System

Abstract

An object of a system according to an embodiment is to extract useful information from an enormous amount of case report data and convert the information into a form that can be utilized on site.SOLUTION: A system includes an analysis unit, a generation unit, an brush-up unit, and a share unit. The analysis unit analyzes the case report. The generator aggregates the successful cases including the topic word extracted by the text mining unit. The brush-up unit performs brush-up of the script generated by the generation unit. The share section shares the content demonstrated by the brush-up section by a moving image.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has faced the challenge of making it difficult to efficiently extract useful information from vast amounts of case report data and utilize it in the field.

[0005] The system according to the embodiment aims to extract useful information from a huge amount of case report data and make it usable in the field. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a brush-up unit, and a sharing unit. The analysis unit analyzes case reports. The generation unit aggregates success stories that include topic words extracted by the text mining unit. The brush-up unit brushes up the script generated by the generation unit. The sharing unit shares the content demonstrated by the brush-up unit as a video. [Effects of the Invention]

[0007] The system according to the embodiment can extract useful information from a huge amount of case report data and make it usable in the field. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The case report analysis system according to an embodiment of the present invention is a system that efficiently analyzes case reports from on-site crews, uses a generation AI to summarize, and compiles the results into a form that can be used on-site. As a result, the case report analysis system can efficiently analyze case reports from on-site crews and compile them into a form that can be used on-site.

[0029] A case report analysis system according to an embodiment includes a text mining unit, a generation unit, a brush-up unit, and a sharing unit. The text mining unit analyzes case reports. For example, approximately 130 case reports are analyzed per week using text mining technology to analyze trends. The text mining unit also analyzes the text data of the case reports using a generation AI to extract frequently occurring topic words. For example, the generation AI extracts topic words based on the text data of the case reports. The generation unit aggregates success stories that include the topic words extracted by the text mining unit. For example, the generation AI selects cases that include the topic words and compiles them into a single story. The generation unit also generates new talk scripts using the generation AI. For example, the generation AI generates new talk scripts based on the topic words and the text data of the case reports related to those topic words. The brush-up unit brushes up the scripts generated by the generation unit. For example, a skilled crew member arranges the content of the script and performs an actual on-site demonstration. The brush-up department also records the demonstration as a video and shares it with other members. For example, by using a video sharing platform, members can view the video anytime, anywhere. The sharing department shares the content demonstrated by the brush-up department as a video. For example, by using a video sharing platform, members can view the video anytime, anywhere. The sharing department also shares the video in a format that is easy to understand visually. For example, it converts it into a visual note or mind map and shares it. This allows the case report analysis system according to the embodiment to efficiently analyze case reports from on-site operation crews and compile them into a form that can be used on-site. For example, by extracting topic words, it is possible to identify common on-site challenges and success patterns. Furthermore, by creating scripts using generative AI, it is possible to effectively share success cases. Furthermore, brush-up and demonstrations by excellent crews can improve response capabilities in actual on-site situations.

[0030] The text mining unit can collect background information on the submitter of a case report and evaluate the importance of topic words based on that information. For example, the text mining unit registers the expertise and industry experience of the submitter of the case report in a database and evaluates the importance of topic words based on that information. For example, reports from submitters with extensive expertise are given priority in analysis. This allows for a more reliable analysis by evaluating the importance of topic words based on the submitter's background information.

[0031] The text mining unit can extract topic words by referring to related industry news and trend information. For example, when the generation AI extracts topic words, the text mining unit automatically collects related industry news and trend information and reflects this in the analysis. For example, topic words are extracted based on the latest technological trends and market needs. This makes it possible to extract topic words with greater accuracy by referring to industry news and trend information.

[0032] The text mining unit can analyze image and audio data in addition to the text data of case reports to extract topic words. For example, the text mining unit allows image and audio data to be uploaded in addition to the text data of case reports, and extracts topic words based on visual and auditory information. For example, analysis can be performed using prototype images and explanatory audio. This enables more comprehensive analysis by extracting topic words from multimodal information.

[0033] The text mining unit can collect case reports from different industries and fields and extract topic words from a cross-industry perspective. For example, the text mining unit collects case reports from different industries and extracts topic words from a cross-industry perspective. For example, it analyzes cases from the fields of technology, design, and marketing. This makes it possible to discover new topic words by incorporating perspectives from different industries and fields.

[0034] When aggregating success stories, the generation unit can generate a chronological story by taking into account the temporal relevance of the cases. For example, when aggregating success stories, the generation unit generates a chronological story by taking into account the submission time and update time of the cases. For example, the generation unit prioritizes aggregating the most recent cases and reflects changes over time. This allows for the generation of a more consistent story by taking into account the temporal relevance.

[0035] When creating a script, the generation unit can generate multiple different scenarios and select the most effective scenario from among them. For example, when the generation AI creates a script, the generation unit generates multiple different scenarios and selects the most effective scenario from among them. For example, multiple stories are generated and the selection is made based on the evaluation score. In this way, the quality of the script is improved by generating multiple scenarios and selecting the most effective scenario.

[0036] The generation unit can automatically translate success stories into different languages ​​and aggregate success stories from an international perspective. For example, when aggregating success stories, the generation unit automatically translates them into different languages ​​and aggregates success stories from an international perspective. For example, translation into multiple languages ​​such as English, French, and Chinese is performed. This makes it possible to aggregate success stories from an international perspective by automatically translating them into different languages.

[0037] The generation unit can convert success stories into visual notes or mind maps to create scripts that are visually easy to understand. For example, the generation unit can convert success stories into visual notes to create scripts that are visually easy to understand. For example, important points are indicated with diagrams or icons. By converting the success stories into visual notes or mind maps, it is possible to create scripts that are visually easy to understand.

[0038] The brush-up unit can implement the content of the demonstration under multiple scenarios and select the most effective scenario. For example, the brush-up unit implements the content of the demonstration under multiple scenarios and selects the most effective scenario. For example, the brush-up unit implements multiple stories and selects the most effective scenario based on the evaluation scores. In this way, the quality of the demonstration is improved by implementing multiple scenarios and selecting the most effective scenario.

[0039] The brush-up department can customize the content of the demonstration according to different industries and applications, making it possible to use it in a wide range of situations. For example, the brush-up department can customize the content of the demonstration according to different industries and applications, making it possible to use it in a wide range of situations. For example, the brush-up department can customize the content of demonstrations for technology, design, and marketing. In this way, by customizing according to different industries and applications, it becomes possible to use it in a wide range of situations.

[0040] The brush-up department can implement the content of the demonstration as a prototype and introduce an agile methodology to improve it based on feedback. The brush-up department can, for example, implement the content of the demonstration as a prototype and introduce an agile methodology to improve it based on feedback from users. For example, a prototype can be developed in a short period of time and user opinions can be reflected. In this way, by introducing an agile methodology, the content of the demonstration can be improved quickly.

[0041] The sharing unit can automatically summarize the content of a video using a generation AI, allowing viewers to grasp the important points in a short amount of time. The sharing unit can, for example, automatically summarize the content of a video using a generation AI, allowing viewers to grasp the important points in a short amount of time. For example, the sharing unit can display the main points of a video in text. By automatically summarizing the content of the video, viewers can grasp the important points in a short amount of time.

[0042] The sharing unit can develop an algorithm that recommends videos that match the viewer's interests and concerns based on the video viewing history. The sharing unit develops an algorithm that recommends videos that match the viewer's interests and concerns based on the video viewing history, for example. For example, the sharing unit recommends related videos based on the viewing history. This improves viewer satisfaction by recommending videos that match the viewer's interests and concerns based on the viewing history.

[0043] The sharing unit can automatically translate videos into different languages ​​to promote sharing from an international perspective. The sharing unit, for example, builds a system that automatically translates videos into different languages ​​to promote sharing from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. In this way, by automatically translating videos into different languages, sharing from an international perspective is promoted.

[0044] The sharing unit converts the content of the video into visual notes or mind maps, allowing it to be shared in a format that is visually easy to understand. For example, the sharing unit converts the content of the video into visual notes and shares it in a format that is visually easy to understand. For example, important points can be indicated with diagrams or icons. In this way, by converting the content of the video into visual notes or mind maps, it can be shared in a format that is visually easy to understand.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The case report analysis system further includes a voice recognition unit. The voice recognition unit can input case reports by voice and converts the voice data into text data. For example, an on-site crew member can give a case report by voice, and the voice recognition unit converts the voice data into text data. This makes it possible to report cases by voice input, reducing the effort required for reporting. The voice recognition unit can also support multiple languages. For example, it converts voice data in English, French, Chinese, etc. into text data. This makes it possible to report cases from an international perspective.

[0047] The case report analysis system also includes an image analysis unit. The image analysis unit can analyze image data attached to case reports and extract important information. For example, it can analyze on-site photographs and drawings to identify abnormalities and areas for improvement. The image analysis unit can also extract text data from image data. For example, it can convert handwritten notes and whiteboard content into text data. This makes it possible to analyze case reports using image data.

[0048] The text mining section can extract topic words by referencing related industry news and trend information. For example, when the generation AI extracts topic words, it automatically collects related industry news and trend information and reflects this in the analysis. For example, topic words are extracted based on the latest technological trends and market needs. This makes it possible to extract topic words with greater accuracy by referencing industry news and trend information.

[0049] The case report analysis system further includes a data visualization unit. The data visualization unit can visually display the analysis results of the case reports. For example, it can display the frequency of occurrence of topic words in a graph or chart. The data visualization unit can also display trends using time-series data. For example, it can display monthly trends in case reports in a line graph. This makes it possible to provide analysis results in a visually easy-to-understand format.

[0050] The generation unit can automatically translate success stories into different languages ​​and aggregate success stories from an international perspective. For example, when aggregating success stories, the generation unit can automatically translate the success stories into different languages ​​and aggregate success stories from an international perspective. For example, the generation unit can translate the success stories into multiple languages, such as English, French, and Chinese. This makes it possible to aggregate success stories from an international perspective by automatically translating the success stories into different languages.

[0051] The sharing unit can develop an algorithm that recommends videos that match the viewer's interests and concerns based on the video viewing history. For example, an algorithm is developed that recommends videos that match the viewer's interests and concerns based on the video viewing history. For example, related videos are recommended based on the viewing history. This improves viewer satisfaction by recommending videos that match the viewer's interests and concerns based on the viewing history.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The text mining department analyzes case reports and extracts frequently occurring topic words. For example, approximately 130 case reports per week are analyzed using text mining technology to analyze trends. Generative AI is also used to analyze the text data of case reports and extract topic words. Step 2: The generation unit aggregates successful cases containing the topic words extracted by the text mining unit and generates a new talk script. For example, the generation AI selects cases containing the topic words and compiles them into a single story. It also generates a new talk script based on the topic words and the text data of related case reports. Step 3: The polishing team polishes the script generated by the generation team and records a video of the performance. For example, a talented crew member will adapt the script and actually demonstrate it on location. A video sharing platform is also used to make the video available for viewing by team members anytime, anywhere. Step 4: The Sharing Team shares the content demonstrated by the Polishing Team in the form of a video. For example, they can use a video sharing platform to allow members to watch the video anytime, anywhere. They also share the video in a format that is easy to understand visually. For example, they can convert it into a visual note or mind map and share it.

[0054] (Example 2) The case report analysis system according to an embodiment of the present invention is a system that efficiently analyzes case reports from on-site crews, uses a generation AI to summarize, and compiles the results into a form that can be used on-site. As a result, the case report analysis system can efficiently analyze case reports from on-site crews and compile them into a form that can be used on-site.

[0055] A case report analysis system according to an embodiment includes a text mining unit, a generation unit, a brush-up unit, and a sharing unit. The text mining unit analyzes case reports. For example, approximately 130 case reports are analyzed per week using text mining technology to analyze trends. The text mining unit also analyzes the text data of the case reports using a generation AI to extract frequently occurring topic words. For example, the generation AI extracts topic words based on the text data of the case reports. The generation unit aggregates success stories that include the topic words extracted by the text mining unit. For example, the generation AI selects cases that include the topic words and compiles them into a single story. The generation unit also generates new talk scripts using the generation AI. For example, the generation AI generates new talk scripts based on the topic words and the text data of the case reports related to those topic words. The brush-up unit brushes up the scripts generated by the generation unit. For example, a skilled crew member arranges the content of the script and performs an actual on-site demonstration. The brush-up department also records the demonstration as a video and shares it with other members. For example, by using a video sharing platform, members can view the video anytime, anywhere. The sharing department shares the content demonstrated by the brush-up department as a video. For example, by using a video sharing platform, members can view the video anytime, anywhere. The sharing department also shares the video in a format that is easy to understand visually. For example, it converts it into a visual note or mind map and shares it. This allows the case report analysis system according to the embodiment to efficiently analyze case reports from on-site operation crews and compile them into a form that can be used on-site. For example, by extracting topic words, it is possible to identify common on-site challenges and success patterns. Furthermore, by creating scripts using generative AI, it is possible to effectively share success cases. Furthermore, brush-up and demonstrations by excellent crews can improve response capabilities in actual on-site situations.

[0056] The text mining unit performs sentiment analysis on case reports and can extract topic words based on the intensity and type of emotion. For example, the text mining unit uses generative AI to perform sentiment analysis on the text data of case reports and quantify the intensity and type of emotion. For example, it calculates sentiment scores of positive, negative, and neutral, and extracts topic words based on these. This enables more accurate analysis by extracting topic words based on sentiment analysis.

[0057] The text mining unit can collect background information on the submitter of a case report and evaluate the importance of topic words based on that information. For example, the text mining unit registers the expertise and industry experience of the submitter of the case report in a database and evaluates the importance of topic words based on that information. For example, reports from submitters with extensive expertise are given priority in analysis. This allows for a more reliable analysis by evaluating the importance of topic words based on the submitter's background information.

[0058] The text mining unit can extract topic words by referring to related industry news and trend information. For example, when the generation AI extracts topic words, the text mining unit automatically collects related industry news and trend information and reflects this in the analysis. For example, topic words are extracted based on the latest technological trends and market needs. This makes it possible to extract topic words with greater accuracy by referring to industry news and trend information.

[0059] The text mining unit can analyze image and audio data in addition to the text data of case reports to extract topic words. For example, the text mining unit allows image and audio data to be uploaded in addition to the text data of case reports, and extracts topic words based on visual and auditory information. For example, analysis can be performed using prototype images and explanatory audio. This enables more comprehensive analysis by extracting topic words from multimodal information.

[0060] The text mining unit can collect case reports from different industries and fields and extract topic words from a cross-industry perspective. For example, the text mining unit collects case reports from different industries and extracts topic words from a cross-industry perspective. For example, it analyzes cases from the fields of technology, design, and marketing. This makes it possible to discover new topic words by incorporating perspectives from different industries and fields.

[0061] The text mining unit can use the emotion estimation function to analyze the emotional tone of the case report and preferentially extract cases with positive emotions as topic words. The text mining unit, for example, uses the emotion estimation function to analyze the emotional tone of the case report and preferentially extract cases with positive emotions as topic words. For example, it focuses on analyzing cases with high emotion scores. This allows for preferential extraction of cases with positive emotions, making it possible to extract topic words more effectively.

[0062] The generation unit can perform sentiment analysis of success stories and generate stories that are easy to empathize with emotionally. For example, the generation unit uses a generation AI to perform sentiment analysis on text data of success stories and generate stories that are easy to empathize with emotionally. For example, it creates stories based on cases with high positive sentiment scores. This generates stories that are easy to empathize with emotionally, improving the effectiveness of sharing success stories.

[0063] When aggregating success stories, the generation unit can generate a chronological story by taking into account the temporal relevance of the cases. For example, when aggregating success stories, the generation unit generates a chronological story by taking into account the submission time and update time of the cases. For example, the generation unit prioritizes aggregating the most recent cases and reflects changes over time. This allows for the generation of a more consistent story by taking into account the temporal relevance.

[0064] When creating a script, the generation unit can generate multiple different scenarios and select the most effective scenario from among them. For example, when the generation AI creates a script, the generation unit generates multiple different scenarios and selects the most effective scenario from among them. For example, multiple stories are generated and the selection is made based on the evaluation score. In this way, the quality of the script is improved by generating multiple scenarios and selecting the most effective scenario.

[0065] The generation unit can automatically translate success stories into different languages ​​and aggregate success stories from an international perspective. For example, when aggregating success stories, the generation unit automatically translates them into different languages ​​and aggregates success stories from an international perspective. For example, translation into multiple languages ​​such as English, French, and Chinese is performed. This makes it possible to aggregate success stories from an international perspective by automatically translating them into different languages.

[0066] The generation unit can convert success stories into visual notes or mind maps to create scripts that are visually easy to understand. For example, the generation unit can convert success stories into visual notes to create scripts that are visually easy to understand. For example, important points are indicated with diagrams or icons. By converting the success stories into visual notes or mind maps, it is possible to create scripts that are visually easy to understand.

[0067] The generation unit can use the emotion estimation function to evaluate the emotional impact of a success story and create a script that is most likely to resonate emotionally. The generation unit, for example, uses the emotion estimation function to evaluate the emotional impact of a success story and create a script that is most likely to resonate emotionally. For example, a script is created based on a story with a high emotion score. This creates a script that is most likely to resonate emotionally, thereby improving the effectiveness of sharing success stories.

[0068] The brush-up unit can use the emotion estimation function to monitor the audience's emotional reactions in real time during a demonstration and adjust the content of the demonstration on the spot. For example, the brush-up unit can use the emotion estimation function to monitor the audience's emotional reactions in real time during a demonstration and adjust the content of the demonstration on the spot. For example, it can analyze the audience's facial expressions and voices and change the content of the demonstration based on the emotion score. This allows the audience's emotional reactions to be monitored in real time and the content of the demonstration to be adjusted on the spot, enabling a more effective demonstration.

[0069] The brush-up unit can implement the content of the demonstration under multiple scenarios and select the most effective scenario. For example, the brush-up unit implements the content of the demonstration under multiple scenarios and selects the most effective scenario. For example, the brush-up unit implements multiple stories and selects the most effective scenario based on the evaluation scores. In this way, the quality of the demonstration is improved by implementing multiple scenarios and selecting the most effective scenario.

[0070] The brush-up department can customize the content of the demonstration according to different industries and applications, making it possible to use it in a wide range of situations. For example, the brush-up department can customize the content of the demonstration according to different industries and applications, making it possible to use it in a wide range of situations. For example, the brush-up department can customize the content of demonstrations for technology, design, and marketing. In this way, by customizing according to different industries and applications, it becomes possible to use it in a wide range of situations.

[0071] The brush-up department can implement the content of the demonstration as a prototype and introduce an agile methodology to improve it based on feedback. The brush-up department can, for example, implement the content of the demonstration as a prototype and introduce an agile methodology to improve it based on feedback from users. For example, a prototype can be developed in a short period of time and user opinions can be reflected. In this way, by introducing an agile methodology, the content of the demonstration can be improved quickly.

[0072] The brush-up department can use the emotion estimation function to analyze the emotional reactions of the audience during a performance and continuously search for a performance method that will elicit the most positive reaction. For example, the brush-up department will use the emotion estimation function to develop a system that monitors the emotional reactions of the audience during a performance in real time. For example, it will analyze the audience's facial expressions and voice and calculate an emotion score. This will allow the department to analyze the audience's emotional reactions and continuously search for a performance method that will elicit the most positive reaction, thereby improving the quality of the performance.

[0073] The sharing unit can analyze the viewing data of the video and optimize the content of the video based on the viewer's emotional response. The sharing unit, for example, analyzes the viewing data of the video and optimizes the content of the video based on the viewer's emotional response. For example, the sharing unit analyzes the viewer's facial expressions and voice and edits the video based on the emotional score. This enables more effective video sharing by analyzing the viewing data and optimizing the content of the video based on the viewer's emotional response.

[0074] The sharing unit can automatically summarize the content of a video using a generation AI, allowing viewers to grasp the important points in a short amount of time. The sharing unit can, for example, automatically summarize the content of a video using a generation AI, allowing viewers to grasp the important points in a short amount of time. For example, the sharing unit can display the main points of a video in text. By automatically summarizing the content of the video, viewers can grasp the important points in a short amount of time.

[0075] The sharing unit can develop an algorithm that recommends videos that match the viewer's interests and concerns based on the video viewing history. The sharing unit develops an algorithm that recommends videos that match the viewer's interests and concerns based on the video viewing history, for example. For example, the sharing unit recommends related videos based on the viewing history. This improves viewer satisfaction by recommending videos that match the viewer's interests and concerns based on the viewing history.

[0076] The sharing unit can automatically translate videos into different languages ​​to promote sharing from an international perspective. The sharing unit, for example, builds a system that automatically translates videos into different languages ​​to promote sharing from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. In this way, by automatically translating videos into different languages, sharing from an international perspective is promoted.

[0077] The sharing unit converts the content of the video into visual notes or mind maps, allowing it to be shared in a format that is visually easy to understand. For example, the sharing unit converts the content of the video into visual notes and shares it in a format that is visually easy to understand. For example, important points can be indicated with diagrams or icons. In this way, by converting the content of the video into visual notes or mind maps, it can be shared in a format that is visually easy to understand.

[0078] The sharing unit can use the emotion estimation function to monitor viewers' emotional responses in real time and continuously search for video content that will elicit the most positive responses. The sharing unit will develop a system that uses the emotion estimation function to monitor viewers' emotional responses in real time, for example, by analyzing viewers' facial expressions and voices and calculating an emotion score. This will enable the system to monitor viewers' emotional responses in real time and continuously search for video content that will elicit the most positive responses, thereby improving the quality of videos.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The case report analysis system further includes a voice recognition unit. The voice recognition unit can input case reports by voice and converts the voice data into text data. For example, an on-site crew member can give a case report by voice, and the voice recognition unit converts the voice data into text data. This makes it possible to report cases by voice input, reducing the effort required for reporting. The voice recognition unit can also support multiple languages. For example, it converts voice data in English, French, Chinese, etc. into text data. This makes it possible to report cases from an international perspective.

[0081] The text mining unit performs sentiment analysis on case reports and can extract topic words based on the intensity and type of emotion. For example, generative AI can be used to perform sentiment analysis on the text data of case reports and quantify the intensity and type of emotion. For example, it can calculate positive, negative, and neutral sentiment scores and extract topic words based on these. This allows for more accurate analysis by extracting topic words based on sentiment analysis.

[0082] The case report analysis system also includes an image analysis unit. The image analysis unit can analyze image data attached to case reports and extract important information. For example, it can analyze on-site photographs and drawings to identify abnormalities and areas for improvement. The image analysis unit can also extract text data from image data. For example, it can convert handwritten notes and whiteboard content into text data. This makes it possible to analyze case reports using image data.

[0083] The text mining section can extract topic words by referencing related industry news and trend information. For example, when the generation AI extracts topic words, it automatically collects related industry news and trend information and reflects this in the analysis. For example, topic words are extracted based on the latest technological trends and market needs. This makes it possible to extract topic words with greater accuracy by referencing industry news and trend information.

[0084] The generation unit performs sentiment analysis on success stories and generates stories that are easy to empathize with emotionally. For example, using generation AI, sentiment analysis is performed on text data of success stories to generate stories that are easy to empathize with emotionally. For example, a story is created based on cases with a high positive sentiment score. This generates stories that are easy to empathize with emotionally, improving the effectiveness of sharing success stories.

[0085] The case report analysis system further includes a data visualization unit. The data visualization unit can visually display the analysis results of the case reports. For example, it can display the frequency of occurrence of topic words in a graph or chart. The data visualization unit can also display trends using time-series data. For example, it can display monthly trends in case reports in a line graph. This makes it possible to provide analysis results in a visually easy-to-understand format.

[0086] The text mining unit can use the emotion estimation function to analyze the emotional tone of the case report and preferentially extract cases with positive emotions as topic words. For example, the emotion estimation function can be used to analyze the emotional tone of the case report and preferentially extract cases with positive emotions as topic words. For example, cases with high emotion scores can be analyzed with a focus. This allows for preferential extraction of cases with positive emotions, making it possible to extract topic words more effectively.

[0087] The generation unit can automatically translate success stories into different languages ​​and aggregate success stories from an international perspective. For example, when aggregating success stories, the generation unit can automatically translate the success stories into different languages ​​and aggregate success stories from an international perspective. For example, the generation unit can translate the success stories into multiple languages, such as English, French, and Chinese. This makes it possible to aggregate success stories from an international perspective by automatically translating the success stories into different languages.

[0088] The brush-up unit uses the emotion estimation function to monitor the audience's emotional reactions in real time during a demonstration and can adjust the content of the demonstration on the spot. For example, during a demonstration, the emotion estimation function can be used to monitor the audience's emotional reactions in real time and adjust the content of the demonstration on the spot. For example, the brush-up unit can analyze the audience's facial expressions and voices and change the content of the demonstration based on the emotion score. This allows the brush-up unit to monitor the audience's emotional reactions in real time and adjust the content of the demonstration on the spot, making for a more effective demonstration.

[0089] The sharing unit can develop an algorithm that recommends videos that match the viewer's interests and concerns based on the video viewing history. For example, an algorithm is developed that recommends videos that match the viewer's interests and concerns based on the video viewing history. For example, related videos are recommended based on the viewing history. This improves viewer satisfaction by recommending videos that match the viewer's interests and concerns based on the viewing history.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The text mining department analyzes case reports and extracts frequently occurring topic words. For example, approximately 130 case reports per week are analyzed using text mining technology to analyze trends. Generative AI is also used to analyze the text data of case reports and extract topic words. Step 2: The generation unit aggregates successful cases containing the topic words extracted by the text mining unit and generates a new talk script. For example, the generation AI selects cases containing the topic words and compiles them into a single story. It also generates a new talk script based on the topic words and the text data of related case reports. Step 3: The polishing team polishes the script generated by the generation team and records a video of the performance. For example, a talented crew member will adapt the script and actually demonstrate it on location. A video sharing platform is also used to make the video available for viewing by team members anytime, anywhere. Step 4: The Sharing Team shares the content demonstrated by the Polishing Team in the form of a video. For example, they can use a video sharing platform to allow members to watch the video anytime, anywhere. They also share the video in a format that is easy to understand visually. For example, they can convert it into a visual note or mind map and share it.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] 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.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] 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.

[0104] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] 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.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] 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.

[0118] 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.

[0119] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] 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.

[0134] 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.

[0135] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] 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.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0151] 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.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A department that analyzes case reports; a generation unit that aggregates success stories that include the topic words extracted by the text mining unit; a brush-up unit that brushes up the script generated by the generation unit; a sharing section that shares the content demonstrated by the brush-up section as a video; A system characterized by:

2. The text mining department Conduct sentiment analysis of case reports and extract topic words based on the intensity and type of sentiment 2. The system of claim 1.

3. The text mining department In addition to the text data of case reports, image and audio data are also analyzed to extract topic words.

2. The system of claim 1.

4. The brush-up section Monitor the audience's emotional reactions in real time during the demonstration and adjust the content of the demonstration on the fly 2. The system of claim 1.

5. The common area is Analyze video viewing data and optimize video content based on viewers' emotional responses 2. The system of claim 1.

6. The text mining department Analyze the emotional tone of case reports and preferentially extract topic words from cases with positive emotions 2. The system of claim 1.

7. The generation part is Conduct sentiment analysis of success stories to generate emotionally relatable stories 2. The system of claim 1.

8. The brush-up section Analyze the emotional reactions of the audience during the demonstration and continually search for the most positive response.

2. The system of claim 1.

Images