system

Abstract

The system according to this embodiment aims to provide advice to customers based on past failures and successes regarding their ideas. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a specification unit, a success case specification unit, and a provision unit. The reception unit receives customer ideas. The analysis unit analyzes the ideas received by the reception unit and finds similar past failures. The specification unit analyzes the failures found by the analysis unit and identifies the causes of the failures. The success case specification unit finds success cases based on the causes of failures identified by the specification unit. The provision unit provides advice based on the success cases found by the success case specification 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, past failure cases and success cases for customers' ideas have not been fully utilized effectively to provide advice, and there is room for improvement.

[0005] The system according to the embodiment aims to provide advice based on past failure cases and success cases for customers' ideas.

Means for Solving the Problems

[0007] The system according to this embodiment can provide advice to customers based on past failures and successes regarding their ideas. [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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The consultation system according to an embodiment of the present invention is a system that uses AI and past service data to guide businesses from failure to success. This consultation system takes customer ideas as input, and the AI ​​analyzes similar failure cases and provides strategic recommendations. Furthermore, the AI ​​identifies success stories and clarifies the elements that lead to success. It captures market changes in real time, updates advice, and strengthens business resilience. For example, a customer inputs a new product concept or business model. This information is input into the AI. Next, the AI ​​analyzes the input idea and finds similar past failure cases. The AI ​​searches a database of past data and identifies similar failure cases. For example, it finds cases where similar products were launched on the market but failed. The AI ​​analyzes the identified failure cases and identifies the causes of failure. For example, it identifies causes such as low product quality or insufficient marketing strategy. Next, the AI ​​finds success stories and clarifies the elements that lead to success. The AI ​​searches past success stories and identifies the factors for success. For example, it identifies factors such as high product quality or an effective marketing strategy. Furthermore, the AI ​​captures market changes in real time and updates advice. AI analyzes the latest market data and adjusts clients' business strategies accordingly. For example, if a new competitor emerges, it provides advice that takes that impact into account. This allows clients to recover from failures and walk the path to success. Based on the AI's analysis, clients can receive strategic recommendations and strengthen their business resilience. For instance, it can identify areas for improvement in failed products and develop new strategies by referencing successful cases. This enables the consultation system to efficiently analyze clients' ideas, identify the root causes of failures, and provide advice based on success stories.

[0029] The consultation system according to this embodiment comprises a reception unit, an analysis unit, a specification unit, a success case specification unit, and a provision unit. The reception unit receives customer ideas. Customer ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. The reception unit can receive ideas by methods such as text input, voice input, and image input. For example, the reception unit allows customers to input ideas in text format. The reception unit can also allow customers to input ideas by voice using voice recognition technology. Furthermore, the reception unit can also allow customers to input ideas using images using image recognition technology. The analysis unit analyzes the ideas input by the reception unit and finds similar past failures. The analysis unit analyzes ideas by methods such as text analysis, data mining, and machine learning algorithms. For example, the analysis unit analyzes the content of the input ideas using text analysis technology. The analysis unit can also find similar failures from past databases using data mining technology. Furthermore, the analysis unit can use machine learning algorithms to evaluate the similarity between input ideas and past failures. The identification unit analyzes the failures identified by the analysis unit and identifies the causes of failure. The identification unit identifies the causes of failure using methods such as root cause analysis and causal relationship identification. For example, the identification unit uses root cause analysis techniques to identify the causes of failure. The identification unit can also use causal relationship identification techniques to identify the causes of failure. Furthermore, the identification unit can refer to past failure data to identify the causes of failure. The success case identification unit finds success cases based on the causes of failure identified by the identification unit. For example, the success case identification unit searches the success case database and identifies the factors for success. For example, the success case identification unit searches the success case database and identifies the factors for success. Furthermore, the success case identification unit can analyze the success case database and identify common success factors. Furthermore, the success case identification unit can improve the success factor identification algorithm based on the success case database.The service provider provides advice based on success stories identified by the success story identification service provider. The service provider provides advice such as specific proposals, improvement measures, and implementation plans. For example, the service provider provides specific proposals based on success stories. The service provider can also provide improvement measures based on success stories. Furthermore, the service provider can also provide implementation plans based on success stories. As a result, the consultation system according to this embodiment can efficiently analyze customer ideas, identify the causes of failures, and provide advice based on success stories.

[0030] The reception desk receives customer ideas. These ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. The reception desk can accept ideas through various methods, such as text input, voice input, and image input. For example, the reception desk allows customers to input ideas in text format. It can also use voice recognition technology to allow customers to input ideas by voice. Furthermore, it can use image recognition technology to allow customers to input ideas using images. Specifically, for text input, customers write their ideas in a dedicated input form and send them to the reception desk by pressing a submit button. For voice input, customers speak their ideas into a microphone, and voice recognition technology converts the content into text and sends it to the reception desk. For image input, customers draw their ideas as diagrams or sketches, take pictures of them with a camera, and upload them. Image recognition technology analyzes the content and sends it to the reception desk as text information. This allows the reception desk to provide diverse input methods, enabling customers to submit ideas in the way that is most convenient for them. In addition, the reception desk also has a function to perform initial filtering of submitted ideas, excluding obviously inappropriate content and spam. This can improve the overall efficiency and reliability of the system.

[0031] The analysis department analyzes ideas entered by the reception department and identifies similar past failures. The analysis department analyzes ideas using methods such as text analysis, data mining, and machine learning algorithms. For example, the analysis department uses text analysis techniques to analyze the content of the entered ideas. It can also use data mining techniques to find similar failures in past databases. Furthermore, the analysis department can use machine learning algorithms to evaluate the similarity between the entered ideas and past failures. Specifically, it uses text analysis techniques to extract keywords and phrases from the ideas and searches past databases based on these. Data mining techniques are used to find patterns and trends from large amounts of data and evaluate their relevance to past failures. Machine learning algorithms are used to learn from past data and evaluate the similarity with new ideas with high accuracy. This allows the analysis department to quickly identify which past failures the entered ideas are similar to, streamlining the analysis in the next step. Furthermore, the analysis department also has a function to visualize the analysis results and present them to the user in an easy-to-understand manner. This allows users to intuitively understand how their ideas have been analyzed.

[0032] The Identification Department analyzes failure cases identified by the Analysis Department and identifies the causes of failures. The Identification Department identifies the causes of failures using methods such as root cause analysis and causal relationship identification. For example, the Identification Department uses root cause analysis techniques to identify the causes of failures. It can also use causal relationship identification techniques to identify the causes of failures. Furthermore, the Identification Department can refer to past failure data to identify the causes of failures. Specifically, it uses root cause analysis techniques to identify the underlying factors behind the failure. For example, if the cause of the failure is a technical problem, it analyzes in detail which part of that technology caused the problem. Causal relationship identification techniques are used to clarify the cause-and-effect relationship of the failure and identify which factors influenced how. This allows the Identification Department to pinpoint the causes of failures in detail and provide a basis for formulating countermeasures in the next steps. Furthermore, the Identification Department can perform more accurate analyses by referring to past failure data and comparing similar cases to identify the causes of failures. This allows the Identification Department to quickly and accurately identify the causes of failures and improve the overall reliability of the system.

[0033] The success case identification unit finds success cases based on the causes of failure identified by the identification unit. For example, the success case identification unit searches the success case database to identify factors for success. The success case identification unit can also analyze the success case database to identify common success factors. Furthermore, the success case identification unit can improve its success factor identification algorithm based on the success case database. Specifically, it searches the success case database to find success cases where effective countermeasures were taken against the identified causes of failure. The success case identification unit conducts a detailed analysis of success cases to identify factors for success. This includes the background of the success case, the countermeasures implemented, and the results obtained. To identify common success factors, it compares multiple success cases to find common elements and patterns. This allows the success case identification unit to find the most effective countermeasures against the identified causes of failure and use this information to provide advice for the next steps. Furthermore, the success case identification unit continuously improves its success factor identification algorithm based on the success case database, enabling more accurate identification of success factors. This allows the success case identification unit to constantly identify success factors with high accuracy based on the latest information, thereby improving the overall effectiveness of the system.

[0034] The Service Provider provides advice based on success stories identified by the Success Story Identification Unit. The Service Provider provides advice such as specific suggestions, improvement measures, and implementation plans. For example, the Service Provider provides specific suggestions based on success stories. The Service Provider can also provide improvement measures based on success stories. Furthermore, the Service Provider can also provide implementation plans based on success stories. Specifically, based on success stories, they propose concrete steps and methods for realizing the client's idea. Improvement measures include clearly indicating what changes or adjustments are needed for the client's idea. Implementation plans include providing a detailed schedule and resource allocation for the client to implement the idea. This allows the Service Provider to support clients in taking concrete actions based on success stories and increase the feasibility of their ideas. Furthermore, the Service Provider can collect feedback from clients and continuously improve the accuracy and effectiveness of the advice they provide. This allows the Service Provider to always provide clients with the best possible advice and improve the overall reliability and effectiveness of the system.

[0035] The service provider can make strategic recommendations based on success stories. For example, the service provider can propose business strategies based on success stories. For example, the service provider can also propose marketing strategies based on success stories. Furthermore, the service provider can propose technology strategies based on success stories. For example, the service provider can propose product improvements based on success stories. Furthermore, the service provider can propose strategies for entering new markets based on success stories. In addition, the service provider can propose revisions to customer targets based on success stories. In this way, by making strategic recommendations based on success stories, the service provider can effectively support the customer's business strategy. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can make strategic recommendations using an AI model that takes success stories identified by the success story identification unit as input and outputs strategic recommendations.

[0036] The service provider can capture market changes in real time and update advice accordingly. For example, the service provider can analyze the latest market data and adjust the client's business strategy as needed. For instance, if a new competitor emerges, the service provider can provide advice that takes this impact into account. The service provider can also analyze market trends and update the client's business strategy. For example, the service provider can analyze market trends and suggest improvements to the client's products and services. The service provider can also review the client's marketing strategy in response to market changes. For example, the service provider can suggest the use of new marketing channels. By providing up-to-date advice that responds to market changes, the service provider can strengthen the client's business resilience. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can update advice using an AI model that takes the latest market data as input and outputs advice.

[0037] The analysis unit can search past databases and identify similar failure cases. For example, the analysis unit can search past project data and identify similar failure cases. The analysis unit can also analyze failure patterns and identify similar failure cases. For example, the analysis unit can analyze failure patterns and identify common causes of failure. Furthermore, the analysis unit can improve its failure identification algorithm based on past databases. This allows for the rapid identification of similar failure cases by searching past databases. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can identify failure cases using an AI model that takes past databases as input and outputs similar failure cases.

[0038] The identification unit can identify the cause of failure. The identification unit can identify the cause of failure, for example, by using root cause analysis techniques. The identification unit can also identify the cause of failure, for example, by using causal relationship identification techniques. Furthermore, the identification unit can refer to past failure data to identify the cause of failure. For example, the identification unit can refer to past failure data to identify the cause of failure. This makes it possible to clarify areas for improvement in the customer's idea by identifying the cause of failure. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can identify the cause of failure using an AI model that takes data for identifying the cause of failure as input and outputs the cause of failure.

[0039] The success story identification unit can search for success stories and identify the factors contributing to their success. For example, the success story identification unit can search a success story database and identify the factors contributing to their success. The success story identification unit can also analyze the success story database and identify common factors contributing to their success. Furthermore, the success story identification unit can improve its success factor identification algorithm based on the success story database. This allows the system to clarify the customer's path to success by searching for success stories and identifying the factors contributing to their success. Some or all of the above-described processes in the success story identification unit may be performed using AI, for example, or without AI. For example, the success story identification unit can identify factors contributing to their success using an AI model that takes a success story database as input and outputs factors contributing to their success.

[0040] The reception desk can analyze the customer's past idea submission history and select the optimal input method. For example, if the customer has previously preferred using text input, the reception desk will prioritize suggesting text input. The reception desk can also prioritize suggesting voice input if the customer has previously used voice input. Furthermore, if the customer has previously submitted ideas using images, the reception desk can prioritize suggesting image input. In this way, the reception desk can select the optimal input method by analyzing the customer's past idea submission history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can select the optimal input method using an AI model that takes the customer's past idea submission history as input and outputs the optimal input method.

[0041] The reception desk can filter ideas based on the customer's current projects and areas of interest when they are entered. For example, the reception desk can prioritize ideas related to the customer's current projects. The reception desk can also prioritize ideas related to the customer's areas of interest. Furthermore, the reception desk can also prioritize ideas related to areas the customer has shown interest in in the past. This allows for the priority input of highly relevant ideas by filtering based on the customer's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can perform filtering using an AI model that takes data on the customer's current projects and areas of interest as input and outputs filtered results.

[0042] The reception desk can prioritize inputting highly relevant ideas by considering the customer's geographical location when ideas are entered. For example, if the customer is in a specific region, the reception desk will prioritize inputting ideas related to that region. The reception desk can also prioritize inputting ideas related to the customer's travel destination if the customer is traveling. Furthermore, if the customer is at home, the reception desk can prioritize inputting ideas related to their home. In this way, highly relevant ideas can be prioritized by considering the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input ideas using an AI model that takes the customer's geographical location as input and outputs highly relevant ideas.

[0043] The reception desk can analyze the customer's social media activity and input relevant ideas when an idea is entered. For example, the reception desk can input ideas based on the customer's interests and passions shared on social media. The reception desk can also input ideas related to topics the customer follows on social media. Furthermore, the reception desk can input ideas related to groups and communities the customer participates in on social media. This allows the reception desk to prioritize the input of relevant ideas by analyzing the customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input ideas using an AI model that takes customer social media activity data as input and outputs relevant ideas.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the ideas during the analysis. For example, the analysis unit can perform a detailed analysis for ideas of high importance. The analysis unit can also perform a concise analysis for ideas of low importance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail for ideas of medium importance. In this way, efficient analysis can be performed by adjusting the level of detail of the analysis based on the importance of the ideas. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes idea importance data as input and outputs the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the idea during analysis. For example, the analysis unit can apply a technical analysis algorithm to a technical idea. For example, the analysis unit can apply a marketing analysis algorithm to a marketing idea. For example, the analysis unit can apply a marketing analysis algorithm to a marketing idea. Furthermore, the analysis unit can apply a business strategy analysis algorithm to a business strategy idea. For example, the analysis unit can apply a business strategy analysis algorithm to a business strategy idea. By applying different analysis algorithms depending on the category of the idea, a more appropriate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply an analysis algorithm using an AI model that takes idea category data as input and outputs an analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on when the ideas were submitted. For example, the analysis unit can prioritize the analysis of recently submitted ideas. The analysis unit can also postpone the analysis of older ideas. Furthermore, the analysis unit can analyze ideas submitted at a moderate pace. This allows for efficient analysis by determining the priority of analysis based on when the ideas were submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority of analysis using an AI model that takes idea submission date data as input and outputs the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of ideas during the analysis process. For example, the analysis unit may prioritize the analysis of ideas related to the customer's current project. The analysis unit may also prioritize the analysis of ideas related to the customer's areas of interest. Furthermore, the analysis unit may prioritize the analysis of ideas related to areas the customer has shown interest in in the past. By adjusting the order of analysis based on the relevance of ideas, more relevant ideas can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order of analysis using an AI model that takes idea relevance data as input and outputs the order of analysis.

[0048] The identification unit can improve the accuracy of cause identification by referring to past failure data when identifying the cause of a failure. For example, the identification unit can improve the accuracy of cause identification by referring to similar past failure data. The identification unit can also analyze past failure data and identify common causes. For example, the identification unit analyzes past failure data and identifies common causes. Furthermore, the identification unit can improve the cause identification algorithm based on past failure data. For example, the identification unit improves the cause identification algorithm based on past failure data. As a result, the accuracy of cause identification is improved by referring to past failure data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can take past failure data as input and perform cause identification using an AI model that improves the accuracy of cause identification.

[0049] The identification unit can apply different cause identification methods to each category of idea when identifying the cause of failure. For example, the identification unit can apply a technical cause identification method to a technical idea. For example, the identification unit can apply a technical cause identification method to a technical idea. The identification unit can also apply a marketing cause identification method to a marketing idea. For example, the identification unit can apply a business strategy cause identification method to a business strategy idea. In addition, the identification unit can apply a business strategy cause identification method to a business strategy idea. For example, the identification unit can apply a business strategy cause identification method to a business strategy idea. By applying different cause identification methods to each category of idea, more appropriate cause identification can be performed. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can apply a cause identification method using an AI model that takes idea category data as input and outputs a cause identification method.

[0050] The identification unit can determine the priority of cause identification based on the submission date of the ideas when identifying the cause of failure. For example, the identification unit may prioritize identifying the cause of failure of recently submitted ideas. The identification unit may also postpone identifying the cause of failure of older ideas. Furthermore, the identification unit may moderately identify the cause of failure of ideas submitted at a moderate time. This allows for efficient cause identification by determining the priority of cause identification based on the submission date of the ideas. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can determine the priority using an AI model that takes idea submission date data as input and outputs a priority order for cause identification.

[0051] The identification unit can improve the accuracy of cause identification by referring to relevant market data for the idea when identifying the cause of failure. For example, the identification unit can improve the accuracy of cause identification by referring to relevant market data. The identification unit can also analyze market data and identify common causes of failure. For example, the identification unit can analyze market data and identify common causes of failure. Furthermore, the identification unit can improve the algorithm for identifying the cause of failure based on market data. For example, the identification unit can improve the algorithm for identifying the cause of failure based on market data. This improves the accuracy of cause identification by referring to relevant market data for the idea. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can use relevant market data as input and perform cause identification using an AI model that improves the accuracy of cause identification.

[0052] The success case identification unit can improve the accuracy of identifying success factors by referring to past success data when identifying success cases. For example, the success case identification unit can improve the accuracy of identifying success factors by referring to similar past success data. The success case identification unit can also analyze past success data and identify common success factors. Furthermore, the success case identification unit can improve the algorithm for identifying success factors based on past success data. For example, the success case identification unit can improve the algorithm for identifying success factors based on past success data. As a result, the accuracy of identifying success factors is improved by referring to past success data. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without AI. For example, the success case identification unit can identify success factors using an AI model that takes past success data as input and improves the accuracy of identifying success factors.

[0053] The success case identification unit can apply different success factor identification methods to each category of idea when identifying success cases. For example, the success case identification unit can apply a technical success factor identification method to a technical idea. For example, the success case identification unit can apply a marketing success factor identification method to a marketing idea. For example, the success case identification unit can apply a marketing success factor identification method to a marketing idea. Furthermore, the success case identification unit can apply a business strategy success factor identification method to a business strategy idea. For example, the success case identification unit can apply a business strategy success factor identification method to a business strategy idea. By applying different success factor identification methods to each category of idea, more appropriate success factors can be identified. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without AI. For example, the success case identification unit can apply a success factor identification method using an AI model that takes idea category data as input and outputs a success factor identification method.

[0054] The success case identification unit can determine the priority of success cases based on the submission date of the ideas when identifying success cases. For example, the success case identification unit can prioritize the identification of recently submitted ideas. The success case identification unit can also postpone the identification of older ideas. Furthermore, the success case identification unit can moderately identify success cases of ideas submitted at a moderate time. This allows for efficient identification of success cases by determining the priority of success cases based on the submission date of the ideas. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without AI. For example, the success case identification unit can determine the priority using an AI model that takes idea submission date data as input and outputs the priority of success cases.

[0055] The success case identification unit can improve the accuracy of identifying success factors by referring to relevant market data for ideas when identifying success cases. For example, the success case identification unit can improve the accuracy of identifying success factors by referring to relevant market data. The success case identification unit can also analyze market data and identify common success factors. For example, the success case identification unit can analyze market data and identify common success factors. Furthermore, the success case identification unit can improve the algorithm for identifying success factors based on market data. For example, the success case identification unit can improve the algorithm for identifying success factors based on market data. This improves the accuracy of identifying success factors by referring to relevant market data for ideas. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without AI. For example, the success case identification unit can identify success factors using an AI model that takes relevant market data as input and improves the accuracy of identifying success factors.

[0056] The service provider can adjust the level of detail of the advice based on the importance of the success stories when providing advice. For example, the service provider can provide detailed advice based on highly important success stories. The service provider can also provide concise advice based on less important success stories. Furthermore, the service provider can provide advice with a moderate level of detail based on moderately important success stories. By adjusting the level of detail of the advice based on the importance of the success stories, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can adjust the level of detail using an AI model that takes success story importance data as input and outputs the level of detail of the advice.

[0057] The service provider can apply different advice algorithms depending on the category of the success story when providing advice. For example, the service provider can apply a technical advice algorithm to a technical success story. For example, the service provider can apply a marketing advice algorithm to a marketing success story. For example, the service provider can apply a business strategy advice algorithm to a business strategy success story. For example, the service provider can apply a business strategy advice algorithm to a business strategy success story. By applying different advice algorithms depending on the category of the success story, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can apply an advice algorithm using an AI model that takes success story category data as input and outputs an advice algorithm.

[0058] The service provider can prioritize advice based on the submission timing of success stories when providing advice. For example, the service provider may prioritize advice based on recent success stories. The service provider may also postpone advice based on older success stories. Furthermore, the service provider may provide advice moderately based on success stories with a moderate submission timing. This allows for the efficient provision of advice by prioritizing it based on the submission timing of success stories. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can determine priorities using an AI model that takes success story submission timing data as input and outputs advice priorities.

[0059] The service provider can adjust the order of advice based on the relevance of success stories when providing advice. For example, the service provider may prioritize advice based on success stories related to the customer's current project. The service provider may also prioritize advice based on success stories related to the customer's areas of interest. Furthermore, the service provider may prioritize advice based on success stories related to areas the customer has shown interest in in the past. By adjusting the order of advice based on the relevance of success stories, more relevant advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can adjust the order using an AI model that takes relevance data of success stories as input and outputs the order of advice.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze the customer's past idea submission history and select the optimal input method. For example, if the customer has previously preferred text input, it can prioritize suggesting text input. Similarly, if the customer has previously used voice input, it can prioritize suggesting voice input. Furthermore, if the customer has previously submitted ideas using images, it can prioritize suggesting image input. In this way, the optimal input method can be selected by analyzing the customer's past idea submission history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can select the optimal input method using an AI model that takes the customer's past idea submission history as input and outputs the optimal input method.

[0062] The service provider can capture market changes in real time and update advice accordingly. For example, it can analyze the latest market data and adjust the client's business strategy as needed. It can also provide advice that takes into account the impact of new competitors that emerge. Furthermore, it can analyze market trends and suggest improvements to the client's products and services. By providing up-to-date advice that responds to market changes, it can strengthen the client's business resilience. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can update advice using an AI model that takes the latest market data as input and outputs advice.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the ideas during the analysis. For example, a detailed analysis can be performed on highly important ideas. Conversely, a concise analysis can be performed on less important ideas. Furthermore, an analysis with an appropriate level of detail can be performed on moderately important ideas. By adjusting the level of detail of the analysis based on the importance of the ideas, efficient analysis can be performed. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes idea importance data as input and outputs the level of detail of the analysis.

[0064] The identification unit can improve the accuracy of cause identification by referring to past failure data when identifying the cause of a failure. For example, it can improve the accuracy of cause identification by referring to similar past failure data. It can also analyze past failure data to identify common causes. Furthermore, it can improve the cause identification algorithm based on past failure data. As a result, the accuracy of cause identification is improved by referring to past failure data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can take past failure data as input and perform cause identification using an AI model that improves the accuracy of cause identification.

[0065] The success case identification unit can improve the accuracy of identifying success factors by referring to past success data when identifying success cases. For example, it can improve the accuracy of identifying success factors by referring to similar past success data. It can also analyze past success data to identify common success factors. Furthermore, it can improve the algorithm for identifying success factors based on past success data. As a result, the accuracy of identifying success factors is improved by referring to past success data. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without using AI. For example, the success case identification unit can take past success data as input and identify success factors using an AI model that improves the accuracy of identifying success factors.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives the customer's ideas. These ideas can include business ideas, technical ideas, and creative ideas. The reception desk can accept ideas via text input, voice input, image input, etc. For example, customers can input ideas in text format, voice using speech recognition technology, or images using image recognition technology. Step 2: The analysis department analyzes the ideas submitted by the reception department and identifies similar past failures. The analysis department analyzes the ideas using methods such as text analysis, data mining, and machine learning algorithms. For example, it can analyze the content of the submitted ideas using text analysis techniques and find similar failures from past databases using data mining techniques. It can also evaluate the similarity between the submitted ideas and past failures using machine learning algorithms. Step 3: The Identification Department analyzes the failure cases identified by the Analysis Department and identifies the causes of the failures. The Identification Department identifies the causes of failures using methods such as root cause analysis and causal relationship identification. For example, it may use root cause analysis techniques and causal relationship identification techniques to identify the causes of failures and refer to past failure data. Step 4: The success case identification unit finds success cases based on the causes of failure identified by the identification unit. The success case identification unit searches the success case database and identifies the factors for success. For example, it can search the success case database, identify factors for success, and identify common success factors. It can also improve the success factor identification algorithm based on the success case database. Step 5: The Service Provider provides advice based on the success stories identified by the Success Story Identification Provider. The Service Provider provides advice such as specific suggestions, improvement measures, and implementation plans. For example, they can provide specific suggestions, improvement measures, and implementation plans based on success stories.

[0068] (Example of form 2) The consultation system according to an embodiment of the present invention is a system that uses AI and past service data to guide businesses from failure to success. This consultation system takes customer ideas as input, and the AI ​​analyzes similar failure cases and provides strategic recommendations. Furthermore, the AI ​​identifies success stories and clarifies the elements that lead to success. It captures market changes in real time, updates advice, and strengthens business resilience. For example, a customer inputs a new product concept or business model. This information is input into the AI. Next, the AI ​​analyzes the input idea and finds similar past failure cases. The AI ​​searches a database of past data and identifies similar failure cases. For example, it finds cases where similar products were launched on the market but failed. The AI ​​analyzes the identified failure cases and identifies the causes of failure. For example, it identifies causes such as low product quality or insufficient marketing strategy. Next, the AI ​​finds success stories and clarifies the elements that lead to success. The AI ​​searches past success stories and identifies the factors for success. For example, it identifies factors such as high product quality or an effective marketing strategy. Furthermore, the AI ​​captures market changes in real time and updates advice. AI analyzes the latest market data and adjusts clients' business strategies accordingly. For example, if a new competitor emerges, it provides advice that takes that impact into account. This allows clients to recover from failures and walk the path to success. Based on the AI's analysis, clients can receive strategic recommendations and strengthen their business resilience. For instance, it can identify areas for improvement in failed products and develop new strategies by referencing successful cases. This enables the consultation system to efficiently analyze clients' ideas, identify the root causes of failures, and provide advice based on success stories.

[0069] The consultation system according to this embodiment comprises a reception unit, an analysis unit, a specification unit, a success case specification unit, and a provision unit. The reception unit receives customer ideas. Customer ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. The reception unit can receive ideas by methods such as text input, voice input, and image input. For example, the reception unit allows customers to input ideas in text format. The reception unit can also allow customers to input ideas by voice using voice recognition technology. Furthermore, the reception unit can also allow customers to input ideas using images using image recognition technology. The analysis unit analyzes the ideas input by the reception unit and finds similar past failures. The analysis unit analyzes ideas by methods such as text analysis, data mining, and machine learning algorithms. For example, the analysis unit analyzes the content of the input ideas using text analysis technology. The analysis unit can also find similar failures from past databases using data mining technology. Furthermore, the analysis unit can use machine learning algorithms to evaluate the similarity between input ideas and past failures. The identification unit analyzes the failures identified by the analysis unit and identifies the causes of failure. The identification unit identifies the causes of failure using methods such as root cause analysis and causal relationship identification. For example, the identification unit uses root cause analysis techniques to identify the causes of failure. The identification unit can also use causal relationship identification techniques to identify the causes of failure. Furthermore, the identification unit can refer to past failure data to identify the causes of failure. The success case identification unit finds success cases based on the causes of failure identified by the identification unit. For example, the success case identification unit searches the success case database and identifies the factors for success. For example, the success case identification unit searches the success case database and identifies the factors for success. Furthermore, the success case identification unit can analyze the success case database and identify common success factors. Furthermore, the success case identification unit can improve the success factor identification algorithm based on the success case database.The service provider provides advice based on success stories identified by the success story identification service provider. The service provider provides advice such as specific proposals, improvement measures, and implementation plans. For example, the service provider provides specific proposals based on success stories. The service provider can also provide improvement measures based on success stories. Furthermore, the service provider can also provide implementation plans based on success stories. As a result, the consultation system according to this embodiment can efficiently analyze customer ideas, identify the causes of failures, and provide advice based on success stories.

[0070] The reception desk receives customer ideas. These ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. The reception desk can accept ideas through various methods, such as text input, voice input, and image input. For example, the reception desk allows customers to input ideas in text format. It can also use voice recognition technology to allow customers to input ideas by voice. Furthermore, it can use image recognition technology to allow customers to input ideas using images. Specifically, for text input, customers write their ideas in a dedicated input form and send them to the reception desk by pressing a submit button. For voice input, customers speak their ideas into a microphone, and voice recognition technology converts the content into text and sends it to the reception desk. For image input, customers draw their ideas as diagrams or sketches, take pictures of them with a camera, and upload them. Image recognition technology analyzes the content and sends it to the reception desk as text information. This allows the reception desk to provide diverse input methods, enabling customers to submit ideas in the way that is most convenient for them. In addition, the reception desk also has a function to perform initial filtering of submitted ideas, excluding obviously inappropriate content and spam. This can improve the overall efficiency and reliability of the system.

[0071] The analysis department analyzes ideas entered by the reception department and identifies similar past failures. The analysis department analyzes ideas using methods such as text analysis, data mining, and machine learning algorithms. For example, the analysis department uses text analysis techniques to analyze the content of the entered ideas. It can also use data mining techniques to find similar failures in past databases. Furthermore, the analysis department can use machine learning algorithms to evaluate the similarity between the entered ideas and past failures. Specifically, it uses text analysis techniques to extract keywords and phrases from the ideas and searches past databases based on these. Data mining techniques are used to find patterns and trends from large amounts of data and evaluate their relevance to past failures. Machine learning algorithms are used to learn from past data and evaluate the similarity with new ideas with high accuracy. This allows the analysis department to quickly identify which past failures the entered ideas are similar to, streamlining the analysis in the next step. Furthermore, the analysis department also has a function to visualize the analysis results and present them to the user in an easy-to-understand manner. This allows users to intuitively understand how their ideas have been analyzed.

[0072] The Identification Department analyzes failure cases identified by the Analysis Department and identifies the causes of failures. The Identification Department identifies the causes of failures using methods such as root cause analysis and causal relationship identification. For example, the Identification Department uses root cause analysis techniques to identify the causes of failures. It can also use causal relationship identification techniques to identify the causes of failures. Furthermore, the Identification Department can refer to past failure data to identify the causes of failures. Specifically, it uses root cause analysis techniques to identify the underlying factors behind the failure. For example, if the cause of the failure is a technical problem, it analyzes in detail which part of that technology caused the problem. Causal relationship identification techniques are used to clarify the cause-and-effect relationship of the failure and identify which factors influenced how. This allows the Identification Department to pinpoint the causes of failures in detail and provide a basis for formulating countermeasures in the next steps. Furthermore, the Identification Department can perform more accurate analyses by referring to past failure data and comparing similar cases to identify the causes of failures. This allows the Identification Department to quickly and accurately identify the causes of failures and improve the overall reliability of the system.

[0073] The success case identification unit finds success cases based on the causes of failure identified by the identification unit. For example, the success case identification unit searches the success case database to identify factors for success. The success case identification unit can also analyze the success case database to identify common success factors. Furthermore, the success case identification unit can improve its success factor identification algorithm based on the success case database. Specifically, it searches the success case database to find success cases where effective countermeasures were taken against the identified causes of failure. The success case identification unit conducts a detailed analysis of success cases to identify factors for success. This includes the background of the success case, the countermeasures implemented, and the results obtained. To identify common success factors, it compares multiple success cases to find common elements and patterns. This allows the success case identification unit to find the most effective countermeasures against the identified causes of failure and use this information to provide advice for the next steps. Furthermore, the success case identification unit continuously improves its success factor identification algorithm based on the success case database, enabling more accurate identification of success factors. This allows the success case identification unit to constantly identify success factors with high accuracy based on the latest information, thereby improving the overall effectiveness of the system.

[0074] The Service Provider provides advice based on success stories identified by the Success Story Identification Unit. The Service Provider provides advice such as specific suggestions, improvement measures, and implementation plans. For example, the Service Provider provides specific suggestions based on success stories. The Service Provider can also provide improvement measures based on success stories. Furthermore, the Service Provider can also provide implementation plans based on success stories. Specifically, based on success stories, they propose concrete steps and methods for realizing the client's idea. Improvement measures include clearly indicating what changes or adjustments are needed for the client's idea. Implementation plans include providing a detailed schedule and resource allocation for the client to implement the idea. This allows the Service Provider to support clients in taking concrete actions based on success stories and increase the feasibility of their ideas. Furthermore, the Service Provider can collect feedback from clients and continuously improve the accuracy and effectiveness of the advice they provide. This allows the Service Provider to always provide clients with the best possible advice and improve the overall reliability and effectiveness of the system.

[0075] The service provider can make strategic recommendations based on success stories. For example, the service provider can propose business strategies based on success stories. For example, the service provider can also propose marketing strategies based on success stories. Furthermore, the service provider can propose technology strategies based on success stories. For example, the service provider can propose product improvements based on success stories. Furthermore, the service provider can propose strategies for entering new markets based on success stories. In addition, the service provider can propose revisions to customer targets based on success stories. In this way, by making strategic recommendations based on success stories, the service provider can effectively support the customer's business strategy. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can make strategic recommendations using an AI model that takes success stories identified by the success story identification unit as input and outputs strategic recommendations.

[0076] The service provider can capture market changes in real time and update advice accordingly. For example, the service provider can analyze the latest market data and adjust the client's business strategy as needed. For instance, if a new competitor emerges, the service provider can provide advice that takes this impact into account. The service provider can also analyze market trends and update the client's business strategy. For example, the service provider can analyze market trends and suggest improvements to the client's products and services. The service provider can also review the client's marketing strategy in response to market changes. For example, the service provider can suggest the use of new marketing channels. By providing up-to-date advice that responds to market changes, the service provider can strengthen the client's business resilience. Some or all of the above processes performed by the service provider may be carried out using AI, for example, or not. For example, the service provider can update advice using an AI model that takes the latest market data as input and outputs advice.

[0077] The analysis unit can search past databases and identify similar failure cases. For example, the analysis unit can search past project data and identify similar failure cases. The analysis unit can also analyze failure patterns and identify similar failure cases. For example, the analysis unit can analyze failure patterns and identify common causes of failure. Furthermore, the analysis unit can improve its failure identification algorithm based on past databases. This allows for the rapid identification of similar failure cases by searching past databases. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can identify failure cases using an AI model that takes past databases as input and outputs similar failure cases.

[0078] The identification unit can identify the cause of failure. The identification unit can identify the cause of failure, for example, by using root cause analysis techniques. The identification unit can also identify the cause of failure, for example, by using causal relationship identification techniques. Furthermore, the identification unit can refer to past failure data to identify the cause of failure. For example, the identification unit can refer to past failure data to identify the cause of failure. This makes it possible to clarify areas for improvement in the customer's idea by identifying the cause of failure. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can identify the cause of failure using an AI model that takes data for identifying the cause of failure as input and outputs the cause of failure.

[0079] The success story identification unit can search for success stories and identify the factors contributing to their success. For example, the success story identification unit can search a success story database and identify the factors contributing to their success. The success story identification unit can also analyze the success story database and identify common factors contributing to their success. Furthermore, the success story identification unit can improve its success factor identification algorithm based on the success story database. This allows the system to clarify the customer's path to success by searching for success stories and identifying the factors contributing to their success. Some or all of the above-described processes in the success story identification unit may be performed using AI, for example, or without AI. For example, the success story identification unit can identify factors contributing to their success using an AI model that takes a success story database as input and outputs factors contributing to their success.

[0080] The reception desk can estimate the customer's emotions and adjust the timing of idea input based on the estimated emotions. For example, if the customer is feeling stressed, the reception desk can prompt them to input ideas during a time when they can relax. The reception desk can also prompt the customer to input ideas when they are concentrating. For example, if the customer is concentrating, the reception desk can prompt them to input ideas during a time when they can relax. Furthermore, if the customer is tired, the reception desk can prompt them to input ideas after a break. For example, if the customer is tired, the reception desk can prompt them to input ideas after a break. By adjusting the timing of idea input according to the customer's emotions, ideas can be input at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can use an AI model that takes customer emotional data as input and outputs the timing of idea input to adjust the timing of idea input.

[0081] The reception desk can analyze the customer's past idea submission history and select the optimal input method. For example, if the customer has previously preferred using text input, the reception desk will prioritize suggesting text input. The reception desk can also prioritize suggesting voice input if the customer has previously used voice input. Furthermore, if the customer has previously submitted ideas using images, the reception desk can prioritize suggesting image input. In this way, the reception desk can select the optimal input method by analyzing the customer's past idea submission history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can select the optimal input method using an AI model that takes the customer's past idea submission history as input and outputs the optimal input method.

[0082] The reception desk can filter ideas based on the customer's current projects and areas of interest when they are entered. For example, the reception desk can prioritize ideas related to the customer's current projects. The reception desk can also prioritize ideas related to the customer's areas of interest. Furthermore, the reception desk can also prioritize ideas related to areas the customer has shown interest in in the past. This allows for the priority input of highly relevant ideas by filtering based on the customer's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can perform filtering using an AI model that takes data on the customer's current projects and areas of interest as input and outputs filtered results.

[0083] The reception desk can estimate the customer's emotions and determine the priority of ideas to input based on the estimated emotions. For example, if the customer is excited, the reception desk will prioritize inputting creative ideas. For example, if the customer is excited, the reception desk will prioritize inputting creative ideas. The reception desk can also prioritize inputting detailed ideas if the customer is relaxed. For example, if the customer is stressed, the reception desk will prioritize inputting simple ideas. For example, if the customer is stressed, the reception desk will prioritize inputting simple ideas. This allows for the input of more appropriate ideas by determining the priority of ideas to input according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can use an AI model that takes customer emotional data as input and outputs idea priorities to determine the priority of ideas.

[0084] The reception desk can prioritize inputting highly relevant ideas by considering the customer's geographical location when ideas are entered. For example, if the customer is in a specific region, the reception desk will prioritize inputting ideas related to that region. The reception desk can also prioritize inputting ideas related to the customer's travel destination if the customer is traveling. Furthermore, if the customer is at home, the reception desk can prioritize inputting ideas related to their home. In this way, highly relevant ideas can be prioritized by considering the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input ideas using an AI model that takes the customer's geographical location as input and outputs highly relevant ideas.

[0085] The reception desk can analyze the customer's social media activity and input relevant ideas when an idea is entered. For example, the reception desk can input ideas based on the customer's interests and passions shared on social media. The reception desk can also input ideas related to topics the customer follows on social media. Furthermore, the reception desk can input ideas related to groups and communities the customer participates in on social media. This allows the reception desk to prioritize the input of relevant ideas by analyzing the customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input ideas using an AI model that takes customer social media activity data as input and outputs relevant ideas.

[0086] The analysis unit can estimate customer emotions and adjust the failure analysis method based on the estimated customer emotions. For example, if the customer is depressed, the analysis unit can perform a concise analysis of the failure. For example, if the customer is depressed, the analysis unit can perform a concise analysis of the failure. For example, if the customer is positive, the analysis unit can perform a detailed analysis. For example, if the customer is positive, the analysis unit can perform a detailed analysis. Furthermore, if the customer is anxious, the analysis unit can perform a rapid analysis. For example, if the customer is anxious, the analysis unit can perform a rapid analysis. In this way, by adjusting the failure analysis method according to customer emotions, more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can use an AI model that takes customer sentiment data as input and outputs methods for analyzing failure cases, thereby adjusting the analysis method.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the ideas during the analysis. For example, the analysis unit can perform a detailed analysis for ideas of high importance. The analysis unit can also perform a concise analysis for ideas of low importance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail for ideas of medium importance. In this way, efficient analysis can be performed by adjusting the level of detail of the analysis based on the importance of the ideas. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes idea importance data as input and outputs the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the idea during analysis. For example, the analysis unit can apply a technical analysis algorithm to a technical idea. For example, the analysis unit can apply a marketing analysis algorithm to a marketing idea. For example, the analysis unit can apply a marketing analysis algorithm to a marketing idea. Furthermore, the analysis unit can apply a business strategy analysis algorithm to a business strategy idea. For example, the analysis unit can apply a business strategy analysis algorithm to a business strategy idea. By applying different analysis algorithms depending on the category of the idea, a more appropriate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply an analysis algorithm using an AI model that takes idea category data as input and outputs an analysis algorithm.

[0089] The analysis unit can estimate the customer's emotions and determine the priority of analysis based on the estimated emotions. For example, if the customer is anxious, the analysis unit can perform a rapid analysis. For example, if the customer is anxious, the analysis unit can perform a rapid analysis. For example, if the customer is relaxed, the analysis unit can perform a detailed analysis. For example, if the customer is depressed, the analysis unit can perform a concise analysis. For example, if the customer is depressed, the analysis unit can perform a concise analysis. By determining the priority of analysis according to the customer's emotions, analyses can be performed in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority of analysis using an AI model that takes customer emotion data as input and outputs the priority of analysis.

[0090] The analysis unit can determine the priority of analysis based on when the ideas were submitted. For example, the analysis unit can prioritize the analysis of recently submitted ideas. The analysis unit can also postpone the analysis of older ideas. Furthermore, the analysis unit can analyze ideas submitted at a moderate pace. This allows for efficient analysis by determining the priority of analysis based on when the ideas were submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority of analysis using an AI model that takes idea submission date data as input and outputs the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of ideas during the analysis process. For example, the analysis unit may prioritize the analysis of ideas related to the customer's current project. The analysis unit may also prioritize the analysis of ideas related to the customer's areas of interest. Furthermore, the analysis unit may prioritize the analysis of ideas related to areas the customer has shown interest in in the past. By adjusting the order of analysis based on the relevance of ideas, more relevant ideas can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order of analysis using an AI model that takes idea relevance data as input and outputs the order of analysis.

[0092] The identification unit can estimate the customer's emotions and adjust the method for identifying the cause of failure based on the estimated customer emotions. For example, if the customer is depressed, the identification unit can concisely identify the cause of failure. For example, if the customer is depressed, the identification unit can concisely identify the cause of failure. The identification unit can also identify the cause of failure in detail if the customer is positive. For example, if the customer is positive, the identification unit can identify the cause of failure in detail. Furthermore, if the customer is anxious, the identification unit can quickly identify the cause of failure. For example, if the customer is anxious, the identification unit can quickly identify the cause of failure. In this way, by adjusting the method for identifying the cause of failure according to the customer's emotions, more appropriate cause identification can be performed. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the specific unit can use an AI model that takes customer emotional data as input and outputs a method for identifying the cause of failure, thereby adjusting the cause identification method.

[0093] The identification unit can improve the accuracy of cause identification by referring to past failure data when identifying the cause of a failure. For example, the identification unit can improve the accuracy of cause identification by referring to similar past failure data. The identification unit can also analyze past failure data and identify common causes. For example, the identification unit analyzes past failure data and identifies common causes. Furthermore, the identification unit can improve the cause identification algorithm based on past failure data. For example, the identification unit improves the cause identification algorithm based on past failure data. As a result, the accuracy of cause identification is improved by referring to past failure data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can take past failure data as input and perform cause identification using an AI model that improves the accuracy of cause identification.

[0094] The identification unit can apply different cause identification methods to each category of idea when identifying the cause of failure. For example, the identification unit can apply a technical cause identification method to a technical idea. For example, the identification unit can apply a technical cause identification method to a technical idea. The identification unit can also apply a marketing cause identification method to a marketing idea. For example, the identification unit can apply a business strategy cause identification method to a business strategy idea. In addition, the identification unit can apply a business strategy cause identification method to a business strategy idea. For example, the identification unit can apply a business strategy cause identification method to a business strategy idea. By applying different cause identification methods to each category of idea, more appropriate cause identification can be performed. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can apply a cause identification method using an AI model that takes idea category data as input and outputs a cause identification method.

[0095] The identification unit can estimate the customer's emotions and determine the priority for identifying the cause of failure based on the estimated customer emotions. For example, if the customer is anxious, the identification unit can quickly identify the cause of failure. For example, if the customer is anxious, the identification unit can quickly identify the cause of failure. For example, if the customer is relaxed, the identification unit can identify the cause of failure in detail. For example, if the customer is relaxed, the identification unit can identify the cause of failure in detail. Furthermore, if the customer is depressed, the identification unit can identify the cause of failure concisely. For example, if the customer is depressed, the identification unit can identify the cause of failure concisely. This allows for cause identification in a more appropriate order by determining the priority for identifying the cause of failure according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the specific unit can determine priorities using an AI model that takes customer emotional data as input and outputs priorities for identifying the causes of failure.

[0096] The identification unit can determine the priority of cause identification based on the submission date of the ideas when identifying the cause of failure. For example, the identification unit may prioritize identifying the cause of failure of recently submitted ideas. The identification unit may also postpone identifying the cause of failure of older ideas. Furthermore, the identification unit may moderately identify the cause of failure of ideas submitted at a moderate time. This allows for efficient cause identification by determining the priority of cause identification based on the submission date of the ideas. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can determine the priority using an AI model that takes idea submission date data as input and outputs a priority order for cause identification.

[0097] The identification unit can improve the accuracy of cause identification by referring to relevant market data for the idea when identifying the cause of failure. For example, the identification unit can improve the accuracy of cause identification by referring to relevant market data. The identification unit can also analyze market data and identify common causes of failure. For example, the identification unit can analyze market data and identify common causes of failure. Furthermore, the identification unit can improve the algorithm for identifying the cause of failure based on market data. For example, the identification unit can improve the algorithm for identifying the cause of failure based on market data. This improves the accuracy of cause identification by referring to relevant market data for the idea. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can use relevant market data as input and perform cause identification using an AI model that improves the accuracy of cause identification.

[0098] The success case identification unit can estimate customer emotions and adjust the success case identification method based on the estimated customer emotions. For example, if the customer is depressed, the success case identification unit can identify success cases concisely. For example, if the customer is depressed, the success case identification unit can identify success cases concisely. For example, if the customer is positive, the success case identification unit can identify success cases in detail. For example, if the customer is positive, the success case identification unit can identify success cases in detail. Furthermore, if the customer is anxious, the success case identification unit can identify success cases quickly. For example, if the customer is anxious, the success case identification unit can quickly identify success cases. In this way, by adjusting the success case identification method according to customer emotions, more appropriate success cases can be identified. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without AI. For example, the success case identification unit can use an AI model that takes customer sentiment data as input and outputs methods for identifying success cases, thereby adjusting the identification method.

[0099] The success case identification unit can improve the accuracy of identifying success factors by referring to past success data when identifying success cases. For example, the success case identification unit can improve the accuracy of identifying success factors by referring to similar past success data. The success case identification unit can also analyze past success data and identify common success factors. Furthermore, the success case identification unit can improve the algorithm for identifying success factors based on past success data. For example, the success case identification unit can improve the algorithm for identifying success factors based on past success data. As a result, the accuracy of identifying success factors is improved by referring to past success data. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without AI. For example, the success case identification unit can identify success factors using an AI model that takes past success data as input and improves the accuracy of identifying success factors.

[0100] The success case identification unit can apply different success factor identification methods to each category of idea when identifying success cases. For example, the success case identification unit can apply a technical success factor identification method to a technical idea. For example, the success case identification unit can apply a marketing success factor identification method to a marketing idea. For example, the success case identification unit can apply a marketing success factor identification method to a marketing idea. Furthermore, the success case identification unit can apply a business strategy success factor identification method to a business strategy idea. For example, the success case identification unit can apply a business strategy success factor identification method to a business strategy idea. By applying different success factor identification methods to each category of idea, more appropriate success factors can be identified. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without AI. For example, the success case identification unit can apply a success factor identification method using an AI model that takes idea category data as input and outputs a success factor identification method.

[0101] The success case identification unit can estimate customer emotions and prioritize success cases based on the estimated customer emotions. For example, if the customer is anxious, the success case identification unit can quickly identify success cases. For example, if the customer is anxious, the success case identification unit can quickly identify success cases. For example, if the customer is relaxed, the success case identification unit can identify success cases in detail. For example, if the customer is relaxed, the success case identification unit can identify success cases in detail. Furthermore, if the customer is depressed, the success case identification unit can identify success cases concisely. For example, if the customer is depressed, the success case identification unit can identify success cases concisely. This allows for the identification of success cases in a more appropriate order by prioritizing success cases according to customer emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without AI. For example, the success case identification unit can determine priorities using an AI model that takes customer sentiment data as input and outputs a priority ranking for success cases.

[0102] The success case identification unit can determine the priority of success cases based on the submission date of the ideas when identifying success cases. For example, the success case identification unit can prioritize the identification of recently submitted ideas. The success case identification unit can also postpone the identification of older ideas. Furthermore, the success case identification unit can moderately identify success cases of ideas submitted at a moderate time. This allows for efficient identification of success cases by determining the priority of success cases based on the submission date of the ideas. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without AI. For example, the success case identification unit can determine the priority using an AI model that takes idea submission date data as input and outputs the priority of success cases.

[0103] The success case identification unit can improve the accuracy of identifying success factors by referring to relevant market data for ideas when identifying success cases. For example, the success case identification unit can improve the accuracy of identifying success factors by referring to relevant market data. The success case identification unit can also analyze market data and identify common success factors. For example, the success case identification unit can analyze market data and identify common success factors. Furthermore, the success case identification unit can improve the algorithm for identifying success factors based on market data. For example, the success case identification unit can improve the algorithm for identifying success factors based on market data. This improves the accuracy of identifying success factors by referring to relevant market data for ideas. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without AI. For example, the success case identification unit can identify success factors using an AI model that takes relevant market data as input and improves the accuracy of identifying success factors.

[0104] The service provider can estimate the customer's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the customer is depressed, the service provider can provide advice that includes words of encouragement. For example, if the customer is depressed, the service provider can provide advice that includes words of encouragement. The service provider can also provide advice that includes specific action plans if the customer is positive. For example, if the customer is anxious, the service provider can provide advice that can be implemented quickly. For example, if the customer is anxious, the service provider can provide advice that can be implemented quickly. By adjusting the way advice is expressed according to the customer's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use an AI model that takes customer emotional data as input and outputs different ways of expressing advice, thereby adjusting the expression method.

[0105] The service provider can adjust the level of detail of the advice based on the importance of the success stories when providing advice. For example, the service provider can provide detailed advice based on highly important success stories. The service provider can also provide concise advice based on less important success stories. Furthermore, the service provider can provide advice with a moderate level of detail based on moderately important success stories. By adjusting the level of detail of the advice based on the importance of the success stories, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can adjust the level of detail using an AI model that takes success story importance data as input and outputs the level of detail of the advice.

[0106] The service provider can apply different advice algorithms depending on the category of the success story when providing advice. For example, the service provider can apply a technical advice algorithm to a technical success story. For example, the service provider can apply a marketing advice algorithm to a marketing success story. For example, the service provider can apply a business strategy advice algorithm to a business strategy success story. For example, the service provider can apply a business strategy advice algorithm to a business strategy success story. By applying different advice algorithms depending on the category of the success story, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can apply an advice algorithm using an AI model that takes success story category data as input and outputs an advice algorithm.

[0107] The service provider can estimate the customer's emotions and prioritize advice based on those emotions. For example, if the customer is anxious, the service provider will prioritize advice that can be acted upon quickly. The service provider can also prioritize detailed advice if the customer is relaxed. Furthermore, if the customer is depressed, the service provider can prioritize advice that includes words of encouragement. This allows advice to be delivered in a more appropriate order by prioritizing it according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can use an AI model that takes customer emotional data as input and outputs an order of priority for advice to determine priorities.

[0108] The service provider can prioritize advice based on the submission timing of success stories when providing advice. For example, the service provider may prioritize advice based on recent success stories. The service provider may also postpone advice based on older success stories. Furthermore, the service provider may provide advice moderately based on success stories with a moderate submission timing. This allows for the efficient provision of advice by prioritizing it based on the submission timing of success stories. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can determine priorities using an AI model that takes success story submission timing data as input and outputs advice priorities.

[0109] The service provider can adjust the order of advice based on the relevance of success stories when providing advice. For example, the service provider may prioritize advice based on success stories related to the customer's current project. The service provider may also prioritize advice based on success stories related to the customer's areas of interest. Furthermore, the service provider may prioritize advice based on success stories related to areas the customer has shown interest in in the past. By adjusting the order of advice based on the relevance of success stories, more relevant advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can adjust the order using an AI model that takes relevance data of success stories as input and outputs the order of advice.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can estimate the customer's emotions and adjust the timing of idea input based on the estimated emotions. For example, if the customer is stressed, it can prompt them to input ideas during a time when they can relax. It can also prompt them to input ideas when they are focused. Furthermore, if the customer is tired, it can prompt them to input ideas after a break. This allows for more appropriate timing of idea input by adjusting the timing according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can adjust the timing of idea input using an AI model that takes customer emotion data as input and outputs the timing of idea input.

[0112] The service provider can estimate the customer's emotions and adjust the way advice is expressed based on those emotions. For example, if the customer is depressed, it can provide advice that includes words of encouragement. If the customer is positive, it can provide advice that includes specific action plans. Furthermore, if the customer is anxious, it can provide advice that can be acted upon quickly. By adjusting the way advice is expressed according to the customer's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can adjust the way advice is expressed using an AI model that takes customer emotion data as input and outputs the way advice is expressed.

[0113] The analysis unit can estimate customer emotions and adjust the failure analysis method based on the estimated customer emotions. For example, if a customer is depressed, the failure analysis can be simplified. If a customer is positive, a detailed analysis can be performed. Furthermore, if a customer is anxious, a rapid analysis can be performed. By adjusting the failure analysis method according to customer emotions, more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the analysis method using an AI model that takes customer emotion data as input and outputs a failure analysis method.

[0114] The identification unit can estimate the customer's emotions and adjust the failure cause identification method based on the estimated customer emotions. For example, if the customer is depressed, the failure cause can be identified concisely. If the customer is positive, the failure cause can be identified in detail. Furthermore, if the customer is anxious, the failure cause can be identified quickly. By adjusting the failure cause identification method according to the customer's emotions, more appropriate cause identification can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can adjust the cause identification method using an AI model that takes customer emotion data as input and outputs a failure cause identification method.

[0115] The success case identification unit can estimate customer emotions and adjust the success case identification method based on the estimated customer emotions. For example, if a customer is depressed, success cases can be identified concisely. If a customer is positive, success cases can be identified in detail. Furthermore, if a customer is anxious, success cases can be identified quickly. By adjusting the success case identification method according to customer emotions, more appropriate success cases can be identified. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without AI. For example, the success case identification unit can adjust the identification method using an AI model that takes customer emotion data as input and outputs a success case identification method.

[0116] The reception desk can analyze the customer's past idea submission history and select the optimal input method. For example, if the customer has previously preferred text input, it can prioritize suggesting text input. Similarly, if the customer has previously used voice input, it can prioritize suggesting voice input. Furthermore, if the customer has previously submitted ideas using images, it can prioritize suggesting image input. In this way, the optimal input method can be selected by analyzing the customer's past idea submission history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can select the optimal input method using an AI model that takes the customer's past idea submission history as input and outputs the optimal input method.

[0117] The service provider can capture market changes in real time and update advice accordingly. For example, it can analyze the latest market data and adjust the client's business strategy as needed. It can also provide advice that takes into account the impact of new competitors that emerge. Furthermore, it can analyze market trends and suggest improvements to the client's products and services. By providing up-to-date advice that responds to market changes, it can strengthen the client's business resilience. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can update advice using an AI model that takes the latest market data as input and outputs advice.

[0118] The analysis unit can adjust the level of detail of the analysis based on the importance of the ideas during the analysis. For example, a detailed analysis can be performed on highly important ideas. Conversely, a concise analysis can be performed on less important ideas. Furthermore, an analysis with an appropriate level of detail can be performed on moderately important ideas. By adjusting the level of detail of the analysis based on the importance of the ideas, efficient analysis can be performed. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that takes idea importance data as input and outputs the level of detail of the analysis.

[0119] The identification unit can improve the accuracy of cause identification by referring to past failure data when identifying the cause of a failure. For example, it can improve the accuracy of cause identification by referring to similar past failure data. It can also analyze past failure data to identify common causes. Furthermore, it can improve the cause identification algorithm based on past failure data. As a result, the accuracy of cause identification is improved by referring to past failure data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can take past failure data as input and perform cause identification using an AI model that improves the accuracy of cause identification.

[0120] The success case identification unit can improve the accuracy of identifying success factors by referring to past success data when identifying success cases. For example, it can improve the accuracy of identifying success factors by referring to similar past success data. It can also analyze past success data to identify common success factors. Furthermore, it can improve the algorithm for identifying success factors based on past success data. As a result, the accuracy of identifying success factors is improved by referring to past success data. Some or all of the above processing in the success case identification unit may be performed using AI, for example, or without using AI. For example, the success case identification unit can take past success data as input and identify success factors using an AI model that improves the accuracy of identifying success factors.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives the customer's ideas. These ideas can include business ideas, technical ideas, and creative ideas. The reception desk can accept ideas via text input, voice input, image input, etc. For example, customers can input ideas in text format, voice using speech recognition technology, or images using image recognition technology. Step 2: The analysis department analyzes the ideas submitted by the reception department and identifies similar past failures. The analysis department analyzes the ideas using methods such as text analysis, data mining, and machine learning algorithms. For example, it can analyze the content of the submitted ideas using text analysis techniques and find similar failures from past databases using data mining techniques. It can also evaluate the similarity between the submitted ideas and past failures using machine learning algorithms. Step 3: The Identification Department analyzes the failure cases identified by the Analysis Department and identifies the causes of the failures. The Identification Department identifies the causes of failures using methods such as root cause analysis and causal relationship identification. For example, it may use root cause analysis techniques and causal relationship identification techniques to identify the causes of failures and refer to past failure data. Step 4: The success case identification unit finds success cases based on the causes of failure identified by the identification unit. The success case identification unit searches the success case database and identifies the factors for success. For example, it can search the success case database, identify factors for success, and identify common success factors. It can also improve the success factor identification algorithm based on the success case database. Step 5: The Service Provider provides advice based on the success stories identified by the Success Story Identification Provider. The Service Provider provides advice such as specific suggestions, improvement measures, and implementation plans. For example, they can provide specific suggestions, improvement measures, and implementation plans based on success stories.

[0123] 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, success case identification unit, and provision unit, is implemented, for example, by 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, which can input customer ideas in text, voice, or image format. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the input ideas and finds similar past failure cases. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes failure cases and identifies the causes of failure. The success case identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which finds success cases and identifies the factors for success. The provision unit is implemented, for example, by the output device 40 of the smart device 14, which provides advice based on success cases. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, success case identification 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, which can input customer ideas in voice format. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the input ideas and finds similar past failure cases. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes failure cases and identifies the causes of failure. The success case identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which finds success cases and identifies the factors for success. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214, which provides advice based on success cases. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, success case identification 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, allowing customers to input their ideas in voice format. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the input ideas and finds similar past failure cases. The identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes failure cases and identifies the causes of failure. The success case identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which finds success cases and identifies the factors for success. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314, which provides advice based on success cases. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, success case identification 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, which can input customer ideas in voice format. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the input ideas and finds similar past failure cases. The identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes failure cases and identifies the causes of failure. The success case identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which finds success cases and identifies the factors for success. The provision unit is implemented by, for example, the speaker 240 of the robot 414, which provides advice based on success cases. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reception area where customers input their ideas, The analysis unit analyzes the ideas entered by the reception unit and finds similar past failures, The analysis unit analyzes the failure cases identified by the aforementioned analysis unit and identifies the cause of the failure, A success case identification unit that finds success cases based on the cause of failure identified by the aforementioned identification unit, The system includes a provision unit that provides advice based on success stories identified by the aforementioned success story identification unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, We make strategic recommendations based on successful case studies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We track market changes in real time and update our advice accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Search past databases to identify similar failure cases. The system described in Appendix 1, characterized by the features described herein. (Note 5) The specified part is, Identify the cause of the failure The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned success case identification unit is: Search for success stories and identify the factors behind their success. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Estimate customer emotions and adjust the timing of idea input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the customer's past idea submission history to select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When customers submit ideas, the system filters them based on their current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Estimate customer emotions and prioritize input ideas based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering ideas, the system prioritizes highly relevant ideas by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering ideas, the system analyzes the customer's social media activity and inputs relevant ideas. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate customer emotions and adjust the analysis method for failure cases based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the ideas. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is The system estimates customer emotions and prioritizes analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, the priority of the analysis will be determined based on when the ideas were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the ideas. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, We estimate customer emotions and adjust the methods for identifying the causes of failure based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, When identifying the cause of a failure, referencing past failure data improves the accuracy of cause identification. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, When identifying the cause of failure, apply different cause identification methods to each category of idea. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, Estimate customer emotions and prioritize identifying the causes of failure based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, When identifying the cause of failure, prioritize the cause identification based on when the idea was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The specified part is, When identifying the cause of failure, referencing relevant market data for the idea improves the accuracy of root cause identification. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned success case identification unit is: We estimate customer emotions and adjust the method for identifying success stories based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned success case identification unit is: When identifying success stories, referencing past success data improves the accuracy of identifying success factors. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned success case identification unit is: When identifying success stories, apply different success factor identification methods to each idea category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned success case identification unit is: Estimate customer emotions and prioritize success stories based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned success case identification unit is: When identifying successful cases, prioritize them based on when the ideas were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned success case identification unit is: When identifying success stories, referencing relevant market data for the idea improves the accuracy of identifying success factors. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate the customer's emotions and adjust the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the success stories. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of the success story. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates customer emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when success stories are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing advice, adjust the order of advice based on the relevance of success stories. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 area where customers input their ideas, The analysis unit analyzes the ideas entered by the reception unit and finds similar past failures, The analysis unit analyzes the failure cases identified by the aforementioned analysis unit and identifies the cause of the failure, A success case identification unit that finds success cases based on the cause of failure identified by the aforementioned identification unit, The system includes a provision unit that provides advice based on success stories identified by the aforementioned success story identification unit. A system characterized by the following features.

2. The aforementioned supply unit is, We make strategic recommendations based on successful case studies. The system according to feature 1.

3. The aforementioned supply unit is, We track market changes in real time and update our advice accordingly. The system according to feature 1.

4. The aforementioned analysis unit is Search past databases to identify similar failure cases. The system according to feature 1.

5. The specified part is, Identify the cause of the failure The system according to feature 1.

6. The aforementioned success case identification unit is: Search for success stories and identify the factors behind their success. The system according to feature 1.

7. The aforementioned reception unit is Estimate customer emotions and adjust the timing of idea input based on those estimated emotions. The system according to feature 1.

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

9. The aforementioned reception unit is When customers submit ideas, the system filters them based on their current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is Estimate customer emotions and prioritize input ideas based on those estimated emotions. The system according to feature 1.

Images