System

Abstract

An object of the system according to the embodiment is to efficiently summarize a long mail or a multi-page material and to personalize the mail or the material according to an attribute of a user.SOLUTION: A system according to an embodiment includes an analysis unit, a summarization unit, and a personalization unit. The analysis part analyzes the contents of the mail and the material. The summarizing unit extracts and summarizes important points from the contents of the mail and the material analyzed by the analyzing unit. The personalizing unit personalizes the content summarized by the summarizing unit according to the attribute of 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technologies have had the problem of not being able to efficiently summarize long emails or multi-page documents, and not being able to fully personalize the summary based on the user's attributes.

[0005] The system according to the embodiment aims to efficiently summarize long emails and multi-page documents and to personalize the summarization according to the attributes of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a summarization unit, and a personalization unit. The analysis unit analyzes the content of emails and documents. The summarization unit extracts and summarizes important points from the content of the emails and documents analyzed by the analysis unit. The personalization unit personalizes the content summarized by the summarization unit according to the user's attributes. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently summarize long emails and multi-page documents and personalize the summarization according to the user's attributes. [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 summarization system according to an embodiment of the present invention utilizes generative AI to instantly summarize the key points of long emails and documents with many pages, dramatically improving work efficiency. As a result, the summarization system can quickly grasp the key points of long emails and documents, dramatically improving work efficiency.

[0029] A summarization system according to an embodiment includes an analysis unit, a summarization unit, and a personalization unit. The analysis unit analyzes the content of emails and documents. For example, the analysis unit uses a generation AI to understand the content of emails and documents and extract key points. The analysis unit can also analyze context and identify important information using natural language processing technology. For example, the generation AI analyzes the body of an email and extracts important keywords and phrases. The summarization unit extracts and summarizes key points from the content of emails and documents analyzed by the analysis unit. For example, the summarization unit uses a generation AI to concisely summarize the content of emails and documents. The summarization unit can also refer to past summarization results to improve the accuracy of the summarization. For example, the generation AI searches a database for similar documents from the past and generates a new summary based on the summary results. The personalization unit personalizes the content summarized by the summarization unit according to user attributes. For example, the personalization unit provides summaries that emphasize information useful for communication with customers to sales representatives and summaries that emphasize information related to business procedures to back-office personnel. The personalization unit can also analyze users' past behavioral data and optimize the summary format based on individual user preferences. For example, the generation AI adjusts the summary format based on the user's browsing history and click data. This allows the summarization system according to the embodiment to quickly grasp the important points of long emails and documents, dramatically improving work efficiency. For example, by checking the content summarized by the generation AI, users can quickly grasp important information and work more efficiently. Furthermore, the summarization system can provide information more effectively by personalizing the summary format according to the user's attributes.

[0030] The analysis unit detects changes in context and tone, enabling more precise extraction of important points. For example, when the generative AI analyzes emails or documents, the analysis unit uses an algorithm to detect changes in context. For example, it analyzes changes in topic between paragraphs and the frequency of keywords in sentences to extract important points. The analysis unit also uses a sentiment analysis algorithm to detect changes in tone. For example, it can detect changes in emotional tone in emails or documents and highlight those parts to reflect them in the summary. This allows for more precise summaries by detecting changes in context and tone.

[0031] The analysis unit compares the new summary with similar documents from the past and can refer to past summary results to improve accuracy. For example, the generation AI searches a database for similar documents from the past and uses the summary results as a reference. For example, it compares documents on the same theme or topic and extracts important common points. The analysis unit also uses an algorithm to generate a new summary based on past summary results. For example, it uses past summary results as learning data to improve the accuracy of the summary. In this way, the accuracy of the summary can be improved by referring to past summary results.

[0032] The analysis unit can also generate summaries from multimedia data, including audio and video inputs. For example, the analysis unit uses a generation AI to analyze audio input and incorporate that content into the summary. For example, it can analyze audio recordings of meetings and extract important remarks and key points of discussion. The analysis unit can also analyze video input and incorporate that content into the summary. For example, it can analyze video conference recordings and extract important scenes and remarks. Furthermore, the analysis unit can convert audio and video data into text data and generate summaries based on that text data. This allows for the generation of summaries from audio and video, making it possible to extract information from a wider variety of data.

[0033] The analysis unit can simultaneously analyze documents in different languages ​​and generate summaries that support multiple languages. For example, the analysis unit uses a generation AI to simultaneously analyze documents in different languages ​​and generate summaries that support multiple languages. For example, it analyzes documents in English and Japanese and provides summaries in both languages. The analysis unit can also use a translation algorithm to translate documents in different languages ​​and generate summaries based on the translation results. For example, it translates an English document into Japanese and generates summaries based on the translation results. This makes it possible to simultaneously analyze documents in different languages ​​and generate summaries that support multiple languages.

[0034] The personalization unit can analyze a user's past behavioral data and optimize the summary format based on the preferences of each individual user. For example, the personalization unit uses a generation AI to analyze a user's past behavioral data and optimize the summary format based on the preferences of each individual user. For example, the personalization unit adjusts the summary format based on the content of documents or emails viewed in the past. The personalization unit can also change the summary layout and information priority based on the user's click data and browsing history. For example, it can provide a summary that highlights keywords that the user clicks frequently. This makes it possible to optimize the summary format based on the user's past behavioral data.

[0035] The personalization unit can refer to the user's work schedule and generate a summary that provides necessary information in a timely manner. For example, the generation AI refers to the user's work schedule and generates a summary that provides necessary information in a timely manner. For example, a summary of relevant materials is provided before a meeting. The personalization unit can also adjust the timing of providing the summary based on the user's schedule. For example, a summary is provided just before an important task. This makes it possible to provide a summary in a timely manner based on the user's work schedule.

[0036] The personalization unit can provide a summary in an optimized format for different devices according to the user's attributes. For example, the generation AI can provide a summary in an optimized format for different devices according to the user's attributes. For example, a concise summary can be provided for smartphones, and a detailed summary for PCs. The personalization unit can also adjust the summary layout based on the device-specific UI / UX. For example, a portrait layout can be used for smartphones, and a landscape layout can be used for PCs. This makes it possible to provide summaries optimized for different devices.

[0037] The personalization unit can integrate the summary format into different business tools according to the user's attributes. For example, the generation AI in the personalization unit integrates the summary format into different business tools according to the user's attributes. For example, it automatically transfers email summaries to a chat tool. The personalization unit can also integrate summaries into project management tools using API integration. For example, it adds summaries to the project management tool as tasks. This makes it possible to provide summaries integrated into different business tools.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The summarization system may further include a search history analysis unit that analyzes a user's past search history and includes relevant information in the summary. For example, relevant information is added to the summary based on keywords or topics that the user has previously searched for. The search history analysis unit may also customize the content of the summary based on the user's frequent searches. For example, if a user frequently searches for information about a particular project, information related to that project may be included in the summary. This allows the system to provide a more relevant summary based on the user's search history.

[0040] The summarization system may further include a location information analysis unit that analyzes the user's geographical location information and adjusts the content of the summary based on the location information. For example, if the user is on a business trip, information related to the business trip destination may be included in the summary. The location information analysis unit may also emphasize information related to a specific location when the user is in that location. For example, if the user is in a conference room, information related to the conference may be included in the summary. This allows for providing a more appropriate summary based on the user's location information.

[0041] The summarization system may further include a social media analysis unit that analyzes the user's social media activity and adjusts the content of the summary based on social media trends. For example, the social media analysis unit may analyze trends on social media platforms frequently used by the user and include information related to the trends in the summary. The social media analysis unit may also customize the content of the summary based on information shared by the user's followers and friends. For example, the summary may include topics that are of interest to the user's followers. This may provide a more relevant summary based on the user's social media activity.

[0042] The summarization system may further include a purchase history analysis unit that analyzes the user's past purchase history and adjusts the content of the summary based on the purchase history. For example, information related to products and services purchased by the user in the past may be included in the summary. The purchase history analysis unit may also customize the content of the summary based on the products and services frequently purchased by the user. For example, if the user frequently purchases products from a particular brand, information related to that brand may be included in the summary. This allows the system to provide a more relevant summary based on the user's purchase history.

[0043] The summarization system may further include a learning history analysis unit that analyzes the user's learning history and adjusts the content of the summary based on the learning history. For example, information related to topics or courses that the user has previously studied may be included in the summary. The learning history analysis unit may also customize the content of the summary based on themes that the user frequently studies. For example, if the user frequently takes courses in a particular field, information related to that field may be included in the summary. This allows the system to provide a more relevant summary based on the user's learning history.

[0044] The summarization system may further include a feedback analysis unit that analyzes user feedback and adjusts the content of the summary based on the feedback. For example, the feedback provided by the user on the summary may be analyzed and the content of the summary may be improved based on the feedback. The feedback analysis unit may also use the user feedback as learning data to improve the accuracy of the summary. For example, the feedback analysis unit may generate a similar summary based on positive feedback provided by the user on the summary. This allows the system to provide a more appropriate summary based on the user feedback.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The analysis unit analyzes the content of emails and documents. For example, the analysis unit uses generation AI to understand the content of emails and documents and extract key points. The analysis unit can also use natural language processing technology to analyze context and identify important information. For example, generation AI analyzes the body of an email and extracts important keywords and phrases. Step 2: The summarization unit extracts and summarizes key points from the contents of the email or document analyzed by the analysis unit. For example, the summarization unit uses a generation AI to concisely summarize the contents of the email or document. The summarization unit can also refer to past summary results to improve the accuracy of the summary. For example, the generation AI searches a database for similar past documents and generates a new summary based on those summary results. Step 3: The personalization unit personalizes the content summarized by the summarization unit according to the user's attributes. For example, the personalization unit provides a summary that emphasizes information useful for communicating with customers to sales representatives, and a summary that emphasizes information related to business procedures to back-office personnel. The personalization unit can also analyze users' past behavioral data and optimize the summary format based on each individual user's preferences. For example, the generation AI adjusts the summary format based on the user's browsing history and click data.

[0047] (Example 2) The summarization system according to an embodiment of the present invention utilizes generative AI to instantly summarize the key points of long emails and documents with many pages, dramatically improving work efficiency. As a result, the summarization system can quickly grasp the key points of long emails and documents, dramatically improving work efficiency.

[0048] A summarization system according to an embodiment includes an analysis unit, a summarization unit, and a personalization unit. The analysis unit analyzes the content of emails and documents. For example, the analysis unit uses a generation AI to understand the content of emails and documents and extract key points. The analysis unit can also analyze context and identify important information using natural language processing technology. For example, the generation AI analyzes the body of an email and extracts important keywords and phrases. The summarization unit extracts and summarizes key points from the content of emails and documents analyzed by the analysis unit. For example, the summarization unit uses a generation AI to concisely summarize the content of emails and documents. The summarization unit can also refer to past summarization results to improve the accuracy of the summarization. For example, the generation AI searches a database for similar documents from the past and generates a new summary based on the summary results. The personalization unit personalizes the content summarized by the summarization unit according to user attributes. For example, the personalization unit provides summaries that emphasize information useful for communication with customers to sales representatives and summaries that emphasize information related to business procedures to back-office personnel. The personalization unit can also analyze users' past behavioral data and optimize the summary format based on individual user preferences. For example, the generation AI adjusts the summary format based on the user's browsing history and click data. This allows the summarization system according to the embodiment to quickly grasp the important points of long emails and documents, dramatically improving work efficiency. For example, by checking the content summarized by the generation AI, users can quickly grasp important information and work more efficiently. Furthermore, the summarization system can provide information more effectively by personalizing the summary format according to the user's attributes.

[0049] The analysis unit detects changes in context and tone, enabling more precise extraction of important points. For example, when the generative AI analyzes emails or documents, the analysis unit uses an algorithm to detect changes in context. For example, it analyzes changes in topic between paragraphs and the frequency of keywords in sentences to extract important points. The analysis unit also uses a sentiment analysis algorithm to detect changes in tone. For example, it can detect changes in emotional tone in emails or documents and highlight those parts to reflect them in the summary. This allows for more precise summaries by detecting changes in context and tone.

[0050] The analysis unit compares the new summary with similar documents from the past and can refer to past summary results to improve accuracy. For example, the generation AI searches a database for similar documents from the past and uses the summary results as a reference. For example, it compares documents on the same theme or topic and extracts important common points. The analysis unit also uses an algorithm to generate a new summary based on past summary results. For example, it uses past summary results as learning data to improve the accuracy of the summary. In this way, the accuracy of the summary can be improved by referring to past summary results.

[0051] The analysis unit uses the emotion estimation function to identify emotionally significant parts of emails and documents, and can emphasize and summarize those parts. For example, the analysis unit uses a generative AI to perform emotion analysis of emails and documents and identify emotionally significant parts. For example, it extracts parts with high emotion scores and reflects those parts in the summary. The analysis unit also uses an emotion estimation algorithm to emphasize emotionally significant parts of emails and documents. For example, it sets an emotion score threshold and includes parts that exceed that threshold in the summary. This improves the accuracy of the summary by emphasizing emotionally significant parts.

[0052] The analysis unit can also generate summaries from multimedia data, including audio and video inputs. For example, the analysis unit uses a generation AI to analyze audio input and incorporate that content into the summary. For example, it can analyze audio recordings of meetings and extract important remarks and key points of discussion. The analysis unit can also analyze video input and incorporate that content into the summary. For example, it can analyze video conference recordings and extract important scenes and remarks. Furthermore, the analysis unit can convert audio and video data into text data and generate summaries based on that text data. This allows for the generation of summaries from audio and video, making it possible to extract information from a wider variety of data.

[0053] The analysis unit can simultaneously analyze documents in different languages ​​and generate summaries that support multiple languages. For example, the analysis unit uses a generation AI to simultaneously analyze documents in different languages ​​and generate summaries that support multiple languages. For example, it analyzes documents in English and Japanese and provides summaries in both languages. The analysis unit can also use a translation algorithm to translate documents in different languages ​​and generate summaries based on the translation results. For example, it translates an English document into Japanese and generates summaries based on the translation results. This makes it possible to simultaneously analyze documents in different languages ​​and generate summaries that support multiple languages.

[0054] The analysis unit can monitor the emotional response of the user when reviewing the summary in real time and dynamically adjust the content of the summary. The analysis unit, for example, uses an emotion estimation function to monitor the emotional response of the user when reviewing the summary in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit also monitors the emotional response in real time and dynamically adjusts the content of the summary based on the results. For example, if the user is feeling stressed, it provides a concise, positive summary. This makes it possible to monitor the user's emotional response in real time and dynamically adjust the content of the summary.

[0055] The personalization unit can analyze a user's past behavioral data and optimize the summary format based on the preferences of each individual user. For example, the personalization unit uses a generation AI to analyze a user's past behavioral data and optimize the summary format based on the preferences of each individual user. For example, the personalization unit adjusts the summary format based on the content of documents or emails viewed in the past. The personalization unit can also change the summary layout and information priority based on the user's click data and browsing history. For example, it can provide a summary that highlights keywords that the user clicks frequently. This makes it possible to optimize the summary format based on the user's past behavioral data.

[0056] The personalization unit can refer to the user's work schedule and generate a summary that provides necessary information in a timely manner. For example, the generation AI refers to the user's work schedule and generates a summary that provides necessary information in a timely manner. For example, a summary of relevant materials is provided before a meeting. The personalization unit can also adjust the timing of providing the summary based on the user's schedule. For example, a summary is provided just before an important task. This makes it possible to provide a summary in a timely manner based on the user's work schedule.

[0057] The personalization unit can use the emotion estimation function to adjust the tone and content of the summary according to the user's current emotional state. For example, the personalization unit uses the emotion estimation function to analyze the user's current emotional state and adjust the tone and content of the summary based on the results. For example, if the user is feeling stressed, the personalization unit provides a concise, positive summary. The personalization unit can also dynamically change the content of the summary based on the emotion score. For example, if the user is relaxed, the personalization unit provides a detailed summary. This allows the tone and content of the summary to be adjusted according to the user's emotional state.

[0058] The personalization unit can provide a summary in an optimized format for different devices according to the user's attributes. For example, the generation AI can provide a summary in an optimized format for different devices according to the user's attributes. For example, a concise summary can be provided for smartphones, and a detailed summary for PCs. The personalization unit can also adjust the summary layout based on the device-specific UI / UX. For example, a portrait layout can be used for smartphones, and a landscape layout can be used for PCs. This makes it possible to provide summaries optimized for different devices.

[0059] The personalization unit can integrate the summary format into different business tools according to the user's attributes. For example, the generation AI in the personalization unit integrates the summary format into different business tools according to the user's attributes. For example, it automatically transfers email summaries to a chat tool. The personalization unit can also integrate summaries into project management tools using API integration. For example, it adds summaries to the project management tool as tasks. This makes it possible to provide summaries integrated into different business tools.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The summarization system may further include a health monitoring unit that monitors the user's health condition and adjusts the content of the summary according to the user's health condition. For example, if the user feels tired, the summary may be made brief and only the important points may be emphasized. Alternatively, if the user feels relaxed, a more detailed summary may be provided. Furthermore, the health monitoring unit may measure the user's heart rate and stress level and dynamically adjust the content of the summary based on that data. This allows the system to provide an optimal summary according to the user's health condition.

[0062] The summarization system may further include a search history analysis unit that analyzes a user's past search history and includes relevant information in the summary. For example, relevant information is added to the summary based on keywords or topics that the user has previously searched for. The search history analysis unit may also customize the content of the summary based on the user's frequent searches. For example, if a user frequently searches for information about a particular project, information related to that project may be included in the summary. This allows the system to provide a more relevant summary based on the user's search history.

[0063] The summarization system may further include a location information analysis unit that analyzes the user's geographical location information and adjusts the content of the summary based on the location information. For example, if the user is on a business trip, information related to the business trip destination may be included in the summary. The location information analysis unit may also emphasize information related to a specific location when the user is in that location. For example, if the user is in a conference room, information related to the conference may be included in the summary. This allows for providing a more appropriate summary based on the user's location information.

[0064] The summarization system may further include a social media analysis unit that analyzes the user's social media activity and adjusts the content of the summary based on social media trends. For example, the social media analysis unit may analyze trends on social media platforms frequently used by the user and include information related to the trends in the summary. The social media analysis unit may also customize the content of the summary based on information shared by the user's followers and friends. For example, the summary may include topics that are of interest to the user's followers. This may provide a more relevant summary based on the user's social media activity.

[0065] The summarization system may further include an emotion estimation unit that estimates the user's emotion and adjusts the content of the summary based on the estimated emotion. For example, if the user is feeling stressed, a concise and positive summary may be provided. On the other hand, if the user is relaxed, a detailed summary may be provided. Furthermore, the emotion estimation unit may analyze the user's facial expressions and voice to estimate the emotion in real time. This makes it possible to provide an optimal summary according to the user's emotion.

[0066] The summarization system may further include a purchase history analysis unit that analyzes the user's past purchase history and adjusts the content of the summary based on the purchase history. For example, information related to products and services purchased by the user in the past may be included in the summary. The purchase history analysis unit may also customize the content of the summary based on the products and services frequently purchased by the user. For example, if the user frequently purchases products from a particular brand, information related to that brand may be included in the summary. This allows the system to provide a more relevant summary based on the user's purchase history.

[0067] The summarization system may further include an emotion estimation unit that estimates the user's emotion and adjusts the tone and content of the summary based on the estimated emotion. For example, if the user feels tired, a concise and positive summary may be provided. On the other hand, if the user feels relaxed, a detailed summary may be provided. Furthermore, the emotion estimation unit may analyze the user's facial expressions and voice to estimate the emotion in real time. This allows the system to provide an optimal summary according to the user's emotion.

[0068] The summarization system may further include a learning history analysis unit that analyzes the user's learning history and adjusts the content of the summary based on the learning history. For example, information related to topics or courses that the user has previously studied may be included in the summary. The learning history analysis unit may also customize the content of the summary based on themes that the user frequently studies. For example, if the user frequently takes courses in a particular field, information related to that field may be included in the summary. This allows the system to provide a more relevant summary based on the user's learning history.

[0069] The summarization system may further include an emotion estimation unit that estimates the user's emotion and adjusts the content of the summary based on the estimated emotion. For example, if the user is feeling stressed, a concise and positive summary may be provided. On the other hand, if the user is relaxed, a detailed summary may be provided. Furthermore, the emotion estimation unit may analyze the user's facial expressions and voice to estimate the emotion in real time. This makes it possible to provide an optimal summary according to the user's emotion.

[0070] The summarization system may further include a feedback analysis unit that analyzes user feedback and adjusts the content of the summary based on the feedback. For example, the feedback provided by the user on the summary may be analyzed and the content of the summary may be improved based on the feedback. The feedback analysis unit may also use the user feedback as learning data to improve the accuracy of the summary. For example, the feedback analysis unit may generate a similar summary based on positive feedback provided by the user on the summary. This allows the system to provide a more appropriate summary based on the user feedback.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The analysis unit analyzes the content of emails and documents. For example, the analysis unit uses generation AI to understand the content of emails and documents and extract key points. The analysis unit can also use natural language processing technology to analyze context and identify important information. For example, generation AI analyzes the body of an email and extracts important keywords and phrases. Step 2: The summarization unit extracts and summarizes key points from the contents of the email or document analyzed by the analysis unit. For example, the summarization unit uses a generation AI to concisely summarize the contents of the email or document. The summarization unit can also refer to past summary results to improve the accuracy of the summary. For example, the generation AI searches a database for similar past documents and generates a new summary based on those summary results. Step 3: The personalization unit personalizes the content summarized by the summarization unit according to the user's attributes. For example, the personalization unit provides a summary that emphasizes information useful for communicating with customers to sales representatives, and a summary that emphasizes information related to business procedures to back-office personnel. The personalization unit can also analyze users' past behavioral data and optimize the summary format based on each individual user's preferences. For example, the generation AI adjusts the summary format based on the user's browsing history and click data.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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 analysis section that analyzes the contents of emails and documents, a summarizing unit that extracts and summarizes important points from the contents of the email or the document analyzed by the analyzing unit; a personalization unit that personalizes the content summarized by the summarization unit in accordance with the attributes of the user. A system characterized by:

2. The analysis unit The analysis includes audio and video inputs, and the summary is generated from multimedia data as well.

2. The system of claim 1.

3. The personalization unit Analyzing the user's past behavioral data and optimizing the summary format based on the individual user's preferences.

2. The system of claim 1.

4. The personalization unit and providing the summary in a format optimized for the different devices according to the attributes of the user.

2. The system of claim 1.

5. The analysis unit Identify, highlight, and summarize the emotionally significant parts of the email or document 2. The system of claim 1.

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