analysis analyzes political, economic, social, and technological elements, while a SWOT analysis identifies strengths, weaknesses, opportunities, and threats. This analysis is performed by AI. The analysis results are provided to the user. For example, a function to visually display the analysis results or a function to propose specific action plans could be considered. This allows the user to intuitively understand the analysis results and make effective decisions. Furthermore, specific action plans are proposed based on the results of applying each framework. For example, based on the results of a SWOT analysis, strategies to leverage strengths and strategies to overcome weaknesses are proposed. These proposals are made by AI. This mechanism enables the user to make effective decisions.For example, it can be used in various business scenarios, such as formulating business strategies, conducting market analysis, and developing competitive strategies. This allows the business framework thinking support system to select appropriate business frameworks based on keywords entered by the user and provide concrete analysis results, thereby supporting effective decision-making.

[0061] The business framework thinking support system according to this embodiment comprises a reception unit, a selection unit, an analysis unit, and a provision unit. The reception unit receives keywords entered by the user. Keywords entered by the user include, for example, "market environment" and "competition," but are not limited to such examples. The reception unit receives the keywords entered by the user in text format, for example. The reception unit can also receive keywords using voice input. For example, it uses speech recognition technology to convert the keywords spoken by the user into text data. The selection unit selects an appropriate business framework based on the keywords received by the reception unit. For example, if the keyword is "market environment," the selection unit selects a PEST analysis. The selection unit can also select a Five Forces analysis if the keyword is "competition." For example, the selection unit selects the correspondence between keywords and business frameworks based on a database. The analysis unit performs a specific analysis based on the framework selected by the selection unit. For example, if it is a PEST analysis, the analysis unit analyzes each element: political, economic, social, and technological. Furthermore, the analysis unit can also identify strengths, weaknesses, opportunities, and threats in a SWOT analysis. For example, the analysis unit uses natural language processing technology to collect and analyze information related to keywords. The provision unit provides the user with the analysis results obtained by the analysis unit. The provision unit displays the analysis results visually, for example, using graphs and charts to make the results intuitively understandable. The provision unit can also propose concrete action plans. For example, based on the results of the SWOT analysis, it can propose strategies to leverage strengths and strategies to overcome weaknesses. In this way, the business framework thinking support system according to the embodiment can support effective decision-making by selecting an appropriate business framework based on keywords entered by the user and providing concrete analysis results.

[0062] The selection team can choose a PEST analysis if the keyword is "market environment," or a Five Forces analysis if the keyword is "competition." For example, if the keyword is "market environment," the selection team will choose a PEST analysis. PEST analysis is a framework that analyzes political, economic, social, and technological elements. For example, political elements include government policies, regulations, and laws. Economic elements include economic growth rate, inflation rate, and unemployment rate. Social elements include demographics, culture, and education levels. Technological elements include the speed of technological innovation and the adoption status of new technologies. The selection team can also choose a Five Forces analysis if the keyword is "competition." Five Forces analysis is a framework that analyzes competitive factors. For example, competitive factors include the strength of existing competitors, the threat of new entrants, the threat of substitutes, the bargaining power of suppliers, and the bargaining power of customers. By selecting the appropriate business framework according to the keyword, the selection team can improve the accuracy of the analysis.

[0063] The analysis department can analyze political, economic, social, and technological elements in a PEST analysis, and identify strengths, weaknesses, opportunities, and threats in a SWOT analysis. For example, when conducting a PEST analysis, the analysis department analyzes political, economic, social, and technological elements. For example, political elements include government policies, regulations, and laws. Economic elements include economic growth rates, inflation rates, and unemployment rates. Social elements include demographics, culture, and education levels. Technological elements include the speed of technological innovation and the adoption of new technologies. The analysis department can also identify strengths, weaknesses, opportunities, and threats when conducting a SWOT analysis. For example, strengths include identifying a company's resources, capabilities, and competitive advantages. Weaknesses include identifying a company's shortcomings, resource shortages, and competitive disadvantages. Opportunities include identifying market growth, technological innovation, and the development of new markets. Threats include identifying increased competition, regulatory changes, and economic uncertainty. This allows the analytics department to provide users with useful information by conducting detailed analyses based on each business framework.

[0064] The service provider can visually display the analysis results. For example, the service provider can visually display the analysis results. Methods of visual display include graphs, charts, and infographics. For example, the service provider can display the results of a PEST analysis as a bar graph or pie chart. The service provider can also display the results of a SWOT analysis in a matrix format. For example, it can display strengths, weaknesses, opportunities, and threats divided into four quadrants. Furthermore, the service provider can display the results of a Five Forces analysis as a radar chart. In this way, the service provider can make it easier for users to intuitively understand the analysis results by visually displaying them.

[0065] The service provider can propose concrete action plans. For example, the service provider can propose concrete action plans based on the results of a SWOT analysis. For instance, as a strategy to leverage strengths, they can propose measures to strengthen the company's competitive advantage. As a strategy to overcome weaknesses, they can propose measures to compensate for resource shortages. As a strategy to capitalize on opportunities, they can propose measures to develop new markets and promote technological innovation. As a strategy to address threats, they can propose measures to cope with intensifying competition. In this way, by proposing concrete action plans, the service provider can make it easier for users to make effective decisions.

[0066] The service provider can display analysis results in a way that is easy to understand intuitively. For example, the service provider can display analysis results in a way that is easy to understand intuitively. Intuitive display methods include the design of the user interface and the arrangement of information. For example, the service provider can display analysis results in a card format, summarizing the key points on each card. The service provider can also display analysis results in a timeline format, organizing information chronologically. Furthermore, the service provider can display analysis results in a dashboard format, allowing users to view multiple pieces of information on a single screen. As a result, the service provider enables users to make quick decisions through an intuitively understandable display method.

[0067] The reception unit can estimate the user's emotions and adjust the timing of keyword reception based on the estimated emotions. For example, the reception unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the reception unit can estimate the user's emotions using behavioral pattern analysis. For example, it can analyze the user's input speed and frequency of operations to estimate their emotions. Based on the estimated emotions of the user, the reception unit adjusts the timing of keyword reception. For example, if the user is stressed, the timing of keyword reception can be delayed to allow them to input in a relaxed state. Conversely, if the user is concentrating, the timing of keyword reception can be advanced to encourage rapid input. Furthermore, if the user is tired, the timing of keyword reception can be adjusted to allow them to input while taking breaks. In this way, the reception unit can reduce user stress and promote efficient input by adjusting the timing of keyword reception according to the user's emotions.

[0068] The reception desk can analyze a user's past keyword input history and select the optimal reception method. For example, the reception desk analyzes a user's past keyword input history, which includes input frequency and trends in input content. For instance, the reception desk can automatically display keywords that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest keywords that the user will use at specific times based on their past input history. In this way, by analyzing past input history, the reception desk can provide the user with the most suitable reception method and improve input efficiency.

[0069] The reception system can filter keywords based on the user's current projects and areas of interest. For example, the reception system can filter keywords based on the user's current projects and areas of interest. Current projects include the project's objectives, progress, and members involved. For example, the reception system will prioritize receiving keywords related to the user's current projects. The reception system can also filter and receive highly relevant keywords based on the user's areas of interest. Furthermore, the reception system can suggest relevant keywords based on areas the user has shown interest in in the past. This allows the reception system to prioritize receiving highly relevant keywords by filtering based on the user's current projects and areas of interest.

[0070] The reception system can estimate the user's emotions and determine the priority of keywords to process based on those estimated emotions. For example, the reception system can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception system can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the reception system can estimate the user's emotions using behavioral pattern analysis. For example, it can analyze the user's input speed and frequency of operations to estimate their emotions. Based on the estimated user emotions, the reception system determines the priority of keywords to process. For example, if the user is stressed, it will postpone less important keywords and prioritize more important ones. If the user is relaxed, it can process all keywords equally. Furthermore, if the user is in a hurry, it can prioritize keywords that require quick processing. In this way, the reception system can prioritize important keywords by determining keyword priorities according to the user's emotions.

[0071] The reception system can prioritize receiving keywords that are highly relevant to the user's geographical location, taking this information into consideration when receiving keywords. For example, the reception system can consider the user's geographical location when receiving keywords. This geographical location information includes GPS data, IP addresses, and location services. For example, if the user is in a specific region, the reception system will prioritize receiving keywords related to that region. Furthermore, if the user is traveling, the reception system can prioritize receiving keywords related to their travel destination. Additionally, if the user is at home, the reception system can prioritize receiving keywords related to their home location. In this way, the reception system can prioritize receiving region-related keywords by considering geographical location information.

[0072] The reception desk can analyze a user's social media activity when receiving keywords and accept relevant keywords. For example, the reception desk analyzes a user's social media activity, which includes the content of posts, the number of likes, and the number of followers. For example, the reception desk prioritizes receiving keywords that the user frequently uses on social media. The reception desk can also accept keywords related to topics the user is interested in, based on their social media activity. Furthermore, the reception desk can accept keywords related to accounts the user follows on social media. In this way, by analyzing social media activity, the reception desk can prioritize receiving keywords based on the user's interests.

[0073] The selection unit can estimate the user's emotions and adjust the framework selection criteria based on the estimated emotions. For example, the selection unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The selection unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the selection unit can estimate the user's emotions using behavioral pattern analysis. For example, it can analyze the user's input speed and operation frequency to estimate the emotions. Based on the estimated emotions of the user, the selection unit adjusts the framework selection criteria. For example, if the user is stressed, it will prioritize selecting a simple framework. If the user is relaxed, it may select a more detailed framework. Furthermore, if the user is in a hurry, it may select a framework that can provide results quickly. In this way, the selection unit can select an appropriate framework by adjusting the framework selection criteria according to the user's emotions.

[0074] The selection unit can improve the accuracy of framework selection by considering the interrelationships of keywords during the selection process. For example, the selection unit analyzes the interrelationships of keywords. Keyword interrelationships include co-occurrence networks and relevance scores. For example, the selection unit analyzes the interrelationships between keywords and selects the framework with the highest relevance. The selection unit can also combine multiple frameworks based on the interrelationships of keywords. Furthermore, the selection unit can select the optimal framework by considering the interrelationships of keywords. In this way, the selection unit can improve the accuracy of framework selection by considering the interrelationships of keywords.

[0075] The selection team can choose a framework by considering the attribute information of the keyword submitter during the selection process. For example, the selection team considers the attribute information of the keyword submitter. This attribute information includes age, gender, occupation, and field of expertise. For example, the selection team can select an appropriate framework based on the keyword submitter's occupation and position. The selection team can also select a highly relevant framework based on the keyword submitter's industry and field. Furthermore, the selection team can select the optimal framework based on the keyword submitter's experience and skills. In this way, the selection team can select an appropriate framework by considering the submitter's attribute information.

[0076] The selection unit can estimate the user's emotions and adjust the display order of the selected frameworks based on the estimated emotions. For example, the selection unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The selection unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the selection unit can estimate the user's emotions using behavioral pattern analysis. For example, it can analyze the user's input speed and operation frequency to estimate their emotions. Based on the estimated emotions of the user, the selection unit adjusts the display order of the selected frameworks. For example, if the user is stressed, a simple framework can be displayed first. If the user is relaxed, a detailed framework can be displayed first. Furthermore, if the user is in a hurry, a framework that provides quick results can be displayed first. In this way, the selection unit can quickly select the appropriate framework by adjusting the display order of the frameworks according to the user's emotions.

[0077] The selection unit can choose a framework while considering the geographical distribution of keywords. For example, the selection unit analyzes the geographical distribution of keywords. This geographical distribution includes the frequency of keywords in each region and region-specific topics. For example, the selection unit analyzes the geographical distribution of keywords and selects an appropriate framework for each region. The selection unit can also select region-specific frameworks based on the geographical distribution of keywords. Furthermore, the selection unit can select the optimal framework by considering the geographical distribution of keywords. In this way, the selection unit can select a framework suitable for each region by considering geographical distribution.

[0078] The selection unit can improve the accuracy of framework selection by referring to related literature for keywords during the selection process. For example, the selection unit can refer to related literature for keywords. Related literature includes academic papers, industry reports, and patent documents. For example, the selection unit can refer to literature related to keywords and select the most suitable framework. The selection unit can also combine multiple frameworks based on the related literature for keywords. Furthermore, the selection unit can select the most suitable framework by considering the related literature for keywords. In this way, the selection unit can improve the accuracy of framework selection by referring to related literature.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the analysis unit can estimate the user's emotions using behavioral pattern analysis. For example, it can analyze the user's input speed and operation frequency to estimate their emotions. Based on the estimated emotions of the user, the analysis unit adjusts the presentation of the analysis. For example, if the user is stressed, it provides a simple and highly visual presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a presentation that gets straight to the point. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions.

[0080] The analysis department can adjust the level of detail in its analysis based on the importance of the frameworks. For example, the analysis department can adjust the level of detail based on the importance of the frameworks. Framework importance includes factors such as business impact and feasibility. For instance, the analysis department can perform detailed analyses on highly important frameworks, while performing simplified analyses on less important frameworks. Furthermore, the analysis department can adjust the level of detail based on the importance of the frameworks. This allows the analysis department to perform efficient analyses by adjusting the level of detail according to the importance of the frameworks.

[0081] The analysis department can apply different analytical algorithms depending on the framework category during analysis. For example, the analysis department can apply different analytical algorithms depending on the framework category. Framework categories include strategic, marketing, and operational frameworks. For instance, in a PEST analysis, the analysis department can apply different algorithms to each element: political, economic, social, and technological. Similarly, in a SWOT analysis, the analysis department can apply different algorithms to each element: strengths, weaknesses, opportunities, and threats. Furthermore, in a Five Forces analysis, the analysis department can apply different algorithms to each competitive factor. This allows the analysis department to provide highly accurate analytical results by applying the optimal analytical algorithm according to the framework category.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the analysis unit can estimate the user's emotions using behavioral pattern analysis. For example, it can analyze the user's input speed and operation frequency to estimate the emotions. Based on the estimated emotions of the user, the analysis unit adjusts the length of the analysis. For example, if the user is in a hurry, it provides a short, to-the-point analysis. If the user is relaxed, it can provide a longer analysis with detailed explanations. Furthermore, if the user is excited, it can provide an analysis with visually stimulating effects. In this way, the analysis unit can provide the optimal amount of information for the user by adjusting the length of the analysis according to the user's emotions.

[0083] The analysis department can prioritize analyses based on the submission date of the frameworks. For example, the analysis department might prioritize the analysis based on the submission date, time, and frequency. For instance, the analysis department might prioritize analyzing the most recent frameworks. It could also postpone analyzing older frameworks. Furthermore, the analysis department can adjust the analysis priority based on the submission date. This allows the analysis department to prioritize the analysis of the most up-to-date information by prioritizing analyses based on submission date.

[0084] The analysis department can adjust the order of analysis based on the relevance of the frameworks during the analysis process. For example, the analysis department can adjust the order of analysis based on the relevance of the frameworks. This relevance includes factors such as keyword matching and past performance. For instance, the analysis department can prioritize analyzing frameworks with high relevance. It can also postpone analyzing frameworks with low relevance. Furthermore, the analysis department can adjust the order of analysis based on the relevance of the frameworks. This allows the analysis department to perform more efficient analysis by adjusting the order of analysis based on relevance.

[0085] The service provider can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the service provider can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The service provider can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the service provider can estimate the user's emotions using behavioral pattern analysis. For example, it can analyze the user's input speed and operation frequency to estimate the emotions. Based on the estimated emotions of the user, the service provider adjusts the display method of the analysis results. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, the service provider can adjust the display method according to the user's emotions, making it easier for the user to understand.

[0086] The service provider can select the optimal display method by referring to the user's past operation history at the time of delivery. For example, the service provider can refer to the user's past operation history. Past operation history includes click history, browsing history, and operation frequency. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. The service provider can also suggest the optimal display method based on the user's past operation history. Furthermore, the service provider can provide a customized display method based on the display methods the user has used in the past. In this way, the service provider can provide the user with the optimal display method by referring to past operation history.

[0087] The service provider can customize the content provided based on the user's current project status at the time of delivery. For example, the service provider can customize the content based on the user's current project status. This current project status includes the project's progress, the members involved, and the project's objectives. For instance, the service provider can prioritize providing analysis results relevant to the user's current project. Furthermore, the service provider can provide highly relevant information based on the user's project status. In addition, the service provider can customize the content according to the user's project progress. This allows the service provider to provide highly relevant information to the user by customizing the content based on the current project status.

[0088] The service provider can estimate the user's emotions and prioritize the action plans to be provided based on those estimated emotions. For example, the service provider can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the service provider can estimate the user's emotions using behavioral pattern analysis. For example, it can analyze the user's input speed and frequency of operations to estimate their emotions. Based on the estimated user emotions, the service provider determines the priority of the action plans to be provided. For example, if the user is stressed, it can postpone less important action plans and prioritize providing more important ones. If the user is relaxed, it can provide all action plans equally. Furthermore, if the user is in a hurry, it can prioritize providing action plans that require immediate action. In this way, the service provider can prioritize important action plans by determining the priority of action plans according to the user's emotions.

[0089] The service provider can provide the most suitable action plan at the time of delivery, taking into account the user's geographical location information. For example, the service provider can provide an action plan that takes into account the user's geographical location information. This geographical location information includes GPS data, IP addresses, and location services. For example, if the service provider is in a specific region, it can prioritize providing an action plan related to that region. Furthermore, if the service provider is traveling, it can prioritize providing an action plan related to the travel destination. Additionally, if the service provider is at home, it can prioritize providing an action plan related to the user's home. In this way, by considering geographical location information, the service provider can prioritize providing action plans relevant to a specific region.

[0090] The service provider can analyze the user's social media activity and propose an action plan at the time of delivery. For example, the service provider can analyze the user's social media activity, which includes the content of posts, the number of likes, and the number of followers. For example, the service provider can provide an action plan related to keywords that the user frequently uses on social media. The service provider can also provide an action plan related to topics that the user has shown interest in, based on their social media activity. Furthermore, the service provider can provide an action plan related to accounts that the user follows on social media. In this way, the service provider can provide an action plan based on the user's interests by analyzing their social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, selection unit, analysis unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives keywords entered by the user in text format or voice input. The selection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and selects an appropriate business framework based on the received keywords. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and performs a specific analysis based on the selected framework. The provision unit is implemented by, for example, the output device 40 of the smart device 14 and visually displays the analysis results and proposes a specific action plan. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, selection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives keywords spoken by the user as voice input. The selection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects an appropriate business framework based on the received keywords. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and performs a specific analysis based on the selected framework. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the analysis results by voice and proposes a specific action plan. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, selection unit, analysis unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives keywords spoken by the user as voice input. The selection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and selects an appropriate business framework based on the received keywords. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and performs a specific analysis based on the selected framework. The provision unit is implemented by, for example, the display 343 of the headset terminal 314 and visually displays the analysis results and proposes a specific action plan. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, selection unit, analysis unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives keywords spoken by the user as voice input. The selection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and selects an appropriate business framework based on the received keywords. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and performs a specific analysis based on the selected framework. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the analysis results by voice and proposes a specific action plan.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The reception desk can analyze a user's past input history and automatically suggest frequently used keywords. For example, if a user has frequently entered keywords such as "market environment" or "competition" in the past, these keywords will be displayed preferentially. The reception desk can also predict and suggest highly relevant keywords based on the user's input history. Furthermore, the reception desk can predict and suggest keywords used during specific time periods based on the user's input history. In this way, the reception desk can leverage the user's past input history to support efficient keyword input.

[0093] The selection team can choose an appropriate business framework based on the user's current project status. For example, if the user is launching a new business, they might select the Business Model Canvas. Alternatively, if the user is improving an existing business, they might select the PDCA cycle. Furthermore, if the user is developing a competitive strategy, they might select the Five Forces analysis. This allows the selection team to choose the optimal business framework according to the user's project situation.

[0094] The analytics unit can estimate the user's emotions and adjust the level of detail in the analysis based on that estimation. For example, if the user is stressed, it can provide a simple and easy-to-understand analysis. If the user is relaxed, it can provide an analysis with more detailed information. Furthermore, if the user is in a hurry, it can provide a concise analysis that gets straight to the point. In this way, the analytics unit can provide the user with the most relevant information by adjusting the level of detail in the analysis according to the user's emotions.

[0095] The service provider can provide highly relevant analysis results by taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize providing market analysis results relevant to that region. Furthermore, if the user is traveling, it can provide business information relevant to their travel destination. Additionally, if the user is at home, it can provide business information relevant to their home location. In this way, the service provider can provide information that is highly relevant to the user by considering their geographical location.

[0096] The service provider can analyze users' social media activity and provide analysis results related to topics they are interested in. For example, if a user frequently posts on social media about topics such as "market environment" or "competition," the service provider will prioritize providing analysis results related to these topics. It can also provide business information related to accounts the user follows. Furthermore, it can predict new topics of interest based on the user's social media activity and provide relevant analysis results. This allows the service provider to provide information based on the user's interests.

[0097] The reception system can estimate the user's emotions and adjust the keyword input method based on that estimation. For example, if the user is stressed, it can prioritize suggesting voice input to allow them to enter keywords in a relaxed state. If the user is focused, it can prioritize suggesting text input to encourage faster input. Furthermore, if the user is tired, it can adjust the timing of keyword input to allow for breaks during input. In this way, the reception system can promote efficient input by adjusting the keyword input method according to the user's emotions.

[0098] The selection process can combine multiple business frameworks, taking into account the interrelationships of keywords. For example, if the keywords "market environment" and "competition" are entered simultaneously, the selection will combine PEST analysis and Five Forces analysis. Similarly, if the keywords "new business" and "risk management" are entered, the selection can combine the Business Model Canvas and SWOT analysis. Furthermore, if the keywords "growth strategy" and "customer satisfaction" are entered, the selection can combine the PDCA cycle and OODA loop. This allows the selection process to choose a more accurate business framework by considering the interrelationships of keywords.

[0099] The analytics department can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, it can provide simple, highly visual graphs and charts. If the user is relaxed, it can provide infographics with detailed information. Furthermore, if the user is in a hurry, it can provide the analysis results in concise, to-the-point text. In this way, the analytics department can provide information that is easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions.

[0100] The service provider can estimate the user's emotions and prioritize the action plans based on those emotions. For example, if the user is stressed, less important action plans can be postponed, and more important ones can be prioritized. If the user is relaxed, all action plans can be provided equally. Furthermore, if the user is in a hurry, action plans that require immediate action can be prioritized. In this way, the service provider can prioritize important action plans by determining their priority according to the user's emotions.

[0101] The service provider can select the optimal display method by referring to the user's past operation history. For example, it can prioritize providing the display method the user has preferred in the past. It can also suggest the optimal display method based on the user's past operation history. Furthermore, it can provide a customized display method based on the display methods the user has used in the past. In this way, the service provider can provide the user with the optimal display method by referring to past operation history.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The reception desk receives keywords entered by the user. Keywords entered by the user may include, but are not limited to, "market environment" or "competition." The reception desk may, for example, receive keywords entered by the user in text format. The reception desk can also receive keywords using voice input. For example, it may use speech recognition technology to convert keywords spoken by the user into text data. Step 2: The selection department selects an appropriate business framework based on the keywords received by the reception department. For example, if the keyword is "market environment," the selection department might select PEST analysis. Alternatively, if the keyword is "competition," the selection department might select Five Forces analysis. For example, the selection department selects the corresponding business framework based on a database. Step 3: The analysis department conducts a specific analysis based on the framework selected by the selection department. For example, in a PEST analysis, the analysis department analyzes each element: political, economic, social, and technological. Alternatively, in a SWOT analysis, the analysis department can identify strengths, weaknesses, opportunities, and threats. For example, the analysis department uses natural language processing technology to collect and analyze information related to keywords. Step 4: The service provider provides the user with the analysis results obtained by the analysis provider. The service provider may, for example, display the analysis results visually. For example, it may use graphs and charts to display the analysis results in an intuitive way. The service provider can also propose concrete action plans. For example, based on the results of the SWOT analysis, it may propose strategies to leverage strengths and strategies to overcome weaknesses.

[0104] 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.

[0105] 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 the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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. 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.

[0122] 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.

[0123] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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. 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.

[0138] 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.

[0139] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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. 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.

[0155] 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.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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. A reception desk that accepts keywords entered by the user, A selection unit selects an appropriate business framework based on the keywords received by the reception unit, An analysis unit that performs specific analysis based on the framework selected by the aforementioned selection unit, The system includes a provisioning unit that provides the user with the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned selection unit is If the keyword is market environment, select PEST analysis; if the keyword is competition, select Five Forces analysis. The system according to feature 1.

3. The aforementioned analysis unit is PEST analysis examines political, economic, social, and technological elements, while SWOT analysis identifies strengths, weaknesses, opportunities, and threats. The system according to feature 1.

4. The aforementioned supply unit is, Visually display the analysis results. The system according to feature 1.

5. The aforementioned supply unit is, Propose a concrete action plan. The system according to feature 1.

6. The aforementioned supply unit is, Display the analysis results in a way that is easy to understand intuitively. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of keyword submissions based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past keyword input history to select the optimal reception method. The system according to feature 1.

Images