system

Abstract

The system according to this embodiment aims to efficiently collect information from social media and various websites, convert it into a schedule, and provide it to users. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information from SNS or various websites. The analysis unit analyzes the information collected by the collection unit and converts it into a schedule. The provision unit provides the user with a schedule based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003] [[ID=]22]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, information from SNS and each site has not been sufficiently collected efficiently and converted into a schedule, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect information from SNS and each site, convert it into a schedule, and provide it.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information from SNS or each site. The analysis unit analyzes the information collected by the collection unit and converts it into a schedule. The provision unit provides a schedule to the user based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect information from social media and various websites, convert it into a schedule, and provide it to users. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 [i]10[ / i] includes a data processing device [i]12[ / i] and a smart device [i]14[ / i]. An example of the data processing device [i]12[ / i] is a server.

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

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

[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 schedule management system according to an embodiment of the present invention is a system that collects application schedules for musical and stage tickets from social media and various websites, and incorporates them into a schedule using AI. This schedule management system collects application schedules for musical and stage tickets from social media and various websites, analyzes the collected information with AI, and incorporates it into a schedule. Based on the analyzed information, it provides the schedule to the user. This service allows users to save the trouble of checking multiple social media and websites and to efficiently grasp the ticket application schedule. For example, in the part where information is collected from social media and various websites, methods such as web scraping and APIs can be used. Next, in the part where the collected information is analyzed, the AI ​​extracts the information using natural language processing. Furthermore, calendar format and list format can be considered as the format of the schedule provided to the user. In addition, in order to ensure the reliability of the collected information, there is a method of prioritizing the collection of information from official websites. Furthermore, a filtering function that allows users to customize the schedule can also be added. Finally, from the perspective of privacy and security, it is necessary to consider how the collected information will be handled. As a result, the schedule management system allows users to save the trouble of checking multiple social media and websites and to efficiently grasp the ticket application schedule.

[0029] The schedule management system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information from social media and various websites. For example, the collection unit uses web scraping technology to collect application schedules for musical and stage tickets from social media and various websites. The collection unit can also collect information using APIs. For example, the collection unit uses the API of a specific social media or website to obtain the latest ticket information. Furthermore, the collection unit can prioritize the collection of information from official websites. For example, the collection unit uses the RSS feed of an official website to obtain the latest ticket information. The analysis unit analyzes the information collected by the collection unit and incorporates it into a schedule. For example, the analysis unit uses natural language processing technology to extract necessary data from the collected information. For example, the analysis unit uses morphological analysis to extract information such as date, time, and location from text. The analysis unit can also use machine learning algorithms to classify the collected information and convert it into a schedule. For example, the analysis unit uses a neural network to analyze the collected information and convert it into a schedule. The provision unit provides the user with a schedule based on the information analyzed by the analysis unit. The system displays schedules in various formats, such as calendar, monthly, weekly, and daily views. It can also display schedules in a list format, such as chronologically. Furthermore, the system includes a filtering function that allows users to customize their schedules. For example, it provides a filtering function that displays only events of interest to the user. This allows the schedule management system according to the embodiment to eliminate the need for users to check multiple social media platforms and websites, enabling them to efficiently grasp ticket application schedules.

[0030] The data collection unit can collect information using web scraping or APIs. For example, the data collection unit can use web scraping technology to collect application schedules for musical and stage tickets from social media and various websites. For example, the data collection unit can analyze the HTML structure of a specific social media or website and extract the necessary information. The data collection unit can also collect information using APIs. For example, the data collection unit can use the API of a specific social media or website to obtain the latest ticket information. This allows for efficient information collection by utilizing web scraping and APIs. 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 URL of the website to be web scraped into the AI, and the AI ​​can automatically collect the information.

[0031] The analysis unit can analyze information using natural language processing and translate it into a schedule. For example, the analysis unit can extract necessary data from collected information using natural language processing techniques. For example, the analysis unit can extract information such as date, time, and location from text using morphological analysis. The analysis unit can also classify collected information and convert it into a schedule using machine learning algorithms. For example, the analysis unit can analyze collected information and convert it into a schedule using a neural network. This improves the accuracy of information analysis by using natural language processing. 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 collected information into AI, which can then automatically analyze the information and convert it into a schedule.

[0032] The service provider can display schedules in calendar or list format. For example, the service provider can display schedules in calendar format. For example, the service provider can display schedules in monthly, weekly, or daily formats. The service provider can also display schedules in list format. For example, the service provider can list schedules in chronological order. This allows users to view schedules in their preferred format. 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 may use AI to automatically select the optimal display format according to the user's preference.

[0033] The data collection unit can prioritize collecting information from the official website. For example, the data collection unit can use the official website's RSS feed to obtain the latest ticket information. For example, the data collection unit can periodically check the official website's RSS feed and automatically collect any new information that is added. The data collection unit can also collect information using the official website's API. For example, the data collection unit can use the official website's API to obtain the latest ticket information. This improves the reliability of the information by prioritizing the collection of information from the official website. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the URL of the official website's RSS feed into the AI, and the AI ​​can automatically collect the information.

[0034] The service provider may include a filtering function that allows users to customize their schedules. For example, the service provider may provide a filtering function that displays only events of interest to the user. For example, the service provider may provide a filtering function that displays only information about a specific musical or play. The service provider may also provide a filtering function that displays only information about a specific date and time. For example, the service provider may provide a filtering function that displays only events held within a specific period. This allows users to customize their schedules to their preferences. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's filtering conditions into the AI, which can then automatically perform the optimal filtering.

[0035] The data collection unit can implement measures to protect users' personal information. For example, the data collection unit can protect collected information using data encryption technology. For example, the data collection unit can encrypt the collected information to prevent unauthorized access by third parties. The data collection unit can also restrict access to collected information using access control technology. For example, the data collection unit can ensure that only specific users can access the collected information. Furthermore, the data collection unit can develop a privacy policy to clarify how the collected information will be handled. For example, the data collection unit can clarify the retention period of the collected information and the conditions under which it will be provided to third parties. This ensures that users' personal information is securely protected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the collected information into AI, which can then automatically encrypt the information.

[0036] The analysis unit may have policies regarding the handling of collected information. For example, the analysis unit may develop policies that clarify the retention period of collected information and the conditions for providing it to third parties. For example, the analysis unit may have a policy to store collected information for a certain period and then automatically delete it. The analysis unit may also clarify the conditions for providing collected information to third parties. For example, the analysis unit may have a policy to provide information to third parties only with the user's consent. This clarifies the policy regarding the handling of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may set the retention period of collected information using AI, and the AI ​​may automatically delete the information.

[0037] The service provider can be equipped with functions to securely manage users' personal information. For example, the service provider can protect users' personal information by implementing database security measures. For example, the service provider can install a firewall to prevent unauthorized access to the database. The service provider can also manage access logs and record the history of access to users' personal information. For example, the service provider can record who accessed which information and when, and detect unauthorized access. Furthermore, the service provider can encrypt and store users' personal information. For example, the service provider can encrypt users' personal information to prevent unauthorized access by third parties. This ensures that users' personal information is managed securely. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input users' personal information into AI, and the AI ​​can automatically encrypt and securely manage the information.

[0038] The data collection unit can analyze the user's past search history and select the optimal data collection method. For example, the data collection unit may use data mining techniques to analyze the user's past search history. For example, the data collection unit may analyze keywords and websites that the user has frequently searched in the past and select the optimal data collection method. The data collection unit can also prioritize the collection of information related to specific keywords from the user's search history. For example, the data collection unit prioritizes the collection of relevant information based on keywords that the user has searched in the past. Furthermore, the data collection unit can analyze the user's search history and propose the most efficient data collection method. For example, the data collection unit may propose the optimal timing and method of information collection based on the user's search history. This enables optimal information collection based on the user's past search history. Some or all of the above 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 search history into AI, which can then automatically select the optimal data collection method.

[0039] The data collection unit can filter information based on the user's current areas of interest. For example, the data collection unit can analyze the user's social media posts and search history to identify the user's current areas of interest. For example, the data collection unit can prioritize collecting information related to musicals or stage performances that the user is currently interested in. The data collection unit can also collect information from relevant social media and websites based on the user's areas of interest. For example, the data collection unit can prioritize collecting information related to a specific theme that the user is interested in. Furthermore, the data collection unit can analyze the user's areas of interest and filter and provide the most relevant information. For example, the data collection unit can collect and provide only the most relevant information based on the user's areas of interest. This allows the data collection unit to provide highly relevant information based on the user's 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 the user's areas of interest into AI, which can automatically perform optimal filtering.

[0040] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location information during data collection. For example, the data collection unit uses GPS technology to obtain the user's geographical location information. For example, the data collection unit obtains the user's current location information and prioritizes collecting information related to that location. The data collection unit can also prioritize collecting nearby performance information based on the user's geographical location information. For example, the data collection unit prioritizes collecting information on musicals and plays related to the area where the user is currently located. Furthermore, the data collection unit can analyze the user's location information and provide the most relevant information. For example, the data collection unit provides the most relevant performance information based on the user's location information. This allows the data collection unit to provide highly relevant information based on the user's geographical location information. 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 location information into AI, and the AI ​​can automatically collect the most suitable information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit may use data mining techniques to analyze the user's social media activity. For example, the data collection unit may prioritize collecting information from accounts that the user follows. The data collection unit can also analyze the content of the user's social media posts and collect relevant information. For example, the data collection unit may prioritize collecting information related to topics that the user is interested in. Furthermore, the data collection unit can provide the most relevant information based on the user's social media activity. For example, the data collection unit can analyze the user's social media activity and provide the most relevant information. This allows the data collection unit to provide highly relevant information based on the user's social media activity. 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 social media activity into AI, which can then automatically collect the most 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 calculates an importance score to evaluate the importance of the information. For example, the analysis unit calculates an importance score based on the content and relevance of the collected information. The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a concise analysis on information with low importance. Furthermore, the analysis unit can analyze the importance of the information and select the optimal analysis method. For example, the analysis unit selects the optimal analysis algorithm based on the importance score. This enables optimal 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 the collected information into AI, which can automatically calculate an importance score and 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 use clustering techniques to identify the category of information. For example, the analysis unit can cluster the collected information and classify it into categories. Furthermore, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a specific analysis algorithm to musical information and a different analysis algorithm to stage information. In addition, the analysis unit can analyze the category of information and select the optimal analysis algorithm. For example, the analysis unit can select the optimal analysis algorithm based on the characteristics of each category. This allows the optimal analysis algorithm to be applied according to the category of 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 the collected information into AI, which can automatically identify the categories and apply the optimal analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the information collection timing during analysis. For example, the analysis unit uses timestamps to evaluate the information collection timing. For example, the analysis unit analyzes the timestamps attached to the collected information to identify the collection timing. The analysis unit can also determine the priority of analysis based on the information collection timing. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also analyze older information as needed. Furthermore, the analysis unit can analyze the information collection timing and determine the optimal analysis order. For example, the analysis unit selects the optimal analysis algorithm based on the collection timing. This allows the optimal analysis order to be determined based on the information collection timing. 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 the collected information into AI, which can automatically identify the collection timing and 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 process. For example, the analysis unit calculates a relevance score to evaluate the relevance of the information. For example, the analysis unit calculates a relevance score based on the content and relevance of the collected information. The analysis unit can also adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. Furthermore, the analysis unit can analyze the relevance of the information and determine the optimal order of analysis. For example, the analysis unit selects the optimal analysis algorithm based on the relevance score. This allows the analysis unit to determine the optimal order of analysis based on the relevance 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 the collected information into AI, which can automatically calculate the relevance score and adjust the order of analysis.

[0046] The service provider can select the optimal display method by referring to the user's past operation history when providing a schedule. The service provider can, for example, use data mining techniques to analyze the user's past operation history. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. The service provider can also propose the most efficient display method based on the user's operation history. For example, the service provider can propose the optimal display method based on the user's operation history. Furthermore, the service provider can analyze the user's past operation history and select the optimal display method. For example, the service provider selects the optimal display method based on the user's operation history. This allows the service provider to provide the optimal display method based on the user's past operation history. 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 user's operation history into AI, and the AI ​​can automatically select the optimal display method.

[0047] The service provider can customize the displayed content based on the user's current areas of interest when providing schedules. For example, the service provider can analyze the user's social media posts and search history to identify the user's current areas of interest. For example, the service provider can prioritize displaying schedules related to musicals or plays that the user is currently interested in. The service provider can also customize relevant schedules based on the user's areas of interest. For example, the service provider can prioritize displaying schedules related to specific themes that the user is interested in. Furthermore, the service provider can analyze the user's areas of interest and provide the most relevant schedules. For example, the service provider can provide relevant schedules based on the user's areas of interest. This allows the service provider to provide highly relevant schedules based on the user's areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's areas of interest into AI, and the AI ​​can automatically customize the optimal displayed content.

[0048] The service provider can select the optimal display method when providing a schedule, taking into account the user's device information. For example, the service provider may use device identification technology to obtain the user's device information. For example, the service provider may identify the type and screen size of the device the user is using. The service provider can also select the optimal display method based on the user's device information. For example, if the user is using a smartphone, the service provider may provide a display method that matches the screen size. The service provider may also provide a display method optimized for a larger screen if the user is using a tablet. Furthermore, if the service provider is using a smartwatch, the service provider may provide a concise and highly visible display method. This allows the service provider to provide the optimal display method based on the user's device information. 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 may input the user's device information into AI, and the AI ​​may automatically select the optimal display method.

[0049] The service provider can analyze a user's social media activity and provide relevant schedules when providing them. For example, the service provider can use data mining techniques to analyze a user's social media activity. For example, the service provider can provide schedules based on information from accounts that the user follows. The service provider can also analyze the content of a user's social media posts and provide relevant schedules. For example, the service provider can prioritize providing schedules related to themes that the user is interested in. Furthermore, the service provider can provide the most relevant schedules based on the user's social media activity. For example, the service provider can analyze a user's social media activity and provide the most relevant schedules. This allows the service provider to provide highly relevant schedules based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity into AI, and the AI ​​can automatically provide the optimal schedule.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can analyze a user's past purchase history and prioritize the collection of relevant ticket information. For example, based on the user's past purchases of musicals and stage performances, the unit can prioritize the collection of performance information of similar genres and artists. The unit can also prioritize the collection of information related to specific theaters or venues from the user's purchase history. Furthermore, the unit can analyze the user's purchase history and provide the most relevant information. This allows the system to provide highly relevant information based on the user's past purchase history.

[0052] The analytics unit can learn user preferences and automatically select recommended performances. For example, it can analyze data from events a user has attended in the past and recommend similar events. It can also analyze a user's social media activity and posts to identify performances they might be interested in. Furthermore, it can analyze the activity of a user's friends and followers and recommend performances that align with their shared interests. This allows the system to recommend the most suitable performances based on the user's preferences.

[0053] The data collection unit can prioritize collecting nearby performance information based on the user's geographical location. For example, the unit can identify the user's current location and collect performance information held in that vicinity. It can also collect relevant performance information based on information about places the user has visited in the past. Furthermore, the unit can analyze the user's location information and provide the most relevant performance information. This allows the system to provide highly relevant information based on the user's geographical location.

[0054] The service provider can select the optimal display method by referring to the user's past operation history. For example, the service provider can prioritize providing the display method that the user has preferred to use in the past. Furthermore, the service provider can suggest the most efficient display method based on the user's operation history. In addition, the service provider can analyze the user's past operation history and select the optimal display method. This allows the service provider to provide the optimal display method based on the user's past operation history.

[0055] The analysis unit can apply different analysis algorithms depending on the category of information. For example, it can apply a specific analysis algorithm to musical information and a different one to stage information. The analysis unit can also use clustering techniques to identify the category of information. Furthermore, it can analyze the category of information and select the optimal analysis algorithm. This allows the system to apply the most suitable analysis algorithm depending on the category of information.

[0056] The service provider can select the optimal display method when providing a schedule, taking into account the user's device information. For example, the service provider can identify the type and screen size of the device the user is using. Furthermore, the service provider can select the optimal display method based on the user's device information. For instance, if the user is using a smartphone, the service provider can provide a display method adapted to the screen size. If the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method based on the user's device information.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit gathers information from social media and various websites. For example, the data collection unit uses web scraping technology to collect application schedules for musical and stage tickets from social media and various websites. The data collection unit can also collect information using APIs. For example, the data collection unit uses the API of a specific social media or website to obtain the latest ticket information. Furthermore, the data collection unit can prioritize collecting information from official websites. For example, the data collection unit uses the RSS feed of an official website to obtain the latest ticket information. Step 2: The analysis unit analyzes the information collected by the collection unit and incorporates it into a schedule. The analysis unit extracts necessary data from the collected information, for example, using natural language processing technology. For example, the analysis unit uses morphological analysis to extract information such as date, time, and location from text. The analysis unit can also classify the collected information and convert it into a schedule using machine learning algorithms. For example, the analysis unit uses a neural network to analyze the collected information and convert it into a schedule. Step 3: The service provider provides the user with a schedule based on the information analyzed by the analysis unit. The service provider displays the schedule in a calendar format, for example. For example, the service provider can display the schedule in monthly, weekly, or daily views. The service provider can also display the schedule in a list format. For example, the service provider can list the schedule in chronological order. Furthermore, the service provider includes a filtering function that allows the user to customize the schedule. For example, the service provider can provide a filtering function that displays only the events that the user is interested in.

[0059] (Example of form 2) The schedule management system according to an embodiment of the present invention is a system that collects application schedules for musical and stage tickets from social media and various websites, and incorporates them into a schedule using AI. This schedule management system collects application schedules for musical and stage tickets from social media and various websites, analyzes the collected information with AI, and incorporates it into a schedule. Based on the analyzed information, it provides the schedule to the user. This service allows users to save the trouble of checking multiple social media and websites and to efficiently grasp the ticket application schedule. For example, in the part where information is collected from social media and various websites, methods such as web scraping and APIs can be used. Next, in the part where the collected information is analyzed, the AI ​​extracts the information using natural language processing. Furthermore, calendar format and list format can be considered as the format of the schedule provided to the user. In addition, in order to ensure the reliability of the collected information, there is a method of prioritizing the collection of information from official websites. Furthermore, a filtering function that allows users to customize the schedule can also be added. Finally, from the perspective of privacy and security, it is necessary to consider how the collected information will be handled. As a result, the schedule management system allows users to save the trouble of checking multiple social media and websites and to efficiently grasp the ticket application schedule.

[0060] The schedule management system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information from social media and various websites. For example, the collection unit uses web scraping technology to collect application schedules for musical and stage tickets from social media and various websites. The collection unit can also collect information using APIs. For example, the collection unit uses the API of a specific social media or website to obtain the latest ticket information. Furthermore, the collection unit can prioritize the collection of information from official websites. For example, the collection unit uses the RSS feed of an official website to obtain the latest ticket information. The analysis unit analyzes the information collected by the collection unit and incorporates it into a schedule. For example, the analysis unit uses natural language processing technology to extract necessary data from the collected information. For example, the analysis unit uses morphological analysis to extract information such as date, time, and location from text. The analysis unit can also use machine learning algorithms to classify the collected information and convert it into a schedule. For example, the analysis unit uses a neural network to analyze the collected information and convert it into a schedule. The provision unit provides the user with a schedule based on the information analyzed by the analysis unit. The system displays schedules in various formats, such as calendar, monthly, weekly, and daily views. It can also display schedules in a list format, such as chronologically. Furthermore, the system includes a filtering function that allows users to customize their schedules. For example, it provides a filtering function that displays only events of interest to the user. This allows the schedule management system according to the embodiment to eliminate the need for users to check multiple social media platforms and websites, enabling them to efficiently grasp ticket application schedules.

[0061] The data collection unit can collect information using web scraping or APIs. For example, the data collection unit can use web scraping technology to collect application schedules for musical and stage tickets from social media and various websites. For example, the data collection unit can analyze the HTML structure of a specific social media or website and extract the necessary information. The data collection unit can also collect information using APIs. For example, the data collection unit can use the API of a specific social media or website to obtain the latest ticket information. This allows for efficient information collection by utilizing web scraping and APIs. 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 URL of the website to be web scraped into the AI, and the AI ​​can automatically collect the information.

[0062] The analysis unit can analyze information using natural language processing and translate it into a schedule. For example, the analysis unit can extract necessary data from collected information using natural language processing techniques. For example, the analysis unit can extract information such as date, time, and location from text using morphological analysis. The analysis unit can also classify collected information and convert it into a schedule using machine learning algorithms. For example, the analysis unit can analyze collected information and convert it into a schedule using a neural network. This improves the accuracy of information analysis by using natural language processing. 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 collected information into AI, which can then automatically analyze the information and convert it into a schedule.

[0063] The service provider can display schedules in calendar or list format. For example, the service provider can display schedules in calendar format. For example, the service provider can display schedules in monthly, weekly, or daily formats. The service provider can also display schedules in list format. For example, the service provider can list schedules in chronological order. This allows users to view schedules in their preferred format. 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 may use AI to automatically select the optimal display format according to the user's preference.

[0064] The data collection unit can prioritize collecting information from the official website. For example, the data collection unit can use the official website's RSS feed to obtain the latest ticket information. For example, the data collection unit can periodically check the official website's RSS feed and automatically collect any new information that is added. The data collection unit can also collect information using the official website's API. For example, the data collection unit can use the official website's API to obtain the latest ticket information. This improves the reliability of the information by prioritizing the collection of information from the official website. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the URL of the official website's RSS feed into the AI, and the AI ​​can automatically collect the information.

[0065] The service provider may include a filtering function that allows users to customize their schedules. For example, the service provider may provide a filtering function that displays only events of interest to the user. For example, the service provider may provide a filtering function that displays only information about a specific musical or play. The service provider may also provide a filtering function that displays only information about a specific date and time. For example, the service provider may provide a filtering function that displays only events held within a specific period. This allows users to customize their schedules to their preferences. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's filtering conditions into the AI, which can then automatically perform the optimal filtering.

[0066] The data collection unit can implement measures to protect users' personal information. For example, the data collection unit can protect collected information using data encryption technology. For example, the data collection unit can encrypt the collected information to prevent unauthorized access by third parties. The data collection unit can also restrict access to collected information using access control technology. For example, the data collection unit can ensure that only specific users can access the collected information. Furthermore, the data collection unit can develop a privacy policy to clarify how the collected information will be handled. For example, the data collection unit can clarify the retention period of the collected information and the conditions under which it will be provided to third parties. This ensures that users' personal information is securely protected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the collected information into AI, which can then automatically encrypt the information.

[0067] The analysis unit may have policies regarding the handling of collected information. For example, the analysis unit may develop policies that clarify the retention period of collected information and the conditions for providing it to third parties. For example, the analysis unit may have a policy to store collected information for a certain period and then automatically delete it. The analysis unit may also clarify the conditions for providing collected information to third parties. For example, the analysis unit may have a policy to provide information to third parties only with the user's consent. This clarifies the policy regarding the handling of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may set the retention period of collected information using AI, and the AI ​​may automatically delete the information.

[0068] The service provider can be equipped with functions to securely manage users' personal information. For example, the service provider can protect users' personal information by implementing database security measures. For example, the service provider can install a firewall to prevent unauthorized access to the database. The service provider can also manage access logs and record the history of access to users' personal information. For example, the service provider can record who accessed which information and when, and detect unauthorized access. Furthermore, the service provider can encrypt and store users' personal information. For example, the service provider can encrypt users' personal information to prevent unauthorized access by third parties. This ensures that users' personal information is managed securely. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input users' personal information into AI, and the AI ​​can automatically encrypt and securely manage the information.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the data collection unit may use an emotion analysis algorithm to estimate the user's emotions. For example, the data collection unit may analyze the content of the user's social media posts and messages and calculate an emotion score. The data collection unit can also adjust the timing of information collection based on the user's emotions. For example, if the user is excited, the data collection unit can collect information in real time and provide it immediately. If the user is relaxed, the data collection unit can also collect information periodically and provide it all at once. Furthermore, if the user is stressed, the data collection unit can reduce the frequency of information collection and provide only the minimum necessary information. This allows for more appropriate information provision by adjusting the timing of information collection according to the user's emotions. 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 users' social media posts into an AI, which can automatically estimate the sentiment and adjust the timing of information collection.

[0070] The data collection unit can analyze the user's past search history and select the optimal data collection method. For example, the data collection unit may use data mining techniques to analyze the user's past search history. For example, the data collection unit may analyze keywords and websites that the user has frequently searched in the past and select the optimal data collection method. The data collection unit can also prioritize the collection of information related to specific keywords from the user's search history. For example, the data collection unit prioritizes the collection of relevant information based on keywords that the user has searched in the past. Furthermore, the data collection unit can analyze the user's search history and propose the most efficient data collection method. For example, the data collection unit may propose the optimal timing and method of information collection based on the user's search history. This enables optimal information collection based on the user's past search history. Some or all of the above 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 search history into AI, which can then automatically select the optimal data collection method.

[0071] The data collection unit can filter information based on the user's current areas of interest. For example, the data collection unit can analyze the user's social media posts and search history to identify the user's current areas of interest. For example, the data collection unit can prioritize collecting information related to musicals or stage performances that the user is currently interested in. The data collection unit can also collect information from relevant social media and websites based on the user's areas of interest. For example, the data collection unit can prioritize collecting information related to a specific theme that the user is interested in. Furthermore, the data collection unit can analyze the user's areas of interest and filter and provide the most relevant information. For example, the data collection unit can collect and provide only the most relevant information based on the user's areas of interest. This allows the data collection unit to provide highly relevant information based on the user's 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 the user's areas of interest into AI, which can automatically perform optimal filtering.

[0072] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, the data collection unit may use an emotion analysis algorithm to estimate the user's emotions. For example, the data collection unit may analyze the content of the user's social media posts and messages and calculate an emotion score. The data collection unit can also determine the priority of information to collect based on the user's emotions. For example, if the user is excited, the data collection unit may prioritize collecting the latest information. If the user is relaxed, the data collection unit may also prioritize collecting information of high importance. Furthermore, if the user is stressed, the data collection unit may prioritize collecting only the minimum necessary information. This allows for more appropriate information to be provided by prioritizing information according to the user's emotions. 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 users' social media posts into an AI, which can then automatically estimate the sentiment behind them and determine the priority of the information.

[0073] The data collection unit can prioritize collecting highly relevant information based on the user's geographical location information during data collection. For example, the data collection unit uses GPS technology to obtain the user's geographical location information. For example, the data collection unit obtains the user's current location information and prioritizes collecting information related to that location. The data collection unit can also prioritize collecting nearby performance information based on the user's geographical location information. For example, the data collection unit prioritizes collecting information on musicals and plays related to the area where the user is currently located. Furthermore, the data collection unit can analyze the user's location information and provide the most relevant information. For example, the data collection unit provides the most relevant performance information based on the user's location information. This allows the data collection unit to provide highly relevant information based on the user's geographical location information. 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 location information into AI, and the AI ​​can automatically collect the most suitable information.

[0074] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit may use data mining techniques to analyze the user's social media activity. For example, the data collection unit may prioritize collecting information from accounts that the user follows. The data collection unit can also analyze the content of the user's social media posts and collect relevant information. For example, the data collection unit may prioritize collecting information related to topics that the user is interested in. Furthermore, the data collection unit can provide the most relevant information based on the user's social media activity. For example, the data collection unit can analyze the user's social media activity and provide the most relevant information. This allows the data collection unit to provide highly relevant information based on the user's social media activity. 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 social media activity into AI, which can then automatically collect the most relevant information.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit uses an emotion analysis algorithm to estimate the user's emotions. For example, the analysis unit analyzes the content of the user's social media posts and messages and calculates an emotion score. The analysis unit can also adjust the presentation of the analysis based on the user's emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a user's social media posts into the AI, which can then automatically estimate the emotions behind them and adjust the way the analysis is presented.

[0076] 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 calculates an importance score to evaluate the importance of the information. For example, the analysis unit calculates an importance score based on the content and relevance of the collected information. The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a concise analysis on information with low importance. Furthermore, the analysis unit can analyze the importance of the information and select the optimal analysis method. For example, the analysis unit selects the optimal analysis algorithm based on the importance score. This enables optimal 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 the collected information into AI, which can automatically calculate an importance score and adjust the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can use clustering techniques to identify the category of information. For example, the analysis unit can cluster the collected information and classify it into categories. Furthermore, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a specific analysis algorithm to musical information and a different analysis algorithm to stage information. In addition, the analysis unit can analyze the category of information and select the optimal analysis algorithm. For example, the analysis unit can select the optimal analysis algorithm based on the characteristics of each category. This allows the optimal analysis algorithm to be applied according to the category of 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 the collected information into AI, which can automatically identify the categories and apply the optimal analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit uses an emotion analysis algorithm to estimate the user's emotions. For example, the analysis unit analyzes the content of the user's social media posts or messages and calculates an emotion score. The analysis unit can also adjust the length of the analysis based on the user's emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a user's social media posts into the AI, which can then automatically estimate the sentiment and adjust the length of the analysis.

[0079] The analysis unit can determine the priority of analysis based on the information collection timing during analysis. For example, the analysis unit uses timestamps to evaluate the information collection timing. For example, the analysis unit analyzes the timestamps attached to the collected information to identify the collection timing. The analysis unit can also determine the priority of analysis based on the information collection timing. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also analyze older information as needed. Furthermore, the analysis unit can analyze the information collection timing and determine the optimal analysis order. For example, the analysis unit selects the optimal analysis algorithm based on the collection timing. This allows the optimal analysis order to be determined based on the information collection timing. 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 the collected information into AI, which can automatically identify the collection timing and determine the priority of analysis.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit calculates a relevance score to evaluate the relevance of the information. For example, the analysis unit calculates a relevance score based on the content and relevance of the collected information. The analysis unit can also adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. Furthermore, the analysis unit can analyze the relevance of the information and determine the optimal order of analysis. For example, the analysis unit selects the optimal analysis algorithm based on the relevance score. This allows the analysis unit to determine the optimal order of analysis based on the relevance 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 the collected information into AI, which can automatically calculate the relevance score and adjust the order of analysis.

[0081] The service provider can estimate the user's emotions and adjust the schedule display method based on the estimated emotions. For example, the service provider may use an emotion analysis algorithm to estimate the user's emotions. For example, the service provider may analyze the content of the user's social media posts or messages and calculate an emotion score. The service provider can also adjust the schedule display method based on the user's emotions. For example, if the user is stressed, the service provider may provide a simple and highly visible display method. If the user is relaxed, the service provider may also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider may provide a display method that gets straight to the point. By adjusting the schedule display method according to the user's emotions, a more appropriate display becomes possible. 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input a user's social media posts into the AI, which can then automatically estimate the user's emotions and adjust how the schedule is displayed.

[0082] The service provider can select the optimal display method by referring to the user's past operation history when providing a schedule. The service provider can, for example, use data mining techniques to analyze the user's past operation history. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. The service provider can also propose the most efficient display method based on the user's operation history. For example, the service provider can propose the optimal display method based on the user's operation history. Furthermore, the service provider can analyze the user's past operation history and select the optimal display method. For example, the service provider selects the optimal display method based on the user's operation history. This allows the service provider to provide the optimal display method based on the user's past operation history. 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 user's operation history into AI, and the AI ​​can automatically select the optimal display method.

[0083] The service provider can customize the displayed content based on the user's current areas of interest when providing schedules. For example, the service provider can analyze the user's social media posts and search history to identify the user's current areas of interest. For example, the service provider can prioritize displaying schedules related to musicals or plays that the user is currently interested in. The service provider can also customize relevant schedules based on the user's areas of interest. For example, the service provider can prioritize displaying schedules related to specific themes that the user is interested in. Furthermore, the service provider can analyze the user's areas of interest and provide the most relevant schedules. For example, the service provider can provide relevant schedules based on the user's areas of interest. This allows the service provider to provide highly relevant schedules based on the user's areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's areas of interest into AI, and the AI ​​can automatically customize the optimal displayed content.

[0084] The service provider can estimate the user's emotions and adjust the schedule operation procedures based on the estimated emotions. For example, the service provider may use an emotion analysis algorithm to estimate the user's emotions. For example, the service provider may analyze the content of the user's social media posts or messages and calculate an emotion score. The service provider can also adjust the schedule operation procedures based on the user's emotions. For example, if the user is stressed, the service provider may provide simple and intuitive operation procedures. If the user is relaxed, the service provider may also provide detailed operation procedures. Furthermore, if the user is in a hurry, the service provider may provide procedures that allow for quick operation. By adjusting the operation procedures according to the user's emotions, more appropriate operation becomes possible. 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input a user's social media posts into the AI, which can then automatically estimate the emotions behind the posts and adjust the schedule's operation procedures accordingly.

[0085] The service provider can select the optimal display method when providing a schedule, taking into account the user's device information. For example, the service provider may use device identification technology to obtain the user's device information. For example, the service provider may identify the type and screen size of the device the user is using. The service provider can also select the optimal display method based on the user's device information. For example, if the user is using a smartphone, the service provider may provide a display method that matches the screen size. The service provider may also provide a display method optimized for a larger screen if the user is using a tablet. Furthermore, if the service provider is using a smartwatch, the service provider may provide a concise and highly visible display method. This allows the service provider to provide the optimal display method based on the user's device information. 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 may input the user's device information into AI, and the AI ​​may automatically select the optimal display method.

[0086] The service provider can analyze a user's social media activity and provide relevant schedules when providing them. For example, the service provider can use data mining techniques to analyze a user's social media activity. For example, the service provider can provide schedules based on information from accounts that the user follows. The service provider can also analyze the content of a user's social media posts and provide relevant schedules. For example, the service provider can prioritize providing schedules related to themes that the user is interested in. Furthermore, the service provider can provide the most relevant schedules based on the user's social media activity. For example, the service provider can analyze a user's social media activity and provide the most relevant schedules. This allows the service provider to provide highly relevant schedules based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity into AI, and the AI ​​can automatically provide the optimal schedule. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis 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 is implemented by the control unit 46A of the smart device 14 and collects information from SNS and various sites using web scraping and APIs. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information using natural language processing technology and converts it into a schedule. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the user with a schedule in calendar format or list format. The collection unit can estimate the user's emotions using an emotion engine and adjust the timing of information collection. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information from SNS and various sites using web scraping and APIs. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information using natural language processing technology and converts it into a schedule. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the user with a schedule in calendar format or list format. The collection unit can estimate the user's emotions using an emotion engine and adjust the timing of information collection. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis 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 is implemented by the control unit 46A of the headset terminal 314 and collects information from SNS and various sites using web scraping and APIs. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information using natural language processing technology and converts it into a schedule. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the user with a schedule in calendar format or list format. The collection unit can estimate the user's emotions using an emotion engine and adjust the timing of information collection. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information from SNS and various sites using web scraping and APIs. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information using natural language processing technology and converts it into a schedule. The provision unit is implemented by the control unit 46A of the robot 414 and provides the user with a schedule in calendar format or list format. The collection unit can estimate the user's emotions using an emotion engine and adjust the timing of information collection.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The data collection unit can analyze a user's past purchase history and prioritize the collection of relevant ticket information. For example, based on the user's past purchases of musicals and stage performances, the unit can prioritize the collection of performance information of similar genres and artists. The unit can also prioritize the collection of information related to specific theaters or venues from the user's purchase history. Furthermore, the unit can analyze the user's purchase history and provide the most relevant information. This allows the system to provide highly relevant information based on the user's past purchase history.

[0089] The analytics unit can learn user preferences and automatically select recommended performances. For example, it can analyze data from events a user has attended in the past and recommend similar events. It can also analyze a user's social media activity and posts to identify performances they might be interested in. Furthermore, it can analyze the activity of a user's friends and followers and recommend performances that align with their shared interests. This allows the system to recommend the most suitable performances based on the user's preferences.

[0090] The service provider can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is relaxed, the service provider can send a notification immediately. It can also postpone notifications if the user is busy. Furthermore, if the user is excited, the service provider can prioritize sending important notifications. This allows for more appropriate information delivery by adjusting notification timing according to the user's emotions.

[0091] The data collection unit can prioritize collecting nearby performance information based on the user's geographical location. For example, the unit can identify the user's current location and collect performance information held in that vicinity. It can also collect relevant performance information based on information about places the user has visited in the past. Furthermore, the unit can analyze the user's location information and provide the most relevant performance information. This allows the system to provide highly relevant information based on the user's geographical location.

[0092] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the level of detail in the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0093] The service provider can select the optimal display method by referring to the user's past operation history. For example, the service provider can prioritize providing the display method that the user has preferred to use in the past. Furthermore, the service provider can suggest the most efficient display method based on the user's operation history. In addition, the service provider can analyze the user's past operation history and select the optimal display method. This allows the service provider to provide the optimal display method based on the user's past operation history.

[0094] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on those emotions. For example, if the user is excited, the data collection unit will prioritize collecting the latest information. If the user is relaxed, the unit can also prioritize collecting information of high importance. Furthermore, if the user is stressed, the unit can prioritize collecting only the essential information. This allows for more appropriate information delivery by prioritizing information according to the user's emotions.

[0095] The analysis unit can apply different analysis algorithms depending on the category of information. For example, it can apply a specific analysis algorithm to musical information and a different one to stage information. The analysis unit can also use clustering techniques to identify the category of information. Furthermore, it can analyze the category of information and select the optimal analysis algorithm. This allows the system to apply the most suitable analysis algorithm depending on the category of information.

[0096] The system can estimate the user's emotions and adjust the schedule display method based on those emotions. For example, if the user is stressed, the system can provide a simple and highly visible display method. If the user is relaxed, the system can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the system can provide a concise display method. By adjusting the schedule display method according to the user's emotions, a more appropriate display becomes possible.

[0097] The service provider can select the optimal display method when providing a schedule, taking into account the user's device information. For example, the service provider can identify the type and screen size of the device the user is using. Furthermore, the service provider can select the optimal display method based on the user's device information. For instance, if the user is using a smartphone, the service provider can provide a display method adapted to the screen size. If the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method based on the user's device information.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit gathers information from social media and various websites. For example, the data collection unit uses web scraping technology to collect application schedules for musical and stage tickets from social media and various websites. The data collection unit can also collect information using APIs. For example, the data collection unit uses the API of a specific social media or website to obtain the latest ticket information. Furthermore, the data collection unit can prioritize collecting information from official websites. For example, the data collection unit uses the RSS feed of an official website to obtain the latest ticket information. Step 2: The analysis unit analyzes the information collected by the collection unit and incorporates it into a schedule. The analysis unit extracts necessary data from the collected information, for example, using natural language processing technology. For example, the analysis unit uses morphological analysis to extract information such as date, time, and location from text. The analysis unit can also classify the collected information and convert it into a schedule using machine learning algorithms. For example, the analysis unit uses a neural network to analyze the collected information and convert it into a schedule. Step 3: The service provider provides the user with a schedule based on the information analyzed by the analysis unit. The service provider displays the schedule in a calendar format, for example. For example, the service provider can display the schedule in monthly, weekly, or daily views. The service provider can also display the schedule in a list format. For example, the service provider can list the schedule in chronological order. Furthermore, the service provider includes a filtering function that allows the user to customize the schedule. For example, the service provider can provide a filtering function that displays only the events that the user is interested in.

[0100] 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.

[0101] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0102] 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.

[0103] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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. 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.

[0118] 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.

[0119] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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. 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.

[0134] 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.

[0135] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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. 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.

[0151] 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.

[0152] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 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 department that collects information from social media or various websites, An analysis unit analyzes the information collected by the aforementioned collection unit and converts it into a schedule, The system includes a provisioning unit that provides a schedule to the user based on the information analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information using web scraping or APIs. The system according to feature 1.

3. The aforementioned analysis unit, Analyze information using natural language processing and incorporate it into a schedule. The system according to feature 1.

4. The aforementioned supply unit is, Display your schedule in calendar or list format. The system according to feature 1.

5. The aforementioned collection unit is Prioritize gathering information from the official website. The system according to feature 1.

6. The aforementioned supply unit is, Features a filtering function that allows users to customize their schedules. The system according to feature 1.

7. The aforementioned collection unit is We have measures in place to protect users' personal information. The system according to feature 1.

8. The aforementioned analysis unit, We have a policy regarding the handling of collected information. The system according to feature 1.

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