system

Abstract

The system according to this embodiment aims to create speech drafts that quickly incorporate region-specific information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information for a region specified by the user. The analysis unit analyzes the information collected by the collection unit and extracts region-specific information. The generation unit creates a speech draft based on the information extracted by the analysis unit. The provision unit provides the speech draft created by the generation unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to quickly incorporate region-specific information in speech or light talks at the deployment site.

[0005] The system according to the embodiment aims to create a speech manuscript that quickly incorporates region-specific information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information from a region specified by the user. The analysis unit analyzes the information collected by the collection unit and extracts region-specific information. The generation unit creates a speech draft based on the information extracted by the analysis unit. The provision unit provides the speech draft created by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can quickly create speech drafts that incorporate region-specific information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The speech script creation system according to an embodiment of the present invention is a system that instantly creates speech scripts incorporating local current events and expressions when business people, executives, and celebrities who travel all over the country for work are asked to give speeches or light talks while on business trips. The speech script creation system works by having the user specify the region of their trip, and the generating AI reads the latest news and trend information for that region and creates a speech script based on this information. Because this script includes local current events and expressions, it is more likely to capture the audience's interest. This mechanism not only reduces the burden on the person in charge of creating the script, but also allows the user to better engage the audience and improve the cost-effectiveness of the trip. For example, the user specifies the region of their trip. For example, they specify "Tokyo". This information is input into the generating AI. Next, the generating AI reads the latest news and trend information for the specified region. The generating AI collects and analyzes the latest information from news sites and social media on the internet. For example, it collects the latest event information and trending news for Tokyo. The generating AI creates a speech script based on the collected information. For example, the system generates speech drafts incorporating current events and news topics in Tokyo. These drafts include localized current events and expressions, making them more engaging for the audience. This reduces the burden on those responsible for drafting. Users can use the drafts generated by the AI ​​directly, saving time and effort. Furthermore, it allows for a more engaging audience experience. Dramas incorporating localized current events and expressions create a sense of familiarity and capture the audience's interest. This system also improves the cost-effectiveness of travel. By incorporating local information into their speeches, users can improve audience engagement and enhance the success of their trips. For example, a business person incorporating the latest Tokyo topics into a speech in Tokyo can attract audience interest and expand business opportunities. Thus, a system that instantly generates speech drafts incorporating localized current events and expressions using AI is extremely useful for business people, executives, and celebrities, improving the quality of speeches and light talk during trips.This allows the speech script creation system to instantly generate speech scripts that include local current events and expressions specific to the region, by collecting and analyzing information on the region specified by the user, creating and providing the script.

[0029] The speech script creation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information from a region specified by the user. The collection unit collects information from, for example, news sites and social networking services (SNS) on the internet. The collection unit can obtain the latest information from news sites using, for example, RSS feeds. The collection unit can also collect data from SNS using APIs. The collection unit filters news articles based on specific keywords and collects relevant information. The analysis unit analyzes the information collected by the collection unit and extracts region-specific information. The analysis unit analyzes news articles using, for example, text analysis technology and extracts important keywords and phrases. The analysis unit can also extract trend information from the collected data using, for example, data mining technology. The analysis unit understands the context of the collected information and extracts region-specific current events and expressions using, for example, natural language processing technology. The generation unit creates a speech script based on the information extracted by the analysis unit. The generation unit generates a speech script based on the extracted information using, for example, generation AI. The generation unit can create a speech script using, for example, a text generation AI (e.g., LLM). The generation unit can also create a speech script that includes not only text but also images and audio using, for example, a multimodal generation AI. The generation unit adjusts the structure and tone of the speech based on the information extracted by the generation AI to create a script that will capture the audience's interest. The provision unit provides the speech script created by the generation unit to the user. The provision unit provides the speech script through, for example, a smartphone app or a web service. The provision unit allows, for example, the user to download the speech script using a smartphone app. The provision unit also allows the user to view the speech script through a web service. The provision unit can also send the speech script to the user via email, for example. As a result, the speech script creation system according to the embodiment can instantly create a speech script that includes local current events and expressions specific to the region by collecting and analyzing information on the region specified by the user, creating a speech script, and providing it.

[0030] The data collection unit collects information from a region specified by the user. For example, it collects information from news sites and social networking services (SNS) on the internet. Specifically, the unit can obtain the latest information from news sites using RSS feeds. RSS feeds are a mechanism that automatically retrieves updates provided by websites, allowing the unit to collect the latest news in real time. The unit can also collect data from SNS using APIs. APIs are interfaces for applications to exchange data, enabling the unit to efficiently collect posts and comments on SNS. Furthermore, the unit filters news articles based on specific keywords and collects relevant information. For example, if a user specifies keywords such as "environmental issues" or "local events," the unit automatically collects news articles and SNS posts related to these keywords. The unit centrally manages the collected data and stores it in a database for access by the analysis unit. This allows the unit to efficiently collect data from diverse sources and improve the overall information gathering capabilities of the system.

[0031] The analysis unit analyzes the information collected by the collection unit and extracts region-specific information. For example, the analysis unit uses text analysis techniques to analyze news articles and extract important keywords and phrases. Text analysis techniques include morphological analysis, grammatical analysis, and semantic analysis, which allow for a detailed analysis of the content of news articles. The analysis unit can also use data mining techniques to extract trend information from the collected data. Data mining techniques are used to find useful patterns and relationships from large amounts of data, thereby identifying region-specific trends and topics. Furthermore, the analysis unit uses natural language processing techniques to understand the context of the collected information and extract region-specific current events and expressions. Natural language processing techniques are used to understand the meaning and context of text, allowing for a deeper understanding of the content of news articles and social media posts and the extraction of important information. The analysis unit organizes the extracted information and provides it to the generation unit as basic data for creating speech drafts. This allows the analysis unit to efficiently analyze the collected information and accurately extract region-specific information.

[0032] The generation unit creates a speech script based on the information extracted by the analysis unit. The generation unit can generate a speech script based on the extracted information, for example, using a generation AI. The generation AI can create a speech script using a text generation AI (e.g., LLM). LLM is a model trained on a large amount of text data, enabling it to generate natural-sounding sentences. The generation unit can also create a speech script that includes not only text, but also images and audio, for example, using a multimodal generation AI. Multimodal generation AI is a technology that integrates and processes multiple data formats such as text, images, and audio, enabling the creation of a speech script that is visually and aurally engaging. Based on the information extracted by the generation AI, the generation unit adjusts the structure and tone of the speech to create a script that captures the audience's interest. For example, the generation AI automatically generates a structure that captures the audience's attention in the introduction and effectively conveys the main points. The generation AI can also adjust the tone of the speech and incorporate expressions that appeal to the audience's emotions. This allows the generation unit to efficiently create a high-quality speech script that captures the audience's interest.

[0033] The delivery unit provides users with speech drafts created by the generation unit. The delivery unit provides speech drafts, for example, through a smartphone app or web service. The smartphone app provides an interface that allows users to easily download speech drafts, while the web service allows users to view speech drafts through a browser. The delivery unit can also send speech drafts to users via email. Email is a convenient way for users to receive speech drafts, allowing them to access them anytime, anywhere. Furthermore, the delivery unit can collect user feedback and continuously improve the quality of speech drafts. For example, users can provide feedback to the delivery unit regarding their impressions and suggestions for improvement after using the speech draft, and the delivery unit can then feed this information back to the generation unit, which can then incorporate it into future speech draft creation. This allows the delivery unit to continue providing users with high-quality speech drafts.

[0034] The data collection unit can collect information from news sites and social networking services (SNS) on the internet. For example, the data collection unit can obtain the latest information from news sites using RSS feeds. The data collection unit can also collect data from SNS using APIs. For example, the data collection unit can filter news articles based on specific keywords and collect relevant information. This allows for obtaining the latest local information by collecting information from news sites and SNS on the internet. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data collected from news sites and SNS into a generating AI, which can then analyze the data.

[0035] The analysis unit can analyze the collected information and extract local current events and expressions. For example, the analysis unit can analyze news articles using text analysis technology and extract important keywords and phrases. The analysis unit can also extract trend information from the collected data using data mining technology. For example, the analysis unit can understand the context of the collected information using natural language processing technology and extract local current events and expressions. This allows for the creation of speech scripts that are more likely to attract the audience's interest by analyzing the collected information and extracting local current events and expressions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI, which can then analyze the information.

[0036] The generation unit can create a speech draft based on the extracted information. The generation unit can generate a speech draft based on the extracted information, for example, using a generation AI. The generation unit can create a speech draft using a text generation AI (e.g., LLM). The generation unit can also create a speech draft that includes not only text but also images and audio, for example, using a multimodal generation AI. The generation unit can adjust the structure and tone of the speech based on the information extracted by the generation AI to create a draft that will attract the audience's interest. This allows for the rapid creation of speech drafts that include region-specific information by creating a speech draft based on the extracted information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the extracted information into a generation AI, which can then generate the speech draft.

[0037] The service provider can provide the generated speech draft to users through a smartphone app or web service. For example, the service provider can allow users to download the speech draft using a smartphone app. The service provider can also allow users to view the speech draft through a web service. The service provider can also send the speech draft to users via email. This makes it easy for users to receive the generated speech draft by providing it through a smartphone app or web service. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated speech draft into a generating AI, and the generating AI can determine how to provide it to the user.

[0038] The data collection unit can analyze the user's past speech content and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the themes of speeches the user has used in the past. For example, the data collection unit can extract specific keywords from the user's past speech content and collect information related to them. For example, the data collection unit can analyze successful examples of the user's past speeches and apply similar information collection methods. In this way, by analyzing the user's past speech content, the optimal information collection method can be selected and information can be collected efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past speech content into a generating AI, which can then select the optimal information collection method.

[0039] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting news related to topics the user is currently interested in. For example, the data collection unit can analyze the user's social media activity and collect information related to their areas of interest. For example, the data collection unit can filter and collect relevant information based on keywords the user has recently searched for. This allows for the efficient collection of highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's areas of interest into a generating AI, which can then filter the information.

[0040] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location. For example, the data collection unit can prioritize collecting the latest news from the user's current location. For example, the data collection unit can collect relevant regional information based on the user's travel history. For example, the data collection unit can collect information in advance about regions the user plans to visit. This allows for the efficient collection of region-specific information by prioritizing the collection of highly relevant information based on the user's geographical location. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then collect highly relevant information.

[0041] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze the content of posts from accounts that the user follows and collect relevant information. For example, the data collection unit can collect information related to the user's areas of interest based on posts that the user has liked or shared. For example, the data collection unit can collect relevant news and trend information based on the user's social media activity history. This allows for the efficient collection of highly relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis of important information to provide deep insights. For example, the analysis unit performs a concise analysis of less important information to provide only the essentials. For example, the analysis unit determines the priority of the analysis according to the importance of the information and performs the analysis efficiently. This allows for efficient analysis and the provision of detailed information on important information by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI, which can then adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a text analysis algorithm to news information to extract important keywords. For example, the analysis unit can apply a sentiment analysis algorithm to social media information to analyze user reactions. For example, the analysis unit can apply a time-series analysis algorithm to event information to grasp changes in trends. By applying different analysis algorithms depending on the category of information, the analysis unit can provide optimal analysis results for each category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI, and the generating AI can apply different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit prioritizes the analysis of the latest information and provides it quickly. For example, the analysis unit determines the priority of analysis of past information according to its importance. For example, the analysis unit adjusts the analysis schedule based on the timing of information collection. This allows for the rapid provision of the latest information by determining the priority of analysis based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI, which can then determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information and provides it quickly. For example, the analysis unit postpones the analysis of less relevant information. For example, the analysis unit adjusts the analysis schedule based on the relevance of the information. This allows for the priority provision of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI, and the generating AI can adjust the order of analysis.

[0046] The generation unit can adjust the level of detail in the manuscript based on the importance of the information during manuscript generation. For example, the generation unit can create a manuscript that includes detailed explanations for important information. For example, the generation unit can create a manuscript that includes concise explanations for less important information. For example, the generation unit can determine the priority of the manuscript according to the importance of the information and create it efficiently. This allows for efficient manuscript creation and the provision of important information in detail by adjusting the level of detail in the manuscript based on the importance of the information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information importance data into the generation AI, and the generation AI can adjust the level of detail in the manuscript.

[0047] The generation unit can apply different generation algorithms depending on the information category when generating manuscripts. For example, the generation unit can apply a text generation algorithm to news information to create a manuscript that includes important keywords. For example, the generation unit can apply a sentiment analysis algorithm to social media information to create a manuscript that reflects user reactions. For example, the generation unit can apply a time-series analysis algorithm to event information to create a manuscript that reflects changes in trends. In this way, by applying different generation algorithms depending on the information category, the generation unit can provide the most suitable manuscript for each category. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information category data into a generation AI, and the generation AI can apply different generation algorithms.

[0048] The generation unit can determine the priority of manuscripts based on the timing of information collection when generating them. For example, the generation unit prioritizes the inclusion of the latest information in the manuscript. For example, the generation unit determines the priority of past information according to its importance. For example, the generation unit adjusts the manuscript schedule based on the timing of information collection. This allows for the rapid provision of the latest information by determining the priority of manuscripts based on the timing of information collection. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information collection timing data into the generation AI, which can then determine the priority of the manuscripts.

[0049] The generation unit can adjust the order of the manuscript based on the relevance of the information during manuscript generation. For example, the generation unit prioritizes reflecting highly relevant information in the manuscript. For example, the generation unit postpones the creation of the manuscript for less relevant information. For example, the generation unit adjusts the manuscript schedule based on the relevance of the information. This allows for the priority provision of highly relevant information by adjusting the order of the manuscript based on the relevance of the information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information relevance data into a generation AI, and the generation AI can adjust the order of the manuscript.

[0050] The delivery department can select the optimal delivery method by referring to the user's past usage history when a manuscript is submitted. For example, the delivery department may prioritize suggesting delivery methods that the user has used in the past (email, app notifications, etc.). For example, the delivery department may select a delivery method for a specific time period based on the user's past usage history. For example, the delivery department may analyze the user's past usage history and suggest the most efficient delivery method. In this way, by referring to the user's past usage history, the optimal delivery method can be selected and the manuscript can be delivered efficiently. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department may input the user's past usage history data into a generating AI, which can then select the optimal delivery method.

[0051] The delivery unit can select the optimal delivery method based on the user's device information when a manuscript is submitted. For example, if the user is using a smartphone, the delivery unit will deliver the manuscript via app notification or SMS. If the user is using a tablet, the delivery unit will deliver the manuscript in a way optimized for a larger screen. If the user is using a personal computer, the delivery unit will deliver the manuscript via email or web service. By selecting the optimal delivery method based on the user's device information, the delivery unit can deliver the manuscript in the most optimal way for the user. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into a generating AI, which can then select the optimal delivery method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can analyze the user's past speech content and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the themes of speeches the user has used in the past. For example, the data collection unit can extract specific keywords from the user's past speech content and collect information related to them. For example, the data collection unit can analyze successful examples of the user's past speeches and apply similar information collection methods. In this way, by analyzing the user's past speech content, the optimal information collection method can be selected and information can be collected efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past speech content into a generating AI, which can then select the optimal information collection method.

[0054] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting news related to topics the user is currently interested in. For example, the data collection unit can analyze the user's social media activity and collect information related to their areas of interest. For example, the data collection unit can filter and collect relevant information based on keywords the user has recently searched for. This allows for the efficient collection of highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's areas of interest into a generating AI, which can then filter the information.

[0055] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis of important information to provide deep insights. For example, the analysis unit performs a concise analysis of less important information to provide only the essentials. For example, the analysis unit determines the priority of the analysis according to the importance of the information and performs the analysis efficiently. This allows for efficient analysis and the provision of detailed information on important information by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI, which can then adjust the level of detail of the analysis.

[0056] The generation unit can adjust the level of detail in the manuscript based on the importance of the information during manuscript generation. For example, the generation unit can create a manuscript that includes detailed explanations for important information. For example, the generation unit can create a manuscript that includes concise explanations for less important information. For example, the generation unit can determine the priority of the manuscript according to the importance of the information and create it efficiently. This allows for efficient manuscript creation and the provision of important information in detail by adjusting the level of detail in the manuscript based on the importance of the information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information importance data into the generation AI, and the generation AI can adjust the level of detail in the manuscript.

[0057] The generation unit can apply different generation algorithms depending on the information category when generating manuscripts. For example, the generation unit can apply a text generation algorithm to news information to create a manuscript that includes important keywords. For example, the generation unit can apply a sentiment analysis algorithm to social media information to create a manuscript that reflects user reactions. For example, the generation unit can apply a time-series analysis algorithm to event information to create a manuscript that reflects changes in trends. In this way, by applying different generation algorithms depending on the information category, the generation unit can provide the most suitable manuscript for each category. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information category data into a generation AI, and the generation AI can apply different generation algorithms.

[0058] The delivery department can select the optimal delivery method by referring to the user's past usage history when a manuscript is submitted. For example, the delivery department may prioritize suggesting delivery methods that the user has used in the past (email, app notifications, etc.). For example, the delivery department may select a delivery method for a specific time period based on the user's past usage history. For example, the delivery department may analyze the user's past usage history and suggest the most efficient delivery method. In this way, by referring to the user's past usage history, the optimal delivery method can be selected and the manuscript can be delivered efficiently. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department may input the user's past usage history data into a generating AI, which can then select the optimal delivery method.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects information for the region specified by the user. The data collection unit collects information from sources such as news sites and social media on the internet. The data collection unit can, for example, obtain the latest information from news sites using RSS feeds. The data collection unit can also collect data from social media using APIs. The data collection unit can, for example, filter news articles based on specific keywords and collect relevant information. Step 2: The analysis unit analyzes the information collected by the collection unit and extracts region-specific information. For example, the analysis unit uses text analysis technology to analyze news articles and extract important keywords and phrases. The analysis unit can also use data mining technology to extract trend information from the collected data. For example, the analysis unit uses natural language processing technology to understand the context of the collected information and extract region-specific current events and expressions. Step 3: The generation unit creates a speech draft based on the information extracted by the analysis unit. The generation unit generates a speech draft based on the extracted information, for example, using a generation AI. The generation unit can create a speech draft using, for example, a text generation AI (e.g., LLM). The generation unit can also create a speech draft that includes not only text but also images and audio, for example, using a multimodal generation AI. The generation unit adjusts the structure and tone of the speech based on the information extracted by the generation AI, for example, to create a draft that will capture the audience's interest. Step 4: The provider provides the speech draft created by the generator to the user. The provider provides the speech draft, for example, through a smartphone app or web service. The provider allows the user to download the speech draft using a smartphone app. The provider also allows the user to view the speech draft through a web service. The provider can also send the speech draft to the user via email, for example.

[0061] (Example of form 2) The speech script creation system according to an embodiment of the present invention is a system that instantly creates speech scripts incorporating local current events and expressions when business people, executives, and celebrities who travel all over the country for work are asked to give speeches or light talks while on business trips. The speech script creation system works by having the user specify the region of their trip, and the generating AI reads the latest news and trend information for that region and creates a speech script based on this information. Because this script includes local current events and expressions, it is more likely to capture the audience's interest. This mechanism not only reduces the burden on the person in charge of creating the script, but also allows the user to better engage the audience and improve the cost-effectiveness of the trip. For example, the user specifies the region of their trip. For example, they specify "Tokyo". This information is input into the generating AI. Next, the generating AI reads the latest news and trend information for the specified region. The generating AI collects and analyzes the latest information from news sites and social media on the internet. For example, it collects the latest event information and trending news for Tokyo. The generating AI creates a speech script based on the collected information. For example, the system generates speech drafts incorporating current events and news topics in Tokyo. These drafts include localized current events and expressions, making them more engaging for the audience. This reduces the burden on those responsible for drafting. Users can use the drafts generated by the AI ​​directly, saving time and effort. Furthermore, it allows for a more engaging audience experience. Dramas incorporating localized current events and expressions create a sense of familiarity and capture the audience's interest. This system also improves the cost-effectiveness of travel. By incorporating local information into their speeches, users can improve audience engagement and enhance the success of their trips. For example, a business person incorporating the latest Tokyo topics into a speech in Tokyo can attract audience interest and expand business opportunities. Thus, a system that instantly generates speech drafts incorporating localized current events and expressions using AI is extremely useful for business people, executives, and celebrities, improving the quality of speeches and light talk during trips.This allows the speech script creation system to instantly generate speech scripts that include local current events and expressions specific to the region, by collecting and analyzing information on the region specified by the user, creating and providing the script.

[0062] The speech script creation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information from a region specified by the user. The collection unit collects information from, for example, news sites and social networking services (SNS) on the internet. The collection unit can obtain the latest information from news sites using, for example, RSS feeds. The collection unit can also collect data from SNS using APIs. The collection unit filters news articles based on specific keywords and collects relevant information. The analysis unit analyzes the information collected by the collection unit and extracts region-specific information. The analysis unit analyzes news articles using, for example, text analysis technology and extracts important keywords and phrases. The analysis unit can also extract trend information from the collected data using, for example, data mining technology. The analysis unit understands the context of the collected information and extracts region-specific current events and expressions using, for example, natural language processing technology. The generation unit creates a speech script based on the information extracted by the analysis unit. The generation unit generates a speech script based on the extracted information using, for example, generation AI. The generation unit can create a speech script using, for example, a text generation AI (e.g., LLM). The generation unit can also create a speech script that includes not only text but also images and audio using, for example, a multimodal generation AI. The generation unit adjusts the structure and tone of the speech based on the information extracted by the generation AI to create a script that will capture the audience's interest. The provision unit provides the speech script created by the generation unit to the user. The provision unit provides the speech script through, for example, a smartphone app or a web service. The provision unit allows, for example, the user to download the speech script using a smartphone app. The provision unit also allows the user to view the speech script through a web service. The provision unit can also send the speech script to the user via email, for example. As a result, the speech script creation system according to the embodiment can instantly create a speech script that includes local current events and expressions specific to the region by collecting and analyzing information on the region specified by the user, creating a speech script, and providing it.

[0063] The data collection unit collects information from a region specified by the user. For example, it collects information from news sites and social networking services (SNS) on the internet. Specifically, the unit can obtain the latest information from news sites using RSS feeds. RSS feeds are a mechanism that automatically retrieves updates provided by websites, allowing the unit to collect the latest news in real time. The unit can also collect data from SNS using APIs. APIs are interfaces for applications to exchange data, enabling the unit to efficiently collect posts and comments on SNS. Furthermore, the unit filters news articles based on specific keywords and collects relevant information. For example, if a user specifies keywords such as "environmental issues" or "local events," the unit automatically collects news articles and SNS posts related to these keywords. The unit centrally manages the collected data and stores it in a database for access by the analysis unit. This allows the unit to efficiently collect data from diverse sources and improve the overall information gathering capabilities of the system.

[0064] The analysis unit analyzes the information collected by the collection unit and extracts region-specific information. For example, the analysis unit uses text analysis techniques to analyze news articles and extract important keywords and phrases. Text analysis techniques include morphological analysis, grammatical analysis, and semantic analysis, which allow for a detailed analysis of the content of news articles. The analysis unit can also use data mining techniques to extract trend information from the collected data. Data mining techniques are used to find useful patterns and relationships from large amounts of data, thereby identifying region-specific trends and topics. Furthermore, the analysis unit uses natural language processing techniques to understand the context of the collected information and extract region-specific current events and expressions. Natural language processing techniques are used to understand the meaning and context of text, allowing for a deeper understanding of the content of news articles and social media posts and the extraction of important information. The analysis unit organizes the extracted information and provides it to the generation unit as basic data for creating speech drafts. This allows the analysis unit to efficiently analyze the collected information and accurately extract region-specific information.

[0065] The generation unit creates a speech script based on the information extracted by the analysis unit. The generation unit can generate a speech script based on the extracted information, for example, using a generation AI. The generation AI can create a speech script using a text generation AI (e.g., LLM). LLM is a model trained on a large amount of text data, enabling it to generate natural-sounding sentences. The generation unit can also create a speech script that includes not only text, but also images and audio, for example, using a multimodal generation AI. Multimodal generation AI is a technology that integrates and processes multiple data formats such as text, images, and audio, enabling the creation of a speech script that is visually and aurally engaging. Based on the information extracted by the generation AI, the generation unit adjusts the structure and tone of the speech to create a script that captures the audience's interest. For example, the generation AI automatically generates a structure that captures the audience's attention in the introduction and effectively conveys the main points. The generation AI can also adjust the tone of the speech and incorporate expressions that appeal to the audience's emotions. This allows the generation unit to efficiently create a high-quality speech script that captures the audience's interest.

[0066] The delivery unit provides users with speech drafts created by the generation unit. The delivery unit provides speech drafts, for example, through a smartphone app or web service. The smartphone app provides an interface that allows users to easily download speech drafts, while the web service allows users to view speech drafts through a browser. The delivery unit can also send speech drafts to users via email. Email is a convenient way for users to receive speech drafts, allowing them to access them anytime, anywhere. Furthermore, the delivery unit can collect user feedback and continuously improve the quality of speech drafts. For example, users can provide feedback to the delivery unit regarding their impressions and suggestions for improvement after using the speech draft, and the delivery unit can then feed this information back to the generation unit, which can then incorporate it into future speech draft creation. This allows the delivery unit to continue providing users with high-quality speech drafts.

[0067] The data collection unit can collect information from news sites and social networking services (SNS) on the internet. For example, the data collection unit can obtain the latest information from news sites using RSS feeds. The data collection unit can also collect data from SNS using APIs. For example, the data collection unit can filter news articles based on specific keywords and collect relevant information. This allows for obtaining the latest local information by collecting information from news sites and SNS on the internet. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data collected from news sites and SNS into a generating AI, which can then analyze the data.

[0068] The analysis unit can analyze the collected information and extract local current events and expressions. For example, the analysis unit can analyze news articles using text analysis technology and extract important keywords and phrases. The analysis unit can also extract trend information from the collected data using data mining technology. For example, the analysis unit can understand the context of the collected information using natural language processing technology and extract local current events and expressions. This allows for the creation of speech scripts that are more likely to attract the audience's interest by analyzing the collected information and extracting local current events and expressions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI, which can then analyze the information.

[0069] The generation unit can create a speech draft based on the extracted information. The generation unit can generate a speech draft based on the extracted information, for example, using a generation AI. The generation unit can create a speech draft using a text generation AI (e.g., LLM). The generation unit can also create a speech draft that includes not only text but also images and audio, for example, using a multimodal generation AI. The generation unit can adjust the structure and tone of the speech based on the information extracted by the generation AI to create a draft that will attract the audience's interest. This allows for the rapid creation of speech drafts that include region-specific information by creating a speech draft based on the extracted information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the extracted information into a generation AI, which can then generate the speech draft.

[0070] The service provider can provide the generated speech draft to users through a smartphone app or web service. For example, the service provider can allow users to download the speech draft using a smartphone app. The service provider can also allow users to view the speech draft through a web service. The service provider can also send the speech draft to users via email. This makes it easy for users to receive the generated speech draft by providing it through a smartphone app or web service. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated speech draft into a generating AI, and the generating AI can determine how to provide it to the user.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect information regularly to always maintain up-to-date information. For example, if the user is stressed, the data collection unit will reduce the frequency of information collection to alleviate the user's burden. For example, if the user is in a hurry, the data collection unit will collect information quickly to provide the necessary information immediately. In this way, by adjusting the timing of information collection based on the user's emotions, the user's burden is reduced and information can be collected efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of information collection.

[0072] The data collection unit can analyze the user's past speech content and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the themes of speeches the user has used in the past. For example, the data collection unit can extract specific keywords from the user's past speech content and collect information related to them. For example, the data collection unit can analyze successful examples of the user's past speeches and apply similar information collection methods. In this way, by analyzing the user's past speech content, the optimal information collection method can be selected and information can be collected efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past speech content into a generating AI, which can then select the optimal information collection method.

[0073] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting news related to topics the user is currently interested in. For example, the data collection unit can analyze the user's social media activity and collect information related to their areas of interest. For example, the data collection unit can filter and collect relevant information based on keywords the user has recently searched for. This allows for the efficient collection of highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's areas of interest into a generating AI, which can then filter the information.

[0074] The data collection unit can estimate the user's emotions and prioritize the information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect a wide range of information and perform a detailed analysis. If the user is stressed, the data collection unit will prioritize collecting only the most important information. If the user is in a hurry, the data collection unit will quickly collect the most relevant information. This reduces the user's burden and allows for efficient information collection by prioritizing the information to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of the information.

[0075] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location. For example, the data collection unit can prioritize collecting the latest news from the user's current location. For example, the data collection unit can collect relevant regional information based on the user's travel history. For example, the data collection unit can collect information in advance about regions the user plans to visit. This allows for the efficient collection of region-specific information by prioritizing the collection of highly relevant information based on the user's geographical location. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then collect highly relevant information.

[0076] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze the content of posts from accounts that the user follows and collect relevant information. For example, the data collection unit can collect information related to the user's areas of interest based on posts that the user has liked or shared. For example, the data collection unit can collect relevant news and trend information based on the user's social media activity history. This allows for the efficient collection of highly relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant information.

[0077] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is in a hurry, the analysis unit provides analysis results in a format that can be quickly understood. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can adjust the presentation of the analysis.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis of important information to provide deep insights. For example, the analysis unit performs a concise analysis of less important information to provide only the essentials. For example, the analysis unit determines the priority of the analysis according to the importance of the information and performs the analysis efficiently. This allows for efficient analysis and the provision of detailed information on important information by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI, which can then adjust the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a text analysis algorithm to news information to extract important keywords. For example, the analysis unit can apply a sentiment analysis algorithm to social media information to analyze user reactions. For example, the analysis unit can apply a time-series analysis algorithm to event information to grasp changes in trends. By applying different analysis algorithms depending on the category of information, the analysis unit can provide optimal analysis results for each category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI, and the generating AI can apply different analysis algorithms.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is in a hurry, the analysis unit provides analysis results in a format that can be quickly understood. By adjusting the length of the analysis based on the user's emotions, the analysis results can be made easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, which can then adjust the length of the analysis.

[0081] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit prioritizes the analysis of the latest information and provides it quickly. For example, the analysis unit determines the priority of analysis of past information according to its importance. For example, the analysis unit adjusts the analysis schedule based on the timing of information collection. This allows for the rapid provision of the latest information by determining the priority of analysis based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI, which can then determine the priority of analysis.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information and provides it quickly. For example, the analysis unit postpones the analysis of less relevant information. For example, the analysis unit adjusts the analysis schedule based on the relevance of the information. This allows for the priority provision of highly relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI, and the generating AI can adjust the order of analysis.

[0083] The generation unit can estimate the user's emotions and adjust the way the manuscript is written based on the estimated emotions. For example, if the user is relaxed, the generation unit will use friendly language. If the user is stressed, the generation unit will use concise and to-the-point language. If the user is in a hurry, the generation unit will create the manuscript in a format that can be quickly understood. In this way, by adjusting the way the manuscript is written based on the user's emotions, the manuscript can be provided in the most optimal way for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, and the generation AI can adjust the way the manuscript is written.

[0084] The generation unit can adjust the level of detail in the manuscript based on the importance of the information during manuscript generation. For example, the generation unit can create a manuscript that includes detailed explanations for important information. For example, the generation unit can create a manuscript that includes concise explanations for less important information. For example, the generation unit can determine the priority of the manuscript according to the importance of the information and create it efficiently. This allows for efficient manuscript creation and the provision of important information in detail by adjusting the level of detail in the manuscript based on the importance of the information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information importance data into the generation AI, and the generation AI can adjust the level of detail in the manuscript.

[0085] The generation unit can apply different generation algorithms depending on the information category when generating manuscripts. For example, the generation unit can apply a text generation algorithm to news information to create a manuscript that includes important keywords. For example, the generation unit can apply a sentiment analysis algorithm to social media information to create a manuscript that reflects user reactions. For example, the generation unit can apply a time-series analysis algorithm to event information to create a manuscript that reflects changes in trends. In this way, by applying different generation algorithms depending on the information category, the generation unit can provide the most suitable manuscript for each category. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information category data into a generation AI, and the generation AI can apply different generation algorithms.

[0086] The generation unit can estimate the user's emotions and adjust the length of the manuscript based on the estimated emotions. For example, if the user is relaxed, the generation unit will create a longer manuscript with detailed explanations. For example, if the user is stressed, the generation unit will create a shorter manuscript that is concise and to the point. For example, if the user is in a hurry, the generation unit will create a shorter manuscript in a format that can be quickly understood. In this way, by adjusting the length of the manuscript based on the user's emotions, the generation unit can provide the user with a manuscript of the optimal length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, and the generation AI can adjust the length of the manuscript.

[0087] The generation unit can determine the priority of manuscripts based on the timing of information collection when generating them. For example, the generation unit prioritizes the inclusion of the latest information in the manuscript. For example, the generation unit determines the priority of past information according to its importance. For example, the generation unit adjusts the manuscript schedule based on the timing of information collection. This allows for the rapid provision of the latest information by determining the priority of manuscripts based on the timing of information collection. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information collection timing data into the generation AI, which can then determine the priority of the manuscripts.

[0088] The generation unit can adjust the order of the manuscript based on the relevance of the information during manuscript generation. For example, the generation unit prioritizes reflecting highly relevant information in the manuscript. For example, the generation unit postpones the creation of the manuscript for less relevant information. For example, the generation unit adjusts the manuscript schedule based on the relevance of the information. This allows for the priority provision of highly relevant information by adjusting the order of the manuscript based on the relevance of the information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information relevance data into a generation AI, and the generation AI can adjust the order of the manuscript.

[0089] The delivery unit can estimate the user's emotions and adjust the method of delivering the manuscript based on the estimated emotions. For example, if the user is relaxed, the delivery unit will deliver the manuscript in a way that includes detailed explanations. For example, if the user is stressed, the delivery unit will deliver the manuscript in a concise and to-the-point way. For example, if the user is in a hurry, the delivery unit will deliver the manuscript in a format that can be quickly understood. In this way, by adjusting the method of delivering the manuscript based on the user's emotions, the manuscript can be delivered in the most optimal way for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI, and the generative AI can adjust the method of delivering the manuscript.

[0090] The delivery department can select the optimal delivery method by referring to the user's past usage history when a manuscript is submitted. For example, the delivery department may prioritize suggesting delivery methods that the user has used in the past (email, app notifications, etc.). For example, the delivery department may select a delivery method for a specific time period based on the user's past usage history. For example, the delivery department may analyze the user's past usage history and suggest the most efficient delivery method. In this way, by referring to the user's past usage history, the optimal delivery method can be selected and the manuscript can be delivered efficiently. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department may input the user's past usage history data into a generating AI, which can then select the optimal delivery method.

[0091] The delivery unit can estimate the user's emotions and adjust the timing of manuscript delivery based on the estimated emotions. For example, if the user is relaxed, the delivery unit will deliver the manuscript at a relaxed pace. For example, if the user is stressed, the delivery unit will deliver the manuscript quickly. For example, if the user is in a hurry, the delivery unit will deliver the manuscript immediately. By adjusting the timing of manuscript delivery based on the user's emotions, the delivery unit can deliver the manuscript at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI, and the generative AI can adjust the timing of manuscript delivery.

[0092] The delivery unit can select the optimal delivery method based on the user's device information when a manuscript is submitted. For example, if the user is using a smartphone, the delivery unit will deliver the manuscript via app notification or SMS. If the user is using a tablet, the delivery unit will deliver the manuscript in a way optimized for a larger screen. If the user is using a personal computer, the delivery unit will deliver the manuscript via email or web service. By selecting the optimal delivery method based on the user's device information, the delivery unit can deliver the manuscript in the most optimal way for the user. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into a generating AI, which can then select the optimal delivery method.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The data collection unit can analyze the user's past speech content and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the themes of speeches the user has used in the past. For example, the data collection unit can extract specific keywords from the user's past speech content and collect information related to them. For example, the data collection unit can analyze successful examples of the user's past speeches and apply similar information collection methods. In this way, by analyzing the user's past speech content, the optimal information collection method can be selected and information can be collected efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past speech content into a generating AI, which can then select the optimal information collection method.

[0095] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting news related to topics the user is currently interested in. For example, the data collection unit can analyze the user's social media activity and collect information related to their areas of interest. For example, the data collection unit can filter and collect relevant information based on keywords the user has recently searched for. This allows for the efficient collection of highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's areas of interest into a generating AI, which can then filter the information.

[0096] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect information regularly to always maintain up-to-date information. For example, if the user is stressed, the data collection unit will reduce the frequency of information collection to alleviate the user's burden. For example, if the user is in a hurry, the data collection unit will collect information quickly to provide the necessary information immediately. In this way, by adjusting the timing of information collection based on the user's emotions, the user's burden is reduced and information can be collected efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of information collection.

[0097] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis of important information to provide deep insights. For example, the analysis unit performs a concise analysis of less important information to provide only the essentials. For example, the analysis unit determines the priority of the analysis according to the importance of the information and performs the analysis efficiently. This allows for efficient analysis and the provision of detailed information on important information by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI, which can then adjust the level of detail of the analysis.

[0098] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is in a hurry, the analysis unit provides analysis results in a format that can be quickly understood. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can adjust the presentation of the analysis.

[0099] The generation unit can estimate the user's emotions and adjust the way the manuscript is written based on the estimated emotions. For example, if the user is relaxed, the generation unit will use friendly language. If the user is stressed, the generation unit will use concise and to-the-point language. If the user is in a hurry, the generation unit will create the manuscript in a format that can be quickly understood. In this way, by adjusting the way the manuscript is written based on the user's emotions, the manuscript can be provided in the most optimal way for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, and the generation AI can adjust the way the manuscript is written.

[0100] The generation unit can adjust the level of detail in the manuscript based on the importance of the information during manuscript generation. For example, the generation unit can create a manuscript that includes detailed explanations for important information. For example, the generation unit can create a manuscript that includes concise explanations for less important information. For example, the generation unit can determine the priority of the manuscript according to the importance of the information and create it efficiently. This allows for efficient manuscript creation and the provision of important information in detail by adjusting the level of detail in the manuscript based on the importance of the information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information importance data into the generation AI, and the generation AI can adjust the level of detail in the manuscript.

[0101] The generation unit can apply different generation algorithms depending on the information category when generating manuscripts. For example, the generation unit can apply a text generation algorithm to news information to create a manuscript that includes important keywords. For example, the generation unit can apply a sentiment analysis algorithm to social media information to create a manuscript that reflects user reactions. For example, the generation unit can apply a time-series analysis algorithm to event information to create a manuscript that reflects changes in trends. In this way, by applying different generation algorithms depending on the information category, the generation unit can provide the most suitable manuscript for each category. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information category data into a generation AI, and the generation AI can apply different generation algorithms.

[0102] The delivery unit can estimate the user's emotions and adjust the method of delivering the manuscript based on the estimated emotions. For example, if the user is relaxed, the delivery unit will deliver the manuscript in a way that includes detailed explanations. For example, if the user is stressed, the delivery unit will deliver the manuscript in a concise and to-the-point way. For example, if the user is in a hurry, the delivery unit will deliver the manuscript in a format that can be quickly understood. In this way, by adjusting the method of delivering the manuscript based on the user's emotions, the manuscript can be delivered in the most optimal way for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI, and the generative AI can adjust the method of delivering the manuscript.

[0103] The delivery department can select the optimal delivery method by referring to the user's past usage history when a manuscript is submitted. For example, the delivery department may prioritize suggesting delivery methods that the user has used in the past (email, app notifications, etc.). For example, the delivery department may select a delivery method for a specific time period based on the user's past usage history. For example, the delivery department may analyze the user's past usage history and suggest the most efficient delivery method. In this way, by referring to the user's past usage history, the optimal delivery method can be selected and the manuscript can be delivered efficiently. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department may input the user's past usage history data into a generating AI, which can then select the optimal delivery method.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The data collection unit collects information for the region specified by the user. The data collection unit collects information from sources such as news sites and social media on the internet. The data collection unit can, for example, obtain the latest information from news sites using RSS feeds. The data collection unit can also collect data from social media using APIs. The data collection unit can, for example, filter news articles based on specific keywords and collect relevant information. Step 2: The analysis unit analyzes the information collected by the collection unit and extracts region-specific information. For example, the analysis unit uses text analysis technology to analyze news articles and extract important keywords and phrases. The analysis unit can also use data mining technology to extract trend information from the collected data. For example, the analysis unit uses natural language processing technology to understand the context of the collected information and extract region-specific current events and expressions. Step 3: The generation unit creates a speech draft based on the information extracted by the analysis unit. The generation unit generates a speech draft based on the extracted information, for example, using a generation AI. The generation unit can create a speech draft using, for example, a text generation AI (e.g., LLM). The generation unit can also create a speech draft that includes not only text but also images and audio, for example, using a multimodal generation AI. The generation unit adjusts the structure and tone of the speech based on the information extracted by the generation AI, for example, to create a draft that will capture the audience's interest. Step 4: The provider provides the speech draft created by the generator to the user. The provider provides the speech draft, for example, through a smartphone app or web service. The provider allows the user to download the speech draft using a smartphone app. The provider also allows the user to view the speech draft through a web service. The provider can also send the speech draft to the user via email, for example.

[0106] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0107] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0108] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0109] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information from news sites and social networking services on the internet via the communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected information and extract region-specific information. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to create a speech draft based on the analyzed information. The provision unit is implemented in the control unit 46A of the smart device 14, for example, to provide the created speech draft to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] As shown in Figure 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.

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

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information from news sites and social networking services on the internet via the communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and extracts region-specific information. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which creates a speech draft based on the analyzed information. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, which provides the created speech draft to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information from news sites and social networking services on the internet via the communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected information and extract region-specific information. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to create a speech draft based on the analyzed information. The provision unit is implemented in the control unit 46A of the headset terminal 314, for example, to provide the created speech draft to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0150] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information from news sites and social networking services on the internet via the communication I / F 44 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information and extracts region-specific information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which creates a speech draft based on the analyzed information. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides the created speech draft to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0159] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0161] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0162] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0163] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0167] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0168] 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.

[0169] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0170] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0171] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0172] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0174] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0175] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0176] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0177] (Note 1) A collection unit that collects information from a region specified by the user, An analysis unit analyzes the information collected by the aforementioned collection unit and extracts region-specific information, A generation unit that creates a speech draft based on the information extracted by the analysis unit, The system includes a providing unit that provides the speech manuscript created by the generation unit to the user. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information from news sites and social media on the internet. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to extract local current events and expressions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Create a speech draft based on the extracted information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The generated speech transcript is provided to users via a smartphone app or web service. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past speech content and select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the user's emotions and adjusts the wording of the manuscript based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating the manuscript, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a manuscript, different generation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the manuscript based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating manuscripts, prioritize the manuscripts based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating the manuscript, adjust the order of the information based on its relevance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We estimate the user's emotions and adjust the method of providing the manuscript based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When submitting a manuscript, the system will select the most suitable delivery method by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of manuscript delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When submitting a manuscript, the optimal delivery method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information from a region specified by the user, An analysis unit analyzes the information collected by the aforementioned collection unit and extracts region-specific information, A generation unit that creates a speech draft based on the information extracted by the analysis unit, The system includes a providing unit that provides the speech manuscript created by the generation unit to the user. A system characterized by the following features.

2. The aforementioned collection unit is Gather information from news sites and social media on the internet. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to extract local current events and expressions. The system according to feature 1.

4. The generating unit is Create a speech draft based on the extracted information. The system according to feature 1.

5. The aforementioned supply unit is, The generated speech transcript is provided to users via a smartphone app or web service. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past speech content and select the optimal information gathering method. The system according to feature 1.

8. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system according to feature 1.

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