System

Abstract

To provide a system according to an embodiment that collects a conversation content efficiently, and analyzes the conversation content.SOLUTION: A system according to an embodiment comprises: a collection unit; a transcription unit; an analysis unit; and a providing unit. The collection unit is configured to collect conversation contents. The transcription unit is configured to transcribe the conversation content collected by the collection unit. The analysis unit is configured to analyze data transcribed by the transcription unit. The providing unit is configured to provide an analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have not been able to efficiently collect and analyze conversation content, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect and analyze conversation contents. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a transcription unit, an analysis unit, and a provision unit. The collection unit collects conversation content. The transcription unit transcribes the conversation content collected by the collection unit. The analysis unit analyzes the data transcribed by the transcription unit. The provision unit provides the analysis result obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently collect and analyze conversation contents. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A conversation analysis system according to an embodiment of the present invention is a system that transcribes all conversation content of messenger apps and brands and analyzes the voices of citizens using AI. This conversation analysis system collects conversation content of messenger apps and brands, and AI transcribes it. Next, AI analyzes the transcribed data and analyzes people's voices and thoughts in detail while avoiding personal identification. This analysis result is used for politics and commerce. For example, the conversation analysis system collects conversation content of messenger apps and brands. At this time, the conversation content is anonymized so that individuals cannot be identified, taking privacy into consideration. For example, user IDs and personal information are deleted, and only the conversation content is collected. Next, AI transcribes the collected conversation content. AI analyzes voice data and text data and converts the conversation content into text data. For example, when a voice message is sent, AI analyzes the voice and converts it into text. The transcribed data is analyzed by AI. AI analyzes the conversation content and analyzes people's voices and thoughts in detail. For example, opinions and emotions on a specific topic can be extracted and statistically analyzed. The results of this analysis are used in politics and commerce. For example, politicians can use it to draft policies that reflect the voices of the people, and companies can use it to understand consumer needs and develop product development and marketing strategies. In this way, conversation analysis systems can efficiently collect and analyze the voices of the people, and use them in politics and commerce. In this way, conversation analysis systems can efficiently collect and analyze the voices of the people, and use them in politics and commerce.

[0029] The conversation analysis system according to the embodiment includes a collection unit, a transcription unit, an analysis unit, and a provision unit. The collection unit collects conversation contents. The conversation contents include, for example, voice conversation, text chat, video conversation, and the like, but are not limited to such examples. The collection unit can acquire data, for example, by using an API of a messenger app. For example, the collection unit can collect conversation contents in real time using the API of the messenger app. The collection unit can also perform batch collection and collect conversation contents collectively at regular intervals. Furthermore, the collection unit can adjust the collection frequency and change the interval of data collection as necessary. The transcription unit transcribes the conversation contents collected by the collection unit. The transcription unit analyzes, for example, voice data and text data, and converts the conversation contents into text data. For example, the transcription unit uses voice recognition technology to convert voice data into text data with high accuracy. The transcription unit can also perform manual input and manually input text data. Furthermore, the transcription unit can set a standard of accuracy and maintain the accuracy of the transcription at a certain level or higher. The analysis unit analyzes the data transcribed by the transcription unit. The analysis unit analyzes the data using, for example, sentiment analysis or topic modeling. For example, the analysis unit can perform sentiment analysis and perform positive / negative analysis of the conversation content. In addition, the analysis unit can use topic modeling to extract topics of the conversation content and classify the data. Furthermore, the analysis unit can also analyze patterns of the conversation content using data mining technology. The provision unit provides the analysis results obtained by the analysis unit. The provision unit provides, for example, the analysis results for use in politics or commerce. For example, the provision unit provides the analysis results in a report format so that politicians and companies can use them. In addition, the provision unit can display a dashboard and visually display the analysis results. Furthermore, the provision unit can provide an API and link the analysis results with other systems. As a result, the conversation analysis system according to the embodiment can efficiently collect, transcribe, analyze, and provide the conversation content.

[0030] The collection unit collects the conversation contents. The conversation contents include, for example, voice conversation, text chat, video conversation, etc., but are not limited to such examples. The collection unit can acquire data, for example, by using an API of a messenger app. Specifically, the collection unit can collect the conversation contents in real time using the API of the messenger app. For example, the collection unit acquires the contents of text messages, voice messages, and video calls between users through the API of the messenger app, and manages these data in a centralized manner. The collection unit can also perform batch collection and collect the conversation contents at regular intervals. For example, data can be collected hourly or daily in a lump sum in preparation for subsequent processing. Furthermore, the collection unit can adjust the collection frequency and change the interval of data collection as necessary. For example, efficient data management can be achieved by increasing the collection frequency during an emergency or a specific event period and decreasing the collection frequency during normal times. Through these functions, the collection unit can efficiently collect conversation data in various formats and strengthen the data infrastructure of the entire system.

[0031] The transcription unit transcribes the conversation content collected by the collection unit. The transcription unit, for example, analyzes voice data and text data and converts the conversation content into text data. Specifically, the transcription unit uses voice recognition technology to convert voice data into text data with high accuracy. For example, a voice recognition engine is used to analyze voice data in real time and convert the spoken content into text. The transcription unit can also perform manual input and manually input text data. For example, when special terms or dialects that cannot be handled by voice recognition technology are included, the accuracy of the transcription is improved by manually correcting or supplementing. Furthermore, the transcription unit can set a standard for accuracy and maintain the accuracy of the transcription at a certain level or higher. For example, the accuracy of the voice recognition engine is periodically evaluated, and the model is updated and tuned as necessary to always maintain high accuracy. This allows the transcription unit to accurately and efficiently convert the collected conversation data into text data and prepare for subsequent analysis processing.

[0032] The analysis unit analyzes the data transcribed by the transcription unit. The analysis unit analyzes the data using, for example, sentiment analysis or topic modeling. Specifically, the analysis unit can perform sentiment analysis and perform positive / negative analysis of the conversation content. For example, natural language processing technology is used to extract emotions from text data and analyze the tone and emotional trends of the conversation. The analysis unit can also use topic modeling to extract topics of the conversation content and classify the data. For example, algorithms such as Latent Dirichlet Allocation (LDA) are used to identify the main topic of the conversation and group related remarks. Furthermore, the analysis unit can also use data mining technology to analyze patterns of the conversation content. For example, frequently occurring keywords and phrases are extracted to clarify the trends and characteristics of the conversation. This allows the analysis unit to extract useful information from the conversation data and provide various analysis results. Furthermore, the analysis unit can also perform predictive analysis of the conversation data using a machine learning model. For example, based on past conversation data, future conversation trends and patterns can be predicted and measures can be taken in advance. This enables the analysis unit to perform multifaceted analysis of the conversation data, thereby increasing the value of the entire system.

[0033] The providing unit provides the analysis results obtained by the analyzing unit. For example, the providing unit provides the analysis results for use in politics or commerce. Specifically, the providing unit provides the analysis results in a report format so that politicians and companies can use them. For example, based on the sentiment analysis results of the conversation data, an election campaign strategy is developed, and a product is improved based on customer feedback. The providing unit can also display a dashboard and visually display the analysis results. For example, the analysis results can be intuitively understood using interactive graphs and charts. Furthermore, the providing unit can provide an API and link the analysis results with other systems. For example, the analysis results can be used in real time by linking with a company's CRM system or marketing tool. This allows the providing unit to provide the analysis results in various formats and make them easily usable by users. Furthermore, the providing unit can collect user feedback and continuously improve the quality and format of the information provided. For example, a more user-friendly system is provided by reviewing the content of the report and the design of the dashboard based on the user's usage status and requests. This allows the providing unit to effectively provide the analysis results and meet the needs of users.

[0034] The collection unit can acquire data by using the API of the messenger app. The collection unit can, for example, collect conversation contents in real time by using the API of the messenger app. For example, the collection unit can collect conversation contents in real time by using the API of the messenger app. The collection unit can also perform batch collection to collect conversation contents collectively at regular intervals. Furthermore, the collection unit can adjust the collection frequency and change the interval of data collection as necessary. As a result, data acquisition can be made more efficient by using the API of the messenger app. A part or all of the above-mentioned processing in the collection unit may be performed by using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by using the API of the messenger app to the generation AI and cause the generation AI to collect data.

[0035] The transcription unit can analyze voice data or text data and convert the contents of the conversation into text data. The transcription unit can, for example, analyze voice data and convert the contents of the conversation into text data. For example, the transcription unit can use voice recognition technology to convert voice data into text data with high accuracy. The transcription unit can also analyze text data and convert the contents of the conversation into text data. For example, the transcription unit can perform manual input and manually input text data. Furthermore, the transcription unit can set a standard for accuracy and maintain the accuracy of transcription at a certain level or higher. This allows the contents of the conversation to be accurately converted into text data by analyzing the voice data or text data. Some or all of the above-mentioned processing in the transcription unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the transcription unit can input voice data into a generation AI and have the generation AI execute the conversion from voice data to text data.

[0036] The analysis unit can analyze data using sentiment analysis or topic modeling. The analysis unit can, for example, perform sentiment analysis and perform positive / negative analysis of the conversation content. For example, the analysis unit can perform sentiment scoring and evaluate the intensity of emotions in the conversation content. The analysis unit can also use topic modeling to extract topics of the conversation content and classify data. For example, the analysis unit can extract topics of the conversation content using LDA (Latent Dirichlet Allocation). Furthermore, the analysis unit can also extract topics of the conversation content using NMF (Non-negative Matrix Factorization). This improves the accuracy of data analysis by using sentiment analysis and topic modeling. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation content to the generation AI and cause the generation AI to execute sentiment analysis and topic modeling.

[0037] The providing unit can provide the analysis results for use in politics or commerce. For example, the providing unit provides the analysis results in a report format so that they can be used by politicians and companies. For example, the providing unit provides the analysis results in a PDF format so that politicians and companies can easily view them. The providing unit can also display a dashboard to visually display the analysis results. For example, the providing unit displays the analysis results as graphs or charts to provide them in a format that is visually easy to understand. Furthermore, the providing unit can provide an API to link the analysis results with other systems. For example, the providing unit provides the analysis results through an API so that other systems can use the analysis results. This creates practical value by using the analysis results in politics or commerce. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generating AI and cause the generating AI to provide the analysis results.

[0038] The collection unit can anonymize the data using a script that deletes personal information. The collection unit, for example, deletes personal information from the conversation content using a script that deletes personal information. For example, the collection unit executes a script that deletes personal information such as a name, address, and phone number to anonymize the data. The collection unit can also delete the user ID and personal information and collect only the conversation content. For example, the collection unit executes a script that deletes the user ID and anonymizes the conversation content. Furthermore, the collection unit can process data in consideration of privacy so that individuals cannot be identified. This allows data to be collected while protecting privacy by deleting personal information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input a script that deletes personal information into the generation AI and cause the generation AI to execute the anonymization of the data.

[0039] The collection unit can select an appropriate collection method based on the frequency of use of the messenger app and the time period. The collection unit selects the optimal collection method based on, for example, the frequency of use of the messenger app and the time period. For example, the collection unit concentrates collection during the time period when the user frequently uses the messenger app. The collection unit can also refrain from collection during the time period when the user does not use the app much. Furthermore, the collection unit can adjust the interval of collection according to the frequency of use of the user. This enables efficient data collection by selecting a collection method based on the frequency of use and the time period. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the usage data of the messenger app to the generation AI and cause the generation AI to select the optimal collection method.

[0040] The collection unit can perform filtering based on a specific keyword or phrase when collecting the conversation content. For example, the collection unit performs filtering based on a specific keyword or phrase when collecting the conversation content. For example, the collection unit collects only conversations that include a specific keyword. The collection unit can also preferentially collect conversations that include a specific phrase. Furthermore, the collection unit can determine the priority of conversations to be collected based on keywords or phrases. This makes it possible to efficiently collect necessary data by filtering based on specific keywords or phrases. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the conversation content to the generation AI and cause the generation AI to perform filtering based on keywords and phrases.

[0041] The collection unit can preferentially collect highly relevant conversations based on the geographical location information of the user when collecting the conversation contents. For example, the collection unit preferentially collects highly relevant conversations by considering the geographical location information of the user when collecting the conversation contents. For example, when the user is in a specific area, the collection unit preferentially collects conversation contents related to the area. In addition, when the user is moving, the collection unit can also preferentially collect conversation contents related to the destination. Furthermore, when the user is participating in a specific event, the collection unit can also preferentially collect conversation contents related to the event. In this way, by considering the geographical location information, highly relevant conversation contents can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to collect highly relevant conversation contents.

[0042] The collection unit can analyze the user's social media activity and collect related conversations when collecting the conversation content. For example, the collection unit analyzes the user's social media activity and collects related conversations when collecting the conversation content. For example, the collection unit collects related conversations based on the content shared by the user on social media. The collection unit can also preferentially collect conversation content of accounts that the user follows on social media. Furthermore, the collection unit can also preferentially collect conversation content of groups in which the user participates on social media. This makes it possible to efficiently collect related conversation content by analyzing social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related conversation content.

[0043] The transcription unit can adjust the level of detail of the transcription based on the importance of the conversation when transcribing. For example, the transcription unit adjusts the level of detail of the transcription based on the importance of the conversation when transcribing. For example, the transcription unit transcribes important conversation contents in detail. The transcription unit can also transcribe everyday conversation contents briefly. Furthermore, the transcription unit can also transcribe conversation contents containing specific keywords in detail. This allows for efficient transcription by adjusting the level of detail based on the importance of the conversation. A part or all of the above-mentioned processing in the transcription unit may be performed using, for example, AI, or may be performed without using AI. For example, the transcription unit can input the conversation contents to the generation AI and cause the generation AI to adjust the level of detail of the transcription based on the importance.

[0044] The transcription unit can apply different transcription algorithms depending on the category of the conversation when transcribing. For example, the transcription unit applies different transcription algorithms depending on the category of the conversation when transcribing. For example, the transcription unit applies a business transcription algorithm to a business conversation. The transcription unit can also apply an everyday transcription algorithm to an everyday conversation. Furthermore, the transcription unit can apply a technical transcription algorithm to a technical conversation. This enables highly accurate transcription by applying an algorithm depending on the category of the conversation. Part or all of the above-mentioned processing in the transcription unit may be performed using, for example, AI, or may be performed without using AI. For example, the transcription unit can input the content of the conversation to the generation AI and cause the generation AI to apply a transcription algorithm depending on the category.

[0045] The transcription unit can determine the priority of transcription based on the time of occurrence of the conversation when transcribing. For example, the transcription unit determines the priority of transcription based on the time of occurrence of the conversation when transcribing. For example, the transcription unit prioritizes transcription of recent conversation content. The transcription unit can also prioritize transcription of conversation content during a specific event period. Furthermore, the transcription unit can also prioritize transcription of important conversation content in the past. This enables efficient transcription by determining the priority based on the time of occurrence. A part or all of the above-mentioned processing in the transcription unit may be performed using, for example, AI, or may be performed without using AI. For example, the transcription unit can input the conversation content to the generation AI and cause the generation AI to determine the priority of transcription based on the time of occurrence.

[0046] The transcription unit can adjust the order of transcription based on the relevance of the conversation during transcription. For example, the transcription unit adjusts the order of transcription based on the relevance of the conversation during transcription. For example, the transcription unit prioritizes transcribing highly relevant conversation content. The transcription unit can also postpone less relevant conversation content. Furthermore, the transcription unit can also prioritize transcribing conversation content related to a specific topic. This allows for efficient transcription by adjusting the order based on relevance. A part or all of the above-mentioned processing in the transcription unit may be performed using, for example, AI, or may be performed without using AI. For example, the transcription unit can input the conversation content to a generation AI and cause the generation AI to adjust the order of transcription based on relevance.

[0047] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the conversation during the analysis. The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the conversation during the analysis, for example. For example, the analysis unit performs the analysis by taking into account the context of the conversation. The analysis unit can also perform the analysis by taking into account the relationship with the other person in the conversation. Furthermore, the analysis unit can also perform the analysis by taking into account the context of the conversation. As a result, the accuracy of the analysis is improved by taking into account the interrelationships of the conversation. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the contents of the conversation to the generation AI and cause the generation AI to perform an analysis taking into account the interrelationships.

[0048] The analysis unit can perform the analysis while taking into consideration the attribute information of the speaker of the conversation. For example, the analysis unit performs the analysis while taking into consideration the attribute information of the speaker of the conversation. For example, the analysis unit performs the analysis while taking into consideration the age and gender of the speaker. The analysis unit can also perform the analysis while taking into consideration the occupation and position of the speaker. Furthermore, the analysis unit can also perform the analysis while taking into consideration the region and cultural background of the speaker. This allows for more detailed analysis by taking into consideration the attribute information of the speaker. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the attribute information of the speaker to the generation AI and cause the generation AI to perform an analysis while taking into consideration the attribute information.

[0049] The analysis unit can perform the analysis while taking into consideration the geographical distribution of the conversation. For example, the analysis unit performs the analysis while taking into consideration the geographical distribution of the conversation. For example, the analysis unit focuses on analyzing the conversation content related to a specific region. The analysis unit can also analyze the conversation content over a wide geographical range. Furthermore, the analysis unit can analyze the difference in the conversation content for each region. In this way, the difference in the conversation content for each region can be analyzed by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the conversation content to the generation AI and cause the generation AI to perform an analysis taking the geographical distribution into consideration.

[0050] The analysis unit can improve the accuracy of the analysis by referring to literature related to the conversation during the analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to the conversation during the analysis. For example, the analysis unit performs the analysis by referring to related academic papers. The analysis unit can also perform the analysis by referring to related news articles. Furthermore, the analysis unit can also perform the analysis by referring to related books. As a result, the accuracy of the analysis is improved by referring to related literature. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the content of the conversation to the generation AI and cause the generation AI to perform an analysis with reference to related literature.

[0051] The providing unit can adjust the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit adjusts the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit provides important analysis results in detail. The providing unit can also provide daily analysis results concisely. Furthermore, the providing unit can also provide analysis results containing specific keywords in detail. This enables efficient information provision by adjusting the level of detail based on the importance. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the analysis result to the generating AI and cause the generating AI to adjust the level of detail of the provision based on the importance.

[0052] The providing unit can apply different providing methods depending on the category of the analysis result at the time of providing. For example, the providing unit applies different providing methods depending on the category of the analysis result at the time of providing. For example, the providing unit provides the analysis result related to politics in the form of a policy proposal. The providing unit can also provide the analysis result related to commerce in the form of a marketing strategy. Furthermore, the providing unit can provide the analysis result related to social issues in the form of a report. This makes it possible to provide appropriate information by applying a providing method according to the category. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the analysis result to the generating AI and cause the generating AI to apply the providing method according to the category.

[0053] The providing unit can determine the priority of provision based on the time of occurrence of the analysis result at the time of provision. For example, the providing unit determines the priority of provision based on the time of occurrence of the analysis result at the time of provision. For example, the providing unit provides recent analysis results preferentially. The providing unit can also provide analysis results during a specific event period preferentially. Furthermore, the providing unit can also provide important analysis results in the past preferentially. This enables efficient information provision by determining the priority based on the time of occurrence. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis result to the generating AI and cause the generating AI to execute the determination of the priority of provision based on the time of occurrence.

[0054] The providing unit can adjust the order of provision based on the relevance of the analysis results at the time of provision. For example, the providing unit adjusts the order of provision based on the relevance of the analysis results at the time of provision. For example, the providing unit provides analysis results with high relevance preferentially. The providing unit can also postpone analysis results with low relevance. Furthermore, the providing unit can also provide analysis results related to a specific topic preferentially. This enables efficient information provision by adjusting the order based on relevance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the analysis results to the generating AI and cause the generating AI to adjust the order of provision based on relevance.

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

[0056] The conversation analysis system may further include a behavior analysis unit that analyzes the behavioral patterns of the user. The behavior analysis unit may, for example, analyze the frequency and time periods of the user's use of a messenger app to identify the user's behavioral patterns. The behavior analysis unit may also analyze the user's past conversation history to extract specific behavioral patterns. Furthermore, the behavior analysis unit may optimize the timing and method of collecting conversation contents based on the user's behavioral patterns. This allows for more efficient data collection by taking the user's behavioral patterns into account.

[0057] The conversation analysis system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit may monitor, for example, the user's heart rate and stress level to grasp the user's health condition. The health monitoring unit may also adjust the timing and method of collecting conversation contents based on the user's health condition. Furthermore, the health monitoring unit may determine the priority of the conversation contents to be collected according to the user's health condition. This allows for more appropriate data collection by taking the user's health condition into consideration.

[0058] The conversation analysis system may further include an interest analysis unit that analyzes the user's hobbies and interests. The interest analysis unit may, for example, analyze the user's conversation content on a messenger app or the content posted on social media to identify the user's hobbies and interests. The interest analysis unit may also determine the priority of the conversation content to be collected based on the user's hobbies and interests. Furthermore, the interest analysis unit may adjust the method of analyzing the conversation content according to the user's hobbies and interests. This allows for more relevant data collection by taking the user's hobbies and interests into consideration.

[0059] The conversation analysis system may further include a network analysis unit that analyzes the user's social network. The network analysis unit may, for example, analyze the user's messenger app contacts or social media followers to identify the user's social network. The network analysis unit may also determine a priority of conversation contents to be collected based on the user's social network. Furthermore, the network analysis unit may adjust the analysis method of the conversation contents according to the user's social network. This allows for more relevant data collection by taking the user's social network into account.

[0060] The conversation analysis system may further include a purchase analysis unit that analyzes the user's purchasing history. The purchase analysis unit may, for example, analyze the user's online shopping history and information on purchased items to identify the user's purchasing patterns. The purchase analysis unit may also determine the priority of conversation contents to be collected based on the user's purchasing patterns. Furthermore, the purchase analysis unit may adjust the method of analyzing the conversation contents according to the user's purchasing patterns. This makes it possible to collect more relevant data by taking the user's purchasing patterns into account.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects the conversation contents. The conversation contents include voice conversations, text chats, video conversations, etc. The collection unit can obtain data using the API of the messenger app, and can collect the conversation contents in real time or perform batch collection to collect the conversation contents at regular intervals. In addition, the collection frequency can be adjusted to change the data collection interval as needed. Step 2: The transcription unit transcribes the conversation content collected by the collection unit. The transcription unit analyzes the voice data and text data and converts the conversation content into text data. Voice data can be converted into text data with high accuracy using voice recognition technology, and text data can also be entered manually. In addition, standards can be set to maintain a certain level of transcription accuracy. Step 3: The analysis unit analyzes the data transcribed by the transcription unit. The analysis unit can analyze the data using sentiment analysis and topic modeling, and can perform positive / negative analysis of the conversation content, extract topics, and classify the data. In addition, the analysis unit can analyze patterns of the conversation content using data mining techniques. Step 4: The provision unit provides the analysis results obtained by the analysis unit. The provision unit provides the analysis results for political or commercial use, and can provide them in report format or display the analysis results visually on a dashboard. In addition, the provision unit can provide APIs to link the analysis results with other systems.

[0063] (Example 2) A conversation analysis system according to an embodiment of the present invention is a system that transcribes all conversation content of messenger apps and brands and analyzes the voices of citizens using AI. This conversation analysis system collects conversation content of messenger apps and brands, and AI transcribes it. Next, AI analyzes the transcribed data and analyzes people's voices and thoughts in detail while avoiding personal identification. This analysis result is used for politics and commerce. For example, the conversation analysis system collects conversation content of messenger apps and brands. At this time, the conversation content is anonymized so that individuals cannot be identified, taking privacy into consideration. For example, user IDs and personal information are deleted, and only the conversation content is collected. Next, AI transcribes the collected conversation content. AI analyzes voice data and text data and converts the conversation content into text data. For example, when a voice message is sent, AI analyzes the voice and converts it into text. The transcribed data is analyzed by AI. AI analyzes the conversation content and analyzes people's voices and thoughts in detail. For example, opinions and emotions on a specific topic can be extracted and statistically analyzed. The results of this analysis are used in politics and commerce. For example, politicians can use it to draft policies that reflect the voices of the people, and companies can use it to understand consumer needs and develop product development and marketing strategies. In this way, conversation analysis systems can efficiently collect and analyze the voices of the people, and use them in politics and commerce. In this way, conversation analysis systems can efficiently collect and analyze the voices of the people, and use them in politics and commerce.

[0064] The conversation analysis system according to the embodiment includes a collection unit, a transcription unit, an analysis unit, and a provision unit. The collection unit collects conversation contents. The conversation contents include, for example, voice conversation, text chat, video conversation, and the like, but are not limited to such examples. The collection unit can acquire data, for example, by using an API of a messenger app. For example, the collection unit can collect conversation contents in real time using the API of the messenger app. The collection unit can also perform batch collection and collect conversation contents collectively at regular intervals. Furthermore, the collection unit can adjust the collection frequency and change the interval of data collection as necessary. The transcription unit transcribes the conversation contents collected by the collection unit. The transcription unit analyzes, for example, voice data and text data, and converts the conversation contents into text data. For example, the transcription unit uses voice recognition technology to convert voice data into text data with high accuracy. The transcription unit can also perform manual input and manually input text data. Furthermore, the transcription unit can set a standard of accuracy and maintain the accuracy of the transcription at a certain level or higher. The analysis unit analyzes the data transcribed by the transcription unit. The analysis unit analyzes the data using, for example, sentiment analysis or topic modeling. For example, the analysis unit can perform sentiment analysis and perform positive / negative analysis of the conversation content. In addition, the analysis unit can use topic modeling to extract topics of the conversation content and classify the data. Furthermore, the analysis unit can also analyze patterns of the conversation content using data mining technology. The provision unit provides the analysis results obtained by the analysis unit. The provision unit provides, for example, the analysis results for use in politics or commerce. For example, the provision unit provides the analysis results in a report format so that politicians and companies can use them. In addition, the provision unit can display a dashboard and visually display the analysis results. Furthermore, the provision unit can provide an API and link the analysis results with other systems. As a result, the conversation analysis system according to the embodiment can efficiently collect, transcribe, analyze, and provide the conversation content.

[0065] The collection unit collects the conversation contents. The conversation contents include, for example, voice conversation, text chat, video conversation, etc., but are not limited to such examples. The collection unit can acquire data, for example, by using an API of a messenger app. Specifically, the collection unit can collect the conversation contents in real time using the API of the messenger app. For example, the collection unit acquires the contents of text messages, voice messages, and video calls between users through the API of the messenger app, and manages these data in a centralized manner. The collection unit can also perform batch collection and collect the conversation contents at regular intervals. For example, data can be collected hourly or daily in a lump sum in preparation for subsequent processing. Furthermore, the collection unit can adjust the collection frequency and change the interval of data collection as necessary. For example, efficient data management can be achieved by increasing the collection frequency during an emergency or a specific event period and decreasing the collection frequency during normal times. Through these functions, the collection unit can efficiently collect conversation data in various formats and strengthen the data infrastructure of the entire system.

[0066] The transcription unit transcribes the conversation content collected by the collection unit. The transcription unit, for example, analyzes voice data and text data and converts the conversation content into text data. Specifically, the transcription unit uses voice recognition technology to convert voice data into text data with high accuracy. For example, a voice recognition engine is used to analyze voice data in real time and convert the spoken content into text. The transcription unit can also perform manual input and manually input text data. For example, when special terms or dialects that cannot be handled by voice recognition technology are included, the accuracy of the transcription is improved by manually correcting or supplementing. Furthermore, the transcription unit can set a standard for accuracy and maintain the accuracy of the transcription at a certain level or higher. For example, the accuracy of the voice recognition engine is periodically evaluated, and the model is updated and tuned as necessary to always maintain high accuracy. This allows the transcription unit to accurately and efficiently convert the collected conversation data into text data and prepare for subsequent analysis processing.

[0067] The analysis unit analyzes the data transcribed by the transcription unit. The analysis unit analyzes the data using, for example, sentiment analysis or topic modeling. Specifically, the analysis unit can perform sentiment analysis and perform positive / negative analysis of the conversation content. For example, natural language processing technology is used to extract emotions from text data and analyze the tone and emotional trends of the conversation. The analysis unit can also use topic modeling to extract topics of the conversation content and classify the data. For example, algorithms such as Latent Dirichlet Allocation (LDA) are used to identify the main topic of the conversation and group related remarks. Furthermore, the analysis unit can also use data mining technology to analyze patterns of the conversation content. For example, frequently occurring keywords and phrases are extracted to clarify the trends and characteristics of the conversation. This allows the analysis unit to extract useful information from the conversation data and provide various analysis results. Furthermore, the analysis unit can also perform predictive analysis of the conversation data using a machine learning model. For example, based on past conversation data, future conversation trends and patterns can be predicted and measures can be taken in advance. This enables the analysis unit to perform multifaceted analysis of the conversation data, thereby increasing the value of the entire system.

[0068] The providing unit provides the analysis results obtained by the analyzing unit. For example, the providing unit provides the analysis results for use in politics or commerce. Specifically, the providing unit provides the analysis results in a report format so that politicians and companies can use them. For example, based on the sentiment analysis results of the conversation data, an election campaign strategy is developed, and a product is improved based on customer feedback. The providing unit can also display a dashboard and visually display the analysis results. For example, the analysis results can be intuitively understood using interactive graphs and charts. Furthermore, the providing unit can provide an API and link the analysis results with other systems. For example, the analysis results can be used in real time by linking with a company's CRM system or marketing tool. This allows the providing unit to provide the analysis results in various formats and make them easily usable by users. Furthermore, the providing unit can collect user feedback and continuously improve the quality and format of the information provided. For example, a more user-friendly system is provided by reviewing the content of the report and the design of the dashboard based on the user's usage status and requests. This allows the providing unit to effectively provide the analysis results and meet the needs of users.

[0069] The collection unit can acquire data by using the API of the messenger app. The collection unit can, for example, collect conversation contents in real time by using the API of the messenger app. For example, the collection unit can collect conversation contents in real time by using the API of the messenger app. The collection unit can also perform batch collection to collect conversation contents collectively at regular intervals. Furthermore, the collection unit can adjust the collection frequency and change the interval of data collection as necessary. As a result, data acquisition can be made more efficient by using the API of the messenger app. A part or all of the above-mentioned processing in the collection unit may be performed by using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by using the API of the messenger app to the generation AI and cause the generation AI to collect data.

[0070] The transcription unit can analyze voice data or text data and convert the contents of the conversation into text data. The transcription unit can, for example, analyze voice data and convert the contents of the conversation into text data. For example, the transcription unit can use voice recognition technology to convert voice data into text data with high accuracy. The transcription unit can also analyze text data and convert the contents of the conversation into text data. For example, the transcription unit can perform manual input and manually input text data. Furthermore, the transcription unit can set a standard for accuracy and maintain the accuracy of transcription at a certain level or higher. This allows the contents of the conversation to be accurately converted into text data by analyzing the voice data or text data. Some or all of the above-mentioned processing in the transcription unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the transcription unit can input voice data into a generation AI and have the generation AI execute the conversion from voice data to text data.

[0071] The analysis unit can analyze data using sentiment analysis or topic modeling. The analysis unit can, for example, perform sentiment analysis and perform positive / negative analysis of the conversation content. For example, the analysis unit can perform sentiment scoring and evaluate the intensity of emotions in the conversation content. The analysis unit can also use topic modeling to extract topics of the conversation content and classify data. For example, the analysis unit can extract topics of the conversation content using LDA (Latent Dirichlet Allocation). Furthermore, the analysis unit can also extract topics of the conversation content using NMF (Non-negative Matrix Factorization). This improves the accuracy of data analysis by using sentiment analysis and topic modeling. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the conversation content to the generation AI and cause the generation AI to execute sentiment analysis and topic modeling.

[0072] The providing unit can provide the analysis results for use in politics or commerce. For example, the providing unit provides the analysis results in a report format so that they can be used by politicians and companies. For example, the providing unit provides the analysis results in a PDF format so that politicians and companies can easily view them. The providing unit can also display a dashboard to visually display the analysis results. For example, the providing unit displays the analysis results as graphs or charts to provide them in a format that is visually easy to understand. Furthermore, the providing unit can provide an API to link the analysis results with other systems. For example, the providing unit provides the analysis results through an API so that other systems can use the analysis results. This creates practical value by using the analysis results in politics or commerce. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generating AI and cause the generating AI to provide the analysis results.

[0073] The collection unit can anonymize the data using a script that deletes personal information. The collection unit, for example, deletes personal information from the conversation content using a script that deletes personal information. For example, the collection unit executes a script that deletes personal information such as a name, address, and phone number to anonymize the data. The collection unit can also delete the user ID and personal information and collect only the conversation content. For example, the collection unit executes a script that deletes the user ID and anonymizes the conversation content. Furthermore, the collection unit can process data in consideration of privacy so that individuals cannot be identified. This allows data to be collected while protecting privacy by deleting personal information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input a script that deletes personal information into the generation AI and cause the generation AI to execute the anonymization of the data.

[0074] The collection unit can estimate the user's emotion and adjust the collection timing of the conversation content based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the collection timing of the conversation content based on the estimated user's emotion. For example, when the user is stressed, the collection unit delays the collection timing to reduce the burden on the user. In addition, when the user is relaxed, the collection unit can also advance the collection timing to collect more data. Furthermore, when the user is excited, the collection unit can adjust the collection timing to collect data at the peak of the emotion. This allows more appropriate data collection by adjusting the collection timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the collection timing.

[0075] The collection unit can select an appropriate collection method based on the frequency of use of the messenger app and the time period. The collection unit selects the optimal collection method based on, for example, the frequency of use of the messenger app and the time period. For example, the collection unit concentrates collection during the time period when the user frequently uses the messenger app. The collection unit can also refrain from collection during the time period when the user does not use the app much. Furthermore, the collection unit can adjust the interval of collection according to the frequency of use of the user. This enables efficient data collection by selecting a collection method based on the frequency of use and the time period. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the usage data of the messenger app to the generation AI and cause the generation AI to select the optimal collection method.

[0076] The collection unit can perform filtering based on a specific keyword or phrase when collecting the conversation content. For example, the collection unit performs filtering based on a specific keyword or phrase when collecting the conversation content. For example, the collection unit collects only conversations that include a specific keyword. The collection unit can also preferentially collect conversations that include a specific phrase. Furthermore, the collection unit can determine the priority of conversations to be collected based on keywords or phrases. This makes it possible to efficiently collect necessary data by filtering based on specific keywords or phrases. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the conversation content to the generation AI and cause the generation AI to perform filtering based on keywords and phrases.

[0077] The collection unit can estimate the user's emotion and determine the priority of the conversation contents to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of the conversation contents to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit preferentially collects conversation contents with strong emotions. In addition, when the user is relaxed, the collection unit can also preferentially collect daily conversation contents. Furthermore, when the user is excited, the collection unit can also preferentially collect conversation contents at the peak of the emotion. In this way, important conversation contents can be preferentially collected by determining the priority based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the conversation contents to be collected.

[0078] The collection unit can preferentially collect highly relevant conversations based on the geographical location information of the user when collecting the conversation contents. For example, the collection unit preferentially collects highly relevant conversations by considering the geographical location information of the user when collecting the conversation contents. For example, when the user is in a specific area, the collection unit preferentially collects conversation contents related to the area. In addition, when the user is moving, the collection unit can also preferentially collect conversation contents related to the destination. Furthermore, when the user is participating in a specific event, the collection unit can also preferentially collect conversation contents related to the event. In this way, by considering the geographical location information, highly relevant conversation contents can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to collect highly relevant conversation contents.

[0079] The collection unit can analyze the user's social media activity and collect related conversations when collecting the conversation content. For example, the collection unit analyzes the user's social media activity and collects related conversations when collecting the conversation content. For example, the collection unit collects related conversations based on the content shared by the user on social media. The collection unit can also preferentially collect conversation content of accounts that the user follows on social media. Furthermore, the collection unit can also preferentially collect conversation content of groups in which the user participates on social media. This makes it possible to efficiently collect related conversation content by analyzing social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related conversation content.

[0080] The transcription unit can estimate the user's emotion and adjust the expression method of the transcription based on the estimated user's emotion. The transcription unit, for example, estimates the user's emotion and adjusts the expression method of the transcription based on the estimated user's emotion. For example, the transcription unit uses a concise and easy-to-understand expression when the user is stressed. In addition, the transcription unit can also use a detailed expression when the user is relaxed. Furthermore, the transcription unit can also use an expression that emphasizes the emotion when the user is excited. This enables more appropriate transcription by adjusting the expression method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the transcription unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transcription unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the transcription.

[0081] The transcription unit can adjust the level of detail of the transcription based on the importance of the conversation when transcribing. For example, the transcription unit adjusts the level of detail of the transcription based on the importance of the conversation when transcribing. For example, the transcription unit transcribes important conversation contents in detail. The transcription unit can also transcribe everyday conversation contents briefly. Furthermore, the transcription unit can also transcribe conversation contents containing specific keywords in detail. This allows for efficient transcription by adjusting the level of detail based on the importance of the conversation. A part or all of the above-mentioned processing in the transcription unit may be performed using, for example, AI, or may be performed without using AI. For example, the transcription unit can input the conversation contents to the generation AI and cause the generation AI to adjust the level of detail of the transcription based on the importance.

[0082] The transcription unit can apply different transcription algorithms depending on the category of the conversation when transcribing. For example, the transcription unit applies different transcription algorithms depending on the category of the conversation when transcribing. For example, the transcription unit applies a business transcription algorithm to a business conversation. The transcription unit can also apply an everyday transcription algorithm to an everyday conversation. Furthermore, the transcription unit can apply a technical transcription algorithm to a technical conversation. This enables highly accurate transcription by applying an algorithm depending on the category of the conversation. Part or all of the above-mentioned processing in the transcription unit may be performed using, for example, AI, or may be performed without using AI. For example, the transcription unit can input the content of the conversation to the generation AI and cause the generation AI to apply a transcription algorithm depending on the category.

[0083] The transcription unit can estimate the user's emotion and adjust the length of the transcription based on the estimated user's emotion. The transcription unit, for example, estimates the user's emotion and adjusts the length of the transcription based on the estimated user's emotion. For example, when the user is stressed, the transcription unit performs a short and to the point transcription. In addition, when the user is relaxed, the transcription unit can also perform a detailed transcription. Furthermore, when the user is excited, the transcription unit can also perform a transcription that emphasizes the emotion. This enables appropriate transcription by adjusting the length based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the transcription unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transcription unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the transcription.

[0084] The transcription unit can determine the priority of transcription based on the time of occurrence of the conversation when transcribing. For example, the transcription unit determines the priority of transcription based on the time of occurrence of the conversation when transcribing. For example, the transcription unit prioritizes transcription of recent conversation content. The transcription unit can also prioritize transcription of conversation content during a specific event period. Furthermore, the transcription unit can also prioritize transcription of important conversation content in the past. This enables efficient transcription by determining the priority based on the time of occurrence. A part or all of the above-mentioned processing in the transcription unit may be performed using, for example, AI, or may be performed without using AI. For example, the transcription unit can input the conversation content to the generation AI and cause the generation AI to determine the priority of transcription based on the time of occurrence.

[0085] The transcription unit can adjust the order of transcription based on the relevance of the conversation during transcription. For example, the transcription unit adjusts the order of transcription based on the relevance of the conversation during transcription. For example, the transcription unit prioritizes transcribing highly relevant conversation content. The transcription unit can also postpone less relevant conversation content. Furthermore, the transcription unit can also prioritize transcribing conversation content related to a specific topic. This allows for efficient transcription by adjusting the order based on relevance. A part or all of the above-mentioned processing in the transcription unit may be performed using, for example, AI, or may be performed without using AI. For example, the transcription unit can input the conversation content to a generation AI and cause the generation AI to adjust the order of transcription based on relevance.

[0086] The analysis unit can estimate the user's emotion and adjust the analysis criteria based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis criteria based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit focuses on analyzing the parts with strong emotions. In addition, when the user is relaxed, the analysis unit can also analyze the overall conversation content. Furthermore, when the user is excited, the analysis unit can also analyze the conversation content at the peak of the emotion. This allows for more appropriate analysis by adjusting the criteria based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis criteria.

[0087] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the conversation during the analysis. The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the conversation during the analysis, for example. For example, the analysis unit performs the analysis by taking into account the context of the conversation. The analysis unit can also perform the analysis by taking into account the relationship with the other person in the conversation. Furthermore, the analysis unit can also perform the analysis by taking into account the context of the conversation. As a result, the accuracy of the analysis is improved by taking into account the interrelationships of the conversation. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the contents of the conversation to the generation AI and cause the generation AI to perform an analysis taking into account the interrelationships.

[0088] The analysis unit can perform the analysis while taking into consideration the attribute information of the speaker of the conversation. For example, the analysis unit performs the analysis while taking into consideration the attribute information of the speaker of the conversation. For example, the analysis unit performs the analysis while taking into consideration the age and gender of the speaker. The analysis unit can also perform the analysis while taking into consideration the occupation and position of the speaker. Furthermore, the analysis unit can also perform the analysis while taking into consideration the region and cultural background of the speaker. This allows for more detailed analysis by taking into consideration the attribute information of the speaker. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the attribute information of the speaker to the generation AI and cause the generation AI to perform an analysis while taking into consideration the attribute information.

[0089] The analysis unit can estimate the user's emotion and adjust the display order of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display order of the analysis results based on the estimated user's emotion. For example, when the user is stressed, the analysis unit displays important analysis results first. In addition, when the user is relaxed, the analysis unit can also display overall analysis results. Furthermore, when the user is excited, the analysis unit can also display analysis results with strong emotions first. In this way, by adjusting the display order based on the user's emotion, important analysis results can be preferentially displayed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order of the analysis results.

[0090] The analysis unit can perform the analysis while taking into consideration the geographical distribution of the conversation. For example, the analysis unit performs the analysis while taking into consideration the geographical distribution of the conversation. For example, the analysis unit focuses on analyzing the conversation content related to a specific region. The analysis unit can also analyze the conversation content over a wide geographical range. Furthermore, the analysis unit can analyze the difference in the conversation content for each region. In this way, the difference in the conversation content for each region can be analyzed by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the conversation content to the generation AI and cause the generation AI to perform an analysis taking the geographical distribution into consideration.

[0091] The analysis unit can improve the accuracy of the analysis by referring to literature related to the conversation during the analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to the conversation during the analysis. For example, the analysis unit performs the analysis by referring to related academic papers. The analysis unit can also perform the analysis by referring to related news articles. Furthermore, the analysis unit can also perform the analysis by referring to related books. As a result, the accuracy of the analysis is improved by referring to related literature. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the content of the conversation to the generation AI and cause the generation AI to perform an analysis with reference to related literature.

[0092] The providing unit can estimate the user's emotion and adjust the expression method of the analysis result to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the expression method of the analysis result to be provided based on the estimated user's emotion. For example, the providing unit uses a concise and easy-to-understand expression when the user is stressed. In addition, the providing unit can also use a detailed expression when the user is relaxed. Furthermore, the providing unit can also use an expression that emphasizes the emotion when the user is excited. In this way, by adjusting the expression method based on the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the analysis result.

[0093] The providing unit can adjust the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit adjusts the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit provides important analysis results in detail. The providing unit can also provide daily analysis results concisely. Furthermore, the providing unit can also provide analysis results containing specific keywords in detail. This enables efficient information provision by adjusting the level of detail based on the importance. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the analysis result to the generating AI and cause the generating AI to adjust the level of detail of the provision based on the importance.

[0094] The providing unit can apply different providing methods depending on the category of the analysis result at the time of providing. For example, the providing unit applies different providing methods depending on the category of the analysis result at the time of providing. For example, the providing unit provides the analysis result related to politics in the form of a policy proposal. The providing unit can also provide the analysis result related to commerce in the form of a marketing strategy. Furthermore, the providing unit can provide the analysis result related to social issues in the form of a report. This makes it possible to provide appropriate information by applying a providing method according to the category. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the analysis result to the generating AI and cause the generating AI to apply the providing method according to the category.

[0095] The providing unit can estimate the user's emotion and determine the priority of the analysis results to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority of the analysis results to be provided based on the estimated user's emotion. For example, when the user is stressed, the providing unit provides important analysis results first. In addition, when the user is relaxed, the providing unit can also provide overall analysis results. Furthermore, when the user is excited, the providing unit can also provide analysis results with strong emotions first. In this way, by determining the priority based on the user's emotion, important analysis results can be provided preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the analysis results.

[0096] The providing unit can determine the priority of provision based on the time of occurrence of the analysis result at the time of provision. For example, the providing unit determines the priority of provision based on the time of occurrence of the analysis result at the time of provision. For example, the providing unit provides recent analysis results preferentially. The providing unit can also provide analysis results during a specific event period preferentially. Furthermore, the providing unit can also provide important analysis results in the past preferentially. This enables efficient information provision by determining the priority based on the time of occurrence. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis result to the generating AI and cause the generating AI to execute the determination of the priority of provision based on the time of occurrence.

[0097] The providing unit can adjust the order of provision based on the relevance of the analysis results at the time of provision. For example, the providing unit adjusts the order of provision based on the relevance of the analysis results at the time of provision. For example, the providing unit provides analysis results with high relevance preferentially. The providing unit can also postpone analysis results with low relevance. Furthermore, the providing unit can also provide analysis results related to a specific topic preferentially. This enables efficient information provision by adjusting the order based on relevance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the analysis results to the generating AI and cause the generating AI to adjust the order of provision based on relevance.

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

[0099] The conversation analysis system may further include a behavior analysis unit that analyzes the behavioral patterns of the user. The behavior analysis unit may, for example, analyze the frequency and time periods of the user's use of a messenger app to identify the user's behavioral patterns. The behavior analysis unit may also analyze the user's past conversation history to extract specific behavioral patterns. Furthermore, the behavior analysis unit may optimize the timing and method of collecting conversation contents based on the user's behavioral patterns. This allows for more efficient data collection by taking the user's behavioral patterns into account.

[0100] The conversation analysis system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit may monitor, for example, the user's heart rate and stress level to grasp the user's health condition. The health monitoring unit may also adjust the timing and method of collecting conversation contents based on the user's health condition. Furthermore, the health monitoring unit may determine the priority of the conversation contents to be collected according to the user's health condition. This allows for more appropriate data collection by taking the user's health condition into consideration.

[0101] The conversation analysis system may further include an interest analysis unit that analyzes the user's hobbies and interests. The interest analysis unit may, for example, analyze the user's conversation content on a messenger app or the content posted on social media to identify the user's hobbies and interests. The interest analysis unit may also determine the priority of the conversation content to be collected based on the user's hobbies and interests. Furthermore, the interest analysis unit may adjust the method of analyzing the conversation content according to the user's hobbies and interests. This allows for more relevant data collection by taking the user's hobbies and interests into consideration.

[0102] The conversation analysis system may further include a network analysis unit that analyzes the user's social network. The network analysis unit may, for example, analyze the user's messenger app contacts or social media followers to identify the user's social network. The network analysis unit may also determine a priority of conversation contents to be collected based on the user's social network. Furthermore, the network analysis unit may adjust the analysis method of the conversation contents according to the user's social network. This allows for more relevant data collection by taking the user's social network into account.

[0103] The conversation analysis system may further include a purchase analysis unit that analyzes the user's purchasing history. The purchase analysis unit may, for example, analyze the user's online shopping history and information on purchased items to identify the user's purchasing patterns. The purchase analysis unit may also determine the priority of conversation contents to be collected based on the user's purchasing patterns. Furthermore, the purchase analysis unit may adjust the method of analyzing the conversation contents according to the user's purchasing patterns. This makes it possible to collect more relevant data by taking the user's purchasing patterns into account.

[0104] The conversation analysis system can further estimate the user's emotions and adjust the method of collecting the conversation contents based on the estimated user's emotions. For example, if the user is feeling stressed, the collection method can be changed to reduce the burden on the user. Also, if the user is relaxed, the collection method can be optimized to collect more data. Furthermore, if the user is excited, the collection method can be adjusted to collect data at the peak of the user's emotions. In this way, more appropriate data collection is possible by adjusting the collection method according to the user's emotions.

[0105] The conversation analysis system can further estimate the user's emotions and adjust the analysis method of the conversation content based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis method can be changed to focus on analyzing parts of the conversation that show strong emotions. Also, if the user is relaxed, the analysis method can be optimized to analyze the overall conversation content. Furthermore, if the user is excited, the analysis method can be adjusted to focus on analyzing the conversation content at the peak of the user's emotions. This allows for more appropriate data analysis by adjusting the analysis method according to the user's emotions.

[0106] The conversation analysis system can further estimate the user's emotions and adjust the method of providing the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, the method of providing the results can be changed to use simple, easy-to-understand expressions. Also, if the user is relaxed, the method of providing the results can be optimized to use detailed expressions. Furthermore, if the user is excited, the method of providing the results can be adjusted to use expressions that emphasize the emotions. In this way, by adjusting the method of providing the results according to the user's emotions, it is possible to provide more appropriate analysis results.

[0107] The conversation analysis system can further estimate the user's emotions and adjust the display order of the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, important analysis results can be displayed first. Also, if the user is relaxed, overall analysis results can be displayed. Furthermore, if the user is excited, analysis results with strong emotions can be displayed first. In this way, by adjusting the display order based on the user's emotions, important analysis results can be displayed preferentially.

[0108] The conversation analysis system can further estimate the user's emotions and determine the priority of analysis results to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, important analysis results can be provided first. Also, if the user is relaxed, overall analysis results can be provided. Furthermore, if the user is excited, analysis results with strong emotions can be provided first. In this way, by determining the priority based on the user's emotions, important analysis results can be provided preferentially.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects the conversation contents. The conversation contents include voice conversations, text chats, video conversations, etc. The collection unit can obtain data using the API of the messenger app, and can collect the conversation contents in real time or perform batch collection to collect the conversation contents at regular intervals. In addition, the collection frequency can be adjusted to change the data collection interval as needed. Step 2: The transcription unit transcribes the conversation content collected by the collection unit. The transcription unit analyzes the voice data and text data and converts the conversation content into text data. Voice data can be converted into text data with high accuracy using voice recognition technology, and text data can also be entered manually. In addition, standards can be set to maintain a certain level of transcription accuracy. Step 3: The analysis unit analyzes the data transcribed by the transcription unit. The analysis unit can analyze the data using sentiment analysis and topic modeling, and can perform positive / negative analysis of the conversation content, extract topics, and classify the data. In addition, the analysis unit can analyze patterns of the conversation content using data mining techniques. Step 4: The provision unit provides the analysis results obtained by the analysis unit. The provision unit provides the analysis results for political or commercial use, and can provide them in report format or display the analysis results visually on a dashboard. In addition, the provision unit can provide APIs to link the analysis results with other systems.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned collection unit, transcription unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit acquires conversation content from the API of a messenger app using the communication I / F 44 of the smart device 14. The transcription unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts voice data into text data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs sentiment analysis and topic modeling. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the analysis results in a report format or dashboard display. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0130] Each of the multiple elements including the above-mentioned collection unit, transcription unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit acquires conversation content from the API of a messenger application using the communication I / F 44 of the smart glasses 214. The transcription unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts voice data into text data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs sentiment analysis and topic modeling. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the analysis results in a report format or dashboard display. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0146] Each of the multiple elements including the above-mentioned collection unit, transcription unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit acquires the conversation content from the API of the messenger application using the communication I / F 44 of the headset type terminal 314. The transcription unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts voice data into text data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs sentiment analysis and topic modeling. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the analysis results in a report format or dashboard display. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0163] Each of the multiple elements including the above-mentioned collection unit, transcription unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit acquires the conversation content from the API of the messenger application using the communication I / F 44 of the robot 414. The transcription unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts voice data into text data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs sentiment analysis and topic modeling. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the analysis results in a report format or dashboard display. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration in which a processor is used that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection unit that collects conversation contents; A transcription unit that transcribes the conversation content collected by the collection unit; an analysis unit that analyzes the data transcribed by the transcription unit; A providing unit that provides the analysis result obtained by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Acquire data using the messenger app's API 2. The system of claim 1, (Appendix 3) The transcription unit includes: Analyzes voice or text data and converts the conversation into text data 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze the data using sentiment analysis or topic modeling 2. The system of claim 1, (Appendix 5) The providing unit is Providing the results of the analysis for political or commercial use 2. The system of claim 1, (Appendix 6) The collecting unit includes: Anonymize your data using a script to remove personal information 2. The system of claim 1, (Appendix 7) The collecting unit includes: The system estimates the user's emotions and adjusts the timing of collection of conversation content based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Select the appropriate collection method based on the frequency and time of use of the messenger app. 2. The system of claim 1, (Appendix 9) The collecting unit includes: As conversations are collected, they can be filtered based on specific keywords or phrases 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate user emotions and prioritize conversation content to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting conversation content, prioritize collection of relevant conversations based on the user's geographic location information 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting conversation content, analyze the user's social media activity and collect related conversations. 2. The system of claim 1, (Appendix 13) The transcription unit includes: Infer user sentiment and adjust transcription expression based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The transcription unit includes: During transcription, adjust transcription detail based on the importance of the conversation 2. The system of claim 1, (Appendix 15) The transcription unit includes: When transcribing, apply different transcription algorithms depending on the category of the conversation 2. The system of claim 1, (Appendix 16) The transcription unit includes: Infer user sentiment and adjust the length of the transcription based on the estimated user sentiment 2. The system of claim 1, (Appendix 17) The transcription unit includes: When transcribing, prioritize transcriptions based on when the conversation occurred 2. The system of claim 1, (Appendix 18) The transcription unit includes: During transcription, adjust the order of the transcript based on the relevance of the conversation 2. The system of claim 1, (Appendix 19) The analysis unit is Inferring user emotions and adjusting analysis criteria based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The analysis unit is During analysis, conversation correlations are taken into account to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 21) The analysis unit is When analyzing, the attribute information of the speaker of the conversation is taken into consideration. 2. The system of claim 1, (Appendix 22) The analysis unit is Infer user emotions and adjust the display order of analysis results based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The analysis unit is During analysis, the geographical distribution of conversations is taken into account. 2. The system of claim 1, (Appendix 24) The analysis unit is During analysis, the accuracy of the analysis is improved based on the related literature of the conversation. 2. The system of claim 1, (Appendix 25) The providing unit is Infer user emotions and adjust the presentation of analysis results based on the inferred user emotions 2. The system of claim 1, (Appendix 26) The providing unit is At the time of provision, the level of detail provided will be adjusted based on the importance of the analysis results. 2. The system of claim 1, (Appendix 27) The providing unit is When providing, apply different provision methods depending on the category of analysis results. 2. The system of claim 1, (Appendix 28) The providing unit is Inferring user emotions and determining the priority of analysis results to provide based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The providing unit is At the time of provision, the priority of provision will be determined based on the timing of the analysis results. 2. The system of claim 1, (Appendix 30) The providing unit is At the time of serving, adjust the order of serving based on the relevance of the analysis results 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. A collection unit that collects conversation contents; A transcription unit that transcribes the conversation content collected by the collection unit; an analysis unit that analyzes the data transcribed by the transcription unit; A providing unit that provides the analysis result obtained by the analysis unit. A system characterized by:

2. The collecting unit includes: Acquire data using the messenger app's API 2. The system of claim 1 .

3. The transcription unit includes: Analyzes voice or text data and converts the conversation into text data 2. The system of claim 1 .

4. The analysis unit is Analyze the data using sentiment analysis or topic modeling 2. The system of claim 1 .

5. The providing unit is Providing the results of the analysis for political or commercial use 2. The system of claim 1 .

6. The collecting unit includes: Anonymize your data using a script to remove personal information 2. The system of claim 1 .

7. The collecting unit includes: The system estimates the user's emotions and adjusts the timing of collection of conversation content based on the estimated user emotions.

2. The system of claim 1 .

8. The collecting unit includes: Select the appropriate collection method based on the frequency and time of use of the messenger app.

2. The system of claim 1 .

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