system

Abstract

The system according to this embodiment aims to aggregate research data, automatically extract important insights, and support rapid decision-making. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a support unit. The data collection unit aggregates research data. The analysis unit analyzes the data aggregated by the data collection unit and automatically extracts important insights. The support unit supports rapid decision-making based on the insights extracted by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the research data is siloed, making it difficult to extract important insights and make quick decisions.

[0005] The system according to the embodiment aims to aggregate research data, automatically extract important insights, and support quick decision-making.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a support unit. The collection unit aggregates research data. The analysis unit analyzes the data aggregated by the collection unit and automatically extracts important insights. The support unit supports quick decision-making based on the insights extracted by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can aggregate research data, automatically extract important insights, and support rapid decision-making. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 multi-AI agent platform according to an embodiment of the present invention is a system that leverages generative AI technology to solve the challenges faced by product managers and UX researchers within an organization. This system uses LLM (Large-Scale Language Model) to aggregate research data from trusted sources, and the generative AI analyzes the aggregated data to automatically extract key insights, thereby preventing data silos and promoting company-wide data utilization. Furthermore, the generative AI supports rapid decision-making based on the extracted insights. For example, the generative AI provides insights into business strategies, marketing initiatives, customer experience strategies, and new business ideas, enabling product managers and UX researchers to make quick decisions. This platform prevents data silos and solves the problem of databases becoming overly reliant on individual expertise. It also enables holistic decision-making by aggregating data from trusted sources and providing insights that grow over time. This promotes data utilization across the entire organization and improves business agility. Thus, the multi-AI agent platform can efficiently solve the challenges faced by product managers and UX researchers within an organization and support rapid decision-making.

[0029] The multi-AI agent platform according to this embodiment comprises a data collection unit, an analysis unit, and a support unit. The data collection unit aggregates research data. The data collection unit aggregates research data from, for example, reliable sources. Reliable sources include, but are not limited to, official databases, reports from professional organizations, and user interviews. The data collection unit can automatically collect research data from reliable sources using generative AI. For example, the data collection unit inputs a prompt to the generative AI, "Please collect research data from reliable sources," and the generative AI automatically collects the data. The analysis unit analyzes the data aggregated by the data collection unit and automatically extracts important insights. The analysis unit analyzes the data using, for example, machine learning algorithms and natural language processing techniques. Important insights include, but are not limited to, business importance, frequency, and impact. The analysis unit can automatically extract important insights from the aggregated data using generative AI. For example, the analysis unit inputs a prompt to the generative AI, "Please extract important insights from the aggregated data," and the generative AI automatically extracts the insights. The support unit assists in rapid decision-making based on insights extracted by the analysis unit. The support unit provides, for example, dialogue-based decision support and automated judgment. Dialogue-based support includes, but is not limited to, chatbots and voice assistants. The support unit can use generative AI to provide dialogue-based decision support and automated judgment. For example, the support unit can input a prompt to the generative AI such as "Please provide dialogue-based decision support," and the generative AI can automatically provide decision support. This enables the multi-AI agent platform according to the embodiment to efficiently aggregate, analyze, and support decision-making based on research data.

[0030] The data collection unit aggregates research data. For example, the data collection unit aggregates research data from reliable sources. Reliable sources include, but are not limited to, official databases, reports from professional organizations, and user interviews. The data collection unit can automatically collect research data from reliable sources using generative AI. For example, the data collection unit can input a prompt to the generative AI such as "Collect research data from reliable sources," and the generative AI will automatically collect the data. Specifically, the generative AI crawls publicly available databases on the internet and the websites of professional organizations to collect the latest reports and statistical data. User interview data can also be transcribed using speech recognition technology and imported into the data collection unit. The data collection unit centrally manages this data and performs filtering to eliminate data duplication and inconsistencies. Furthermore, the data collection unit can implement a scoring system to evaluate data reliability and exclude unreliable data. This allows the data collection unit to efficiently aggregate high-quality, reliable research data. The data collection unit stores the collected data in cloud storage, making it easily accessible to the analysis and support units. Furthermore, the data collection unit can set the data update frequency and regularly collect new data, ensuring that the latest information is always available. This allows the data collection unit to automate the research data aggregation process, enabling efficient and effective data collection.

[0031] The analysis unit analyzes the data aggregated by the collection unit and automatically extracts key insights. The analysis unit analyzes the data using, for example, machine learning algorithms and natural language processing techniques. Key insights include, but are not limited to, business importance, frequency, and impact. The analysis unit can also automatically extract key insights from aggregated data using generative AI. For example, the analysis unit can input a prompt to the generative AI such as "Extract key insights from the aggregated data," and the generative AI will automatically extract the insights. Specifically, the generative AI analyzes text data using natural language processing techniques and extracts frequently occurring keywords and phrases. It also uses machine learning algorithms to analyze data patterns and trends and identify key business insights. For example, it can analyze sales data to predict fluctuations in demand for specific products or services, or analyze customer feedback to identify factors that lead to improved customer satisfaction. The analysis unit visualizes these insights and displays them as graphs and charts, making them easily understandable to users. Furthermore, the analysis unit can evaluate the current situation by comparing it with historical data and predict future risks and opportunities. This allows the analytics department to quickly and accurately analyze collected data and provide critical business insights. The analytics department stores the data analysis results in cloud storage, making them easily accessible to the support department. Furthermore, the analytics department can automate the data analysis process and regularly provide new insights, ensuring that information is always up-to-date. This streamlines the data analysis process and supports business decision-making.

[0032] The support department assists in rapid decision-making based on insights extracted by the analytics department. The support department provides, for example, conversational decision-making support and automated judgment. Conversational support includes, but is not limited to, chatbots and voice assistants. The support department can use generative AI to provide conversational decision-making support and automated judgment. For example, the support department can input a prompt to the generative AI such as "Please provide conversational decision-making support," and the generative AI will automatically provide decision-making support. Specifically, the generative AI generates appropriate answers to user questions and requests based on insights provided by the analytics department. For example, if a user asks, "Which markets should we focus on in the next quarter?", the generative AI will identify the most promising markets and explain the reasons based on collected data and analysis results. Furthermore, the support department can learn from the user's past decision-making history and provide personalized support based on the user's preferences and tendencies. In addition, the support department can simulate multiple scenarios and propose the optimal decision. For example, it can compare multiple strategies for launching a new product, evaluate the risks and returns of each, and propose the most effective strategy. The support department visualizes these suggestions to make them easily understandable to users. Furthermore, the support department can collect user feedback to continuously improve the accuracy and effectiveness of decision support. This allows the support department to provide users with fast and appropriate decision support, helping them succeed in their businesses. The support department stores the results of decision support in cloud storage, making them easily accessible to users. Additionally, the support department can automate the decision support process and provide new suggestions regularly, ensuring users are always up-to-date. This streamlines the decision support process and helps support business decision-making.

[0033] The data collection unit can aggregate research data from reliable sources. For example, it can aggregate research data from official databases. It can also aggregate research data from reports of specialized organizations. It can also aggregate research data from user interview records. This allows for the aggregation of highly reliable research data. Some or all of the above-described processes in the data collection unit may be performed using or without generative AI. For example, the data collection unit can input a prompt to the generative AI, "Please collect research data from official databases," and the generative AI will automatically collect the data.

[0034] The analysis unit can analyze aggregated data and automatically extract important insights. For example, the analysis unit can analyze data using machine learning algorithms. The analysis unit can also analyze data using natural language processing techniques. The analysis unit can also extract insights based on the importance of the data. This allows for the automatic extraction of important insights. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input a prompt to the generative AI, "Please extract important insights from the aggregated data," and the generative AI will automatically extract the insights.

[0035] The support unit can provide dialogue-based decision support and automated judgment. For example, the support unit can provide dialogue-based decision support using a chatbot. The support unit can also provide dialogue-based decision support using a voice assistant. The support unit can also make automated judgments using rule-based judgment. This enables dialogue-based decision support and automated judgment. Some or all of the above processes in the support unit may be performed using generative AI, or not using generative AI. For example, the support unit can input the prompt "Please provide dialogue-based decision support" to the generative AI, and the generative AI will automatically provide decision support.

[0036] The support department can provide insights into business strategy, marketing initiatives, customer experience strategies, and new business ideas. For example, the support department can provide insights into business strategy. The support department can also provide insights into marketing initiatives. The support department can also provide insights into customer experience strategies. The support department can also provide insights into new business ideas. This allows the support department to provide insights into a variety of fields. Some or all of the above processing in the support department may be performed using or without generative AI. For example, the support department may input the prompt "Please provide insights into business strategy" to the generative AI, and the generative AI will automatically provide the insights.

[0037] The support department can provide nurturing insights and enable comprehensive decision-making. For example, the support department can conduct long-term trend analysis and provide nurturing insights. The support department can also conduct continuous data collection and provide nurturing insights. The support department can prevent data silos and promote company-wide data utilization. This enables comprehensive decision-making. Some or all of the above processes in the support department may be performed using generative AI, or not. For example, the support department can input a prompt to the generative AI saying, "Conduct long-term trend analysis and provide nurturing insights," and the generative AI will automatically provide the insights.

[0038] The data collection unit can evaluate the reliability of reliable sources in real time and prioritize data collection from the most reliable sources. For example, the data collection unit can evaluate the reliability of sources in real time and prioritize data collection from highly reliable sources. The data collection unit can also periodically update the reliability of sources and determine data collection priorities based on the latest reliability. The data collection unit can also restrict data collection from less reliable sources and strengthen data collection from highly reliable sources. This allows for efficient data collection from reliable sources. Some or all of the above processes in the data collection unit may be performed using or without generative AI. For example, the data collection unit can input a prompt to the generative AI saying, "Evaluate the reliability of reliable sources and collect data from the most reliable sources," and the generative AI can automatically evaluate reliability and collect data.

[0039] The data collection unit can automatically detect data duplication and redundancy during research data collection and efficiently organize the data. For example, the data collection unit can automatically detect duplicate data during data collection, eliminate the duplication, and efficiently organize the data. The data collection unit can also automatically detect redundant data and collect only the necessary data. The data collection unit can also detect duplication and redundancy in real time during data collection and efficiently organize the data. This eliminates data duplication and redundancy and allows for efficient data organization. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input a prompt to the generative AI saying, "Detect duplication and redundancy during research data collection and efficiently organize the data," and the generative AI will automatically detect duplication and redundancy and organize the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting research data. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. The data collection unit can also collect highly relevant data by referring to the user's past travel history. The data collection unit can also update the user's geographical location information in real time and collect the most relevant data. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input a prompt to the generative AI saying, "Please collect highly relevant data considering the user's geographical location information," and the generative AI will automatically consider the geographical location information and collect the data.

[0041] The data collection unit can analyze users' social media activity and collect relevant data when collecting research data. For example, the data collection unit can analyze users' social media activity and prioritize the collection of relevant data. The data collection unit can also analyze the content of users' social media posts and collect relevant data. The data collection unit can also collect relevant data by referring to users' social media followers and accounts they follow. This allows for the collection of relevant data based on users' social media activity. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input a prompt to the generative AI saying, "Analyze the user's social media activity and collect relevant data," and the generative AI will automatically analyze the social media activity and collect the data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can also evaluate the importance of the data in real time and dynamically adjust the level of detail of the analysis. The analysis unit can also apply multiple analysis methods to high-importance data to extract detailed insights. This enables detailed analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input a prompt to the generative AI saying, "Adjust the level of detail of the analysis based on the importance of the data," and the generative AI will automatically evaluate the importance and adjust the level of detail.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data and a statistical analysis algorithm to numerical data. It can also apply an image analysis algorithm to image data and a speech analysis algorithm to speech data. The analysis unit can also select and apply the most suitable analysis algorithm depending on the data category. This enables optimal analysis according to the data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input a prompt to the generative AI saying, "Please apply the most suitable analysis algorithm according to the data category," and the generative AI will automatically select and apply the algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also dynamically adjust the analysis priority based on the data collection timing. The analysis unit may also prioritize the analysis of data from the time when important events occurred. This enables analysis with priorities based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit may input a prompt to the generative AI saying, "Please determine the priority of analysis based on the data collection timing," and the generative AI will automatically evaluate the collection timing and determine the priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also evaluate the relevance of the data in real time and dynamically adjust the order of analysis. The analysis unit can also group highly relevant data and analyze them all at once. This allows for analysis in an order based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input a prompt to the generative AI saying, "Please adjust the order of analysis based on the relevance of the data," and the generative AI can automatically evaluate the relevance and adjust the order.

[0046] The support unit can analyze the user's past decision-making history to select the optimal support method when providing decision support. For example, the support unit can analyze the user's past decision-making history and propose the optimal support method. The support unit can also select the optimal support method by referring to the user's past decision-making patterns. The support unit can also extract successful decision-making patterns from the user's past decision-making history and reflect them in the support method. This allows the support unit to provide the optimal support method based on the user's past decision-making history. Some or all of the above processing in the support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the support unit can input a prompt to the generative AI saying, "Analyze the user's past decision-making history and select the optimal support method," and the generative AI will automatically analyze the history and select a support method.

[0047] The support unit can customize the support provided during decision-making based on the user's current projects and areas of interest. For example, the support unit can prioritize providing information relevant to the user's current projects to support decision-making. The support unit can also provide relevant insights based on the user's areas of interest. The support unit can also customize the optimal support content according to the progress of the user's projects. This allows the support unit to provide optimal support content based on the user's current projects and areas of interest. Some or all of the above processes in the support unit may be performed using generative AI, or not. For example, the support unit can input a prompt to the generative AI saying, "Please customize the support content based on the user's current projects and areas of interest," and the generative AI can automatically analyze the projects and areas of interest and customize the support content.

[0048] The support unit can select the optimal support method when providing decision support, taking into account the user's geographical location information. For example, the support unit can provide relevant information based on the user's current location to support decision-making. The support unit can also select the optimal support method by referring to the user's past travel history. The support unit can also update the user's geographical location information in real time and provide the optimal support method. This allows the support unit to provide the optimal support method based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the support unit can input a prompt to the generative AI saying, "Please select the optimal support method considering the user's geographical location information," and the generative AI will automatically consider the geographical location information and select a support method.

[0049] The support unit can analyze a user's social media activity and propose support during decision-making support. For example, the support unit can analyze a user's social media activity and provide relevant insights. The support unit can also analyze the content of a user's social media posts and provide information useful for decision-making. The support unit can also propose the most suitable support by referring to the user's social media followers and followed accounts. This allows the support unit to provide the most suitable support based on the user's social media activity. Some or all of the above processes in the support unit may be performed using generative AI, or not. For example, the support unit can input a prompt to the generative AI saying, "Analyze the user's social media activity and propose support," and the generative AI will automatically analyze the social media activity and propose support.

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

[0051] The data collection unit can analyze a user's past behavior history and select the optimal method for collecting research data. For example, the unit can analyze what sources a user has collected data from in the past and collect data from similar sources. The unit can also analyze when a user has collected data in the past and collect data at similar times. The unit can also analyze what data a user has valued in the past and prioritize the collection of similar data. This enables the collection of optimal research data based on the user's past behavior history.

[0052] The data collection unit can evaluate the reliability of research data in real time and prioritize the collection of highly reliable data. For example, the data collection unit can evaluate the reliability of the data to be collected in real time and prioritize the collection of highly reliable data. The data collection unit can also periodically update the data reliability and determine the priority of data collection based on the latest reliability. The data collection unit can also limit the collection of unreliable data and strengthen the collection of highly reliable data. This allows for the efficient collection of highly reliable data.

[0053] The analysis unit can prioritize analysis based on the importance of the data during the analysis process. For example, it can prioritize analyzing high-importance data and postpone analyzing low-importance data. The analysis unit can also evaluate data importance in real time and dynamically adjust analysis priorities. The analysis unit can also apply multiple analysis methods to high-importance data to extract detailed insights. This enables detailed analysis tailored to the importance of the data.

[0054] The data collection unit can automatically detect data duplication and redundancy during research data collection, enabling efficient data organization. For example, the data collection unit can automatically detect duplicate data during data collection, eliminate the duplicates, and efficiently organize the data. The data collection unit can also automatically detect redundant data and collect only the necessary data. The data collection unit can also detect duplication and redundancy in real time during data collection and efficiently organize the data. This eliminates data duplication and redundancy, enabling efficient data organization.

[0055] The support department can analyze the user's past decision-making history to select the optimal support method when providing decision support. For example, the support department can analyze the user's past decision-making history and propose the optimal support method. The support department can also select the optimal support method by referring to the user's past decision-making patterns. The support department can also extract patterns of successful decisions from the user's past decision-making history and reflect them in the support method. This allows the support department to provide the optimal support method based on the user's past decision-making history.

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

[0057] Step 1: The data collection unit aggregates research data. The data collection unit aggregates research data from reliable sources. Reliable sources include official databases, reports from professional organizations, and user interviews. The data collection unit uses generative AI to automatically collect research data from reliable sources. For example, the data collection unit inputs a prompt to the generative AI, "Please collect research data from reliable sources," and the generative AI automatically collects the data. Step 2: The analysis unit analyzes the data aggregated by the collection unit and automatically extracts key insights. The analysis unit analyzes the data using machine learning algorithms and natural language processing techniques. Key insights include business importance, frequency, and impact. The analysis unit uses generative AI to automatically extract key insights from the aggregated data. For example, the analysis unit prompts the generative AI with "Please extract key insights from the aggregated data," and the generative AI automatically extracts the insights. Step 3: The support unit assists in rapid decision-making based on the insights extracted by the analysis unit. The support unit provides conversational decision-making support and automated judgment. Conversational support includes chatbots and voice assistants. The support unit uses generative AI to provide conversational decision-making support and automated judgment. For example, the support unit inputs a prompt to the generative AI saying, "Please provide conversational decision-making support," and the generative AI automatically provides decision-making support.

[0058] (Example of form 2) The multi-AI agent platform according to an embodiment of the present invention is a system that leverages generative AI technology to solve the challenges faced by product managers and UX researchers within an organization. This system uses LLM (Large-Scale Language Model) to aggregate research data from trusted sources, and the generative AI analyzes the aggregated data to automatically extract key insights, thereby preventing data silos and promoting company-wide data utilization. Furthermore, the generative AI supports rapid decision-making based on the extracted insights. For example, the generative AI provides insights into business strategies, marketing initiatives, customer experience strategies, and new business ideas, enabling product managers and UX researchers to make quick decisions. This platform prevents data silos and solves the problem of databases becoming overly reliant on individual expertise. It also enables holistic decision-making by aggregating data from trusted sources and providing insights that grow over time. This promotes data utilization across the entire organization and improves business agility. Thus, the multi-AI agent platform can efficiently solve the challenges faced by product managers and UX researchers within an organization and support rapid decision-making.

[0059] The multi-AI agent platform according to this embodiment comprises a data collection unit, an analysis unit, and a support unit. The data collection unit aggregates research data. The data collection unit aggregates research data from, for example, reliable sources. Reliable sources include, but are not limited to, official databases, reports from professional organizations, and user interviews. The data collection unit can automatically collect research data from reliable sources using generative AI. For example, the data collection unit inputs a prompt to the generative AI, "Please collect research data from reliable sources," and the generative AI automatically collects the data. The analysis unit analyzes the data aggregated by the data collection unit and automatically extracts important insights. The analysis unit analyzes the data using, for example, machine learning algorithms and natural language processing techniques. Important insights include, but are not limited to, business importance, frequency, and impact. The analysis unit can automatically extract important insights from the aggregated data using generative AI. For example, the analysis unit inputs a prompt to the generative AI, "Please extract important insights from the aggregated data," and the generative AI automatically extracts the insights. The support unit assists in rapid decision-making based on insights extracted by the analysis unit. The support unit provides, for example, dialogue-based decision support and automated judgment. Dialogue-based support includes, but is not limited to, chatbots and voice assistants. The support unit can use generative AI to provide dialogue-based decision support and automated judgment. For example, the support unit can input a prompt to the generative AI such as "Please provide dialogue-based decision support," and the generative AI can automatically provide decision support. This enables the multi-AI agent platform according to the embodiment to efficiently aggregate, analyze, and support decision-making based on research data.

[0060] The data collection unit aggregates research data. For example, the data collection unit aggregates research data from reliable sources. Reliable sources include, but are not limited to, official databases, reports from professional organizations, and user interviews. The data collection unit can automatically collect research data from reliable sources using generative AI. For example, the data collection unit can input a prompt to the generative AI such as "Collect research data from reliable sources," and the generative AI will automatically collect the data. Specifically, the generative AI crawls publicly available databases on the internet and the websites of professional organizations to collect the latest reports and statistical data. User interview data can also be transcribed using speech recognition technology and imported into the data collection unit. The data collection unit centrally manages this data and performs filtering to eliminate data duplication and inconsistencies. Furthermore, the data collection unit can implement a scoring system to evaluate data reliability and exclude unreliable data. This allows the data collection unit to efficiently aggregate high-quality, reliable research data. The data collection unit stores the collected data in cloud storage, making it easily accessible to the analysis and support units. Furthermore, the data collection unit can set the data update frequency and regularly collect new data, ensuring that the latest information is always available. This allows the data collection unit to automate the research data aggregation process, enabling efficient and effective data collection.

[0061] The analysis unit analyzes the data aggregated by the collection unit and automatically extracts key insights. The analysis unit analyzes the data using, for example, machine learning algorithms and natural language processing techniques. Key insights include, but are not limited to, business importance, frequency, and impact. The analysis unit can also automatically extract key insights from aggregated data using generative AI. For example, the analysis unit can input a prompt to the generative AI such as "Extract key insights from the aggregated data," and the generative AI will automatically extract the insights. Specifically, the generative AI analyzes text data using natural language processing techniques and extracts frequently occurring keywords and phrases. It also uses machine learning algorithms to analyze data patterns and trends and identify key business insights. For example, it can analyze sales data to predict fluctuations in demand for specific products or services, or analyze customer feedback to identify factors that lead to improved customer satisfaction. The analysis unit visualizes these insights and displays them as graphs and charts, making them easily understandable to users. Furthermore, the analysis unit can evaluate the current situation by comparing it with historical data and predict future risks and opportunities. This allows the analytics department to quickly and accurately analyze collected data and provide critical business insights. The analytics department stores the data analysis results in cloud storage, making them easily accessible to the support department. Furthermore, the analytics department can automate the data analysis process and regularly provide new insights, ensuring that information is always up-to-date. This streamlines the data analysis process and supports business decision-making.

[0062] The support department assists in rapid decision-making based on insights extracted by the analytics department. The support department provides, for example, conversational decision-making support and automated judgment. Conversational support includes, but is not limited to, chatbots and voice assistants. The support department can use generative AI to provide conversational decision-making support and automated judgment. For example, the support department can input a prompt to the generative AI such as "Please provide conversational decision-making support," and the generative AI will automatically provide decision-making support. Specifically, the generative AI generates appropriate answers to user questions and requests based on insights provided by the analytics department. For example, if a user asks, "Which markets should we focus on in the next quarter?", the generative AI will identify the most promising markets and explain the reasons based on collected data and analysis results. Furthermore, the support department can learn from the user's past decision-making history and provide personalized support based on the user's preferences and tendencies. In addition, the support department can simulate multiple scenarios and propose the optimal decision. For example, it can compare multiple strategies for launching a new product, evaluate the risks and returns of each, and propose the most effective strategy. The support department visualizes these suggestions to make them easily understandable to users. Furthermore, the support department can collect user feedback to continuously improve the accuracy and effectiveness of decision support. This allows the support department to provide users with fast and appropriate decision support, helping them succeed in their businesses. The support department stores the results of decision support in cloud storage, making them easily accessible to users. Additionally, the support department can automate the decision support process and provide new suggestions regularly, ensuring users are always up-to-date. This streamlines the decision support process and helps support business decision-making.

[0063] The data collection unit can aggregate research data from reliable sources. For example, it can aggregate research data from official databases. It can also aggregate research data from reports of specialized organizations. It can also aggregate research data from user interview records. This allows for the aggregation of highly reliable research data. Some or all of the above-described processes in the data collection unit may be performed using or without generative AI. For example, the data collection unit can input a prompt to the generative AI, "Please collect research data from official databases," and the generative AI will automatically collect the data.

[0064] The analysis unit can analyze aggregated data and automatically extract important insights. For example, the analysis unit can analyze data using machine learning algorithms. The analysis unit can also analyze data using natural language processing techniques. The analysis unit can also extract insights based on the importance of the data. This allows for the automatic extraction of important insights. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input a prompt to the generative AI, "Please extract important insights from the aggregated data," and the generative AI will automatically extract the insights.

[0065] The support unit can provide dialogue-based decision support and automated judgment. For example, the support unit can provide dialogue-based decision support using a chatbot. The support unit can also provide dialogue-based decision support using a voice assistant. The support unit can also make automated judgments using rule-based judgment. This enables dialogue-based decision support and automated judgment. Some or all of the above processes in the support unit may be performed using generative AI, or not using generative AI. For example, the support unit can input the prompt "Please provide dialogue-based decision support" to the generative AI, and the generative AI will automatically provide decision support.

[0066] The support department can provide insights into business strategy, marketing initiatives, customer experience strategies, and new business ideas. For example, the support department can provide insights into business strategy. The support department can also provide insights into marketing initiatives. The support department can also provide insights into customer experience strategies. The support department can also provide insights into new business ideas. This allows the support department to provide insights into a variety of fields. Some or all of the above processing in the support department may be performed using or without generative AI. For example, the support department may input the prompt "Please provide insights into business strategy" to the generative AI, and the generative AI will automatically provide the insights.

[0067] The support department can provide nurturing insights and enable comprehensive decision-making. For example, the support department can conduct long-term trend analysis and provide nurturing insights. The support department can also conduct continuous data collection and provide nurturing insights. The support department can prevent data silos and promote company-wide data utilization. This enables comprehensive decision-making. Some or all of the above processes in the support department may be performed using generative AI, or not. For example, the support department can input a prompt to the generative AI saying, "Conduct long-term trend analysis and provide nurturing insights," and the generative AI will automatically provide the insights.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of research data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily delay data collection and resume it when the user is relaxed. If the user is focused, the data collection unit can collect data at that time to efficiently gather data. If the user is tired, the data collection unit can adjust the collection timing and collect data after the user has rested. This allows research data to be collected at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input a prompt to the generative AI, "Estimate the user's emotions and adjust the timing of research data collection," and the generative AI will automatically estimate the emotions and adjust the collection timing.

[0069] The data collection unit can evaluate the reliability of reliable sources in real time and prioritize data collection from the most reliable sources. For example, the data collection unit can evaluate the reliability of sources in real time and prioritize data collection from highly reliable sources. The data collection unit can also periodically update the reliability of sources and determine data collection priorities based on the latest reliability. The data collection unit can also restrict data collection from less reliable sources and strengthen data collection from highly reliable sources. This allows for efficient data collection from reliable sources. Some or all of the above processes in the data collection unit may be performed using or without generative AI. For example, the data collection unit can input a prompt to the generative AI saying, "Evaluate the reliability of reliable sources and collect data from the most reliable sources," and the generative AI can automatically evaluate reliability and collect data.

[0070] The data collection unit can automatically detect data duplication and redundancy during research data collection and efficiently organize the data. For example, the data collection unit can automatically detect duplicate data during data collection, eliminate the duplication, and efficiently organize the data. The data collection unit can also automatically detect redundant data and collect only the necessary data. The data collection unit can also detect duplication and redundancy in real time during data collection and efficiently organize the data. This eliminates data duplication and redundancy and allows for efficient data organization. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input a prompt to the generative AI saying, "Detect duplication and redundancy during research data collection and efficiently organize the data," and the generative AI will automatically detect duplication and redundancy and organize the data.

[0071] The data collection unit can estimate the user's emotions and prioritize the research data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data and prioritize the collection of more important data. If the user is relaxed, the data collection unit can also prioritize the collection of detailed data to reduce the user's burden. If the user is in a hurry, the data collection unit can also prioritize the collection of data that can be collected quickly. This allows for the collection of research data with priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without generative AI. For example, the data collection unit may input a prompt to the generative AI saying, "Estimate the user's emotions and determine the priority of the research data to collect," and the generative AI will automatically estimate the emotions and determine the priorities.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting research data. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. The data collection unit can also collect highly relevant data by referring to the user's past travel history. The data collection unit can also update the user's geographical location information in real time and collect the most relevant data. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input a prompt to the generative AI saying, "Please collect highly relevant data considering the user's geographical location information," and the generative AI will automatically consider the geographical location information and collect the data.

[0073] The data collection unit can analyze users' social media activity and collect relevant data when collecting research data. For example, the data collection unit can analyze users' social media activity and prioritize the collection of relevant data. The data collection unit can also analyze the content of users' social media posts and collect relevant data. The data collection unit can also collect relevant data by referring to users' social media followers and accounts they follow. This allows for the collection of relevant data based on users' social media activity. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input a prompt to the generative AI saying, "Analyze the user's social media activity and collect relevant data," and the generative AI will automatically analyze the social media activity and collect the data.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and visually easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results to deepen the user's understanding. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. This allows the analysis results to be presented in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input a prompt to the generative AI saying, "Estimate the user's emotions and adjust the presentation of the analysis results," and the generative AI will automatically estimate the emotions and adjust the presentation.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can also evaluate the importance of the data in real time and dynamically adjust the level of detail of the analysis. The analysis unit can also apply multiple analysis methods to high-importance data to extract detailed insights. This enables detailed analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input a prompt to the generative AI saying, "Adjust the level of detail of the analysis based on the importance of the data," and the generative AI will automatically evaluate the importance and adjust the level of detail.

[0076] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data and a statistical analysis algorithm to numerical data. It can also apply an image analysis algorithm to image data and a speech analysis algorithm to speech data. The analysis unit can also select and apply the most suitable analysis algorithm depending on the data category. This enables optimal analysis according to the data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input a prompt to the generative AI saying, "Please apply the most suitable analysis algorithm according to the data category," and the generative AI will automatically select and apply the algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis results based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a longer analysis result with detailed explanations. If the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. This allows the analysis results to be provided at an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input a prompt to the generative AI, "Estimate the user's emotions and adjust the length of the analysis results," and the generative AI will automatically estimate the emotions and adjust the length.

[0078] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also dynamically adjust the analysis priority based on the data collection timing. The analysis unit may also prioritize the analysis of data from the time when important events occurred. This enables analysis with priorities based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit may input a prompt to the generative AI saying, "Please determine the priority of analysis based on the data collection timing," and the generative AI will automatically evaluate the collection timing and determine the priority.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also evaluate the relevance of the data in real time and dynamically adjust the order of analysis. The analysis unit can also group highly relevant data and analyze them all at once. This allows for analysis in an order based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input a prompt to the generative AI saying, "Please adjust the order of analysis based on the relevance of the data," and the generative AI can automatically evaluate the relevance and adjust the order.

[0080] The support unit can estimate the user's emotions and adjust the decision support method based on the estimated emotions. For example, if the user is stressed, the support unit can provide simple and visually easy-to-understand decision support. If the user is relaxed, the support unit can also provide detailed information to deepen the user's understanding. If the user is in a hurry, the support unit can also provide concise decision support that gets straight to the point. This enables decision support in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using generative AI or not. For example, the support unit can input a prompt to the generative AI saying, "Estimate the user's emotions and adjust the decision support method," and the generative AI will automatically estimate the emotions and adjust the method.

[0081] The support unit can analyze the user's past decision-making history to select the optimal support method when providing decision support. For example, the support unit can analyze the user's past decision-making history and propose the optimal support method. The support unit can also select the optimal support method by referring to the user's past decision-making patterns. The support unit can also extract successful decision-making patterns from the user's past decision-making history and reflect them in the support method. This allows the support unit to provide the optimal support method based on the user's past decision-making history. Some or all of the above processing in the support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the support unit can input a prompt to the generative AI saying, "Analyze the user's past decision-making history and select the optimal support method," and the generative AI will automatically analyze the history and select a support method.

[0082] The support unit can customize the support provided during decision-making based on the user's current projects and areas of interest. For example, the support unit can prioritize providing information relevant to the user's current projects to support decision-making. The support unit can also provide relevant insights based on the user's areas of interest. The support unit can also customize the optimal support content according to the progress of the user's projects. This allows the support unit to provide optimal support content based on the user's current projects and areas of interest. Some or all of the above processes in the support unit may be performed using generative AI, or not. For example, the support unit can input a prompt to the generative AI saying, "Please customize the support content based on the user's current projects and areas of interest," and the generative AI can automatically analyze the projects and areas of interest and customize the support content.

[0083] The support unit can estimate the user's emotions and prioritize decision support based on those emotions. For example, if the user is stressed, the support unit will postpone less important decision support and prioritize more important ones. If the user is relaxed, the support unit can also provide detailed information to assist with decision-making. If the user is in a hurry, the support unit can provide concise support to enable quick decision-making. This allows for decision support to be prioritized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using generative AI or not. For example, the support unit can input a prompt to the generative AI saying, "Estimate the user's emotions and determine the priority of decision support," and the generative AI will automatically estimate the emotions and determine the priorities.

[0084] The support unit can select the optimal support method when providing decision support, taking into account the user's geographical location information. For example, the support unit can provide relevant information based on the user's current location to support decision-making. The support unit can also select the optimal support method by referring to the user's past travel history. The support unit can also update the user's geographical location information in real time and provide the optimal support method. This allows the support unit to provide the optimal support method based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the support unit can input a prompt to the generative AI saying, "Please select the optimal support method considering the user's geographical location information," and the generative AI will automatically consider the geographical location information and select a support method.

[0085] The support unit can analyze a user's social media activity and propose support during decision-making support. For example, the support unit can analyze a user's social media activity and provide relevant insights. The support unit can also analyze the content of a user's social media posts and provide information useful for decision-making. The support unit can also propose the most suitable support by referring to the user's social media followers and followed accounts. This allows the support unit to provide the most suitable support based on the user's social media activity. Some or all of the above processes in the support unit may be performed using generative AI, or not. For example, the support unit can input a prompt to the generative AI saying, "Analyze the user's social media activity and propose support," and the generative AI will automatically analyze the social media activity and propose support.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The data collection unit can analyze a user's past behavior history and select the optimal method for collecting research data. For example, the unit can analyze what sources a user has collected data from in the past and collect data from similar sources. The unit can also analyze when a user has collected data in the past and collect data at similar times. The unit can also analyze what data a user has valued in the past and prioritize the collection of similar data. This enables the collection of optimal research data based on the user's past behavior history.

[0088] The analysis unit can estimate the user's emotions and adjust how the analysis results are presented based on those estimated emotions. For example, if the user is stressed, the analysis unit provides simple, visually easy-to-understand results. If the user is relaxed, the analysis unit can provide detailed results to deepen the user's understanding. If the user is in a hurry, the analysis unit can provide concise results that get straight to the point. This allows the analysis results to be delivered in an appropriate way according to the user's emotions.

[0089] The support unit can estimate the user's emotions and adjust the timing of decision-making support based on those estimates. For example, if the user is stressed, the support unit can temporarily delay decision-making support and resume it when the user is relaxed. If the user is focused, the support unit can provide decision-making support at that time to ensure efficient support. If the user is tired, the support unit can adjust the timing of support, providing it after the user has rested. This allows for decision-making support to be provided at the appropriate time according to the user's emotions.

[0090] The data collection unit can evaluate the reliability of research data in real time and prioritize the collection of highly reliable data. For example, the data collection unit can evaluate the reliability of the data to be collected in real time and prioritize the collection of highly reliable data. The data collection unit can also periodically update the data reliability and determine the priority of data collection based on the latest reliability. The data collection unit can also limit the collection of unreliable data and strengthen the collection of highly reliable data. This allows for the efficient collection of highly reliable data.

[0091] The analysis unit can prioritize analysis based on the importance of the data during the analysis process. For example, it can prioritize analyzing high-importance data and postpone analyzing low-importance data. The analysis unit can also evaluate data importance in real time and dynamically adjust analysis priorities. The analysis unit can also apply multiple analysis methods to high-importance data to extract detailed insights. This enables detailed analysis tailored to the importance of the data.

[0092] The support unit can estimate the user's emotions and customize the decision-making support based on those estimates. For example, if the user is stressed, the support unit can provide simple, visually easy-to-understand support. If the user is relaxed, the support unit can provide detailed information to deepen the user's understanding. If the user is in a hurry, the support unit can provide concise support that gets straight to the point. This allows for decision-making support tailored to the user's emotions.

[0093] The data collection unit can automatically detect data duplication and redundancy during research data collection, enabling efficient data organization. For example, the data collection unit can automatically detect duplicate data during data collection, eliminate the duplicates, and efficiently organize the data. The data collection unit can also automatically detect redundant data and collect only the necessary data. The data collection unit can also detect duplication and redundancy in real time during data collection and efficiently organize the data. This eliminates data duplication and redundancy, enabling efficient data organization.

[0094] The analysis unit can estimate the user's emotions and adjust the length of the analysis results based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis result. If the user is relaxed, the analysis unit may provide a longer analysis result that includes detailed explanations. If the user is excited, the analysis unit may provide an analysis result with visually stimulating effects. This allows the analysis results to be provided at an appropriate length according to the user's emotions.

[0095] The support department can analyze the user's past decision-making history to select the optimal support method when providing decision support. For example, the support department can analyze the user's past decision-making history and propose the optimal support method. The support department can also select the optimal support method by referring to the user's past decision-making patterns. The support department can also extract patterns of successful decisions from the user's past decision-making history and reflect them in the support method. This allows the support department to provide the optimal support method based on the user's past decision-making history.

[0096] The support team can estimate the user's emotions and prioritize decision-making support based on those estimates. For example, if the user is stressed, the support team will postpone less important decision-making support and prioritize more important ones. If the user is relaxed, the support team can provide detailed information to assist with decision-making. If the user is in a hurry, the support team can provide concise support to enable quick decision-making. This allows for decision-making support to be prioritized according to the user's emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit aggregates research data. The data collection unit aggregates research data from reliable sources. Reliable sources include official databases, reports from professional organizations, and user interviews. The data collection unit uses generative AI to automatically collect research data from reliable sources. For example, the data collection unit inputs a prompt to the generative AI, "Please collect research data from reliable sources," and the generative AI automatically collects the data. Step 2: The analysis unit analyzes the data aggregated by the collection unit and automatically extracts key insights. The analysis unit analyzes the data using machine learning algorithms and natural language processing techniques. Key insights include business importance, frequency, and impact. The analysis unit uses generative AI to automatically extract key insights from the aggregated data. For example, the analysis unit prompts the generative AI with "Please extract key insights from the aggregated data," and the generative AI automatically extracts the insights. Step 3: The support unit assists in rapid decision-making based on the insights extracted by the analysis unit. The support unit provides conversational decision-making support and automated judgment. Conversational support includes chatbots and voice assistants. The support unit uses generative AI to provide conversational decision-making support and automated judgment. For example, the support unit inputs a prompt to the generative AI saying, "Please provide conversational decision-making support," and the generative AI automatically provides decision-making support.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and aggregates research data from reliable sources. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the aggregated data to automatically extract important insights. The support unit is implemented by the control unit 46A of the smart device 14 and provides interactive decision support and automatic judgment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and aggregates research data from reliable sources. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the aggregated data to automatically extract important insights. The support unit is implemented by the control unit 46A of the smart glasses 214 and provides interactive decision support and automatic judgment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and aggregates research data from reliable sources. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the aggregated data to automatically extract important insights. The support unit is implemented by the control unit 46A of the headset terminal 314 and provides interactive decision support and automatic judgment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and aggregates research data from reliable sources. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the aggregated data to automatically extract important insights. The support unit is implemented by the control unit 46A of the robot 414 and provides interactive decision support and automatic judgment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The data collection department aggregates research data, The analysis unit analyzes the data collected by the aforementioned collection unit and automatically extracts important insights, A support unit that assists in rapid decision-making based on the insights extracted by the analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Aggregate research data from reliable sources. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze aggregated data and automatically extract key insights. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit, Provides dialogue-based decision support and automated judgment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit, We provide insights into business strategy, marketing initiatives, customer experience strategies, and new business ideas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, We provide insights that nurture and enable comprehensive decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate user sentiment and adjust the timing of research data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The reliability of trusted sources is assessed in real time, and data is collected preferentially from the most reliable sources. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting research data, it automatically detects data duplication and redundancy and organizes the data efficiently. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate user sentiment and prioritize the research data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting research data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting research data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit, It estimates the user's emotions and adjusts the decision support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit, When providing decision support, the system analyzes the user's past decision-making history to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit, When providing decision support, customize the support based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned support unit, It estimates the user's emotions and prioritizes decision support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit, When providing decision support, the optimal support method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit, When providing decision support, we analyze the user's social media activity and propose support strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department aggregates research data, The analysis unit analyzes the data collected by the aforementioned collection unit and automatically extracts important insights, A support unit that assists in rapid decision-making based on the insights extracted by the analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Aggregate research data from reliable sources. The system according to feature 1.

3. The aforementioned analysis unit, Analyze aggregated data and automatically extract key insights. The system according to feature 1.

4. The aforementioned support unit, Provides dialogue-based decision support and automated judgment. The system according to feature 1.

5. The aforementioned support unit, We provide insights into business strategy, marketing initiatives, customer experience strategies, and new business ideas. The system according to feature 1.

6. The aforementioned support unit, We provide insights that nurture and enable comprehensive decision-making. The system according to feature 1.

7. The aforementioned collection unit is We estimate user sentiment and adjust the timing of research data collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is The reliability of trusted sources is assessed in real time, and data is collected preferentially from the most reliable sources. The system according to feature 1.

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