System

Abstract

To provide a system that converts an emotional voice or negative statement into a rational speech in online calls.SOLUTION: A system comprises an acquisition unit, an analysis unit, a filtering unit, and a provision unit. The acquisition unit acquires speech data. The analysis unit analyzes the speech data acquired by the acquisition unit and classifies an emotion of a speaker. The filtering unit converts a negatively emotional speech into a rational speech on the basis of the emotion classified by the analysis unit. The provision unit provides the speech converted by the filtering unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, emotional voices and negative comments during online calls can lead to reduced work efficiency.

[0005] The system according to the embodiment aims to convert emotional voices and negative comments into rational voices during online calls. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a filtering unit, and a provision unit. The acquisition unit acquires voice data. The analysis unit analyzes the voice data acquired by the acquisition unit and classifies the emotion of the speaker. The filtering unit converts negative emotional voice into rational voice based on the emotion classified by the analysis unit. The provision unit provides the voice converted by the filtering unit to a user. Effect of the Invention

[0007] The system according to the embodiment can convert emotional voices and negative comments into rational voices during online calls. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The online call filtering system according to the embodiment of the present invention is a system that filters out emotional voices and negative comments during online calls. This system acquires voice data from online calls, and an emotion recognition AI analyzes the voice data to classify the speaker's emotions. For example, emotions such as anger, sadness, and joy are identified. In this case, the emotion recognition AI determines the emotion based on characteristics such as the tone, pitch, and speed of the voice. Next, negative emotional voices are filtered into rational voices. For example, a voice containing an angry emotion is converted into a calm tone. This filtering process is performed based on the emotion determined by the emotion recognition AI. Furthermore, for negative comments, a generation AI is used to present voices that omit comments unrelated to the main topic of the discussion. For example, emotional comments and unrelated topics are removed, and only important information is extracted and presented. This process allows the user to work efficiently without feeling stressed. This mechanism prevents emotional voices and negative comments from being heard during online calls, improving work efficiency. In addition, employee satisfaction is improved by creating a stress-free work environment. This enables the online call filtering system to filter out emotional voices and negative comments during online calls, improving work efficiency.

[0029] The online call filtering system according to the embodiment includes an acquisition unit, an analysis unit, a filtering unit, and a provision unit. The acquisition unit acquires voice data of an online call. The acquisition unit collects voice data in real time using, for example, a microphone. The acquisition unit can also read recorded voice files. For example, the acquisition unit supports voice data formats such as audio file formats, sampling rates, and bit rates. The analysis unit uses an emotion recognition AI to analyze the voice data acquired by the acquisition unit and classify the speaker's emotions. The analysis unit determines emotions based on features such as tone, pitch, and speed of the voice. The emotion recognition AI takes voice data as input and outputs an emotion classification result. For example, the analysis unit analyzes the tone of the voice data and identifies emotions such as anger, sadness, and joy. The filtering unit converts negative emotional voice into rational voice based on the emotion classified by the analysis unit. The filtering unit converts, for example, voice including an angry emotion into a calm tone. The filtering unit takes the judgment result of the emotion recognition AI as input and outputs the converted voice. For example, the filtering unit adjusts the tone of the voice and converts it into a calm tone. The providing unit provides the voice converted by the filtering unit to the user. The providing unit plays the voice using, for example, a speaker. The providing unit can also display the converted voice as text data. For example, the providing unit converts the voice data into text and displays it on a screen. In this way, the online call filtering system according to the embodiment can filter out emotional voices and negative comments during online calls, thereby improving business efficiency.

[0030] The acquisition unit acquires voice data of online calls. The acquisition unit, for example, collects voice data in real time using a microphone. Specifically, the acquisition unit acquires clear voice data by using a high-sensitivity microphone and incorporating noise canceling technology that reduces ambient noise. The acquisition unit can also read recorded voice files. For example, the acquisition unit supports voice data formats such as audio file formats, sampling rates, and bit rates. This allows the acquisition unit to flexibly handle voice data in various formats. Furthermore, the acquisition unit also acquires metadata of the voice data and provides it to the analysis unit and filtering unit. The metadata includes the recording date and time, speaker identification information, and the context of the call. This makes it easier for the acquisition unit to understand the context of the voice data and improve the accuracy of subsequent analysis and filtering. The acquisition unit stores the voice data in cloud storage or local storage and makes it accessible as needed. This allows the acquisition unit to efficiently manage not only real-time voice data but also past voice data and utilize it for analysis and filtering.

[0031] The analysis unit uses the emotion recognition AI to analyze the voice data acquired by the acquisition unit and classify the speaker's emotion. The analysis unit determines the emotion based on features such as tone, pitch, and speed of the voice. The emotion recognition AI takes the voice data as input and outputs the emotion classification result. Specifically, the emotion recognition AI uses deep learning technology to extract features of the voice data and classifies the emotion based on a model learned in advance. For example, the analysis unit analyzes the tone of the voice data and identifies emotions such as anger, sadness, and joy. Furthermore, the analysis unit can track emotional fluctuations in real time by taking into account temporal changes in the voice data. This allows the analysis unit to quickly detect changes in the speaker's emotion and provide information for appropriate filtering. The analysis unit periodically re-learns the model and incorporates new data in order to improve the accuracy of the emotion recognition AI. This allows the analysis unit to always utilize the latest emotion recognition technology and achieve highly accurate emotion classification.

[0032] The filtering unit converts negative emotional voice into rational voice based on the emotion classified by the analysis unit. For example, the filtering unit converts an angry emotion into a calm tone. Specifically, the filtering unit adjusts the tone, pitch, and speed of the voice to soften the intensity of the emotion. For example, when the emotion of anger is strong, the filtering unit converts the voice into a calm voice by lowering the pitch, slowing the speed, and gentlening the tone. The filtering unit inputs the judgment result of the emotion recognition AI and outputs the converted voice. Furthermore, the filtering unit utilizes natural language processing technology to adjust the expression of emotion while retaining the content of the voice. This allows the filtering unit to convert an emotional expression into a rational expression without compromising the speaker's intention. The filtering unit can adjust the strength and criteria of filtering according to the user's settings. For example, it is possible to set it to filter only a specific emotion, or to change the degree of filtering according to the intensity of the emotion. This allows the filtering unit to provide flexible filtering according to the user's needs.

[0033] The providing unit provides the user with the voice converted by the filtering unit. The providing unit, for example, uses a speaker to play back the voice. Specifically, the providing unit uses a high-quality speaker to play back the converted voice clearly. The providing unit can also display the converted voice as text data. For example, the providing unit converts the voice data into text and displays it on a screen. This allows the user to confirm information not only as voice but also as text. Furthermore, the providing unit can customize settings for playing back the voice and displaying the text through a user interface. For example, it is possible to adjust the playback speed and volume of the voice, and to change the font size and display position of the text. This allows the providing unit to realize flexible information provision according to the user's preferences. The providing unit can also save the converted voice and text data so that they can be played back or referenced later. This allows the user to check the contents of past calls and reuse them as necessary. The providing unit can also work with other systems and devices to share the converted voice and text data. For example, it is possible to work with a business chat tool or an email system to automatically record and share the contents of calls. This allows the providing unit to provide a variety of information providing means for improving business efficiency.

[0034] The analysis unit can determine the emotion based on the characteristics of the tone, pitch, and speed of the voice. The analysis unit, for example, analyzes the tone of the voice to determine the emotion. For example, the emotion is identified based on the pitch and strength of the tone. The analysis unit can also analyze the pitch of the voice to determine the emotion. For example, the emotion is identified based on the frequency of the pitch. The analysis unit can also analyze the speed of the voice to determine the emotion. For example, the emotion is identified based on the speaking speed. This allows the analysis unit to accurately determine the emotion based on the characteristics of the voice. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the voice data to AI and have the AI ​​execute the emotion determination.

[0035] The filtering unit can convert a voice containing an angry emotion into a calm tone. The filtering unit, for example, converts a voice containing an angry emotion into a calm tone. For example, the filtering unit adjusts the tone of the voice and converts it into a calm tone. The filtering unit can also adjust the pitch of the voice and convert it into a calm tone. For example, the pitch frequency is stabilized. The filtering unit can also adjust the speed of the voice and convert it into a calm tone. For example, the speech rate is kept constant. In this way, the filtering unit can reduce the emotional impact by converting an angry emotion into a calm tone. A part or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI. For example, the filtering unit can input voice data to AI and have the AI ​​perform tone conversion.

[0036] The system includes a generation unit, which can eliminate negative comments and extract and present only important information. The generation unit uses a generation AI to eliminate negative comments and extract and present only important information. The generation unit, for example, removes emotional comments and irrelevant topics and extracts important information. The generation AI receives voice data as input and outputs voice data from which important information has been extracted. For example, the generation unit detects emotional comments and removes them. The generation unit can also detect irrelevant topics and remove them. For example, the generation unit detects comments that are unrelated to the main topic of the discussion and removes them. This allows the generation unit to efficiently obtain only important information by omitting negative comments. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input voice data to the generation AI and cause the generation AI to extract important information.

[0037] The providing unit can provide the converted voice to the user. The providing unit, for example, provides the converted voice to the user using a speaker. For example, the providing unit plays the converted voice in real time. The providing unit can also display the converted voice as text data. For example, the providing unit converts the voice data into text and displays it on a screen. As a result, the providing unit can provide the converted voice to the user, thereby realizing a stress-free work environment. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the converted voice data to AI and have the AI ​​execute the conversion to text data.

[0038] The acquisition unit can analyze the user's past call history and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past call history and selects the optimal acquisition method. For example, the acquisition unit preferentially acquires a call app that the user has used frequently in the past. The acquisition unit can also predict voice data to be acquired in a specific time period from the user's past call history and select the optimal acquisition method. For example, the acquisition unit analyzes the user's past call history and preferentially acquires only important calls. As a result, the acquisition unit can select the optimal acquisition method by analyzing the past call history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input call history data to AI and cause AI to select the optimal acquisition method.

[0039] The acquisition unit can perform filtering based on the user's current work situation or field of interest when acquiring the voice data. For example, when the user is in a meeting, the acquisition unit acquires only voice data related to the meeting. For example, the acquisition unit preferentially acquires voice data related to the agenda of the meeting. In addition, when the user is concentrating on a specific project, the acquisition unit can also preferentially acquire voice data related to the project. For example, the acquisition unit filters related voice data based on the progress of the project. In addition, the acquisition unit can filter and acquire related voice data based on the user's field of interest. For example, the acquisition unit preferentially acquires voice data related to the user's field of expertise or topics of interest. Thereby, the acquisition unit can acquire highly relevant data by filtering based on the work situation or field of interest. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the work situation or field of interest to AI and cause the AI ​​to perform filtering.

[0040] When acquiring voice data, the acquisition unit can preferentially acquire data with high relevance based on the geographical location information of the user. For example, when the user is in a specific area, the acquisition unit preferentially acquires voice data related to the area. For example, the acquisition unit acquires voice data related to events and news in the area. In addition, when the user is moving, the acquisition unit can also acquire highly relevant voice data based on the current location. For example, the acquisition unit acquires voice data including information related to the user who is moving. In addition, the acquisition unit can filter and acquire optimal voice data based on the geographical location information of the user. For example, the acquisition unit filters the voice data based on geographical relevance. As a result, the acquisition unit acquires highly relevant data based on the geographical location information, thereby enabling efficient data collection. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information to AI and cause AI to execute acquisition of highly relevant data.

[0041] The acquisition unit can analyze the user's SNS activity and acquire related data when acquiring the voice data. The acquisition unit acquires, for example, voice data related to a topic that the user frequently mentions on social media. For example, the acquisition unit analyzes the user's SNS posting content and acquires related voice data. The acquisition unit can also analyze a topic of interest from the user's social media activity and acquire related voice data. For example, the acquisition unit acquires voice data related to a topic of interest based on the user's SNS activity. The acquisition unit can also filter and acquire optimal voice data based on the content of the user's comments on social media. For example, the acquisition unit filters the voice data based on trends and topics on SNS. As a result, the acquisition unit can acquire highly relevant data by analyzing social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input SNS data to AI and cause AI to execute acquisition of related data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice data during analysis. For example, the analysis unit performs a detailed analysis on important voice data. For example, the analysis unit analyzes the contents of the voice data in detail to extract important information. The analysis unit can also perform a simplified analysis on voice data with low importance. For example, the analysis unit analyzes an overview of the voice data to provide concise information. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the voice data. For example, the analysis unit adjusts the depth and range of the analysis based on the importance of the voice data. As a result, the analysis unit adjusts the level of detail of the analysis according to the importance of the voice data, thereby enabling efficient analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the voice data to AI and cause AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the voice data during analysis. For example, the analysis unit applies a minutes generation algorithm to conference voice data. For example, the analysis unit automatically summarizes the contents of the conference and generates minutes. The analysis unit can also apply a customer satisfaction analysis algorithm to customer support voice data. For example, the analysis unit analyzes the contents of the customer's remarks and evaluates the satisfaction level. The analysis unit can also apply a learning progress analysis algorithm to educational voice data. For example, the analysis unit analyzes the educational content and evaluates the learning progress. Thereby, the analysis unit applies an appropriate analysis algorithm depending on the category of the voice data, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the voice data to AI and cause the AI ​​to apply the analysis algorithm.

[0044] The analysis unit can determine the priority of the analysis based on the acquisition time of the voice data during the analysis. The analysis unit, for example, prioritizes the analysis of the latest voice data. For example, the analysis unit quickly analyzes the latest voice data and provides the result in real time. The analysis unit can also prioritize the analysis of voice data acquired immediately after an important event. For example, the analysis unit prioritizes the analysis of voice data acquired immediately after the event to extract important information. The analysis unit can also dynamically adjust the priority of the analysis based on the user's schedule. For example, the analysis unit prioritizes the analysis of voice data related to important tasks based on the user's schedule. This allows the analysis unit to determine the priority of the analysis based on the acquisition time of the voice data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquisition time of the voice data to AI and cause the AI ​​to execute the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the voice data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant voice data. For example, the analysis unit quickly analyzes highly relevant voice data to extract important information. The analysis unit can also postpone analysis of less relevant voice data. For example, the analysis unit postpones analysis of less relevant voice data and prioritizes analysis of data with high importance. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the voice data. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the voice data to perform efficient analysis. As a result, the analysis unit adjusts the order of analysis based on the relevance of the voice data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the voice data to AI and cause AI to adjust the order of analysis.

[0046] The filtering unit can improve the accuracy of filtering based on the interrelationships of the voice data during filtering. The filtering unit filters related voices collectively, for example, taking into account the context of the voice data. For example, the filtering unit analyzes the context of the voice data and extracts related information. The filtering unit can also analyze the interrelationships of the voice data, extract important parts, and filter them. For example, the filtering unit extracts important information based on the interrelationships of the voice data. The filtering unit can also dynamically adjust the accuracy of filtering based on the interrelationships of the voice data. For example, the filtering unit improves the accuracy of filtering based on the interrelationships of the voice data. As a result, the filtering unit improves the accuracy of filtering by taking into account the interrelationships of the voice data. A part or all of the above-mentioned processing in the filtering unit may be performed, for example, using AI, or may be performed without using AI. For example, the filtering unit can input the interrelationships of the voice data to AI and cause the AI ​​to improve the accuracy of filtering.

[0047] The filtering unit can perform filtering in consideration of attribute information of the submitter of the voice data at the time of filtering. The filtering unit, for example, preferentially filters important voice data based on the job title of the submitter. For example, the filtering unit preferentially filters voice data including important information based on the job title of the submitter. The filtering unit can also filter related voice data based on the specialty of the submitter. For example, the filtering unit filters voice data including information related to the specialty of the submitter. The filtering unit can also dynamically adjust the filtering criteria based on the attribute information of the submitter. For example, the filtering unit adjusts the filtering criteria based on the attribute information of the submitter. As a result, the filtering unit can perform appropriate filtering by considering the attribute information of the submitter. A part or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI. For example, the filtering unit can input the attribute information of the submitter to AI and have the AI ​​perform filtering.

[0048] The filtering unit can perform filtering in consideration of the geographical distribution of the voice data when filtering. For example, the filtering unit preferentially filters voice data related to a specific region. For example, the filtering unit filters voice data related to events or news in the region. The filtering unit can also filter highly relevant voice data based on the geographical distribution. For example, the filtering unit filters voice data based on geographical relevance. The filtering unit can also improve the accuracy of filtering in consideration of the geographical distribution of the voice data. For example, the filtering unit improves the accuracy of filtering based on the geographical distribution. Thereby, the filtering unit can efficiently filter highly relevant voice data by considering the geographical distribution. A part or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI. For example, the filtering unit can input geographical distribution data to AI and cause the AI ​​to perform filtering.

[0049] The filtering unit can improve the accuracy of filtering by referring to related literature of the voice data during filtering. The filtering unit, for example, preferentially filters important voice data based on related literature. For example, the filtering unit adjusts the filtering criteria of the voice data by referring to related literature. The filtering unit can also analyze related literature of the voice data and dynamically improve the accuracy of filtering. For example, the filtering unit improves the accuracy of filtering based on related literature. As a result, the filtering unit improves the accuracy of filtering by referring to related literature. A part or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI. For example, the filtering unit can input related literature data to AI and cause AI to improve the accuracy of filtering.

[0050] The providing unit can select the optimal display method by referring to the user's past operation history at the time of providing. The providing unit, for example, preferentially provides a display method used by the user in the past. For example, the providing unit selects the optimal display method based on the user's past operation history. The providing unit can also predict and provide the optimal display method from the user's past operation history. For example, the providing unit analyzes past operation patterns and provides the optimal display method. The providing unit can also analyze the user's past operation history and provide a display method with high visibility. For example, the providing unit selects a display method with high visibility based on the past operation history. As a result, the providing unit can provide the optimal display method by referring to the past operation history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input operation history data to AI and cause AI to select the optimal display method.

[0051] The providing unit can select the optimal display method in consideration of the device information of the user at the time of providing. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. For example, the providing unit provides a display method optimized for the screen size of the smartphone. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit provides a display method optimized for the screen size of the tablet. In addition, when the user is using a smart watch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit provides a display method optimized for the screen size of the smart watch. Thereby, the providing unit can provide the optimal display method by considering the device information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input device information to AI and cause AI to select the optimal display method.

[0052] The generation unit can improve the accuracy of generation by considering the interrelationship of the voice data at the time of generation. The generation unit generates related voices in a lump, for example, by considering the context of the voice data. For example, the generation unit analyzes the context of the voice data, extracts related information, and generates it. The generation unit can also analyze the interrelationship of the voice data, extract important parts, and generate it. For example, the generation unit extracts important information based on the interrelationship of the voice data, and generates it. The generation unit can also dynamically adjust the accuracy of generation based on the interrelationship of the voice data. For example, the generation unit improves the accuracy of generation based on the interrelationship of the voice data. As a result, the generation unit improves the accuracy of generation by considering the interrelationship of the voice data. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the interrelationship of the voice data to AI, and cause AI to execute improvement of the accuracy of generation.

[0053] The generation unit can generate the voice data while taking into consideration the attribute information of the person who submitted the voice data. The generation unit, for example, generates important voice data preferentially based on the job title of the person who submitted the voice data. For example, the generation unit generates voice data including important information preferentially based on the job title of the person who submitted the voice data. The generation unit can also generate related voice data based on the specialty of the person who submitted the voice data. For example, the generation unit generates voice data including information related to the specialty of the person who submitted the voice data. The generation unit can also dynamically adjust the generation criteria based on the attribute information of the person who submitted the voice data. For example, the generation unit adjusts the generation criteria based on the attribute information of the person who submitted the voice data. This enables the generation unit to generate appropriate voice by taking into consideration the attribute information of the person who submitted the voice data. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the attribute information of the person who submitted the voice data to AI and cause AI to execute generation.

[0054] The generation unit can generate the voice data while taking into consideration the geographical distribution of the voice data. For example, the generation unit preferentially generates voice data related to a specific region. For example, the generation unit generates voice data related to events or news in the region. The generation unit can also generate highly relevant voice data based on the geographical distribution. For example, the generation unit generates voice data based on geographical relevance. The generation unit can also improve the accuracy of the generation by taking into consideration the geographical distribution of the voice data. For example, the generation unit improves the accuracy of the generation based on the geographical distribution. As a result, the generation unit can efficiently generate highly relevant voice data by taking into consideration the geographical distribution. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input geographical distribution data to AI and cause the AI ​​to execute the generation.

[0055] The generation unit can improve the accuracy of generation by referring to related literature of the voice data at the time of generation. The generation unit, for example, generates important voice data preferentially based on related literature. For example, the generation unit adjusts the generation criteria of the voice data by referring to related literature. The generation unit can also analyze related literature of the voice data and dynamically improve the accuracy of generation. For example, the generation unit improves the accuracy of generation based on related literature. As a result, the generation unit improves the accuracy of generation by referring to related literature. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input related literature data to AI and cause AI to improve the accuracy of generation.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The acquisition unit can analyze the user's past call history and select the optimal acquisition method. For example, the acquisition unit preferentially acquires a call app that the user has used frequently in the past. The acquisition unit can also predict voice data to be acquired in a specific time period from the user's past call history and select the optimal acquisition method. For example, the acquisition unit analyzes the user's past call history and preferentially acquires only important calls. As a result, the acquisition unit can select the optimal acquisition method by analyzing the past call history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input call history data to AI and cause AI to select the optimal acquisition method.

[0058] The analysis unit can determine the priority of the analysis based on the acquisition time of the voice data. For example, the analysis unit preferentially analyzes the latest voice data. For example, the analysis unit quickly analyzes the latest voice data and provides the result in real time. The analysis unit can also preferentially analyze the voice data acquired immediately after an important event. For example, the analysis unit preferentially analyzes the voice data acquired immediately after the event to extract important information. The analysis unit can also dynamically adjust the priority of the analysis based on the user's schedule. For example, the analysis unit preferentially analyzes the voice data related to an important task based on the user's schedule. As a result, the analysis unit determines the priority of the analysis based on the acquisition time of the voice data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquisition time of the voice data to AI and cause AI to execute the determination of the analysis priority.

[0059] The filtering unit can improve the accuracy of filtering based on the interrelationships of the voice data. For example, the filtering unit filters related voices collectively, taking into account the context of the voice data. For example, the filtering unit analyzes the context of the voice data and extracts related information. The filtering unit can also analyze the interrelationships of the voice data, extract important parts, and filter them. For example, the filtering unit extracts important information based on the interrelationships of the voice data. The filtering unit can also dynamically adjust the accuracy of filtering based on the interrelationships of the voice data. For example, the filtering unit improves the accuracy of filtering based on the interrelationships of the voice data. As a result, the filtering unit improves the accuracy of filtering by taking into account the interrelationships of the voice data. A part or all of the above-mentioned processing in the filtering unit may be performed, for example, using AI, or may be performed without using AI. For example, the filtering unit can input the interrelationships of the voice data to AI and cause the AI ​​to improve the accuracy of filtering.

[0060] The providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit preferentially provides the display method used by the user in the past. For example, the providing unit selects the optimal display method based on the user's past operation history. The providing unit can also predict and provide the optimal display method from the user's past operation history. For example, the providing unit analyzes past operation patterns and provides the optimal display method. The providing unit can also analyze the user's past operation history and provide a display method with high visibility. For example, the providing unit selects a display method with high visibility based on the past operation history. As a result, the providing unit can provide the optimal display method by referring to the past operation history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input operation history data to AI and cause AI to select the optimal display method.

[0061] The generation unit can improve the accuracy of generation by taking into account the interrelationship of the voice data. For example, the generation unit generates related voices in a lump by taking into account the context of the voice data. For example, the generation unit analyzes the context of the voice data, extracts related information, and generates it. The generation unit can also analyze the interrelationship of the voice data, extract important parts, and generate it. For example, the generation unit extracts important information based on the interrelationship of the voice data, and generates it. The generation unit can also dynamically adjust the accuracy of generation based on the interrelationship of the voice data. For example, the generation unit improves the accuracy of generation based on the interrelationship of the voice data. As a result, the generation unit improves the accuracy of generation by taking into account the interrelationship of the voice data. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the interrelationship of the voice data to AI, and cause AI to execute improvement of the accuracy of generation.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The acquisition unit acquires voice data of an online call. The acquisition unit, for example, collects voice data in real time using a microphone. The acquisition unit can also read a recorded voice file. For example, the acquisition unit supports the format of the voice data, such as the voice file format, sampling rate, and bit rate. Step 2: The analysis unit uses emotion recognition AI to analyze the voice data acquired by the acquisition unit and classify the speaker's emotion. The analysis unit determines the emotion based on features such as the tone, pitch, and speed of the voice. The emotion recognition AI takes the voice data as input and outputs the emotion classification result. For example, the analysis unit analyzes the tone of the voice data and identifies emotions such as anger, sadness, and joy. Step 3: The filtering unit converts negative emotional voice into rational voice based on the emotions classified by the analysis unit. For example, the filtering unit converts voice containing angry emotions into a calm tone. The filtering unit receives the judgment result of the emotion recognition AI as input and outputs the converted voice. For example, the filtering unit adjusts the tone of the voice and converts it into a calm tone. Step 4: The providing unit provides the voice converted by the filtering unit to the user. The providing unit plays the voice using a speaker, for example. The providing unit can also display the converted voice as text data. For example, the providing unit converts the voice data into text and displays it on a screen.

[0064] (Example 2) The online call filtering system according to the embodiment of the present invention is a system that filters out emotional voices and negative comments during online calls. This system acquires voice data from online calls, and an emotion recognition AI analyzes the voice data to classify the speaker's emotions. For example, emotions such as anger, sadness, and joy are identified. In this case, the emotion recognition AI determines the emotion based on characteristics such as the tone, pitch, and speed of the voice. Next, negative emotional voices are filtered into rational voices. For example, a voice containing an angry emotion is converted into a calm tone. This filtering process is performed based on the emotion determined by the emotion recognition AI. Furthermore, for negative comments, a generation AI is used to present voices that omit comments unrelated to the main topic of the discussion. For example, emotional comments and unrelated topics are removed, and only important information is extracted and presented. This process allows the user to work efficiently without feeling stressed. This mechanism prevents emotional voices and negative comments from being heard during online calls, improving work efficiency. In addition, employee satisfaction is improved by creating a stress-free work environment. This enables the online call filtering system to filter out emotional voices and negative comments during online calls, improving work efficiency.

[0065] The online call filtering system according to the embodiment includes an acquisition unit, an analysis unit, a filtering unit, and a provision unit. The acquisition unit acquires voice data of an online call. The acquisition unit collects voice data in real time using, for example, a microphone. The acquisition unit can also read recorded voice files. For example, the acquisition unit supports voice data formats such as audio file formats, sampling rates, and bit rates. The analysis unit uses an emotion recognition AI to analyze the voice data acquired by the acquisition unit and classify the speaker's emotions. The analysis unit determines emotions based on features such as tone, pitch, and speed of the voice. The emotion recognition AI takes voice data as input and outputs an emotion classification result. For example, the analysis unit analyzes the tone of the voice data and identifies emotions such as anger, sadness, and joy. The filtering unit converts negative emotional voice into rational voice based on the emotion classified by the analysis unit. The filtering unit converts, for example, voice including an angry emotion into a calm tone. The filtering unit takes the judgment result of the emotion recognition AI as input and outputs the converted voice. For example, the filtering unit adjusts the tone of the voice and converts it into a calm tone. The providing unit provides the voice converted by the filtering unit to the user. The providing unit plays the voice using, for example, a speaker. The providing unit can also display the converted voice as text data. For example, the providing unit converts the voice data into text and displays it on a screen. In this way, the online call filtering system according to the embodiment can filter out emotional voices and negative comments during online calls, thereby improving business efficiency.

[0066] The acquisition unit acquires voice data of online calls. The acquisition unit, for example, collects voice data in real time using a microphone. Specifically, the acquisition unit acquires clear voice data by using a high-sensitivity microphone and incorporating noise canceling technology that reduces ambient noise. The acquisition unit can also read recorded voice files. For example, the acquisition unit supports voice data formats such as audio file formats, sampling rates, and bit rates. This allows the acquisition unit to flexibly handle voice data in various formats. Furthermore, the acquisition unit also acquires metadata of the voice data and provides it to the analysis unit and filtering unit. The metadata includes the recording date and time, speaker identification information, and the context of the call. This makes it easier for the acquisition unit to understand the context of the voice data and improve the accuracy of subsequent analysis and filtering. The acquisition unit stores the voice data in cloud storage or local storage and makes it accessible as needed. This allows the acquisition unit to efficiently manage not only real-time voice data but also past voice data and utilize it for analysis and filtering.

[0067] The analysis unit uses the emotion recognition AI to analyze the voice data acquired by the acquisition unit and classify the speaker's emotion. The analysis unit determines the emotion based on features such as tone, pitch, and speed of the voice. The emotion recognition AI takes the voice data as input and outputs the emotion classification result. Specifically, the emotion recognition AI uses deep learning technology to extract features of the voice data and classifies the emotion based on a model learned in advance. For example, the analysis unit analyzes the tone of the voice data and identifies emotions such as anger, sadness, and joy. Furthermore, the analysis unit can track emotional fluctuations in real time by taking into account temporal changes in the voice data. This allows the analysis unit to quickly detect changes in the speaker's emotion and provide information for appropriate filtering. The analysis unit periodically re-learns the model and incorporates new data in order to improve the accuracy of the emotion recognition AI. This allows the analysis unit to always utilize the latest emotion recognition technology and achieve highly accurate emotion classification.

[0068] The filtering unit converts negative emotional voice into rational voice based on the emotion classified by the analysis unit. For example, the filtering unit converts an angry emotion into a calm tone. Specifically, the filtering unit adjusts the tone, pitch, and speed of the voice to soften the intensity of the emotion. For example, when the emotion of anger is strong, the filtering unit converts the voice into a calm voice by lowering the pitch, slowing the speed, and gentlening the tone. The filtering unit inputs the judgment result of the emotion recognition AI and outputs the converted voice. Furthermore, the filtering unit utilizes natural language processing technology to adjust the expression of emotion while retaining the content of the voice. This allows the filtering unit to convert an emotional expression into a rational expression without compromising the speaker's intention. The filtering unit can adjust the strength and criteria of filtering according to the user's settings. For example, it is possible to set it to filter only a specific emotion, or to change the degree of filtering according to the intensity of the emotion. This allows the filtering unit to provide flexible filtering according to the user's needs.

[0069] The providing unit provides the user with the voice converted by the filtering unit. The providing unit, for example, uses a speaker to play back the voice. Specifically, the providing unit uses a high-quality speaker to play back the converted voice clearly. The providing unit can also display the converted voice as text data. For example, the providing unit converts the voice data into text and displays it on a screen. This allows the user to confirm information not only as voice but also as text. Furthermore, the providing unit can customize settings for playing back the voice and displaying the text through a user interface. For example, it is possible to adjust the playback speed and volume of the voice, and to change the font size and display position of the text. This allows the providing unit to realize flexible information provision according to the user's preferences. The providing unit can also save the converted voice and text data so that they can be played back or referenced later. This allows the user to check the contents of past calls and reuse them as necessary. The providing unit can also work with other systems and devices to share the converted voice and text data. For example, it is possible to work with a business chat tool or an email system to automatically record and share the contents of calls. This allows the providing unit to provide a variety of information providing means for improving business efficiency.

[0070] The analysis unit can determine the emotion based on the characteristics of the tone, pitch, and speed of the voice. The analysis unit, for example, analyzes the tone of the voice to determine the emotion. For example, the emotion is identified based on the pitch and strength of the tone. The analysis unit can also analyze the pitch of the voice to determine the emotion. For example, the emotion is identified based on the frequency of the pitch. The analysis unit can also analyze the speed of the voice to determine the emotion. For example, the emotion is identified based on the speaking speed. This allows the analysis unit to accurately determine the emotion based on the characteristics of the voice. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the voice data to AI and have the AI ​​execute the emotion determination.

[0071] The filtering unit can convert a voice containing an angry emotion into a calm tone. The filtering unit, for example, converts a voice containing an angry emotion into a calm tone. For example, the filtering unit adjusts the tone of the voice and converts it into a calm tone. The filtering unit can also adjust the pitch of the voice and convert it into a calm tone. For example, the pitch frequency is stabilized. The filtering unit can also adjust the speed of the voice and convert it into a calm tone. For example, the speech rate is kept constant. In this way, the filtering unit can reduce the emotional impact by converting an angry emotion into a calm tone. A part or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI. For example, the filtering unit can input voice data to AI and have the AI ​​perform tone conversion.

[0072] The system includes a generation unit, which can eliminate negative comments and extract and present only important information. The generation unit uses a generation AI to eliminate negative comments and extract and present only important information. The generation unit, for example, removes emotional comments and irrelevant topics and extracts important information. The generation AI receives voice data as input and outputs voice data from which important information has been extracted. For example, the generation unit detects emotional comments and removes them. The generation unit can also detect irrelevant topics and remove them. For example, the generation unit detects comments that are unrelated to the main topic of the discussion and removes them. This allows the generation unit to efficiently obtain only important information by omitting negative comments. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input voice data to the generation AI and cause the generation AI to extract important information.

[0073] The providing unit can provide the converted voice to the user. The providing unit, for example, provides the converted voice to the user using a speaker. For example, the providing unit plays the converted voice in real time. The providing unit can also display the converted voice as text data. For example, the providing unit converts the voice data into text and displays it on a screen. As a result, the providing unit can provide the converted voice to the user, thereby realizing a stress-free work environment. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the converted voice data to AI and have the AI ​​execute the conversion to text data.

[0074] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the voice data based on the estimated user's emotion. The acquisition unit, for example, estimates the user's emotion and adjusts the timing of acquiring the voice data based on the estimated user's emotion. For example, when the user is stressed, the acquisition unit reduces the frequency of acquiring the voice data and acquires only important parts. In addition, when the user is relaxed, the acquisition unit can increase the frequency of acquiring the voice data and acquire detailed information. For example, when the user is in a hurry, the acquisition unit speeds up the timing of acquiring the voice data and acquires it in real time. As a result, the acquisition unit adjusts the timing of acquiring the voice data according to the user's emotion, thereby enabling efficient data acquisition. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The acquisition unit can analyze the user's past call history and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past call history and selects the optimal acquisition method. For example, the acquisition unit preferentially acquires a call app that the user has used frequently in the past. The acquisition unit can also predict voice data to be acquired in a specific time period from the user's past call history and select the optimal acquisition method. For example, the acquisition unit analyzes the user's past call history and preferentially acquires only important calls. As a result, the acquisition unit can select the optimal acquisition method by analyzing the past call history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input call history data to AI and cause AI to select the optimal acquisition method.

[0076] The acquisition unit can perform filtering based on the user's current work situation or field of interest when acquiring the voice data. For example, when the user is in a meeting, the acquisition unit acquires only voice data related to the meeting. For example, the acquisition unit preferentially acquires voice data related to the agenda of the meeting. In addition, when the user is concentrating on a specific project, the acquisition unit can also preferentially acquire voice data related to the project. For example, the acquisition unit filters related voice data based on the progress of the project. In addition, the acquisition unit can filter and acquire related voice data based on the user's field of interest. For example, the acquisition unit preferentially acquires voice data related to the user's field of expertise or topics of interest. Thereby, the acquisition unit can acquire highly relevant data by filtering based on the work situation or field of interest. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the work situation or field of interest to AI and cause the AI ​​to perform filtering.

[0077] The acquisition unit can estimate the user's emotion and determine the priority of the voice data to be acquired based on the estimated user's emotion. For example, when the user is feeling stressed, the acquisition unit preferentially acquires only important voice data. For example, the acquisition unit preferentially acquires important voice data related to work. In addition, when the user is relaxed, the acquisition unit can also preferentially acquire detailed voice data. For example, the acquisition unit preferentially acquires voice data including detailed information related to work. In addition, when the user is in a hurry, the acquisition unit can also preferentially acquire voice data that can be acquired quickly. For example, the acquisition unit preferentially acquires voice data that can be acquired in real time. Thereby, the acquisition unit can preferentially acquire important data by determining the priority of the voice data according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input a user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0078] When acquiring voice data, the acquisition unit can preferentially acquire data with high relevance based on the geographical location information of the user. For example, when the user is in a specific area, the acquisition unit preferentially acquires voice data related to the area. For example, the acquisition unit acquires voice data related to events and news in the area. In addition, when the user is moving, the acquisition unit can also acquire highly relevant voice data based on the current location. For example, the acquisition unit acquires voice data including information related to the user who is moving. In addition, the acquisition unit can filter and acquire optimal voice data based on the geographical location information of the user. For example, the acquisition unit filters the voice data based on geographical relevance. As a result, the acquisition unit acquires highly relevant data based on the geographical location information, thereby enabling efficient data collection. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information to AI and cause AI to execute acquisition of highly relevant data.

[0079] The acquisition unit can analyze the user's SNS activity and acquire related data when acquiring the voice data. The acquisition unit acquires, for example, voice data related to a topic that the user frequently mentions on social media. For example, the acquisition unit analyzes the user's SNS posting content and acquires related voice data. The acquisition unit can also analyze a topic of interest from the user's social media activity and acquire related voice data. For example, the acquisition unit acquires voice data related to a topic of interest based on the user's SNS activity. The acquisition unit can also filter and acquire optimal voice data based on the content of the user's comments on social media. For example, the acquisition unit filters the voice data based on trends and topics on SNS. As a result, the acquisition unit can acquire highly relevant data by analyzing social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input SNS data to AI and cause AI to execute acquisition of related data.

[0080] The analysis unit can estimate the user's emotion and adjust the method of expressing the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. For example, the analysis unit displays the analysis result in a visually easy-to-understand format using graphs and charts. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, the analysis unit displays an analysis result including detailed text and data. The analysis unit can also provide an analysis result that focuses on the main points when the user is in a hurry. For example, the analysis unit displays a concise analysis result that highlights important points. Thereby, the analysis unit can provide an analysis result with high visibility by adjusting the method of expressing the analysis according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice data during analysis. For example, the analysis unit performs a detailed analysis on important voice data. For example, the analysis unit analyzes the contents of the voice data in detail to extract important information. The analysis unit can also perform a simplified analysis on voice data with low importance. For example, the analysis unit analyzes an overview of the voice data to provide concise information. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the voice data. For example, the analysis unit adjusts the depth and range of the analysis based on the importance of the voice data. As a result, the analysis unit adjusts the level of detail of the analysis according to the importance of the voice data, thereby enabling efficient analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the voice data to AI and cause AI to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the voice data during analysis. For example, the analysis unit applies a minutes generation algorithm to conference voice data. For example, the analysis unit automatically summarizes the contents of the conference and generates minutes. The analysis unit can also apply a customer satisfaction analysis algorithm to customer support voice data. For example, the analysis unit analyzes the contents of the customer's remarks and evaluates the satisfaction level. The analysis unit can also apply a learning progress analysis algorithm to educational voice data. For example, the analysis unit analyzes the educational content and evaluates the learning progress. Thereby, the analysis unit applies an appropriate analysis algorithm depending on the category of the voice data, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the voice data to AI and cause the AI ​​to apply the analysis algorithm.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit performs a short and to the point analysis. For example, the analysis unit provides a concise analysis result that emphasizes important points. The analysis unit can also perform a detailed analysis when the user is relaxed. For example, the analysis unit provides an analysis result including detailed text and data. The analysis unit can also perform an analysis with visually stimulating effects added when the user is excited. For example, the analysis unit displays the analysis result by adding visual effects to a graph or chart. This allows the analysis unit to adjust the length of the analysis according to the user's emotion, thereby enabling efficient analysis. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotion data to the generation AI and have the generation AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of the analysis based on the acquisition time of the voice data during the analysis. The analysis unit, for example, prioritizes the analysis of the latest voice data. For example, the analysis unit quickly analyzes the latest voice data and provides the result in real time. The analysis unit can also prioritize the analysis of voice data acquired immediately after an important event. For example, the analysis unit prioritizes the analysis of voice data acquired immediately after the event to extract important information. The analysis unit can also dynamically adjust the priority of the analysis based on the user's schedule. For example, the analysis unit prioritizes the analysis of voice data related to important tasks based on the user's schedule. This allows the analysis unit to determine the priority of the analysis based on the acquisition time of the voice data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquisition time of the voice data to AI and cause the AI ​​to execute the determination of the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the voice data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant voice data. For example, the analysis unit quickly analyzes highly relevant voice data to extract important information. The analysis unit can also postpone analysis of less relevant voice data. For example, the analysis unit postpones analysis of less relevant voice data and prioritizes analysis of data with high importance. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the voice data. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the voice data to perform efficient analysis. As a result, the analysis unit adjusts the order of analysis based on the relevance of the voice data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the voice data to AI and cause AI to adjust the order of analysis.

[0086] The filtering unit can estimate the user's emotion and adjust the filtering criteria based on the estimated user's emotion. For example, when the user is stressed, the filtering unit applies strict filtering criteria to remove negative voices. For example, the filtering unit removes emotional remarks and irrelevant topics. Also, when the user is relaxed, the filtering unit can apply loose filtering criteria to provide detailed voices. For example, the filtering unit provides voice data including detailed information. Also, when the user is in a hurry, the filtering unit can quickly perform filtering to provide only important voices. For example, the filtering unit quickly provides voice data including important information. Thereby, the filtering unit adjusts the filtering criteria according to the user's emotion, thereby enabling appropriate filtering. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the filtering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the filtering unit can input user emotion data to the generation AI and have the generation AI adjust the filtering criteria.

[0087] The filtering unit can improve the accuracy of filtering based on the interrelationships of the voice data during filtering. The filtering unit filters related voices collectively, for example, taking into account the context of the voice data. For example, the filtering unit analyzes the context of the voice data and extracts related information. The filtering unit can also analyze the interrelationships of the voice data, extract important parts, and filter them. For example, the filtering unit extracts important information based on the interrelationships of the voice data. The filtering unit can also dynamically adjust the accuracy of filtering based on the interrelationships of the voice data. For example, the filtering unit improves the accuracy of filtering based on the interrelationships of the voice data. As a result, the filtering unit improves the accuracy of filtering by taking into account the interrelationships of the voice data. A part or all of the above-mentioned processing in the filtering unit may be performed, for example, using AI, or may be performed without using AI. For example, the filtering unit can input the interrelationships of the voice data to AI and cause the AI ​​to improve the accuracy of filtering.

[0088] The filtering unit can perform filtering in consideration of attribute information of the submitter of the voice data at the time of filtering. The filtering unit, for example, preferentially filters important voice data based on the job title of the submitter. For example, the filtering unit preferentially filters voice data including important information based on the job title of the submitter. The filtering unit can also filter related voice data based on the specialty of the submitter. For example, the filtering unit filters voice data including information related to the specialty of the submitter. The filtering unit can also dynamically adjust the filtering criteria based on the attribute information of the submitter. For example, the filtering unit adjusts the filtering criteria based on the attribute information of the submitter. As a result, the filtering unit can perform appropriate filtering by considering the attribute information of the submitter. A part or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI. For example, the filtering unit can input the attribute information of the submitter to AI and have the AI ​​perform filtering.

[0089] The filtering unit can estimate the user's emotion and adjust the order of displaying the filtering results based on the estimated user's emotion. For example, when the user is stressed, the filtering unit preferentially displays important voice data. For example, the filtering unit preferentially displays voice data including important information. In addition, when the user is relaxed, the filtering unit can also preferentially display detailed voice data. For example, the filtering unit preferentially displays voice data including detailed information. In addition, when the user is in a hurry, the filtering unit can also quickly display the filtering results. For example, the filtering unit quickly displays important information. Thereby, the filtering unit can provide important information preferentially by adjusting the display order of the filtering results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the filtering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the filtering unit can input user emotion data to the generation AI and cause the generation AI to adjust the display order of the filtering results.

[0090] The filtering unit can perform filtering in consideration of the geographical distribution of the voice data when filtering. For example, the filtering unit preferentially filters voice data related to a specific region. For example, the filtering unit filters voice data related to events or news in the region. The filtering unit can also filter highly relevant voice data based on the geographical distribution. For example, the filtering unit filters voice data based on geographical relevance. The filtering unit can also improve the accuracy of filtering in consideration of the geographical distribution of the voice data. For example, the filtering unit improves the accuracy of filtering based on the geographical distribution. Thereby, the filtering unit can efficiently filter highly relevant voice data by considering the geographical distribution. A part or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI. For example, the filtering unit can input geographical distribution data to AI and cause the AI ​​to perform filtering.

[0091] The filtering unit can improve the accuracy of filtering by referring to related literature of the voice data during filtering. The filtering unit, for example, preferentially filters important voice data based on related literature. For example, the filtering unit adjusts the filtering criteria of the voice data by referring to related literature. The filtering unit can also analyze related literature of the voice data and dynamically improve the accuracy of filtering. For example, the filtering unit improves the accuracy of filtering based on related literature. As a result, the filtering unit improves the accuracy of filtering by referring to related literature. A part or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI. For example, the filtering unit can input related literature data to AI and cause AI to improve the accuracy of filtering.

[0092] The providing unit can estimate the user's emotion and adjust the display method of the provided information based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. For example, the providing unit displays information in a visually easy-to-understand format using graphs and charts. In addition, the providing unit can also provide a display method including detailed information when the user is relaxed. For example, the providing unit provides a display method including detailed text and data. In addition, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, the providing unit provides a concise display method that emphasizes important points. Thereby, the providing unit can provide information with high visibility by adjusting the display method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generating AI and cause the generating AI to adjust the display method.

[0093] The providing unit can select the optimal display method by referring to the user's past operation history at the time of providing. The providing unit, for example, preferentially provides a display method used by the user in the past. For example, the providing unit selects the optimal display method based on the user's past operation history. The providing unit can also predict and provide the optimal display method from the user's past operation history. For example, the providing unit analyzes past operation patterns and provides the optimal display method. The providing unit can also analyze the user's past operation history and provide a display method with high visibility. For example, the providing unit selects a display method with high visibility based on the past operation history. As a result, the providing unit can provide the optimal display method by referring to the past operation history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input operation history data to AI and cause AI to select the optimal display method.

[0094] The providing unit can estimate the user's emotion and adjust the provided operation procedure based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple operation procedure. For example, the providing unit provides a step-by-step procedure to allow the user to easily operate. In addition, the providing unit can also provide a detailed operation procedure when the user is relaxed. For example, the providing unit provides an operation procedure including a detailed explanation or guide. In addition, the providing unit can also provide a procedure that allows the user to operate quickly when the user is in a hurry. For example, the providing unit provides a shortcut or a simplified procedure. Thereby, the providing unit adjusts the operation procedure according to the user's emotion, thereby enabling efficient operation. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the operation procedure.

[0095] The providing unit can select the optimal display method in consideration of the device information of the user at the time of providing. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. For example, the providing unit provides a display method optimized for the screen size of the smartphone. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit provides a display method optimized for the screen size of the tablet. In addition, when the user is using a smart watch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit provides a display method optimized for the screen size of the smart watch. Thereby, the providing unit can provide the optimal display method by considering the device information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input device information to AI and cause AI to select the optimal display method.

[0096] The generation unit can estimate the user's emotion and determine the priority of the voice to be generated based on the estimated user's emotion. For example, when the user is stressed, the generation unit generates important voice with priority. For example, the generation unit generates important voice data with priority related to work. In addition, the generation unit can also generate detailed voice with priority when the user is relaxed. For example, the generation unit generates voice data with priority including detailed information related to work. In addition, when the user is in a hurry, the generation unit can also generate voice that can be generated quickly with priority. For example, the generation unit generates voice data that can be generated in real time with priority. Thereby, the generation unit can generate important voice with priority by determining the priority of voice according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input user emotion data to the generation AI and have the generation AI determine the priority of voices.

[0097] The generation unit can improve the accuracy of generation by considering the interrelationship of the voice data at the time of generation. The generation unit generates related voices in a lump, for example, by considering the context of the voice data. For example, the generation unit analyzes the context of the voice data, extracts related information, and generates it. The generation unit can also analyze the interrelationship of the voice data, extract important parts, and generate it. For example, the generation unit extracts important information based on the interrelationship of the voice data, and generates it. The generation unit can also dynamically adjust the accuracy of generation based on the interrelationship of the voice data. For example, the generation unit improves the accuracy of generation based on the interrelationship of the voice data. As a result, the generation unit improves the accuracy of generation by considering the interrelationship of the voice data. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the interrelationship of the voice data to AI, and cause AI to execute improvement of the accuracy of generation.

[0098] The generation unit can generate the voice data while taking into consideration the attribute information of the person who submitted the voice data. The generation unit, for example, generates important voice data preferentially based on the job title of the person who submitted the voice data. For example, the generation unit generates voice data including important information preferentially based on the job title of the person who submitted the voice data. The generation unit can also generate related voice data based on the specialty of the person who submitted the voice data. For example, the generation unit generates voice data including information related to the specialty of the person who submitted the voice data. The generation unit can also dynamically adjust the generation criteria based on the attribute information of the person who submitted the voice data. For example, the generation unit adjusts the generation criteria based on the attribute information of the person who submitted the voice data. This enables the generation unit to generate appropriate voice by taking into consideration the attribute information of the person who submitted the voice data. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the attribute information of the person who submitted the voice data to AI and cause AI to execute generation.

[0099] The generation unit can estimate the user's emotion and adjust the display method of the generated voice based on the estimated user's emotion. For example, when the user is nervous, the generation unit provides a simple and highly visible display method. For example, the generation unit displays information in a visually easy-to-understand format using graphs and charts. In addition, the generation unit can also provide a display method including detailed information when the user is relaxed. For example, the generation unit provides a display method including detailed text and data. In addition, when the user is in a hurry, the generation unit can also provide a display method that focuses on the main points. For example, the generation unit provides a concise display method that emphasizes important points. Thereby, the generation unit can provide information with high visibility by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotion data to the generation AI and have the generation AI adjust the display method.

[0100] The generation unit can generate the voice data while taking into consideration the geographical distribution of the voice data. For example, the generation unit preferentially generates voice data related to a specific region. For example, the generation unit generates voice data related to events or news in the region. The generation unit can also generate highly relevant voice data based on the geographical distribution. For example, the generation unit generates voice data based on geographical relevance. The generation unit can also improve the accuracy of the generation by taking into consideration the geographical distribution of the voice data. For example, the generation unit improves the accuracy of the generation based on the geographical distribution. As a result, the generation unit can efficiently generate highly relevant voice data by taking into consideration the geographical distribution. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input geographical distribution data to AI and cause the AI ​​to execute the generation.

[0101] The generation unit can improve the accuracy of generation by referring to related literature of the voice data at the time of generation. The generation unit, for example, generates important voice data preferentially based on related literature. For example, the generation unit adjusts the generation criteria of the voice data by referring to related literature. The generation unit can also analyze related literature of the voice data and dynamically improve the accuracy of generation. For example, the generation unit improves the accuracy of generation based on related literature. As a result, the generation unit improves the accuracy of generation by referring to related literature. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input related literature data to AI and cause AI to improve the accuracy of generation.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The acquisition unit can analyze the user's past call history and select the optimal acquisition method. For example, the acquisition unit preferentially acquires a call app that the user has used frequently in the past. The acquisition unit can also predict voice data to be acquired in a specific time period from the user's past call history and select the optimal acquisition method. For example, the acquisition unit analyzes the user's past call history and preferentially acquires only important calls. As a result, the acquisition unit can select the optimal acquisition method by analyzing the past call history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input call history data to AI and cause AI to select the optimal acquisition method.

[0104] The analysis unit can determine the priority of the analysis based on the acquisition time of the voice data. For example, the analysis unit preferentially analyzes the latest voice data. For example, the analysis unit quickly analyzes the latest voice data and provides the result in real time. The analysis unit can also preferentially analyze the voice data acquired immediately after an important event. For example, the analysis unit preferentially analyzes the voice data acquired immediately after the event to extract important information. The analysis unit can also dynamically adjust the priority of the analysis based on the user's schedule. For example, the analysis unit preferentially analyzes the voice data related to an important task based on the user's schedule. As a result, the analysis unit determines the priority of the analysis based on the acquisition time of the voice data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquisition time of the voice data to AI and cause AI to execute the determination of the analysis priority.

[0105] The filtering unit can improve the accuracy of filtering based on the interrelationships of the voice data. For example, the filtering unit filters related voices collectively, taking into account the context of the voice data. For example, the filtering unit analyzes the context of the voice data and extracts related information. The filtering unit can also analyze the interrelationships of the voice data, extract important parts, and filter them. For example, the filtering unit extracts important information based on the interrelationships of the voice data. The filtering unit can also dynamically adjust the accuracy of filtering based on the interrelationships of the voice data. For example, the filtering unit improves the accuracy of filtering based on the interrelationships of the voice data. As a result, the filtering unit improves the accuracy of filtering by taking into account the interrelationships of the voice data. A part or all of the above-mentioned processing in the filtering unit may be performed, for example, using AI, or may be performed without using AI. For example, the filtering unit can input the interrelationships of the voice data to AI and cause the AI ​​to improve the accuracy of filtering.

[0106] The providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit preferentially provides the display method used by the user in the past. For example, the providing unit selects the optimal display method based on the user's past operation history. The providing unit can also predict and provide the optimal display method from the user's past operation history. For example, the providing unit analyzes past operation patterns and provides the optimal display method. The providing unit can also analyze the user's past operation history and provide a display method with high visibility. For example, the providing unit selects a display method with high visibility based on the past operation history. As a result, the providing unit can provide the optimal display method by referring to the past operation history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input operation history data to AI and cause AI to select the optimal display method.

[0107] The generation unit can improve the accuracy of generation by taking into account the interrelationship of the voice data. For example, the generation unit generates related voices in a lump by taking into account the context of the voice data. For example, the generation unit analyzes the context of the voice data, extracts related information, and generates it. The generation unit can also analyze the interrelationship of the voice data, extract important parts, and generate it. For example, the generation unit extracts important information based on the interrelationship of the voice data, and generates it. The generation unit can also dynamically adjust the accuracy of generation based on the interrelationship of the voice data. For example, the generation unit improves the accuracy of generation based on the interrelationship of the voice data. As a result, the generation unit improves the accuracy of generation by taking into account the interrelationship of the voice data. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the interrelationship of the voice data to AI, and cause AI to execute improvement of the accuracy of generation.

[0108] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring voice data based on the estimated user's emotion. For example, when the user is stressed, the frequency of acquiring voice data is reduced and only important parts are acquired. Also, when the user is relaxed, the acquisition unit can increase the frequency of acquiring voice data and acquire detailed information. For example, when the user is in a hurry, the acquisition unit speeds up the timing of acquiring voice data and acquires it in real time. As a result, the acquisition unit can adjust the timing of acquiring voice data according to the user's emotion, thereby enabling efficient data acquisition. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0109] The analysis unit can estimate the user's emotion and adjust the method of expressing the analysis based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible analysis result is provided. For example, the analysis unit displays the analysis result in a visually easy-to-understand format using graphs and charts. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, the analysis unit displays an analysis result including detailed text and data. The analysis unit can also provide an analysis result that focuses on the main points when the user is in a hurry. For example, the analysis unit displays a concise analysis result that highlights important points. Thereby, the analysis unit can provide an analysis result with high visibility by adjusting the method of expressing the analysis according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0110] The filtering unit can estimate the user's emotion and adjust the filtering criteria based on the estimated user's emotion. For example, when the user is stressed, strict filtering criteria are applied to remove negative voices. For example, the filtering unit removes emotional remarks and irrelevant topics. In addition, when the user is relaxed, the filtering unit can apply loose filtering criteria to provide detailed voices. For example, the filtering unit provides voice data including detailed information. In addition, when the user is in a hurry, the filtering unit can quickly perform filtering and provide only important voices. For example, the filtering unit quickly provides voice data including important information. Thereby, the filtering unit adjusts the filtering criteria according to the user's emotion, thereby enabling appropriate filtering. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the filtering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the filtering unit can input user emotion data to the generation AI and have the generation AI adjust the filtering criteria.

[0111] The providing unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. For example, the providing unit displays information in a visually easy-to-understand format using graphs and charts. In addition, the providing unit can also provide a display method including detailed information when the user is relaxed. For example, the providing unit provides a display method including detailed text and data. In addition, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, the providing unit provides a concise display method that emphasizes important points. As a result, the providing unit can provide information with high visibility by adjusting the display method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generating AI and cause the generating AI to adjust the display method.

[0112] The generation unit can estimate the user's emotion and determine the priority of the voice to be generated based on the estimated user's emotion. For example, when the user is stressed, important voice is generated preferentially. For example, the generation unit preferentially generates important voice data related to work. In addition, the generation unit can also generate detailed voice preferentially when the user is relaxed. For example, the generation unit preferentially generates voice data including detailed information related to work. In addition, when the user is in a hurry, the generation unit can also generate voice that can be generated quickly preferentially. For example, the generation unit preferentially generates voice data that can be generated in real time. Thereby, the generation unit can generate important voice preferentially by determining the priority of voice according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input user emotion data to the generation AI and have the generation AI determine the priority of voices.

[0113] The process flow of the second embodiment will be briefly described below.

[0114] Step 1: The acquisition unit acquires voice data of an online call. The acquisition unit, for example, collects voice data in real time using a microphone. The acquisition unit can also read a recorded voice file. For example, the acquisition unit supports the format of the voice data, such as the voice file format, sampling rate, and bit rate. Step 2: The analysis unit uses emotion recognition AI to analyze the voice data acquired by the acquisition unit and classify the speaker's emotion. The analysis unit determines the emotion based on features such as the tone, pitch, and speed of the voice. The emotion recognition AI takes the voice data as input and outputs the emotion classification result. For example, the analysis unit analyzes the tone of the voice data and identifies emotions such as anger, sadness, and joy. Step 3: The filtering unit converts negative emotional voice into rational voice based on the emotions classified by the analysis unit. For example, the filtering unit converts voice containing angry emotions into a calm tone. The filtering unit receives the judgment result of the emotion recognition AI as input and outputs the converted voice. For example, the filtering unit adjusts the tone of the voice and converts it into a calm tone. Step 4: The providing unit provides the voice converted by the filtering unit to the user. The providing unit plays the voice using a speaker, for example. The providing unit can also display the converted voice as text data. For example, the providing unit converts the voice data into text and displays it on a screen.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0117] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, filtering unit, provision unit, and generation unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit collects voice data using a microphone of the smart device 14, and the voice data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using an emotion recognition AI. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts negative emotional voice into rational voice. The provision unit plays the converted voice using, for example, the speaker of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and omits negative remarks and extracts and presents only important information. The acquisition unit estimates the user's emotions using, for example, the control unit 46A of the smart device 14, and adjusts the acquisition timing of the voice data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0126] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, filtering unit, provision unit, and generation unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit collects voice data using a microphone of the smart glasses 214, and the voice data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using an emotion recognition AI. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts negative emotional voice into rational voice. The provision unit plays the converted voice using, for example, the speaker of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and omits negative remarks and extracts and presents only important information. The acquisition unit estimates the user's emotions using, for example, the control unit 46A of the smart glasses 214, and adjusts the acquisition timing of the voice data. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] 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.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, filtering unit, provision unit, and generation unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit collects voice data using a microphone of the headset type terminal 314, and the voice data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using an emotion recognition AI. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts negative emotional voice into rational voice. The provision unit plays the converted voice using, for example, the speaker of the headset type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and omits negative remarks and extracts and presents only important information. The acquisition unit estimates the user's emotions using, for example, the control unit 46A of the headset type terminal 314, and adjusts the acquisition timing of the voice data. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] 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.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] 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.

[0160] 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.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0162] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, filtering unit, provision unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit collects voice data using a microphone of the robot 414, and the voice data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using an emotion recognition AI. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts negative emotional voice into rational voice. The provision unit plays the converted voice using, for example, the speaker of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and omits negative remarks and extracts and presents only important information. The acquisition unit, for example, estimates the user's emotions using the control unit 46A of the robot 414, and adjusts the acquisition timing of the voice data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0169] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0173] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0175] 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.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0178] 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.

[0179] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0180] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0184] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0186] (Appendix 1) An acquisition unit that acquires voice data; an analysis unit that analyzes the voice data acquired by the acquisition unit and classifies the emotion of the speaker; a filtering unit that converts a negative emotional voice into a rational voice based on the emotion classified by the analysis unit; a providing unit that provides the voice converted by the filtering unit to a user. A system characterized by: (Appendix 2) The analysis unit is Determine emotions based on tone, pitch, and speed characteristics of speech 2. The system of claim 1, (Appendix 3) The filtering unit is Transform angry voice into a calm tone 2. The system of claim 1, (Appendix 4) A generating unit is provided, The generation unit is Eliminate negative comments and present only the important information. 2. The system of claim 1, (Appendix 5) The providing unit is Providing the converted audio to the user 2. The system of claim 1, (Appendix 6) The acquisition unit is To estimate a user's emotion and adjust a timing of acquiring voice data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The acquisition unit is Analyze the user's past call history and select the optimal acquisition method 2. The system of claim 1, (Appendix 8) The acquisition unit is Filtering voice data as it is captured based on the user's current work situation or areas of interest 2. The system of claim 1, (Appendix 9) The acquisition unit is A user's emotion is estimated, and a priority order of voice data to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The acquisition unit is When acquiring voice data, prioritize acquisition of data that is highly relevant based on the user's geographic location information 2. The system of claim 1, (Appendix 11) The acquisition unit is When voice data is acquired, the user's SNS activity is analyzed to acquire related data. 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of analysis detail based on the importance of the audio data 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of audio data. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when the audio data was acquired 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on the relevance of the audio data 2. The system of claim 1, (Appendix 18) The filtering unit is Inferring a user's emotion and adjusting filtering criteria based on the estimated user's emotion 2. The system of claim 1, (Appendix 19) The filtering unit is When filtering, the accuracy of filtering is improved based on correlations in the audio data. 2. The system of claim 1, (Appendix 20) The filtering unit is When filtering, the attribute information of the person submitting the voice data is taken into consideration. 2. The system of claim 1, (Appendix 21) The filtering unit is Infer user emotion and adjust the order in which filtering results are displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 22) The filtering unit is When filtering, the geographical distribution of voice data is taken into account. 2. The system of claim 1, (Appendix 23) The filtering unit is During filtering, the accuracy of filtering is improved based on related literature of the speech data. 2. The system of claim 1, (Appendix 24) The providing unit is Inferring a user's emotion and adjusting how the offering is presented based on the inferred user emotion 2. The system of claim 1, (Appendix 25) The providing unit is When providing the information, the optimal display method is selected by referring to the user's past operation history. 2. The system of claim 1, (Appendix 26) The providing unit is Estimating a user's emotions and adjusting the provided operation procedure based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The providing unit is When providing the content, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 28) The generation unit is Estimate the user's emotions and prioritize the speech to be generated based on the estimated user's emotions. 2. The system of claim 1, (Appendix 29) The generation unit is During generation, correlations in voice data are taken into account to improve the accuracy of generation. 2. The system of claim 1, (Appendix 30) The generation unit is When generating voice data, attribute information of the person who submitted the voice data is taken into consideration. 2. The system of claim 1, (Appendix 31) The generation unit is Estimating a user's emotion and adjusting the display method of the generated voice based on the estimated user's emotion 2. The system of claim 1, (Appendix 32) The generation unit is When generating, the geographical distribution of voice data is taken into account. 2. The system of claim 1, (Appendix 33) The generation unit is During generation, the accuracy of the generation is improved by referring to related literature on the speech data. 2. The system of claim 1, [Explanation of symbols]

[0187] 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. An acquisition unit that acquires voice data; an analysis unit that analyzes the voice data acquired by the acquisition unit and classifies the emotion of the speaker; a filtering unit that converts a negative emotional voice into a rational voice based on the emotion classified by the analysis unit; a providing unit that provides the voice converted by the filtering unit to a user. A system characterized by:

2. The analysis unit is Determine emotions based on tone, pitch, and speed characteristics of speech 2. The system of claim 1 .

3. The filtering unit is Transform angry voice into a calm tone 2. The system of claim 1 .

4. A generating unit is provided, The generation unit is Eliminate negative comments and present only the important information.

2. The system of claim 1 .

5. The acquisition unit is To estimate a user's emotion and adjust a timing of acquiring voice data based on the estimated user's emotion.

2. The system of claim 1 .

6. The acquisition unit is Analyze the user's past call history and select the optimal acquisition method 2. The system of claim 1 .

7. The acquisition unit is Filtering voice data as it is captured based on the user's current work situation or areas of interest 2. The system of claim 1 .

Images