system

Abstract

The system according to the embodiment aims to significantly reduce the time and effort required to create a presentation. [Solution] A system according to an embodiment includes a reception unit, an information collection unit, a layout determination unit, and a slide generation unit. The reception unit receives a theme and number of pages input by a user. The information collection unit collects related information based on the theme received by the reception unit. The layout determination unit determines the layout of the slides based on the information collected by the information collection unit. The slide generation unit generates slides based on the layout determined by the layout determination unit.

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 techniques have had the problem of requiring a lot of time and effort to create a presentation.

[0005] The system according to the embodiment aims to significantly reduce the time and effort required to create a presentation. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an information collection unit, a layout determination unit, and a slide generation unit. The reception unit receives a theme and number of pages input by a user. The information collection unit collects related information based on the theme received by the reception unit. The layout determination unit determines the layout of the slides based on the information collected by the information collection unit. The slide generation unit generates slides based on the layout determined by the layout determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can significantly reduce the time and effort required to create a presentation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A presentation creation system according to an embodiment of the present invention collects related information and automatically generates slides based on a theme and page count entered by a user. This system automatically generates slides using presentation software in a few seconds, simply by entering the theme and page count through a web interface. Specifically, the user first enters the theme and page count through a web interface. The system then collects related information based on the entered theme and determines the slide layout based on the page count. Finally, slides are automatically generated based on the collected information using presentation software templates. This system allows users to create high-quality presentations in a short amount of time. For example, it uses web crawling and APIs to collect information and an AI-based algorithm to determine the slide layout. This significantly reduces the time required to create a presentation and enables the efficient generation of high-quality slides. The presentation creation system can collect related information and automatically generate slides based on the theme and page count entered by the user.

[0029] A presentation creation system according to an embodiment includes a reception unit, an information collection unit, a layout determination unit, and a slide generation unit. The reception unit receives a theme and page count input by a user. For example, the reception unit allows a user to input the theme and page count through a web interface. The information collection unit collects related information based on the theme received by the reception unit. For example, the information collection unit collects related information on the Internet using web crawling technology. The information collection unit can also obtain information from a database using an API. The layout determination unit determines the layout of slides based on the information collected by the information collection unit. For example, the layout determination unit analyzes the collected information using an AI algorithm to determine the optimal order and content of slides. The slide generation unit generates slides based on the layout determined by the layout determination unit. For example, the slide generation unit uses presentation software templates to reflect the collected information in the slides. This allows the presentation creation system to collect related information and automatically generate slides based on the theme and page count input by the user. For example, the reception unit receives the theme and page count input by the user. The information collection unit collects related information using web crawling or an API. The configuration determination unit determines the optimal slide configuration based on the collected information. The slide generation unit generates slides using templates from the presentation software. This allows the presentation creation system to collect related information and automatically generate slides based on the theme and number of pages entered by the user.

[0030] The reception unit can analyze the user's past theme input history and provide an appropriate input interface. For example, the reception unit can automatically display themes that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes to be used in a specific time period based on the user's past input history. This improves input efficiency by providing an optimal input interface based on the user's past input history. The analysis of the past theme input history is performed using, for example, data mining technology. Data mining technology is a technology for extracting useful information from large amounts of data and can perform detailed analysis of the user's past input history. This allows the reception unit to provide an optimal input interface based on the user's past input history.

[0031] When inputting the theme and page count, the reception unit can present input candidates based on the user's current project or field of interest. For example, the reception unit can automatically display themes related to the user's current project as candidates. The reception unit can also suggest related themes based on the user's field of interest. Furthermore, the reception unit can present theme candidates based on fields in which the user has previously shown interest. This improves input efficiency by presenting input candidates based on the user's current project or field of interest. The current project or field of interest can be identified based on, for example, the user's past activity history or survey results. The user's past activity history can be obtained, for example, from a database that records the user's past projects and themes in which the user has shown interest. The survey results can identify the user's field of interest based on the results of a survey answered by the user. This allows the reception unit to grasp the user's current project or field of interest in detail and provide appropriate input candidates.

[0032] When inputting a theme and page count, the reception unit can prioritize the presentation of highly relevant themes based on the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize the presentation of themes related to that region. Furthermore, if the user is traveling, the reception unit can also suggest themes related to the user's travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize the presentation of themes related to that event. This improves input efficiency by presenting highly relevant themes based on the user's geographical location information. The geographical location information is acquired using, for example, GPS data or an IP address. GPS data can identify the user's current location with high accuracy. An IP address can identify the user's approximate location based on the location information of the user's Internet connection. This allows the reception unit to grasp the user's geographical location in detail and present appropriate themes.

[0033] When the user inputs the theme and page count, the reception unit can analyze the user's social media activity and suggest related themes. For example, the reception unit can automatically display themes that the user frequently mentions on social media as candidates. The reception unit can also suggest themes that the user's social media followers are interested in. Furthermore, the reception unit can present themes related to groups and events in which the user participates on social media. This improves the efficiency of input by suggesting related themes based on the user's social media activity. The analysis of social media activity can be performed, for example, by analyzing the content of posts and the followers. The analysis of the content of posts can analyze the content posted by the user on social media to identify the themes of interest. The analysis of the followers can identify themes that the user's followers are interested in and suggest them to the user. This allows the reception unit to grasp the user's social media activity in detail and suggest appropriate themes.

[0034] When collecting information, the information collecting unit can select an appropriate information source by referring to the user's past search history. For example, the information collecting unit preferentially uses information sources that the user has frequently used in the past. The information collecting unit can also select a highly reliable information source from the user's past search history. Furthermore, the information collecting unit can analyze the user's past search history and select the most relevant information source. This improves the efficiency of information collection by selecting the optimal information source based on the user's past search history. The analysis of the past search history is performed using, for example, data mining technology. Data mining technology is a technology for extracting useful information from large amounts of data and can analyze the user's past search history in detail. This allows the information collecting unit to provide the optimal information source based on the user's past search history.

[0035] When collecting information, the information collection unit can use different information collection algorithms depending on the category of the topic. For example, for a topic related to science and technology, the information collection unit can prioritize collecting specialized academic papers. For an entertainment topic, the information collection unit can also collect the latest news articles and blogs. For a business topic, the information collection unit can also collect industry reports and market analyses. This improves the accuracy of information collection by applying information collection algorithms depending on the topic category. The topic categories are classified based on categories such as business, technology, and entertainment. The information collection algorithm is implemented using, for example, web scraping technology or API. Web scraping technology is a technology for automatically collecting information on the Internet, and an API is an interface for retrieving information from a database. This allows the information collection unit to apply the optimal information collection algorithm depending on the topic category and collect highly accurate information.

[0036] When collecting information, the information collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, the information collection unit prioritizes collecting information related to that area. Furthermore, if the user is traveling, the information collection unit can also collect information related to the travel destination. Furthermore, if the user is participating in a specific event, the information collection unit can prioritize collecting information related to the event. This improves the efficiency of information collection by collecting highly relevant information based on the user's geographical location information. Geographical location information is acquired using, for example, GPS data or an IP address. GPS data can identify the user's current location with high accuracy. An IP address can identify the user's approximate location based on the location information of the user's Internet connection destination. This allows the information collection unit to grasp the user's geographical location in detail and collect appropriate information.

[0037] The information collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the information collection unit can collect information related to topics that the user frequently mentions on social media. The information collection unit can also collect information on which the user's social media followers are interested. Furthermore, the information collection unit can collect information related to groups and events in which the user participates on social media. This improves the efficiency of information collection by collecting related information based on the user's social media activities. The analysis of social media activities can be performed, for example, by analyzing the content of posts and analyzing the followers. The analysis of the content of posts analyzes the content posted by the user on social media to identify topics of interest. The analysis of the followers identifies topics of interest to the user's followers and suggests them to the user. This allows the information collection unit to have a detailed understanding of the user's social media activities and collect appropriate information.

[0038] The configuration determination unit can change the level of detail of slides based on the importance of the collected information when determining the configuration. For example, the configuration determination unit creates slides that explain highly important information in detail. The configuration determination unit can also create slides that briefly summarize less important information. Furthermore, the configuration determination unit can adjust the order of slides according to their importance. This improves the quality of slides by adjusting the level of detail of slides based on the importance of the collected information. The importance of information is evaluated based on, for example, the reliability and relevance of the information. The reliability of information is an index that evaluates the reliability of the information source and the accuracy of the information, and the relevance of information is an index that evaluates the relevance to the theme. This allows the configuration determination unit to evaluate the importance of the collected information in detail and provide an appropriate level of detail for the slides.

[0039] The composition determination unit can use different composition algorithms depending on the theme category when determining the composition. For example, for a science and technology theme, the composition determination unit can prioritize a logical composition. For an entertainment theme, the composition determination unit can also prioritize a visually appealing composition. For a business theme, the composition determination unit can also prioritize a data- and analysis-oriented composition. This improves the quality of slides by applying composition algorithms depending on the theme category. The theme categories are classified based on categories such as business, technology, and entertainment. The composition algorithm can be implemented using, for example, template-based composition or automatic composition using AI. Template-based composition is a method of composing slides based on a pre-prepared template, while automatic composition using AI is a method of automatically determining the slide composition using an AI algorithm. This allows the composition determination unit to apply the optimal composition algorithm depending on the theme category and provide high-quality slides.

[0040] The configuration determination unit can set the priority of slides based on the submission time of collected information when determining the configuration. For example, the configuration determination unit preferentially reflects information with an upcoming submission deadline in the slides. The configuration determination unit can also postpone information with a more distant submission deadline. Furthermore, the configuration determination unit can adjust the order of slides according to the submission time. In this way, by determining the priority of slides based on the submission time of collected information, slides can be created in time for the submission deadline. The submission time is obtained using, for example, a project management tool or a calendar application. A project management tool is a tool for managing the progress of a project and submission deadlines, and a calendar application is an application for schedule management. In this way, the configuration determination unit can grasp the submission time of collected information in detail and set appropriate slide priorities.

[0041] The layout determination unit can change the order of slides based on the relevance of the collected information when determining the layout. For example, the layout determination unit arranges highly relevant information consecutively. The layout determination unit can also postpone less relevant information. Furthermore, the layout determination unit can adjust the order of slides according to the relevance of the information. In this way, adjusting the order of slides based on the relevance of the collected information improves the quality of the slides. The relevance of the information is evaluated based on, for example, the reliability of the information and the relevance to the theme. The reliability of the information is an index that evaluates the reliability of the information source and the accuracy of the information, and the relevance of the information is an index that evaluates the relevance to the theme. In this way, the layout determination unit can evaluate the relevance of the collected information in detail and provide an appropriate slide order.

[0042] When generating slides, the slide generator can select an appropriate template by referring to the user's past presentation history. For example, the slide generator can preferentially suggest templates that the user has used in the past. The slide generator can also select the most effective template from the user's past presentation history. Furthermore, the slide generator can analyze the user's past presentation history and suggest an optimal template. This improves the quality of slides by selecting an optimal template based on the user's past presentation history. The analysis of the past presentation history is performed using, for example, data mining technology. Data mining technology is a technology for extracting useful information from large amounts of data and can perform a detailed analysis of the user's past presentation history. This allows the slide generator to provide an optimal template based on the user's past presentation history.

[0043] The slide generator may use different templates depending on the theme category when generating slides. For example, the slide generator may use a specialized template for a science and technology theme. The slide generator may also use a visually appealing template for an entertainment theme. The slide generator may also use a template that emphasizes data and analysis for a business theme. This improves the quality of slides by applying templates depending on the theme category. The theme categories are classified based on categories such as business, technology, and entertainment. Templates are implemented using, for example, presentation templates and report templates. Presentation templates are templates specially designed for presentations, and report templates are templates specially designed for reports. This allows the slide generator to apply the optimal template depending on the theme category and provide high-quality slides.

[0044] When generating slides, the slide generator can select an appropriate template based on the user's geographical location information. For example, if the user is in a specific area, the slide generator can suggest a template including a design related to that area. Furthermore, if the user is traveling, the slide generator can suggest a template related to the user's travel destination. Furthermore, if the user is attending a specific event, the slide generator can suggest a template related to that event. This improves the quality of slides by selecting an optimal template based on the user's geographical location information. The geographical location information can be acquired using, for example, GPS data or an IP address. GPS data can identify the user's current location with high accuracy. An IP address can identify the user's approximate location based on the location information of the user's Internet connection. This allows the slide generator to grasp the user's geographical location in detail and provide an appropriate template.

[0045] The slide generator can analyze the user's social media activity and suggest relevant templates when generating slides. For example, the slide generator can suggest templates related to topics frequently mentioned by the user on social media. The slide generator can also suggest templates related to topics of interest to the user's social media followers. Furthermore, the slide generator can suggest templates related to groups or events in which the user participates on social media. This improves the quality of slides by suggesting relevant templates based on the user's social media activity. The analysis of social media activity can be performed, for example, by analyzing the content of posts and the followers. The analysis of the content of posts can analyze the content posted by the user on social media to identify topics of interest. The analysis of the followers can identify topics of interest to the user and suggest them to the user. This allows the slide generator to have a detailed understanding of the user's social media activity and provide appropriate templates.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can analyze the user's input speed and adjust the response speed of the interface based on the input speed. For example, if the user is inputting quickly, the response speed of the interface can be increased to provide immediate feedback. Alternatively, if the user is inputting slowly, the response speed of the interface can be decreased to wait until the user has completed input. Furthermore, if the user interrupts input, the interface can display a message prompting the user to resume input. This improves the user's input experience by adjusting the response speed of the interface according to the user's input speed.

[0048] The reception unit can analyze the user's past theme input history and provide an auto-completion function when inputting. For example, when the user inputs part of a theme that he or she has previously input, the system automatically displays candidates. The system can also suggest related themes based on keywords that the user has used in the past. Furthermore, the system can prioritize the most frequently used themes based on the frequency with which the user has input themes in the past. This improves input efficiency by providing an auto-completion function based on the user's past input history.

[0049] The reception unit can automatically suggest relevant materials and references based on the user's current project or area of ​​interest. For example, if the user is working on a specific project, the reception unit can display the latest research papers and articles related to that project. The reception unit can also suggest related books and websites based on the user's area of ​​interest. Furthermore, the reception unit can suggest related materials based on areas in which the user has previously shown interest. This improves the efficiency of information gathering by providing related materials based on the user's current project or area of ​​interest.

[0050] The information collection unit can collect information from region-specific databases and sources based on the user's geographic location information. For example, if the user is in a particular country, information can be collected from government databases and local news sites for that country. If the user is in an urban area, city-specific statistical data and reports can be collected. Furthermore, if the user is in a rural area, agricultural data and local community information can be collected. This improves the relevance of the information by collecting region-specific information based on the user's geographic location information.

[0051] The information collection unit can analyze a user's social media activities and collect related trends and topics in real time. For example, it can collect information related to hashtags that the user frequently mentions on X (formerly Twitter®). It can also collect related information based on articles and posts shared by the user's Facebook® friends. It can also collect related information based on the content of discussions in LinkedIn® groups in which the user participates. This allows the information to be collected in real time based on the user's social media activities, improving the freshness and relevance of the information.

[0052] When collecting information, the information collection unit can refer to the user's past search history and prioritize collection of new related information. For example, it can collect the latest research papers and articles related to keywords searched by the user in the past. It can also collect new content from websites visited by the user in the past. It can also collect new data related to materials downloaded by the user in the past. This improves the freshness and relevance of information by prioritizing collection of new related information based on the user's past search history.

[0053] The information gathering unit can apply different information gathering strategies depending on the topic category. For example, for science and technology topics, information can be collected from specialized academic databases and research institution websites. For entertainment topics, information can be collected from social media and entertainment news sites. Furthermore, for business topics, information can be collected from websites that provide industry reports and market analysis. This allows the application of the most appropriate information gathering strategy depending on the topic category, improving the accuracy and efficiency of information gathering.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit receives the theme and the number of pages input by the user. For example, the reception unit allows the user to input the theme and the number of pages through a Web interface. Step 2: The information collection unit collects related information based on the theme received by the reception unit. For example, the information collection unit may use web crawling technology to collect related information on the Internet. The information collection unit may also use an API to obtain information from a database. Step 3: The composition determination unit determines the composition of the slides based on the information collected by the information collection unit. For example, the composition determination unit uses an AI algorithm to analyze the collected information and determine the optimal order and content of the slides. Step 4: The slide generator generates slides based on the layout determined by the layout determiner. For example, the slide generator uses a template of presentation software to reflect the collected information in the slides.

[0056] (Example 2) A presentation creation system according to an embodiment of the present invention collects related information and automatically generates slides based on a theme and page count entered by a user. This system automatically generates slides using presentation software in a few seconds, simply by entering the theme and page count through a web interface. Specifically, the user first enters the theme and page count through a web interface. The system then collects related information based on the entered theme and determines the slide layout based on the page count. Finally, slides are automatically generated based on the collected information using presentation software templates. This system allows users to create high-quality presentations in a short amount of time. For example, it uses web crawling and APIs to collect information and an AI-based algorithm to determine the slide layout. This significantly reduces the time required to create a presentation and enables the efficient generation of high-quality slides. The presentation creation system can collect related information and automatically generate slides based on the theme and page count entered by the user.

[0057] A presentation creation system according to an embodiment includes a reception unit, an information collection unit, a layout determination unit, and a slide generation unit. The reception unit receives a theme and page count input by a user. For example, the reception unit allows a user to input the theme and page count through a web interface. The information collection unit collects related information based on the theme received by the reception unit. For example, the information collection unit collects related information on the Internet using web crawling technology. The information collection unit can also obtain information from a database using an API. The layout determination unit determines the layout of slides based on the information collected by the information collection unit. For example, the layout determination unit analyzes the collected information using an AI algorithm to determine the optimal order and content of slides. The slide generation unit generates slides based on the layout determined by the layout determination unit. For example, the slide generation unit uses presentation software templates to reflect the collected information in the slides. This allows the presentation creation system to collect related information and automatically generate slides based on the theme and page count input by the user. For example, the reception unit receives the theme and page count input by the user. The information collection unit collects related information using web crawling or an API. The configuration determination unit determines the optimal slide configuration based on the collected information. The slide generation unit generates slides using templates from the presentation software. This allows the presentation creation system to collect related information and automatically generate slides based on the theme and number of pages entered by the user.

[0058] The reception unit can estimate the user's emotions and change the theme input method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the theme and page number. This improves user convenience by adjusting the theme input method according to the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology. Facial expression recognition technology analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows the reception unit to grasp the user's emotions in detail and provide an appropriate input method.

[0059] The reception unit can analyze the user's past theme input history and provide an appropriate input interface. For example, the reception unit can automatically display themes that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes to be used in a specific time period based on the user's past input history. This improves input efficiency by providing an optimal input interface based on the user's past input history. The analysis of the past theme input history is performed using, for example, data mining technology. Data mining technology is a technology for extracting useful information from large amounts of data and can perform detailed analysis of the user's past input history. This allows the reception unit to provide an optimal input interface based on the user's past input history.

[0060] When inputting the theme and page count, the reception unit can present input candidates based on the user's current project or field of interest. For example, the reception unit can automatically display themes related to the user's current project as candidates. The reception unit can also suggest related themes based on the user's field of interest. Furthermore, the reception unit can present theme candidates based on fields in which the user has previously shown interest. This improves input efficiency by presenting input candidates based on the user's current project or field of interest. The current project or field of interest can be identified based on, for example, the user's past activity history or survey results. The user's past activity history can be obtained, for example, from a database that records the user's past projects and themes in which the user has shown interest. The survey results can identify the user's field of interest based on the results of a survey answered by the user. This allows the reception unit to grasp the user's current project or field of interest in detail and provide appropriate input candidates.

[0061] The reception unit can estimate the user's emotions and set the priority of the input themes based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize themes with high importance. Furthermore, when the user is relaxed, the reception unit can allow the user to select the priority of the themes. Furthermore, when the user is in a hurry, the reception unit can prioritize themes with high urgency. In this way, by determining the priority of themes according to the user's emotions, important themes can be prioritized. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology. Facial expression recognition technology analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows the reception unit to grasp the user's emotions in detail and set appropriate priority of themes.

[0062] When inputting a theme and page count, the reception unit can prioritize the presentation of highly relevant themes based on the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize the presentation of themes related to that region. Furthermore, if the user is traveling, the reception unit can also suggest themes related to the user's travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize the presentation of themes related to that event. This improves input efficiency by presenting highly relevant themes based on the user's geographical location information. The geographical location information is acquired using, for example, GPS data or an IP address. GPS data can identify the user's current location with high accuracy. An IP address can identify the user's approximate location based on the location information of the user's Internet connection. This allows the reception unit to grasp the user's geographical location in detail and present appropriate themes.

[0063] When the user inputs the theme and page count, the reception unit can analyze the user's social media activity and suggest related themes. For example, the reception unit can automatically display themes that the user frequently mentions on social media as candidates. The reception unit can also suggest themes that the user's social media followers are interested in. Furthermore, the reception unit can present themes related to groups and events in which the user participates on social media. This improves the efficiency of input by suggesting related themes based on the user's social media activity. The analysis of social media activity can be performed, for example, by analyzing the content of posts and the followers. The analysis of the content of posts can analyze the content posted by the user on social media to identify the themes of interest. The analysis of the followers can identify themes that the user's followers are interested in and suggest them to the user. This allows the reception unit to grasp the user's social media activity in detail and suggest appropriate themes.

[0064] The information collection unit can estimate the user's emotions and change the information collection method based on the estimated user's emotions. For example, when the user is relaxed, the information collection unit collects information from a wide range of information sources. Furthermore, when the user is in a hurry, the information collection unit can quickly collect information from reliable information sources. Furthermore, when the user is excited, the information collection unit can prioritize collecting visually appealing information. This improves the efficiency of information collection by adjusting the information collection method according to the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology. Facial expression recognition technology analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows the information collection unit to grasp the user's emotions in detail and provide an appropriate information collection method.

[0065] When collecting information, the information collecting unit can select an appropriate information source by referring to the user's past search history. For example, the information collecting unit preferentially uses information sources that the user has frequently used in the past. The information collecting unit can also select a highly reliable information source from the user's past search history. Furthermore, the information collecting unit can analyze the user's past search history and select the most relevant information source. This improves the efficiency of information collection by selecting the optimal information source based on the user's past search history. The analysis of the past search history is performed using, for example, data mining technology. Data mining technology is a technology for extracting useful information from large amounts of data and can analyze the user's past search history in detail. This allows the information collecting unit to provide the optimal information source based on the user's past search history.

[0066] When collecting information, the information collection unit can use different information collection algorithms depending on the category of the topic. For example, for a topic related to science and technology, the information collection unit can prioritize collecting specialized academic papers. For an entertainment topic, the information collection unit can also collect the latest news articles and blogs. For a business topic, the information collection unit can also collect industry reports and market analyses. This improves the accuracy of information collection by applying information collection algorithms depending on the topic category. The topic categories are classified based on categories such as business, technology, and entertainment. The information collection algorithm is implemented using, for example, web scraping technology or API. Web scraping technology is a technology for automatically collecting information on the Internet, and an API is an interface for retrieving information from a database. This allows the information collection unit to apply the optimal information collection algorithm depending on the topic category and collect highly accurate information.

[0067] The information collecting unit can estimate the user's emotions and set a priority order for the information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the information collecting unit can prioritize collecting information of high importance. Furthermore, when the user is relaxed, the information collecting unit can also prioritize collecting a wide range of information. Furthermore, when the user is in a hurry, the information collecting unit can prioritize collecting information that can be collected quickly. In this way, by determining the priority order of information according to the user's emotions, important information can be collected preferentially. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology. Facial expression recognition technology analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows the information collecting unit to grasp the user's emotions in detail and set appropriate information priorities.

[0068] When collecting information, the information collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, the information collection unit prioritizes collecting information related to that area. Furthermore, if the user is traveling, the information collection unit can also collect information related to the travel destination. Furthermore, if the user is participating in a specific event, the information collection unit can prioritize collecting information related to the event. This improves the efficiency of information collection by collecting highly relevant information based on the user's geographical location information. Geographical location information is acquired using, for example, GPS data or an IP address. GPS data can identify the user's current location with high accuracy. An IP address can identify the user's approximate location based on the location information of the user's Internet connection destination. This allows the information collection unit to grasp the user's geographical location in detail and collect appropriate information.

[0069] The information collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the information collection unit can collect information related to topics that the user frequently mentions on social media. The information collection unit can also collect information on which the user's social media followers are interested. Furthermore, the information collection unit can collect information related to groups and events in which the user participates on social media. This improves the efficiency of information collection by collecting related information based on the user's social media activities. The analysis of social media activities can be performed, for example, by analyzing the content of posts and analyzing the followers. The analysis of the content of posts analyzes the content posted by the user on social media to identify topics of interest. The analysis of the followers identifies topics of interest to the user's followers and suggests them to the user. This allows the information collection unit to have a detailed understanding of the user's social media activities and collect appropriate information.

[0070] The composition determination unit can estimate the user's emotions and change the slide composition method based on the estimated user's emotions. For example, if the user is relaxed, the composition determination unit can suggest a slide composition that includes detailed information. If the user is in a hurry, the composition determination unit can also suggest a concise slide composition that focuses on the main points. Furthermore, if the user is excited, the composition determination unit can also suggest a visually appealing slide composition. This improves the quality of the slides by adjusting the slide composition method according to the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology. Facial expression recognition technology analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows the composition determination unit to grasp the user's emotions in detail and provide an appropriate slide composition.

[0071] The configuration determination unit can change the level of detail of slides based on the importance of the collected information when determining the configuration. For example, the configuration determination unit creates slides that explain highly important information in detail. The configuration determination unit can also create slides that briefly summarize less important information. Furthermore, the configuration determination unit can adjust the order of slides according to their importance. This improves the quality of slides by adjusting the level of detail of slides based on the importance of the collected information. The importance of information is evaluated based on, for example, the reliability and relevance of the information. The reliability of information is an index that evaluates the reliability of the information source and the accuracy of the information, and the relevance of information is an index that evaluates the relevance to the theme. This allows the configuration determination unit to evaluate the importance of the collected information in detail and provide an appropriate level of detail for the slides.

[0072] The composition determination unit can use different composition algorithms depending on the theme category when determining the composition. For example, for a science and technology theme, the composition determination unit can prioritize a logical composition. For an entertainment theme, the composition determination unit can also prioritize a visually appealing composition. For a business theme, the composition determination unit can also prioritize a data- and analysis-oriented composition. This improves the quality of slides by applying composition algorithms depending on the theme category. The theme categories are classified based on categories such as business, technology, and entertainment. The composition algorithm can be implemented using, for example, template-based composition or automatic composition using AI. Template-based composition is a method of composing slides based on a pre-prepared template, while automatic composition using AI is a method of automatically determining the slide composition using an AI algorithm. This allows the composition determination unit to apply the optimal composition algorithm depending on the theme category and provide high-quality slides.

[0073] The configuration determination unit can estimate the user's emotions and set a priority order for slide configurations based on the estimated user emotions. For example, if the user is feeling stressed, the configuration determination unit can prioritize creating slides with high importance. Furthermore, if the user is relaxed, the configuration determination unit can also allow the user to select the priority order for slides. Furthermore, if the user is in a hurry, the configuration determination unit can prioritize creating slides with high urgency. Thus, by determining the priority order for slide configurations according to the user's emotions, important slides can be created with priority. Emotions can be estimated using, for example, facial expression recognition technology or voice analysis technology. Facial expression recognition technology analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows the configuration determination unit to grasp the user's emotions in detail and set appropriate priorities for slide configurations.

[0074] The configuration determination unit can set the priority of slides based on the submission time of collected information when determining the configuration. For example, the configuration determination unit preferentially reflects information with an upcoming submission deadline in the slides. The configuration determination unit can also postpone information with a more distant submission deadline. Furthermore, the configuration determination unit can adjust the order of slides according to the submission time. In this way, by determining the priority of slides based on the submission time of collected information, slides can be created in time for the submission deadline. The submission time is obtained using, for example, a project management tool or a calendar application. A project management tool is a tool for managing the progress of a project and submission deadlines, and a calendar application is an application for schedule management. In this way, the configuration determination unit can grasp the submission time of collected information in detail and set appropriate slide priorities.

[0075] The layout determination unit can change the order of slides based on the relevance of the collected information when determining the layout. For example, the layout determination unit arranges highly relevant information consecutively. The layout determination unit can also postpone less relevant information. Furthermore, the layout determination unit can adjust the order of slides according to the relevance of the information. In this way, adjusting the order of slides based on the relevance of the collected information improves the quality of the slides. The relevance of the information is evaluated based on, for example, the reliability of the information and the relevance to the theme. The reliability of the information is an index that evaluates the reliability of the information source and the accuracy of the information, and the relevance of the information is an index that evaluates the relevance to the theme. In this way, the layout determination unit can evaluate the relevance of the collected information in detail and provide an appropriate slide order.

[0076] The slide generator can estimate the user's emotions and change the slide design based on the estimated user emotions. For example, if the user is relaxed, the slide generator can provide a design with calm colors. Furthermore, if the user is in a hurry, the slide generator can provide a simple, highly visible design. Furthermore, if the user is excited, the slide generator can provide a visually stimulating design. By adjusting the slide design according to the user's emotions, visually appealing slides can be provided. The emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. The facial expression recognition technology analyzes the user's facial expressions captured by a camera to estimate the emotion. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotion. This allows the slide generator to grasp the user's emotions in detail and provide an appropriate slide design.

[0077] When generating slides, the slide generator can select an appropriate template by referring to the user's past presentation history. For example, the slide generator can preferentially suggest templates that the user has used in the past. The slide generator can also select the most effective template from the user's past presentation history. Furthermore, the slide generator can analyze the user's past presentation history and suggest an optimal template. This improves the quality of slides by selecting an optimal template based on the user's past presentation history. The analysis of the past presentation history is performed using, for example, data mining technology. Data mining technology is a technology for extracting useful information from large amounts of data and can perform a detailed analysis of the user's past presentation history. This allows the slide generator to provide an optimal template based on the user's past presentation history.

[0078] The slide generator may use different templates depending on the theme category when generating slides. For example, the slide generator may use a specialized template for a science and technology theme. The slide generator may also use a visually appealing template for an entertainment theme. The slide generator may also use a template that emphasizes data and analysis for a business theme. This improves the quality of slides by applying templates depending on the theme category. The theme categories are classified based on categories such as business, technology, and entertainment. Templates are implemented using, for example, presentation templates and report templates. Presentation templates are templates specially designed for presentations, and report templates are templates specially designed for reports. This allows the slide generator to apply the optimal template depending on the theme category and provide high-quality slides.

[0079] The slide generation unit can estimate the user's emotions and change the slide display method based on the estimated user's emotions. For example, if the user is nervous, the slide generation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the slide generation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the slide generation unit can provide a display method that focuses on the main points. By adjusting the slide display method according to the user's emotions, highly visible slides can be provided. Emotions can be estimated using, for example, facial expression recognition technology or voice analysis technology. Facial expression recognition technology analyzes the user's facial expressions captured by a camera to estimate emotions. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows the slide generation unit to grasp the user's emotions in detail and provide an appropriate slide display method.

[0080] When generating slides, the slide generator can select an appropriate template based on the user's geographical location information. For example, if the user is in a specific area, the slide generator can suggest a template including a design related to that area. Furthermore, if the user is traveling, the slide generator can suggest a template related to the user's travel destination. Furthermore, if the user is attending a specific event, the slide generator can suggest a template related to that event. This improves the quality of slides by selecting an optimal template based on the user's geographical location information. The geographical location information can be acquired using, for example, GPS data or an IP address. GPS data can identify the user's current location with high accuracy. An IP address can identify the user's approximate location based on the location information of the user's Internet connection. This allows the slide generator to grasp the user's geographical location in detail and provide an appropriate template.

[0081] The slide generator can analyze the user's social media activity and suggest relevant templates when generating slides. For example, the slide generator can suggest templates related to topics frequently mentioned by the user on social media. The slide generator can also suggest templates related to topics of interest to the user's social media followers. Furthermore, the slide generator can suggest templates related to groups or events in which the user participates on social media. This improves the quality of slides by suggesting relevant templates based on the user's social media activity. The analysis of social media activity can be performed, for example, by analyzing the content of posts and the followers. The analysis of the content of posts can analyze the content posted by the user on social media to identify topics of interest. The analysis of the followers can identify topics of interest to the user and suggest them to the user. This allows the slide generator to have a detailed understanding of the user's social media activity and provide appropriate templates. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, information collection unit, configuration determination unit, and slide generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a theme and number of pages input by a user using the reception device 38 of the smart device 14. The information collection unit collects related information using web crawling or an API by the specific processing unit 290 of the data processing device 12. The configuration determination unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and determines an optimal slide configuration. The slide generation unit generates slides using templates of presentation software by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, information collection unit, configuration determination unit, and slide generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a theme and page number input by a user using the microphone 238 of the smart glasses 214. The information collection unit collects related information using web crawling or an API by the specific processing unit 290 of the data processing device 12. The configuration determination unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and determines an optimal slide configuration. The slide generation unit generates slides using templates of presentation software by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, information collection unit, configuration determination unit, and slide generation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives a theme and the number of pages input by a user using the microphone 238 of the headset-type terminal 314. The information collection unit uses the specific processing unit 290 of the data processing device 12 to collect related information by web crawling or using an API. The configuration determination unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and determines the optimal slide configuration. The slide generation unit generates slides using templates of presentation software by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, information collection unit, layout determination unit, and slide generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a theme and the number of pages input by a user using the microphone 238 of the robot 414. The information collection unit uses the specific processing unit 290 of the data processing device 12 to collect related information by web crawling or using an API. The layout determination unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and determines the optimal slide layout. The slide generation unit generates slides using templates of presentation software by the specific processing unit 290 of the data processing device 12.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The reception unit can analyze the user's input speed and adjust the response speed of the interface based on the input speed. For example, if the user is inputting quickly, the response speed of the interface can be increased to provide immediate feedback. Alternatively, if the user is inputting slowly, the response speed of the interface can be decreased to wait until the user has completed input. Furthermore, if the user interrupts input, the interface can display a message prompting the user to resume input. This improves the user's input experience by adjusting the response speed of the interface according to the user's input speed.

[0084] The reception unit can estimate the user's emotion and change the design of the input interface based on the estimated user's emotion. For example, if the user is feeling stressed, a simple and calm design can be provided. If the user is relaxed, a colorful and fun design can be provided. Furthermore, if the user is excited, a visually stimulating design can be provided. In this way, the user's input experience can be improved by adjusting the design of the input interface according to the user's emotion.

[0085] The reception unit can analyze the user's past theme input history and provide an auto-completion function when inputting. For example, when the user inputs part of a theme that he or she has previously input, the system automatically displays candidates. The system can also suggest related themes based on keywords that the user has used in the past. Furthermore, the system can prioritize the most frequently used themes based on the frequency with which the user has input themes in the past. This improves input efficiency by providing an auto-completion function based on the user's past input history.

[0086] The reception unit can automatically suggest relevant materials and references based on the user's current project or area of ​​interest. For example, if the user is working on a specific project, the reception unit can display the latest research papers and articles related to that project. The reception unit can also suggest related books and websites based on the user's area of ​​interest. Furthermore, the reception unit can suggest related materials based on areas in which the user has previously shown interest. This improves the efficiency of information gathering by providing related materials based on the user's current project or area of ​​interest.

[0087] The reception unit can estimate the user's emotions and adjust the difficulty level of the input theme based on the estimated user's emotions. For example, if the user is feeling stressed, easy themes can be preferentially suggested. Also, if the user is relaxed, a more difficult theme can be suggested. Furthermore, if the user is excited, a challenging theme can be suggested. In this way, adjusting the difficulty level of the theme according to the user's emotions improves user satisfaction.

[0088] The information collection unit can collect information from region-specific databases and sources based on the user's geographic location information. For example, if the user is in a particular country, information can be collected from government databases and local news sites for that country. If the user is in an urban area, city-specific statistical data and reports can be collected. Furthermore, if the user is in a rural area, agricultural data and local community information can be collected. This improves the relevance of the information by collecting region-specific information based on the user's geographic location information.

[0089] The information collection unit can analyze a user's social media activity and collect related trends and topics in real time. For example, it can collect information related to hashtags that the user frequently mentions on Twitter. It can also collect related information based on articles and posts shared by the user's Facebook friends. It can also collect related information based on the content of discussions in LinkedIn groups in which the user participates. This allows the information to be collected in real time based on the user's social media activity, improving its freshness and relevance.

[0090] The information collection unit can estimate the user's emotions and evaluate the reliability of information based on the estimated user's emotions. For example, if the user is feeling stressed, information can be collected only from highly reliable information sources. If the user is relaxed, information can be collected from a wide range of information sources. Furthermore, if the user is excited, visually appealing information can be preferentially collected. This improves the accuracy of information collection by evaluating the reliability of information according to the user's emotions.

[0091] When collecting information, the information collection unit can refer to the user's past search history and prioritize collection of new related information. For example, it can collect the latest research papers and articles related to keywords searched by the user in the past. It can also collect new content from websites visited by the user in the past. It can also collect new data related to materials downloaded by the user in the past. This improves the freshness and relevance of information by prioritizing collection of new related information based on the user's past search history.

[0092] The information gathering unit can apply different information gathering strategies depending on the topic category. For example, for science and technology topics, information can be collected from specialized academic databases and research institution websites. For entertainment topics, information can be collected from social media and entertainment news sites. Furthermore, for business topics, information can be collected from websites that provide industry reports and market analysis. This allows the application of the most appropriate information gathering strategy depending on the topic category, improving the accuracy and efficiency of information gathering.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The reception unit receives the theme and the number of pages input by the user. For example, the reception unit allows the user to input the theme and the number of pages through a Web interface. Step 2: The information collection unit collects related information based on the theme received by the reception unit. For example, the information collection unit may use web crawling technology to collect related information on the Internet. The information collection unit may also use an API to obtain information from a database. Step 3: The composition determination unit determines the composition of the slides based on the information collected by the information collection unit. For example, the composition determination unit uses an AI algorithm to analyze the collected information and determine the optimal order and content of the slides. Step 4: The slide generator generates slides based on the layout determined by the layout determiner. For example, the slide generator uses a template of presentation software to reflect the collected information in the slides.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0097] 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.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0118] 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.

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

[0120] 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).

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

[0122] 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.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0134] 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.

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

[0136] 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).

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

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 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 reception unit that receives a theme and a page number input by a user; an information collection unit that collects related information based on the theme accepted by the accepting unit; a configuration determination unit that determines a slide configuration based on the information collected by the information collection unit; a slide generator that generates slides based on the layout determined by the layout determination unit; Equipped with A system characterized by:

2. The reception unit Inferring the user's emotions and changing the theme input method based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyze the user's past theme input history and provide an appropriate input interface 2. The system of claim 1.

4. The reception unit As you type in the topic and page number, suggestions are provided based on the user's current project or area of ​​interest 2. The system of claim 1.

5. The reception unit Estimate user sentiment and prioritize input themes based on the estimated user sentiment.

2. The system of claim 1.

6. The reception unit When entering a topic and page number, the system prioritizes relevant topics based on the user's geographic location.

2. The system of claim 1.

7. The reception unit When you enter a topic and page number, the app analyzes your social media activity to suggest related topics.

2. The system of claim 1.

8. The information collecting unit Estimate user emotions and change information gathering methods based on the estimated user emotions 2. The system of claim 1.

Images