System

Abstract

An object of a system according to an embodiment is to convert call contents into text and immediately search and analyze the text.SOLUTION: A system includes a call content conversion unit, a search unit, and an analysis unit. The call content conversion unit converts the call content into text. The search unit immediately searches for the text data generated by the call content conversion unit. The analysis unit analyzes the text data retrieved by the retrieval 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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately convert call content into text, or perform instant search and analysis, leaving room for improvement in information management and customer service efficiency.

[0005] The system according to the embodiment aims to convert the contents of a call into text and enable instant search and analysis. [Means for solving the problem]

[0006] The system according to the embodiment includes a call content conversion unit, a search unit, and an analysis unit. The call content conversion unit converts the call content into text. The search unit instantly searches the text data generated by the call content conversion unit. The analysis unit analyzes the text data searched by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment converts the contents of a call into text and can be searched and analyzed instantly. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded 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, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The call content analysis system according to the embodiment of the present invention converts call content into text, analyzes it using a generation AI, and performs instant search and analysis. This makes it possible to improve the efficiency of information management, the quality of customer service, and sales.

[0029] A call content analysis system according to an embodiment includes a call content conversion unit, a search unit, and an analysis unit. The call content conversion unit converts call content into text. For example, the call content conversion unit converts call content into text in real time using voice recognition technology. The call content conversion unit can also accurately convert call content into text using natural language processing technology. The call content conversion unit analyzes voice data to generate text data. For example, voice recognition technology analyzes voice waveforms and converts voice data into text. The natural language processing technology understands the context of the voice data and generates accurate text data. The search unit instantly searches the text data generated by the call content conversion unit. For example, the search unit quickly searches for call content containing specific keywords or phrases. The search unit can also efficiently search text data using an indexing method. The search unit instantly searches the text data using a search algorithm. For example, the search unit performs a keyword search to search for call content containing a specific keyword. The indexing method indexes the text data to improve search speed. The search algorithm evaluates the relevance of the text data and provides optimal search results. The analysis unit analyzes the text data searched by the search unit. For example, the analysis unit analyzes the text data using text mining technology to extract important information and trends. The analysis unit can also analyze emotional elements of the text data using sentiment analysis technology. The analysis unit can also extract topics from the text data using topic modeling technology. For example, the analysis unit extracts customer requests and complaints using text mining technology. Sentiment analysis technology analyzes emotional elements of the text data to understand customer emotions. Topic modeling technology extracts topics from the text data and identifies important topics. This enables the call content analysis system according to the embodiment to improve the efficiency of information management, improve the quality of customer service, and increase sales. For example, a company can quickly search call content and understand customer requests and complaints, thereby improving the quality of customer service. A company can also analyze call content to develop strategies to increase sales.

[0030] When converting call content to text, the call content conversion unit can automatically recognize technical terms and industry-specific words and provide appropriate translations and annotations. For example, when converting call content to text, the call content conversion unit uses a generation AI to automatically recognize technical terms and industry-specific words and provide appropriate translations. For example, when converting call content from the medical industry to text, technical terms are translated into general language. When converting call content to text, the generation AI also annotates industry-specific words. For example, when converting call content from the financial industry to text, technical terms are annotated to aid understanding. When converting call content to text, the generation AI also compares technical terms and industry-specific words with a dictionary database and automatically provides appropriate translations and annotations. For example, when converting call content from the IT industry to text, technical terms are annotated. This allows technical terms and industry-specific words to be automatically recognized and provided with appropriate translations and annotations to aid understanding.

[0031] When converting call content into text, the call content conversion unit can analyze the tone and speed of the speaker's voice and reflect that information in the text. For example, when converting call content into text, the call content conversion unit uses a generation AI to analyze the tone of the speaker's voice and reflect that information in the text data. For example, emphasized parts are displayed in bold. When converting call content into text, the call content conversion unit also uses a generation AI to analyze the speed of the speaker's voice and reflect that information in the text data. For example, parts spoken quickly are displayed in italics. When converting call content into text, the call content conversion unit also uses a generation AI to record changes in the speaker's tone and speed over time for later analysis. For example, changes in tone and speed from the start to the end of the call are displayed in a graph. This allows the nuances of the call content to be preserved by analyzing the tone and speed of the speaker's voice and reflecting that information in the text.

[0032] The call content conversion unit can translate the call content into different languages ​​in real time when converting the call content into text, enabling multilingual call analysis. In the call content conversion unit, for example, when converting the call content into text, the generation AI translates it into different languages ​​in real time. For example, English call content is translated into Japanese and saved as text data. In addition, when converting the call content into text, the generation AI translates it into multiple languages ​​simultaneously. For example, English call content is translated into Japanese and Chinese simultaneously and saved as text data. In addition, when converting the call content into text, the generation AI translates it into different languages ​​and displays the translation results in real time. For example, English call content is translated into Japanese and displayed during the call. This enables real-time translation into different languages ​​and multilingual call analysis, making it possible to respond to international customers.

[0033] The call content conversion unit can analyze background sounds and environmental sounds and perform noise cancellation when converting the call content into text. For example, when converting the call content into text, the call content conversion unit uses a generation AI to analyze background sounds and perform noise cancellation. For example, noise during a call is removed to convert clear voice data into text. In addition, when converting the call content into text, the call content conversion unit uses a generation AI to analyze environmental sounds and perform noise cancellation. For example, wind noise and car noise during a call are removed to convert clear voice data into text. In addition, when converting the call content into text, the call content conversion unit uses a generation AI to analyze background sounds and environmental sounds in real time and perform noise cancellation. For example, music and television sounds during a call are removed to convert clear voice data into text. In this way, background sounds and environmental sounds are analyzed and noise cancellation is performed to convert clear voice data into text.

[0034] The search unit can understand the context when searching text data and automatically suggest related keywords and phrases. For example, when searching text data generated by the generation AI, the search unit understands the context and automatically suggests related keywords. For example, it suggests keywords related to "customer satisfaction." Furthermore, when searching text data generated by the generation AI, the search unit understands the context and automatically suggests related phrases. For example, it suggests phrases related to "customer requests." Furthermore, when searching text data generated by the generation AI, the search unit understands the context and suggests related keywords and phrases in real time. For example, it suggests keywords and phrases related to "customer support." This improves the accuracy and efficiency of searches by understanding the context and automatically suggesting related keywords and phrases.

[0035] The search unit can automatically summarize search results for text data, allowing users to quickly obtain the information they need. For example, the search unit automatically summarizes search results for text data generated by the generation AI, allowing users to quickly obtain the information they need. For example, it displays the main points of the search results in short sentences. The search unit also automatically summarizes search results for text data generated by the generation AI and displays related information together. For example, it displays related information for search results in a single view. The search unit also automatically summarizes search results for text data generated by the generation AI, allowing users to quickly obtain the information they need. For example, it highlights important points of the search results. This allows users to quickly obtain the information they need by automatically summarizing the search results.

[0036] The search unit can expand the search function of text data to a multimodal search that also includes images and audio data. For example, the search unit expands the search function of text data generated by the generation AI to a multimodal search that also includes image data. For example, images related to the content of a call are displayed in the search results. The search unit also expands the search function of text data generated by the generation AI to a multimodal search that also includes audio data. For example, audio data related to the content of a call are displayed in the search results. The search unit also expands the search function of text data generated by the generation AI to a multimodal search that also includes images and audio data, allowing users to obtain the information they need from multiple angles. For example, images and audio data related to the content of a call are displayed in the search results. In this way, by expanding the search to a multimodal search that also includes images and audio data, users can obtain the information they need from multiple angles.

[0037] The search unit can automatically cluster search results of text data and group highly related information. For example, the search unit automatically clusters search results of text data generated by the generation AI and group highly related information. For example, it groups together phone call content related to the same topic into one cluster. The search unit also automatically clusters search results of text data generated by the generation AI to allow users to grasp related information at a glance. For example, it displays related phone call content in groups. The search unit also automatically clusters search results of text data generated by the generation AI and visually displays highly related information. For example, it displays related phone call content as a cluster map. In this way, by automatically clustering search results and grouping highly related information, users can grasp related information at a glance.

[0038] When analyzing text data, the analysis unit can use time series data to track changes in trends and identify long-term patterns. For example, when the generation AI analyzes text data, the analysis unit uses time series data to track changes in trends. For example, it analyzes changes in customer requests and complaints over time to identify long-term patterns. When the generation AI analyzes text data, the analysis unit also uses time series data to track fluctuations in sales and identify long-term patterns. For example, it analyzes how sales of a particular product fluctuate by season. When the generation AI analyzes text data, the analysis unit also uses time series data to track customer purchasing history and identify long-term patterns. For example, it analyzes when customers purchase particular products. In this way, using time series data to track changes in trends and identify long-term patterns is useful for formulating business strategies.

[0039] When analyzing text data, the analysis unit can refer to related external data to extract broader trends. For example, when the generation AI analyzes text data, the analysis unit refers to related social media posts to extract broader trends. For example, it analyzes how customer requests and complaints are mentioned on social media. In addition, when the generation AI analyzes text data, the analysis unit refers to related news articles to extract broader trends. For example, it extracts the latest industry trends and market changes from news articles. In addition, when the generation AI analyzes text data, the analysis unit refers to related external data to extract broader trends. For example, it analyzes how customer purchasing behavior is changing in other data sources. This makes it possible to provide comprehensive insights by referring to related external data and extracting broader trends.

[0040] The analysis unit can customize the analysis results of the text data according to different industries and uses to meet specific needs. For example, when the generation AI analyzes text data, the analysis unit customizes the analysis results according to the needs of different industries. For example, it provides analysis results specialized for the medical industry. Furthermore, when the generation AI analyzes text data, the analysis unit customizes the analysis results according to different uses. For example, it provides analysis results specialized for marketing uses. Furthermore, when the generation AI analyzes text data, the analysis unit customizes the analysis results to meet specific needs. For example, it provides analysis results for improving the quality of customer service. In this way, specific needs can be met by customizing the analysis results according to different industries and uses.

[0041] The analysis unit can convert the data analyzed by the generation AI into visual notes or mind maps to make it easier to understand visually. For example, the analysis unit converts the text data analyzed by the generation AI into visual notes to make it easier to understand visually. For example, it indicates important points with diagrams or icons. The analysis unit also converts the text data analyzed by the generation AI into mind map format to visually organize related keywords and concepts. This allows the overall picture of the analysis results to be understood at a glance. The analysis unit also converts the text data analyzed by the generation AI into visual notes or mind maps to allow users to easily display the analysis results visually. For example, it provides a function to visualize the analysis results with drag and drop. By converting the analysis results into visual notes or mind maps, it makes them easier to understand visually.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] When converting call content into text, the call content conversion unit can analyze the tone and speed of the speaker's voice and reflect that information in the text. For example, emphasized parts are displayed in bold. In addition, when converting call content into text, the generation AI analyzes the speed of the speaker's voice and reflects this information in the text data. For example, parts spoken quickly are displayed in italics. In addition, when converting call content into text, the generation AI records changes in the speaker's tone and speed in chronological order so that they can be analyzed later. For example, changes in tone and speed from the start to the end of the call are displayed in a graph. In this way, the nuances of the call content are preserved by analyzing the tone and speed of the speaker's voice and reflecting this information in the text.

[0044] The call content conversion unit can translate call content into different languages ​​in real time when converting it to text, enabling multilingual call analysis. For example, when converting call content into text, the generation AI translates it into different languages ​​in real time. For example, English call content is translated into Japanese and saved as text data. Furthermore, when converting call content into text, the generation AI simultaneously translates it into multiple languages. For example, English call content is simultaneously translated into Japanese and Chinese and saved as text data. Furthermore, when converting call content into text, the generation AI translates it into different languages ​​and displays the translation results in real time. For example, English call content is translated into Japanese and displayed during the call. This enables real-time translation into different languages ​​and multilingual call analysis, making it possible to respond to international customers.

[0045] When converting call content to text, the call content conversion unit can analyze background sounds and environmental sounds and perform noise cancellation. For example, when converting call content to text, the generation AI analyzes background sounds and performs noise cancellation. For example, noise during a call is removed to convert clear voice data into text. In addition, when converting call content to text, the generation AI analyzes environmental sounds and performs noise cancellation. For example, wind noise and car noise during a call are removed to convert clear voice data into text. In addition, when converting call content to text, the generation AI analyzes background sounds and environmental sounds in real time and performs noise cancellation. For example, music and television sounds during a call are removed to convert clear voice data into text. In this way, background sounds and environmental sounds are analyzed and noise cancellation is performed to convert clear voice data into text.

[0046] The search unit can understand the context when searching text data and automatically suggest related keywords and phrases. For example, when searching text data generated by the generation AI, it understands the context and automatically suggests related keywords. For example, it suggests keywords related to "customer satisfaction." Furthermore, when searching text data generated by the generation AI, the search unit understands the context and automatically suggests related phrases. For example, it suggests phrases related to "customer requests." Furthermore, when searching text data generated by the generation AI, the search unit understands the context and suggests related keywords and phrases in real time. For example, it suggests keywords and phrases related to "customer support." This improves the accuracy and efficiency of searches by understanding the context and automatically suggesting related keywords and phrases.

[0047] The search unit can automatically summarize search results for text data, allowing users to quickly obtain the information they need. For example, the search results for text data generated by the generation AI can be automatically summarized, allowing users to quickly obtain the information they need. For example, the main points of the search results can be displayed in short sentences. The search unit also automatically summarizes search results for text data generated by the generation AI and displays related information together. For example, related information for search results can be displayed in a single view. The search unit also automatically summarizes search results for text data generated by the generation AI, allowing users to quickly obtain the information they need. For example, important points of the search results can be highlighted. This allows users to quickly obtain the information they need by automatically summarizing the search results.

[0048] The search unit can expand the search function of text data to a multimodal search that also includes image and audio data. For example, the search function of text data generated by the generation AI is expanded to a multimodal search that also includes image data. For example, images related to the content of a call are displayed in the search results. The search unit also expands the search function of text data generated by the generation AI to a multimodal search that also includes audio data. For example, audio data related to the content of a call are displayed in the search results. The search unit also expands the search function of text data generated by the generation AI to a multimodal search that also includes image and audio data, allowing users to obtain the information they need from multiple angles. For example, images and audio data related to the content of a call are displayed in the search results. In this way, by expanding the search to a multimodal search that also includes image and audio data, users can obtain the information they need from multiple angles.

[0049] The search unit can automatically cluster search results for text data and group highly related information. For example, it can automatically cluster search results for text data generated by the generation AI and group highly related information. For example, it can group phone call content related to the same topic into one cluster. The search unit also automatically clusters search results for text data generated by the generation AI, allowing users to grasp related information at a glance. For example, it can display related phone call content in groups. The search unit also automatically clusters search results for text data generated by the generation AI and visually display highly related information. For example, it can display related phone call content as a cluster map. In this way, by automatically clustering search results and grouping highly related information, users can grasp related information at a glance.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The call content conversion unit converts the call content into text. For example, the call content conversion unit converts the call content into text in real time using voice recognition technology. It can also accurately convert the call content into text using natural language processing technology. The voice data is analyzed and text data is generated. Step 2: The search unit immediately searches the text data generated by the call content conversion unit. For example, the search unit quickly searches for call content containing specific keywords or phrases. The search unit can also use indexing methods and search algorithms to efficiently search the text data. Step 3: The analysis unit analyzes the text data retrieved by the search unit. For example, the analysis unit may use text mining techniques to analyze the text data and extract important information and trends. It may also use sentiment analysis and topic modeling techniques to analyze the emotional elements and topics of the text data.

[0052] (Example 2) The call content analysis system according to the embodiment of the present invention converts call content into text, analyzes it using a generation AI, and performs instant search and analysis. This makes it possible to improve the efficiency of information management, the quality of customer service, and sales.

[0053] A call content analysis system according to an embodiment includes a call content conversion unit, a search unit, and an analysis unit. The call content conversion unit converts call content into text. For example, the call content conversion unit converts call content into text in real time using voice recognition technology. The call content conversion unit can also accurately convert call content into text using natural language processing technology. The call content conversion unit analyzes voice data to generate text data. For example, voice recognition technology analyzes voice waveforms and converts voice data into text. The natural language processing technology understands the context of the voice data and generates accurate text data. The search unit instantly searches the text data generated by the call content conversion unit. For example, the search unit quickly searches for call content containing specific keywords or phrases. The search unit can also efficiently search text data using an indexing method. The search unit instantly searches the text data using a search algorithm. For example, the search unit performs a keyword search to search for call content containing a specific keyword. The indexing method indexes the text data to improve search speed. The search algorithm evaluates the relevance of the text data and provides optimal search results. The analysis unit analyzes the text data searched by the search unit. For example, the analysis unit analyzes the text data using text mining technology to extract important information and trends. The analysis unit can also analyze emotional elements of the text data using sentiment analysis technology. The analysis unit can also extract topics from the text data using topic modeling technology. For example, the analysis unit extracts customer requests and complaints using text mining technology. Sentiment analysis technology analyzes emotional elements of the text data to understand customer emotions. Topic modeling technology extracts topics from the text data and identifies important topics. This enables the call content analysis system according to the embodiment to improve the efficiency of information management, improve the quality of customer service, and increase sales. For example, a company can quickly search call content and understand customer requests and complaints, thereby improving the quality of customer service. A company can also analyze call content to develop strategies to increase sales.

[0054] When converting call content into text, the call content conversion unit estimates the speaker's emotions and assigns emotion tags. For example, when converting call content into text, the call content conversion unit uses a generation AI to analyze the speaker's emotions in real time and assign emotion tags. For example, if the speaker is angry, it assigns an "anger" tag, and if the speaker is happy, it assigns a "joy" tag. When converting call content into text, the generation AI also quantifies the intensity of the speaker's emotions and reflects them in the text data. For example, it expresses the intensity of emotions on a scale from 0 to 10 to track changes in emotions. When converting call content into text, the generation AI also records changes in the speaker's emotions over time so that these changes can be analyzed later. For example, it displays changes in emotions from the start to the end of the call in a graph. This allows the speaker's emotions to be estimated and emotion tags to be assigned, making it possible to track changes in emotions.

[0055] When converting call content to text, the call content conversion unit can automatically recognize technical terms and industry-specific words and provide appropriate translations and annotations. For example, when converting call content to text, the call content conversion unit uses a generation AI to automatically recognize technical terms and industry-specific words and provide appropriate translations. For example, when converting call content from the medical industry to text, technical terms are translated into general language. When converting call content to text, the generation AI also annotates industry-specific words. For example, when converting call content from the financial industry to text, technical terms are annotated to aid understanding. When converting call content to text, the generation AI also compares technical terms and industry-specific words with a dictionary database and automatically provides appropriate translations and annotations. For example, when converting call content from the IT industry to text, technical terms are annotated. This allows technical terms and industry-specific words to be automatically recognized and provided with appropriate translations and annotations to aid understanding.

[0056] When converting call content into text, the call content conversion unit can analyze the tone and speed of the speaker's voice and reflect that information in the text. For example, when converting call content into text, the call content conversion unit uses a generation AI to analyze the tone of the speaker's voice and reflect that information in the text data. For example, emphasized parts are displayed in bold. When converting call content into text, the call content conversion unit also uses a generation AI to analyze the speed of the speaker's voice and reflect that information in the text data. For example, parts spoken quickly are displayed in italics. When converting call content into text, the call content conversion unit also uses a generation AI to record changes in the speaker's tone and speed over time for later analysis. For example, changes in tone and speed from the start to the end of the call are displayed in a graph. This allows the nuances of the call content to be preserved by analyzing the tone and speed of the speaker's voice and reflecting that information in the text.

[0057] The call content conversion unit can translate the call content into different languages ​​in real time when converting the call content into text, enabling multilingual call analysis. In the call content conversion unit, for example, when converting the call content into text, the generation AI translates it into different languages ​​in real time. For example, English call content is translated into Japanese and saved as text data. In addition, when converting the call content into text, the generation AI translates it into multiple languages ​​simultaneously. For example, English call content is translated into Japanese and Chinese simultaneously and saved as text data. In addition, when converting the call content into text, the generation AI translates it into different languages ​​and displays the translation results in real time. For example, English call content is translated into Japanese and displayed during the call. This enables real-time translation into different languages ​​and multilingual call analysis, making it possible to respond to international customers.

[0058] The call content conversion unit can analyze background sounds and environmental sounds and perform noise cancellation when converting the call content into text. For example, when converting the call content into text, the call content conversion unit uses a generation AI to analyze background sounds and perform noise cancellation. For example, noise during a call is removed to convert clear voice data into text. In addition, when converting the call content into text, the call content conversion unit uses a generation AI to analyze environmental sounds and perform noise cancellation. For example, wind noise and car noise during a call are removed to convert clear voice data into text. In addition, when converting the call content into text, the call content conversion unit uses a generation AI to analyze background sounds and environmental sounds in real time and perform noise cancellation. For example, music and television sounds during a call are removed to convert clear voice data into text. In this way, background sounds and environmental sounds are analyzed and noise cancellation is performed to convert clear voice data into text.

[0059] When converting call content to text, the call content conversion unit can display the speaker's emotions in real time, allowing the operator to respond appropriately. For example, when converting call content to text, the call content conversion unit uses a generation AI to analyze the speaker's emotions in real time and display them to the operator. For example, if the speaker is angry, an "Anger" tag is displayed, allowing the operator to respond appropriately. Furthermore, when converting call content to text, the generation AI quantifies the intensity of the speaker's emotions and displays them to the operator. For example, the intensity of emotions can be displayed on a scale from 0 to 10, allowing the operator to respond appropriately. Furthermore, when converting call content to text, the generation AI records changes in the speaker's emotions over time and displays them to the operator. For example, changes in emotions from the start to the end of the call can be displayed in a graph, allowing the operator to respond appropriately. This improves the quality of customer service by displaying the speaker's emotions in real time and allowing the operator to respond appropriately.

[0060] When searching text data, the search unit can filter by emotional tone based on emotion tags. For example, when searching text data generated by the generation AI, the search unit adds a function to filter by emotional tone based on emotion tags. For example, it searches only phone call content tagged with "anger." Furthermore, when searching text data generated by the generation AI, the search unit adds a function to filter based on emotional intensity. For example, it searches only phone call content with a high emotional intensity. Furthermore, when searching text data generated by the generation AI, the search unit adds a function to filter based on changes in emotion. For example, it searches only phone call content in which emotions changed significantly during the call. This makes it possible to quickly search for phone call content related to a specific emotion by filtering by emotional tone based on emotion tags.

[0061] The search unit can understand the context when searching text data and automatically suggest related keywords and phrases. For example, when searching text data generated by the generation AI, the search unit understands the context and automatically suggests related keywords. For example, it suggests keywords related to "customer satisfaction." Furthermore, when searching text data generated by the generation AI, the search unit understands the context and automatically suggests related phrases. For example, it suggests phrases related to "customer requests." Furthermore, when searching text data generated by the generation AI, the search unit understands the context and suggests related keywords and phrases in real time. For example, it suggests keywords and phrases related to "customer support." This improves the accuracy and efficiency of searches by understanding the context and automatically suggesting related keywords and phrases.

[0062] The search unit can automatically summarize search results for text data, allowing users to quickly obtain the information they need. For example, the search unit automatically summarizes search results for text data generated by the generation AI, allowing users to quickly obtain the information they need. For example, it displays the main points of the search results in short sentences. The search unit also automatically summarizes search results for text data generated by the generation AI and displays related information together. For example, it displays related information for search results in a single view. The search unit also automatically summarizes search results for text data generated by the generation AI, allowing users to quickly obtain the information they need. For example, it highlights important points of the search results. This allows users to quickly obtain the information they need by automatically summarizing the search results.

[0063] The search unit can expand the search function of text data to a multimodal search that also includes images and audio data. For example, the search unit expands the search function of text data generated by the generation AI to a multimodal search that also includes image data. For example, images related to the content of a call are displayed in the search results. The search unit also expands the search function of text data generated by the generation AI to a multimodal search that also includes audio data. For example, audio data related to the content of a call are displayed in the search results. The search unit also expands the search function of text data generated by the generation AI to a multimodal search that also includes images and audio data, allowing users to obtain the information they need from multiple angles. For example, images and audio data related to the content of a call are displayed in the search results. In this way, by expanding the search to a multimodal search that also includes images and audio data, users can obtain the information they need from multiple angles.

[0064] The search unit can automatically cluster search results of text data and group highly related information. For example, the search unit automatically clusters search results of text data generated by the generation AI and group highly related information. For example, it groups together phone call content related to the same topic into one cluster. The search unit also automatically clusters search results of text data generated by the generation AI to allow users to grasp related information at a glance. For example, it displays related phone call content in groups. The search unit also automatically clusters search results of text data generated by the generation AI and visually displays highly related information. For example, it displays related phone call content as a cluster map. In this way, by automatically clustering search results and grouping highly related information, users can grasp related information at a glance.

[0065] The search unit can use the emotion estimation function to collect users' emotional responses to search results and improve the accuracy of the search algorithm. The search unit, for example, uses the emotion estimation function to collect users' emotional responses to search results and improve the accuracy of the search algorithm. For example, search results with a high number of positive emotional responses are preferentially displayed. The search unit also uses the emotion estimation function to analyze users' emotional scores for the search results and improve the accuracy of the search algorithm. For example, search results with a high emotional score are preferentially displayed. The search unit also uses the emotion estimation function to collect users' emotional responses to search results in real time and improve the accuracy of the search algorithm. For example, the search results are dynamically adjusted in response to changes in the user's emotions. In this way, by collecting users' emotional responses and improving the accuracy of the search algorithm, more appropriate search results can be provided.

[0066] When analyzing text data, the analysis unit can use an emotion estimation function to extract customer emotion trends and provide emotional insights. For example, when the generation AI analyzes text data, the analysis unit uses the emotion estimation function to extract customer emotion trends. For example, the analysis unit analyzes changes in customer emotion over time and provides emotional insights. Furthermore, when the generation AI analyzes text data, the analysis unit uses the emotion estimation function to quantify the intensity of the customer's emotion and extract emotional trends. For example, the analysis unit expresses the intensity of emotion on a scale of 0 to 10 and provides emotional insights. Furthermore, when the generation AI analyzes text data, the analysis unit uses the emotion estimation function to record changes in the customer's emotion over time and extract emotional trends. For example, the analysis unit displays changes in emotion from the start to the end of a call in a graph and provides emotional insights. In this way, the analysis unit can extract customer emotion trends and provide emotional insights, thereby improving the quality of customer service.

[0067] When analyzing text data, the analysis unit can use time series data to track changes in trends and identify long-term patterns. For example, when the generation AI analyzes text data, the analysis unit uses time series data to track changes in trends. For example, it analyzes changes in customer requests and complaints over time to identify long-term patterns. When the generation AI analyzes text data, the analysis unit also uses time series data to track fluctuations in sales and identify long-term patterns. For example, it analyzes how sales of a particular product fluctuate by season. When the generation AI analyzes text data, the analysis unit also uses time series data to track customer purchasing history and identify long-term patterns. For example, it analyzes when customers purchase particular products. In this way, using time series data to track changes in trends and identify long-term patterns is useful for formulating business strategies.

[0068] When analyzing text data, the analysis unit can refer to related external data to extract broader trends. For example, when the generation AI analyzes text data, the analysis unit refers to related social media posts to extract broader trends. For example, it analyzes how customer requests and complaints are mentioned on social media. In addition, when the generation AI analyzes text data, the analysis unit refers to related news articles to extract broader trends. For example, it extracts the latest industry trends and market changes from news articles. In addition, when the generation AI analyzes text data, the analysis unit refers to related external data to extract broader trends. For example, it analyzes how customer purchasing behavior is changing in other data sources. This makes it possible to provide comprehensive insights by referring to related external data and extracting broader trends.

[0069] The analysis unit can customize the analysis results of the text data according to different industries and uses to meet specific needs. For example, when the generation AI analyzes text data, the analysis unit customizes the analysis results according to the needs of different industries. For example, it provides analysis results specialized for the medical industry. Furthermore, when the generation AI analyzes text data, the analysis unit customizes the analysis results according to different uses. For example, it provides analysis results specialized for marketing uses. Furthermore, when the generation AI analyzes text data, the analysis unit customizes the analysis results to meet specific needs. For example, it provides analysis results for improving the quality of customer service. In this way, specific needs can be met by customizing the analysis results according to different industries and uses.

[0070] The analysis unit can convert the data analyzed by the generation AI into visual notes or mind maps to make it easier to understand visually. For example, the analysis unit converts the text data analyzed by the generation AI into visual notes to make it easier to understand visually. For example, it indicates important points with diagrams or icons. The analysis unit also converts the text data analyzed by the generation AI into mind map format to visually organize related keywords and concepts. This allows the overall picture of the analysis results to be understood at a glance. The analysis unit also converts the text data analyzed by the generation AI into visual notes or mind maps to allow users to easily display the analysis results visually. For example, it provides a function to visualize the analysis results with drag and drop. By converting the analysis results into visual notes or mind maps, it makes them easier to understand visually.

[0071] The analysis unit can use the emotion estimation function to collect the user's emotional reactions to the analysis results and improve the accuracy of the analysis algorithm. The analysis unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the analysis results and improve the accuracy of the analysis algorithm. For example, the analysis unit preferentially displays analysis results with a high number of positive emotional reactions. The analysis unit also uses the emotion estimation function to analyze the user's emotional score for the analysis results and improve the accuracy of the analysis algorithm. For example, the analysis unit preferentially displays analysis results with a high emotional score. The analysis unit also uses the emotion estimation function to collect the user's emotional reactions to the analysis results in real time and improve the accuracy of the analysis algorithm. For example, the analysis result is dynamically adjusted in response to changes in the user's emotions. In this way, by collecting the user's emotional reactions and improving the accuracy of the analysis algorithm, more appropriate analysis results can be provided.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] When converting call content into text, the call content conversion unit can analyze the tone and speed of the speaker's voice and reflect that information in the text. For example, emphasized parts are displayed in bold. In addition, when converting call content into text, the generation AI analyzes the speed of the speaker's voice and reflects this information in the text data. For example, parts spoken quickly are displayed in italics. In addition, when converting call content into text, the generation AI records changes in the speaker's tone and speed in chronological order so that they can be analyzed later. For example, changes in tone and speed from the start to the end of the call are displayed in a graph. In this way, the nuances of the call content are preserved by analyzing the tone and speed of the speaker's voice and reflecting this information in the text.

[0074] The call content conversion unit can translate call content into different languages ​​in real time when converting it to text, enabling multilingual call analysis. For example, when converting call content into text, the generation AI translates it into different languages ​​in real time. For example, English call content is translated into Japanese and saved as text data. Furthermore, when converting call content into text, the generation AI simultaneously translates it into multiple languages. For example, English call content is simultaneously translated into Japanese and Chinese and saved as text data. Furthermore, when converting call content into text, the generation AI translates it into different languages ​​and displays the translation results in real time. For example, English call content is translated into Japanese and displayed during the call. This enables real-time translation into different languages ​​and multilingual call analysis, making it possible to respond to international customers.

[0075] When converting call content to text, the call content conversion unit can analyze background sounds and environmental sounds and perform noise cancellation. For example, when converting call content to text, the generation AI analyzes background sounds and performs noise cancellation. For example, noise during a call is removed to convert clear voice data into text. In addition, when converting call content to text, the generation AI analyzes environmental sounds and performs noise cancellation. For example, wind noise and car noise during a call are removed to convert clear voice data into text. In addition, when converting call content to text, the generation AI analyzes background sounds and environmental sounds in real time and performs noise cancellation. For example, music and television sounds during a call are removed to convert clear voice data into text. In this way, background sounds and environmental sounds are analyzed and noise cancellation is performed to convert clear voice data into text.

[0076] When converting call content to text, the call content conversion unit can display the speaker's emotions in real time, allowing the operator to respond appropriately. For example, when converting call content to text, the generation AI analyzes the speaker's emotions in real time and displays them to the operator. For example, if the speaker is angry, an "Anger" tag is displayed, allowing the operator to respond appropriately. In addition, when converting call content to text, the generation AI quantifies the intensity of the speaker's emotions and displays them to the operator. For example, the intensity of emotions can be displayed on a scale from 0 to 10, allowing the operator to respond appropriately. In addition, when converting call content to text, the generation AI records changes in the speaker's emotions over time and displays them to the operator. For example, it can display a graph of changes in emotions from the start to the end of the call, allowing the operator to respond appropriately. This improves the quality of customer service by displaying the speaker's emotions in real time and allowing the operator to respond appropriately.

[0077] When searching text data, the search unit can filter by emotional tone based on emotion tags. For example, when searching text data generated by the generation AI, a function is added to filter by emotional tone based on emotion tags. For example, only phone call content tagged with "anger" is searched. Furthermore, when searching text data generated by the generation AI, the search unit adds a function to filter based on the intensity of emotion. For example, only phone call content with a high emotional intensity is searched. Furthermore, when searching text data generated by the generation AI, the search unit adds a function to filter based on changes in emotion. For example, only phone call content in which emotions changed significantly during the call is searched. This makes it possible to quickly search for phone call content related to a specific emotion by filtering by emotional tone based on emotion tags.

[0078] The search unit can understand the context when searching text data and automatically suggest related keywords and phrases. For example, when searching text data generated by the generation AI, it understands the context and automatically suggests related keywords. For example, it suggests keywords related to "customer satisfaction." Furthermore, when searching text data generated by the generation AI, the search unit understands the context and automatically suggests related phrases. For example, it suggests phrases related to "customer requests." Furthermore, when searching text data generated by the generation AI, the search unit understands the context and suggests related keywords and phrases in real time. For example, it suggests keywords and phrases related to "customer support." This improves the accuracy and efficiency of searches by understanding the context and automatically suggesting related keywords and phrases.

[0079] The search unit can automatically summarize search results for text data, allowing users to quickly obtain the information they need. For example, the search results for text data generated by the generation AI can be automatically summarized, allowing users to quickly obtain the information they need. For example, the main points of the search results can be displayed in short sentences. The search unit also automatically summarizes search results for text data generated by the generation AI and displays related information together. For example, related information for search results can be displayed in a single view. The search unit also automatically summarizes search results for text data generated by the generation AI, allowing users to quickly obtain the information they need. For example, important points of the search results can be highlighted. This allows users to quickly obtain the information they need by automatically summarizing the search results.

[0080] The search unit can expand the search function of text data to a multimodal search that also includes image and audio data. For example, the search function of text data generated by the generation AI is expanded to a multimodal search that also includes image data. For example, images related to the content of a call are displayed in the search results. The search unit also expands the search function of text data generated by the generation AI to a multimodal search that also includes audio data. For example, audio data related to the content of a call are displayed in the search results. The search unit also expands the search function of text data generated by the generation AI to a multimodal search that also includes image and audio data, allowing users to obtain the information they need from multiple angles. For example, images and audio data related to the content of a call are displayed in the search results. In this way, by expanding the search to a multimodal search that also includes image and audio data, users can obtain the information they need from multiple angles.

[0081] The search unit can automatically cluster search results for text data and group highly related information. For example, it can automatically cluster search results for text data generated by the generation AI and group highly related information. For example, it can group phone call content related to the same topic into one cluster. The search unit also automatically clusters search results for text data generated by the generation AI, allowing users to grasp related information at a glance. For example, it can display related phone call content in groups. The search unit also automatically clusters search results for text data generated by the generation AI and visually display highly related information. For example, it can display related phone call content as a cluster map. In this way, by automatically clustering search results and grouping highly related information, users can grasp related information at a glance.

[0082] The search unit can use the emotion estimation function to collect users' emotional responses to search results and improve the accuracy of the search algorithm. For example, the emotion estimation function is used to collect users' emotional responses to search results and improve the accuracy of the search algorithm. For example, search results with a high number of positive emotional responses are preferentially displayed. The search unit also uses the emotion estimation function to analyze users' emotional scores for the search results and improve the accuracy of the search algorithm. For example, search results with a high emotional score are preferentially displayed. The search unit also uses the emotion estimation function to collect users' emotional responses to search results in real time and improve the accuracy of the search algorithm. For example, the search results are dynamically adjusted in response to changes in the user's emotions. In this way, by collecting users' emotional responses and improving the accuracy of the search algorithm, more appropriate search results can be provided.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The call content conversion unit converts the call content into text. For example, the call content conversion unit converts the call content into text in real time using voice recognition technology. It can also accurately convert the call content into text using natural language processing technology. The voice data is analyzed and text data is generated. Step 2: The search unit immediately searches the text data generated by the call content conversion unit. For example, the search unit quickly searches for call content containing specific keywords or phrases. The search unit can also use indexing methods and search algorithms to efficiently search the text data. Step 3: The analysis unit analyzes the text data retrieved by the search unit. For example, the analysis unit may use text mining techniques to analyze the text data and extract important information and trends. It may also use sentiment analysis and topic modeling techniques to analyze the emotional elements and topics of the text data.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0086] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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, RAM 30, and 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.

[0091] 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.

[0092] 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 audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] 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 carried out in a secure state.

[0095] Fig. 4 shows an example of the 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.

[0096] 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.

[0097] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0101] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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, RAM 30, and 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.

[0106] 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.

[0107] 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 audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] 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 carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0111] 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.

[0112] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 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 specific processing unit 290 using these models.

[0114] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0116] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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, RAM 30, and 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.

[0121] 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.

[0122] 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 audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] 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 carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0126] 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.

[0127] 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.

[0128] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The robot 414 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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 voice indicating a user input regarding 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.

[0132] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] 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.

[0142] 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 USB (Universal Serial Bus) 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.

[0143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0147] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 call content conversion unit that converts the call content into text; a search unit that instantly searches the text data generated by the call content conversion unit; an analysis unit that analyzes the text data searched by the search unit; A system characterized by:

2. The call content conversion unit When converting conversations into text, the speaker's emotions are estimated and emotion tags are added. The system of claim 1 .

3. The call content conversion unit Automatically recognizes technical and industry-specific terms when converting calls to text, and provides appropriate translations and annotations The system of claim 1 .

4. The call content conversion unit When converting phone conversations into text, the tone and speed of the speaker's voice are analyzed and reflected in the text. The system of claim 1 .

5. The call content conversion unit When converting call content to text, it translates it into different languages ​​in real time, enabling multilingual call analysis. The system of claim 1 .

Images