system

Abstract

The system according to the embodiment aims to improve the efficiency of tasks involved in running a neighborhood association. [Solution] The system according to the embodiment includes a distribution unit, a counting unit, and a preparation unit. The distribution unit simultaneously distributes electronic circular notices. The counting unit automatically counts the amount of neighborhood association fees and automatically bills those who have not paid. The preparation unit supports tasks related to preparing for an event.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately streamline the distribution of circulars, the tallying of membership fees, and event preparation in neighborhood association management, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of tasks involved in running a neighborhood association. [Means for solving the problem]

[0006] The system according to the embodiment includes a distribution unit, a counting unit, and a preparation unit. The distribution unit simultaneously distributes electronic circular notices. The counting unit automatically counts the amount of neighborhood association fees and automatically bills those who have not paid. The preparation unit supports tasks related to preparing for an event. [Effects of the Invention]

[0007] The system according to the embodiment can streamline tasks involved in running a neighborhood association. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system according to an embodiment of the present invention utilizes AI technology and is targeted at local governments and neighborhood associations across Japan. This system streamlines various tasks related to association management, assists communities struggling with labor shortages, and contributes to the creation of a sustainable society. The system has the following functions: First, an automatic circular distribution function. This function distributes electronic circulars simultaneously, eliminating the need to hand out paper circulars and ensuring fast and reliable information transmission. For example, important neighborhood association announcements and event information can be distributed electronically. Second, a function to support electronic payment of neighborhood association dues. This function automatically tallies the amount of neighborhood association dues and automatically bills those who have not paid, reducing the burden on accounting staff and facilitating the collection of dues. For example, it can automatically send reminders to those who have not paid when the due date for dues approaches. Third, a function to support pre-event preparation. This function supports tasks related to event preparation, including issuing automatic alerts before the deadline and automatically creating information materials. This allows event preparations to be carried out efficiently and reduces the burden on those in charge. For example, as the event date approaches, a list of necessary preparations can be compiled and notified to those in charge. By introducing this system, the operations of local governments and neighborhood associations can be made more efficient, the problem of labor shortages can be solved, and it can contribute to the creation of a sustainable society. As a result, the system can increase the efficiency of the operations of local governments and neighborhood associations and solve the problem of labor shortages.

[0029] The system according to the embodiment includes a distribution unit, a counting unit, and a preparation unit. The distribution unit simultaneously distributes electronic circulars. Electronic circulars may be in various formats, including, but not limited to, PDF, HTML, and text. The distribution unit can, for example, create electronic circulars and simultaneously distribute them via email, push notification, social media, and other methods. For example, the distribution unit can create electronic circulars using templates and design input forms. The distribution unit can also automatically generate the content of electronic circulars using AI when creating them. For example, the distribution unit can input a prompt to the AI, such as "Please create an important notice for the neighborhood association," and the AI ​​can automatically generate the content of the notice. The counting unit automatically counts the amount of neighborhood association dues and automatically bills those who have not paid. The counting unit can, for example, automatically calculate the amount of dues using a counting algorithm and send reminders to those who have not paid. For example, the counting unit can automatically send reminders to those who have not paid dues when the due date for dues approaches. The tallying unit can also use AI to create a list of defaulters and automatically generate invoices. For example, the tallying unit inputs a prompt to the AI, such as "Please create a list of defaulters," and the AI ​​automatically generates the list. The preparation unit supports tasks related to event preparation. For example, as the event date approaches, the preparation unit can list necessary preparations and notify the person in charge. For example, the preparation unit can also use AI to automatically create event information materials. For example, the preparation unit inputs a prompt to the AI, such as "Please create event information materials," and the AI ​​automatically generates the materials. As a result, the system according to the embodiment can streamline various tasks related to event management and resolve the problem of labor shortages.

[0030] The distribution unit can create an electronic circular. Electronic circulars can be in various formats, including, but not limited to, PDF, HTML, and text. The distribution unit can create an electronic circular using a template. For example, the distribution unit can use the template to create important neighborhood association announcements and event information. The distribution unit can also design an input form and create an electronic circular. For example, the distribution unit can design an input form and create an electronic circular by having neighborhood association members enter information. The distribution unit can also automatically create an electronic circular using AI. For example, the distribution unit can input a prompt to the AI, such as "Please create an important neighborhood association announcement," and the AI ​​can automatically generate the announcement content. This automates the creation of electronic circulars and enables efficient distribution. Some or all of the above-described processing in the distribution unit can be performed using AI, or can be performed without AI. For example, the distribution unit can input a prompt to the AI, such as "Please create neighborhood association event information," and the AI ​​can automatically generate event information.

[0031] The tallying unit can automatically send reminders to defaulters. For example, the tallying unit automatically sends reminders to defaulters when the due date for membership fee payment approaches. Methods for sending reminders include, but are not limited to, email, SMS, and push notification. For example, the tallying unit can send reminders to defaulters via email. For example, the tallying unit can obtain the email address of a defaulter and send the reminder. The tallying unit can also send reminders to defaulters via SMS. For example, the tallying unit can obtain the phone number of a defaulter and send the reminder via SMS. The tallying unit can also send reminders to defaulters via push notification. For example, the tallying unit can send a push notification to the smartphone of a defaulter to notify them of the reminder. This automates the sending of reminders to defaulters, allowing for smooth collection of membership fees. Some or all of the above-described processing in the tallying unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregation department can input a prompt to the AI ​​such as "Please create a list of non-payers," and the AI ​​can automatically generate the list and send reminders.

[0032] The preparation unit can list necessary preparation items as the event date approaches and notify a person in charge. For example, the preparation unit can list necessary preparation items as the event date approaches and notify a person in charge. Listing preparation items includes, but is not limited to, creating a checklist and prioritizing tasks. For example, the preparation unit can create a checklist and list preparation items for the event. For example, the preparation unit can compile necessary preparation items into a checklist as the event date approaches and notify a person in charge. The preparation unit can also prioritize tasks and list important preparation items first. For example, the preparation unit can prioritize preparation items for the event, list important tasks, and notify a person in charge. Furthermore, the preparation unit can use AI to list preparation items and notify a person in charge. For example, the preparation unit can input a prompt to the AI, such as "Please list preparation items for the event," and the AI ​​can automatically list preparation items and notify a person in charge. This allows for efficient preparation of the event and reduces the burden on the person in charge. Some or all of the above-mentioned processing in the preparation unit may be performed using, for example, AI, or may be performed without using AI. For example, the preparation unit can input a prompt to the AI ​​saying, "Please list the preparation items for the event," and the AI ​​can automatically list the preparation items and notify the person in charge.

[0033] When distributing a circular, the distribution unit can analyze a user's past browsing history and select an appropriate distribution method. For example, the distribution unit distributes the circular according to the time period in which the user frequently browsed the circular in the past. The analysis of the past browsing history includes, but is not limited to, a browsing history storage method and an analysis algorithm. For example, the distribution unit can store the user's past browsing history and select the optimal distribution method using an analysis algorithm. For example, the distribution unit stores the user's past browsing history, analyzes it using AI, and selects the optimal distribution method. The distribution unit can also distribute on a specific day of the week or time period based on the user's past browsing history. For example, the distribution unit analyzes the user's past browsing history and distributes on a specific day of the week or time period. Furthermore, the distribution unit can prioritize a preferred distribution method (email, app notification, etc.) based on the user's past browsing history. For example, the distribution unit analyzes the user's past browsing history and selects the preferred distribution method. This improves the information reception rate by selecting the optimal distribution method based on the user's past browsing history. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input a prompt to the AI ​​such as "Please analyze the user's past browsing history," and the AI ​​may automatically analyze and select the optimal distribution method.

[0034] When distributing a circular, the distribution unit can filter the circular based on the user's current areas of interest. For example, the distribution unit prioritizes distribution of circulars related to topics in which the user is currently interested. Filtering of current areas of interest includes, but is not limited to, survey results, past browsing history, etc. For example, the distribution unit can identify and filter the user's current areas of interest based on the user's survey results. For example, the distribution unit can analyze the user's survey results and filter the circular based on the areas of interest. The distribution unit can also identify and filter the user's current areas of interest based on the user's past browsing history. For example, the distribution unit can analyze the user's past browsing history and filter the circular based on the areas of interest. Furthermore, the distribution unit can use AI to identify and filter the user's current areas of interest. For example, the distribution unit can input a prompt to the AI ​​saying, "Please identify the user's current areas of interest," and the AI ​​can automatically identify and filter the areas of interest. This allows more relevant information to be provided by filtering information based on the user's areas of interest. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input a prompt to the AI ​​such as "Please identify the user's current areas of interest," and the AI ​​may automatically identify the areas of interest and perform filtering.

[0035] When distributing a circular, the distribution unit can prioritize distribution of highly relevant information based on the user's geographical location information. For example, the distribution unit prioritizes distribution of local information related to the user's current location. Use of geographical location information includes, but is not limited to, methods for acquiring location information and criteria for evaluating relevance. For example, the distribution unit can acquire the user's location information and prioritize distribution of highly relevant information. For example, the distribution unit acquires the user's location information and identifies and distributes highly relevant information using AI. The distribution unit can also prioritize distribution of nearby event information based on the user's geographical location information. For example, the distribution unit identifies and distributes nearby event information based on the user's geographical location information. Furthermore, the distribution unit can prioritize distribution of highly urgent information taking into account the user's geographical location information. For example, the distribution unit identifies and distributes highly urgent information based on the user's geographical location information. This improves the reception rate of information by distributing highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input a prompt to the AI ​​such as "Please identify relevant information based on the user's geographical location information," and the AI ​​may automatically identify and distribute the information.

[0036] The distribution unit can analyze the user's social media activity and distribute relevant information when distributing the circular. For example, the distribution unit distributes a circular related to a topic in which the user has shown interest on social media. The analysis of social media activity includes, but is not limited to, the data source and analysis algorithm used. For example, the distribution unit can use the user's social media activity as a data source and identify and distribute relevant information using an analysis algorithm. For example, the distribution unit analyzes the user's social media activity and distributes a circular related to a topic in which the user has shown interest. The distribution unit can also identify and distribute event information that the user is likely to be interested in from the user's social media activity. For example, the distribution unit analyzes the user's social media activity and identifies and distributes event information that the user is likely to be interested in. Furthermore, the distribution unit can distribute the circular based on the user's social media activity time. For example, the distribution unit analyzes the user's social media activity time and selects the optimal distribution time. This improves the reception rate of the information by distributing relevant information based on the user's social media activity. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input a prompt to the AI ​​such as "Please analyze the user's social media activity," and the AI ​​may automatically analyze, identify, and distribute relevant information.

[0037] When tallying membership fees, the tallying unit can analyze past payment history and select an appropriate tallying method. For example, the tallying unit prioritizes the most frequently used payment method based on the user's past payment history. Analysis of past payment history includes, but is not limited to, data storage methods, analysis algorithms, and the like. For example, the tallying unit can store the user's past payment history and select an optimal tallying method using an analysis algorithm. For example, the tallying unit can store the user's past payment history, analyze it using AI, and select an optimal tallying method. The tallying unit can also send reminders to defaulters based on the user's past payment history. For example, the tallying unit can analyze the user's past payment history and send reminders to defaulters. Furthermore, the tallying unit can set an optimal tallying period based on the user's past payment history. For example, the tallying unit can analyze the user's past payment history and set an optimal tallying period. This allows for efficient tallying of membership fees by selecting an optimal tallying method based on the past payment history. Some or all of the above-described processing in the tallying unit may be performed using, for example, AI, or may be performed without using AI. For example, the tallying unit may input a prompt to the AI ​​such as "Please analyze the user's past payment history," and the AI ​​may automatically analyze and select the optimal tallying method.

[0038] When tallying the membership fees, the tallying unit may perform filtering based on the user's current financial situation. The tallying unit may, for example, suggest a payment method taking into account the user's current financial situation. Filtering based on financial situation may include, but is not limited to, income data, expense data, etc. The tallying unit may, for example, suggest an appropriate payment method based on the user's income data. For example, the tallying unit may analyze the user's income data and suggest an optimal payment method. The tallying unit may also adjust the payment deadline based on the user's expense data. For example, the tallying unit may analyze the user's expense data and adjust the payment deadline. Furthermore, the tallying unit may provide an installment payment option taking into account the user's financial situation. For example, the tallying unit may analyze the user's financial situation and provide an installment payment option. This allows for more appropriate tallying by filtering based on the user's financial situation. Some or all of the above-described processing in the tallying unit may be performed using, for example, AI, or may be performed without AI. For example, the tallying unit may input a prompt to the AI, such as "Please analyze the user's financial situation," and the AI ​​may automatically analyze and filter.

[0039] When aggregating membership fees, the aggregation unit can prioritize aggregation of highly relevant information taking into account the user's geographical location information. For example, the aggregation unit prioritizes aggregation of membership fee information related to the user's current location. Use of geographical location information includes, but is not limited to, methods for acquiring location information and criteria for evaluating relevance. For example, the aggregation unit can acquire the user's location information and prioritize aggregation of highly relevant information. For example, the aggregation unit acquires the user's location information and uses AI to identify and aggregate highly relevant information. The aggregation unit can also prioritize aggregation of nearby membership fee information based on the user's geographical location information. For example, the aggregation unit identifies and aggregates nearby membership fee information based on the user's geographical location information. Furthermore, the aggregation unit can prioritize aggregation of highly urgent membership fee information taking into account the user's geographical location information. For example, the aggregation unit identifies and aggregates highly urgent membership fee information based on the user's geographical location information. This makes the aggregation of membership fees more efficient by aggregating highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the aggregation unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregation unit may input a prompt to the AI ​​such as "Please identify relevant information based on the user's geographical location information," and the AI ​​may automatically identify and aggregate the information.

[0040] The aggregation unit may analyze the user's social media activity and aggregate related information when aggregating membership fees. For example, the aggregation unit may prioritize aggregation of membership fee information in which the user expressed interest on social media. Analysis of social media activity may include, but is not limited to, the data source and analysis algorithm used. For example, the aggregation unit may use the user's social media activity as a data source and identify and aggregate related information using an analysis algorithm. For example, the aggregation unit may analyze the user's social media activity and prioritize aggregation of membership fee information in which the user expressed interest. The aggregation unit may also identify and aggregate membership fee information that the user is likely to be interested in from the user's social media activity. For example, the aggregation unit may analyze the user's social media activity and identify and aggregate membership fee information that the user is likely to be interested in. Furthermore, the aggregation unit may aggregate membership fee information based on the user's social media activity time. For example, the aggregation unit may analyze the user's social media activity time and select the optimal aggregation time period. This allows for efficient aggregation of membership fees by aggregating related information based on the user's social media activity. Some or all of the above-mentioned processing in the aggregation unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregation unit may input a prompt to the AI ​​such as "Please analyze the user's social media activity," and the AI ​​may automatically analyze, identify, and aggregate the relevant information.

[0041] When issuing an alert for event preparation, the preparation unit can analyze past event history and select an appropriate alert method. For example, the preparation unit prioritizes selecting an alert method (email, app notification, etc.) that the user has previously preferred. Analysis of the past event history includes, but is not limited to, data storage methods and analysis algorithms. For example, the preparation unit can store the user's past event history and select an optimal alert method using an analysis algorithm. For example, the preparation unit can store the user's past event history, analyze it using AI, and select an optimal alert method. The preparation unit can also issue an alert on a specific day of the week or time period based on the user's past event history. For example, the preparation unit analyzes the user's past event history and issues an alert on a specific day of the week or time period. Furthermore, the preparation unit can select the most effective alert method based on the user's past event history. For example, the preparation unit analyzes the user's past event history and selects the most effective alert method. As a result, the effectiveness of the alert is improved by selecting the optimal alert method based on the past event history. Some or all of the above-described processing in the preparation unit may be performed using, for example, AI, or may be performed without using AI. For example, the preparation unit may input a prompt to the AI ​​such as "Please analyze the user's past event history," and the AI ​​may automatically analyze and select the optimal alert method.

[0042] The preparation unit may filter the event preparation alert based on the user's current areas of interest. For example, the preparation unit may prioritize event preparation alerts related to topics in which the user is currently interested. Filtering of current areas of interest may include, but is not limited to, survey results, past browsing history, etc. For example, the preparation unit may identify and filter the user's current areas of interest based on the user's survey results. For example, the preparation unit may analyze the user's survey results and filter the event preparation alert based on the areas of interest. The preparation unit may also identify and filter the user's current areas of interest based on the user's past browsing history. For example, the preparation unit may analyze the user's past browsing history and filter the event preparation alert based on the areas of interest. Furthermore, the preparation unit may use AI to identify and filter the user's current areas of interest. For example, the preparation unit may input a prompt to the AI ​​saying, "Please identify the user's current areas of interest," and the AI ​​may automatically identify and filter the areas of interest. This allows the user to provide more relevant information by filtering information based on the user's areas of interest. Some or all of the above-described processing in the preparation unit may be performed using, for example, AI, or may be performed without using AI. For example, the preparation unit may input a prompt to the AI ​​such as "Please identify the user's current areas of interest," and the AI ​​may automatically identify the areas of interest and perform filtering.

[0043] When issuing an event preparation alert, the preparation unit can prioritize alerting of highly relevant information by taking into account the user's geographical location information. For example, the preparation unit prioritizes alerting of event preparation information related to the user's current location. Use of the geographical location information includes, but is not limited to, for example, a method for acquiring location information and a criterion for evaluating relevance. For example, the preparation unit can acquire the user's location information and prioritize alerting of highly relevant information. For example, the preparation unit can acquire the user's location information and use AI to identify and alert of highly relevant information. The preparation unit can also prioritize alerting of nearby event preparation information based on the user's geographical location information. For example, the preparation unit can identify and alert of nearby event preparation information based on the user's geographical location information. Furthermore, the preparation unit can prioritize alerting of highly urgent event preparation information by taking into account the user's geographical location information. For example, the preparation unit can identify and alert of highly urgent event preparation information based on the user's geographical location information. This improves the information reception rate by alerting of highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the preparation unit may be performed using, for example, AI, or may be performed without using AI. For example, the preparation unit may input a prompt to the AI ​​such as "Please identify relevant information based on the user's geographical location information," and the AI ​​may automatically identify the information and issue an alert.

[0044] When issuing an event preparation alert, the preparation unit may analyze the user's social media activity and alert the user to related information. For example, the preparation unit may prioritize alerting the user to event preparation information in which the user has expressed interest on social media. Analysis of social media activity may include, but is not limited to, the data source and analysis algorithm used. For example, the preparation unit may use the user's social media activity as a data source and identify and alert the user to related information using an analysis algorithm. For example, the preparation unit may analyze the user's social media activity and prioritize alerting the user to event preparation information in which the user has expressed interest. The preparation unit may also identify and alert the user to event preparation information that the user is likely to be interested in from the user's social media activity. For example, the preparation unit may analyze the user's social media activity and identify and alert the user to event preparation information that the user is likely to be interested in. Furthermore, the preparation unit may alert the user to event preparation information based on the user's social media activity. For example, the preparation unit may analyze the user's social media activity time and select the optimal alert time. This improves the reception rate of the information by alerting the user to related information based on the user's social media activity. Some or all of the above-described processing in the preparation unit may be performed using, for example, AI, or may be performed without using AI. For example, the preparation department can input a prompt to the AI ​​such as "Please analyze the user's social media activity," and the AI ​​can automatically analyze, identify relevant information, and provide alerts.

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

[0046] When aggregating membership fees, the aggregation unit can analyze the user's past payment patterns and determine the optimal timing for payment reminders. For example, if the user has made late payments in the past, it can send reminders earlier. Also, if the user makes payments regularly, it can reduce the frequency of reminders. Furthermore, if the user tends to make payments on specific days of the week or at specific times of the day, it can send reminders at those times. This allows for smooth payment of membership fees.

[0047] When distributing circulars, the distribution unit can prioritize distribution of area-specific information based on the user's geographical location information. For example, if the user is in a specific area, event information and important notices for that area can be distributed with priority. Also, if the user is traveling, information about the area of ​​the user's travel destination can be provided. Furthermore, if the user is in a disaster area, emergency information can be distributed with priority. This makes it possible to provide the most appropriate information according to the user's current location.

[0048] When supporting tasks related to event preparation, the preparation unit can analyze the user's past event participation history and suggest the optimal preparation method. For example, it can suggest a similar preparation method based on the preparation method used by the user for events he or she has previously participated in. Also, if the user prefers a specific preparation method, it can preferentially suggest that method. Furthermore, it can make suggestions to avoid preparation methods that the user has had problems with in the past. This allows for efficient event preparation.

[0049] When calculating the membership fees, the calculation unit can propose an optimal payment plan taking into account the user's current financial situation. For example, if the user's income is unstable, the calculation unit can offer an installment payment option. Also, if the user's expenses are high, the calculation unit can offer an extension of the payment deadline. Furthermore, if the user's financial situation is good, the calculation unit can offer a lump-sum payment option. In this way, the optimal payment plan is offered according to the user's financial situation.

[0050] The preparation unit can analyze the user's social media activities and provide relevant information when supporting tasks related to event preparation. For example, it can provide preparation information related to events in which the user has expressed interest on social media. It can also provide information on event organizers that the user follows on social media with priority. It can also provide preparation information related to events that the user has shared on social media. This allows optimal preparation support to be provided based on the user's social media activities.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The distribution department distributes the digitized circulars simultaneously. Digitized circulars can be in PDF, HTML, text, and other formats. The distribution department can create electronic circulars and distribute them simultaneously via email, push notification, social media, and other methods. The distribution department can also create electronic circulars using templates and design input forms. It is also possible to use AI to automatically generate the content; for example, by entering a prompt such as "Please create an important announcement for the neighborhood association," the AI ​​will automatically generate the content of the announcement. Step 2: The calculation unit automatically calculates the amount of neighborhood association fees and automatically bills those who have not paid. The calculation unit can automatically calculate the amount of fees using a calculation algorithm and send reminders to those who have not paid. Furthermore, when the due date for payment of fees approaches, it automatically sends reminders to those who have not paid. It is also possible to use AI to create a list of non-payers and automatically generate invoices; for example, by entering a prompt such as "Please create a list of non-payers," the AI ​​will automatically generate the list. Step 3: The Preparation Department supports tasks related to event preparation. As the event date approaches, the Preparation Department can list the necessary preparations and notify the person in charge. In addition, it is possible to use AI to automatically create event information materials. For example, by entering a prompt such as "Please create event information materials," the AI ​​will automatically generate the materials.

[0053] (Example 2) The system according to an embodiment of the present invention utilizes AI technology and is targeted at local governments and neighborhood associations across Japan. This system streamlines various tasks related to association management, assists communities struggling with labor shortages, and contributes to the creation of a sustainable society. The system has the following functions: First, an automatic circular distribution function. This function distributes electronic circulars simultaneously, eliminating the need to hand out paper circulars and ensuring fast and reliable information transmission. For example, important neighborhood association announcements and event information can be distributed electronically. Second, a function to support electronic payment of neighborhood association dues. This function automatically tallies the amount of neighborhood association dues and automatically bills those who have not paid, reducing the burden on accounting staff and facilitating the collection of dues. For example, it can automatically send reminders to those who have not paid when the due date for dues approaches. Third, a function to support pre-event preparation. This function supports tasks related to event preparation, including issuing automatic alerts before the deadline and automatically creating information materials. This allows event preparations to be carried out efficiently and reduces the burden on those in charge. For example, as the event date approaches, a list of necessary preparations can be compiled and notified to those in charge. By introducing this system, the operations of local governments and neighborhood associations can be made more efficient, the problem of labor shortages can be solved, and it can contribute to the creation of a sustainable society. As a result, the system can increase the efficiency of the operations of local governments and neighborhood associations and solve the problem of labor shortages.

[0054] The system according to the embodiment includes a distribution unit, a counting unit, and a preparation unit. The distribution unit simultaneously distributes electronic circulars. Electronic circulars may be in various formats, including, but not limited to, PDF, HTML, and text. The distribution unit can, for example, create electronic circulars and simultaneously distribute them via email, push notification, social media, and other methods. For example, the distribution unit can create electronic circulars using templates and design input forms. The distribution unit can also automatically generate the content of electronic circulars using AI when creating them. For example, the distribution unit can input a prompt to the AI, such as "Please create an important notice for the neighborhood association," and the AI ​​can automatically generate the content of the notice. The counting unit automatically counts the amount of neighborhood association dues and automatically bills those who have not paid. The counting unit can, for example, automatically calculate the amount of dues using a counting algorithm and send reminders to those who have not paid. For example, the counting unit can automatically send reminders to those who have not paid dues when the due date for dues approaches. The tallying unit can also use AI to create a list of defaulters and automatically generate invoices. For example, the tallying unit inputs a prompt to the AI, such as "Please create a list of defaulters," and the AI ​​automatically generates the list. The preparation unit supports tasks related to event preparation. For example, as the event date approaches, the preparation unit can list necessary preparations and notify the person in charge. For example, the preparation unit can also use AI to automatically create event information materials. For example, the preparation unit inputs a prompt to the AI, such as "Please create event information materials," and the AI ​​automatically generates the materials. As a result, the system according to the embodiment can streamline various tasks related to event management and resolve the problem of labor shortages.

[0055] The distribution unit can create an electronic circular. Electronic circulars can be in various formats, including, but not limited to, PDF, HTML, and text. The distribution unit can create an electronic circular using a template. For example, the distribution unit can use the template to create important neighborhood association announcements and event information. The distribution unit can also design an input form and create an electronic circular. For example, the distribution unit can design an input form and create an electronic circular by having neighborhood association members enter information. The distribution unit can also automatically create an electronic circular using AI. For example, the distribution unit can input a prompt to the AI, such as "Please create an important neighborhood association announcement," and the AI ​​can automatically generate the announcement content. This automates the creation of electronic circulars and enables efficient distribution. Some or all of the above-described processing in the distribution unit can be performed using AI, or can be performed without AI. For example, the distribution unit can input a prompt to the AI, such as "Please create neighborhood association event information," and the AI ​​can automatically generate event information.

[0056] The tallying unit can automatically send reminders to defaulters. For example, the tallying unit automatically sends reminders to defaulters when the due date for membership fee payment approaches. Methods for sending reminders include, but are not limited to, email, SMS, and push notification. For example, the tallying unit can send reminders to defaulters via email. For example, the tallying unit can obtain the email address of a defaulter and send the reminder. The tallying unit can also send reminders to defaulters via SMS. For example, the tallying unit can obtain the phone number of a defaulter and send the reminder via SMS. The tallying unit can also send reminders to defaulters via push notification. For example, the tallying unit can send a push notification to the smartphone of a defaulter to notify them of the reminder. This automates the sending of reminders to defaulters, allowing for smooth collection of membership fees. Some or all of the above-described processing in the tallying unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregation department can input a prompt to the AI ​​such as "Please create a list of non-payers," and the AI ​​can automatically generate the list and send reminders.

[0057] The preparation unit can list necessary preparation items as the event date approaches and notify a person in charge. For example, the preparation unit can list necessary preparation items as the event date approaches and notify a person in charge. Listing preparation items includes, but is not limited to, creating a checklist and prioritizing tasks. For example, the preparation unit can create a checklist and list preparation items for the event. For example, the preparation unit can compile necessary preparation items into a checklist as the event date approaches and notify a person in charge. The preparation unit can also prioritize tasks and list important preparation items first. For example, the preparation unit can prioritize preparation items for the event, list important tasks, and notify a person in charge. Furthermore, the preparation unit can use AI to list preparation items and notify a person in charge. For example, the preparation unit can input a prompt to the AI, such as "Please list preparation items for the event," and the AI ​​can automatically list preparation items and notify a person in charge. This allows for efficient preparation of the event and reduces the burden on the person in charge. Some or all of the above-mentioned processing in the preparation unit may be performed using, for example, AI, or may be performed without using AI. For example, the preparation unit can input a prompt to the AI ​​saying, "Please list the preparation items for the event," and the AI ​​can automatically list the preparation items and notify the person in charge.

[0058] The distribution unit can estimate the user's emotions and adjust the timing of distribution of the circular notice based on the estimated user emotions. For example, if the user is feeling stressed, the distribution unit can delay the timing of distribution and distribute the notice to a time when the user is relaxed. Examples of adjusting the timing of distribution include, but are not limited to, an optimal distribution time period and the user's activity pattern. For example, the distribution unit can analyze the user's activity pattern and select an optimal distribution time period. For example, the distribution unit can analyze the user's past activity history and select an optimal distribution time period. The distribution unit can also estimate the user's emotions and adjust the timing of distribution based on the emotions. For example, the distribution unit can use AI to estimate the user's emotions and adjust the timing of distribution based on the emotions. For example, the distribution unit can input a prompt to the AI, such as "Please estimate the user's emotions," and the AI ​​can automatically estimate the emotions and adjust the timing of distribution. This allows information to be distributed at a more appropriate time by adjusting the timing of distribution according to the user's emotions. Some or all of the above-described processing in the distribution unit can be performed using AI, for example, or without AI. For example, the distribution department can input a prompt to the AI ​​such as "Please estimate the user's emotions," and the AI ​​can automatically estimate the emotions and adjust the timing of distribution.

[0059] When distributing a circular, the distribution unit can analyze a user's past browsing history and select an appropriate distribution method. For example, the distribution unit distributes the circular according to the time period in which the user frequently browsed the circular in the past. The analysis of the past browsing history includes, but is not limited to, a browsing history storage method and an analysis algorithm. For example, the distribution unit can store the user's past browsing history and select the optimal distribution method using an analysis algorithm. For example, the distribution unit stores the user's past browsing history, analyzes it using AI, and selects the optimal distribution method. The distribution unit can also distribute on a specific day of the week or time period based on the user's past browsing history. For example, the distribution unit analyzes the user's past browsing history and distributes on a specific day of the week or time period. Furthermore, the distribution unit can prioritize a preferred distribution method (email, app notification, etc.) based on the user's past browsing history. For example, the distribution unit analyzes the user's past browsing history and selects the preferred distribution method. This improves the information reception rate by selecting the optimal distribution method based on the user's past browsing history. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input a prompt to the AI ​​such as "Please analyze the user's past browsing history," and the AI ​​may automatically analyze and select the optimal distribution method.

[0060] When distributing a circular, the distribution unit can filter the circular based on the user's current areas of interest. For example, the distribution unit prioritizes distribution of circulars related to topics in which the user is currently interested. Filtering of current areas of interest includes, but is not limited to, survey results, past browsing history, etc. For example, the distribution unit can identify and filter the user's current areas of interest based on the user's survey results. For example, the distribution unit can analyze the user's survey results and filter the circular based on the areas of interest. The distribution unit can also identify and filter the user's current areas of interest based on the user's past browsing history. For example, the distribution unit can analyze the user's past browsing history and filter the circular based on the areas of interest. Furthermore, the distribution unit can use AI to identify and filter the user's current areas of interest. For example, the distribution unit can input a prompt to the AI ​​saying, "Please identify the user's current areas of interest," and the AI ​​can automatically identify and filter the areas of interest. This allows more relevant information to be provided by filtering information based on the user's areas of interest. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input a prompt to the AI ​​such as "Please identify the user's current areas of interest," and the AI ​​may automatically identify the areas of interest and perform filtering.

[0061] The distribution unit can estimate the user's emotions and determine the priority of the circulars to be distributed based on the estimated user emotions. For example, if the user is feeling stressed, the distribution unit postpones distribution of less important circulars. Determining the priority of the circulars includes, but is not limited to, evaluation criteria for importance and prioritization based on the user's emotions. For example, the distribution unit can evaluate the importance of the circulars and determine the priority based on the user's emotions. For example, the distribution unit can evaluate the importance of the circulars and determine the priority based on the user's emotions. The distribution unit can also prioritize distribution of more important circulars based on the user's emotions. For example, the distribution unit can estimate the user's emotions and prioritize distribution of more important circulars. The distribution unit can also estimate the user's emotions using AI and determine the priority of the circulars based on the emotions. For example, the distribution unit can input a prompt to the AI ​​saying, "Please estimate the user's emotions," and the AI ​​can automatically estimate the emotions and determine the priority of the circulars. This allows important information to be distributed at the appropriate time by determining the priority of circulars according to the user's emotions. Some or all of the above-described processing in the distribution unit may be performed using AI, for example, or may be performed without using AI. For example, the distribution unit may input a prompt to the AI ​​saying, "Please estimate the user's emotions," and the AI ​​may automatically estimate the emotions and determine the priority of circulars.

[0062] When distributing a circular, the distribution unit can prioritize distribution of highly relevant information based on the user's geographical location information. For example, the distribution unit prioritizes distribution of local information related to the user's current location. Use of geographical location information includes, but is not limited to, methods for acquiring location information and criteria for evaluating relevance. For example, the distribution unit can acquire the user's location information and prioritize distribution of highly relevant information. For example, the distribution unit acquires the user's location information and identifies and distributes highly relevant information using AI. The distribution unit can also prioritize distribution of nearby event information based on the user's geographical location information. For example, the distribution unit identifies and distributes nearby event information based on the user's geographical location information. Furthermore, the distribution unit can prioritize distribution of highly urgent information taking into account the user's geographical location information. For example, the distribution unit identifies and distributes highly urgent information based on the user's geographical location information. This improves the reception rate of information by distributing highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input a prompt to the AI ​​such as "Please identify relevant information based on the user's geographical location information," and the AI ​​may automatically identify and distribute the information.

[0063] The distribution unit can analyze the user's social media activity and distribute relevant information when distributing the circular. For example, the distribution unit distributes a circular related to a topic in which the user has shown interest on social media. The analysis of social media activity includes, but is not limited to, the data source and analysis algorithm used. For example, the distribution unit can use the user's social media activity as a data source and identify and distribute relevant information using an analysis algorithm. For example, the distribution unit analyzes the user's social media activity and distributes a circular related to a topic in which the user has shown interest. The distribution unit can also identify and distribute event information that the user is likely to be interested in from the user's social media activity. For example, the distribution unit analyzes the user's social media activity and identifies and distributes event information that the user is likely to be interested in. Furthermore, the distribution unit can distribute the circular based on the user's social media activity time. For example, the distribution unit analyzes the user's social media activity time and selects the optimal distribution time. This improves the reception rate of the information by distributing relevant information based on the user's social media activity. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit may input a prompt to the AI ​​such as "Please analyze the user's social media activity," and the AI ​​may automatically analyze, identify, and distribute relevant information.

[0064] The tallying unit can estimate the user's emotions and adjust the membership fee tallying method based on the estimated user emotions. For example, if the user is feeling stressed, the tallying unit provides a simple tallying method. Adjustments to the membership fee tallying method include, but are not limited to, tallying algorithms and adjustment criteria based on the user's emotions. For example, the tallying unit can adjust the membership fee tallying method using a tallying algorithm. For example, the tallying unit can estimate the user's emotions and provide a simple tallying method. Furthermore, if the user is relaxed, the tallying unit can provide a detailed tallying method. For example, the tallying unit can estimate the user's emotions and provide a detailed tallying method. Furthermore, if the user is busy, the tallying unit can provide a method for quickly tallying. For example, the tallying unit can estimate the user's emotions and provide a method for quickly tallying. This allows for more appropriate tallying by adjusting the membership fee tallying method according to the user's emotions. Some or all of the above-described processing in the tallying unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregation unit can input a prompt to the AI ​​such as "Please estimate the user's emotions," and the AI ​​can automatically estimate the emotions and adjust the aggregation method.

[0065] When tallying membership fees, the tallying unit can analyze past payment history and select an appropriate tallying method. For example, the tallying unit prioritizes the most frequently used payment method based on the user's past payment history. Analysis of past payment history includes, but is not limited to, data storage methods, analysis algorithms, and the like. For example, the tallying unit can store the user's past payment history and select an optimal tallying method using an analysis algorithm. For example, the tallying unit can store the user's past payment history, analyze it using AI, and select an optimal tallying method. The tallying unit can also send reminders to defaulters based on the user's past payment history. For example, the tallying unit can analyze the user's past payment history and send reminders to defaulters. Furthermore, the tallying unit can set an optimal tallying period based on the user's past payment history. For example, the tallying unit can analyze the user's past payment history and set an optimal tallying period. This allows for efficient tallying of membership fees by selecting an optimal tallying method based on the past payment history. Some or all of the above-described processing in the tallying unit may be performed using, for example, AI, or may be performed without using AI. For example, the tallying unit may input a prompt to the AI ​​such as "Please analyze the user's past payment history," and the AI ​​may automatically analyze and select the optimal tallying method.

[0066] When tallying the membership fees, the tallying unit may perform filtering based on the user's current financial situation. The tallying unit may, for example, suggest a payment method taking into account the user's current financial situation. Filtering based on financial situation may include, but is not limited to, income data, expense data, etc. The tallying unit may, for example, suggest an appropriate payment method based on the user's income data. For example, the tallying unit may analyze the user's income data and suggest an optimal payment method. The tallying unit may also adjust the payment deadline based on the user's expense data. For example, the tallying unit may analyze the user's expense data and adjust the payment deadline. Furthermore, the tallying unit may provide an installment payment option taking into account the user's financial situation. For example, the tallying unit may analyze the user's financial situation and provide an installment payment option. This allows for more appropriate tallying by filtering based on the user's financial situation. Some or all of the above-described processing in the tallying unit may be performed using, for example, AI, or may be performed without AI. For example, the tallying unit may input a prompt to the AI, such as "Please analyze the user's financial situation," and the AI ​​may automatically analyze and filter.

[0067] The tallying unit can estimate the user's emotions and determine the priority of the membership fees to be tallied based on the estimated user emotions. For example, if the user is feeling stressed, the tallying unit postpones the tallying of less important membership fees. Determining the priority of membership fees includes, but is not limited to, prioritization based on importance evaluation criteria and user emotions. For example, the tallying unit can evaluate the importance of membership fees and determine the priority based on the user's emotions. For example, the tallying unit can evaluate the importance of membership fees and estimate the user's emotions using AI to determine the priority. The tallying unit can also prioritize tallying more important membership fees based on the user's emotions. For example, the tallying unit can estimate the user's emotions and prioritize more important membership fees. Furthermore, the tallying unit can estimate the user's emotions using AI and determine the priority of membership fees based on the emotions. For example, the tallying unit can input a prompt to the AI ​​saying, "Please estimate the user's emotions," and the AI ​​can automatically estimate the emotions and determine the priority of membership fees. This allows important membership fees to be appropriately tallied by determining the priority of membership fees according to the user's emotions. Some or all of the above-described processing in the tallying unit may be performed using AI, for example, or may be performed without using AI. For example, the tallying unit may input a prompt to the AI ​​saying, "Please estimate the user's emotions," so that the AI ​​can automatically estimate the emotions and determine the priority of membership fees.

[0068] When aggregating membership fees, the aggregation unit can prioritize aggregation of highly relevant information taking into account the user's geographical location information. For example, the aggregation unit prioritizes aggregation of membership fee information related to the user's current location. Use of geographical location information includes, but is not limited to, methods for acquiring location information and criteria for evaluating relevance. For example, the aggregation unit can acquire the user's location information and prioritize aggregation of highly relevant information. For example, the aggregation unit acquires the user's location information and uses AI to identify and aggregate highly relevant information. The aggregation unit can also prioritize aggregation of nearby membership fee information based on the user's geographical location information. For example, the aggregation unit identifies and aggregates nearby membership fee information based on the user's geographical location information. Furthermore, the aggregation unit can prioritize aggregation of highly urgent membership fee information taking into account the user's geographical location information. For example, the aggregation unit identifies and aggregates highly urgent membership fee information based on the user's geographical location information. This makes the aggregation of membership fees more efficient by aggregating highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the aggregation unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregation unit may input a prompt to the AI ​​such as "Please identify relevant information based on the user's geographical location information," and the AI ​​may automatically identify and aggregate the information.

[0069] The aggregation unit may analyze the user's social media activity and aggregate related information when aggregating membership fees. For example, the aggregation unit may prioritize aggregation of membership fee information in which the user expressed interest on social media. Analysis of social media activity may include, but is not limited to, the data source and analysis algorithm used. For example, the aggregation unit may use the user's social media activity as a data source and identify and aggregate related information using an analysis algorithm. For example, the aggregation unit may analyze the user's social media activity and prioritize aggregation of membership fee information in which the user expressed interest. The aggregation unit may also identify and aggregate membership fee information that the user is likely to be interested in from the user's social media activity. For example, the aggregation unit may analyze the user's social media activity and identify and aggregate membership fee information that the user is likely to be interested in. Furthermore, the aggregation unit may aggregate membership fee information based on the user's social media activity time. For example, the aggregation unit may analyze the user's social media activity time and select the optimal aggregation time period. This allows for efficient aggregation of membership fees by aggregating related information based on the user's social media activity. Some or all of the above-mentioned processing in the aggregation unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregation unit may input a prompt to the AI ​​such as "Please analyze the user's social media activity," and the AI ​​may automatically analyze, identify, and aggregate the relevant information.

[0070] The preparation unit can estimate the user's emotions and adjust the alert timing for event preparation based on the estimated user emotions. For example, if the user is feeling stressed, the preparation unit delays the alert timing and issues an alert during a time period when the user is relaxed. Examples of adjusting the alert timing include, but are not limited to, an optimal alert time period and the user's activity pattern. For example, the preparation unit can analyze the user's activity pattern and select an optimal alert time period. For example, the preparation unit can analyze the user's past activity history and select an optimal alert time period. The preparation unit can also estimate the user's emotions and adjust the alert timing based on the emotions. For example, the preparation unit can estimate the user's emotions using AI and adjust the alert timing based on the emotions. For example, the preparation unit can input a prompt to the AI, such as "Please estimate the user's emotions," and the AI ​​can automatically estimate the emotions and adjust the alert timing. This allows the alert timing to be adjusted according to the user's emotions, thereby issuing an alert at a more appropriate time. Some or all of the above-described processing in the preparation unit may be performed using AI, for example, or without AI. For example, the preparation department can input a prompt to the AI ​​such as "Please estimate the user's emotions," and the AI ​​can automatically estimate the emotions and adjust the alert timing.

[0071] When issuing an alert for event preparation, the preparation unit can analyze past event history and select an appropriate alert method. For example, the preparation unit prioritizes selecting an alert method (email, app notification, etc.) that the user has previously preferred. Analysis of the past event history includes, but is not limited to, data storage methods and analysis algorithms. For example, the preparation unit can store the user's past event history and select an optimal alert method using an analysis algorithm. For example, the preparation unit can store the user's past event history, analyze it using AI, and select an optimal alert method. The preparation unit can also issue an alert on a specific day of the week or time period based on the user's past event history. For example, the preparation unit analyzes the user's past event history and issues an alert on a specific day of the week or time period. Furthermore, the preparation unit can select the most effective alert method based on the user's past event history. For example, the preparation unit analyzes the user's past event history and selects the most effective alert method. As a result, the effectiveness of the alert is improved by selecting the optimal alert method based on the past event history. Some or all of the above-described processing in the preparation unit may be performed using, for example, AI, or may be performed without using AI. For example, the preparation unit may input a prompt to the AI ​​such as "Please analyze the user's past event history," and the AI ​​may automatically analyze and select the optimal alert method.

[0072] The preparation unit may filter the event preparation alert based on the user's current areas of interest. For example, the preparation unit may prioritize event preparation alerts related to topics in which the user is currently interested. Filtering of current areas of interest may include, but is not limited to, survey results, past browsing history, etc. For example, the preparation unit may identify and filter the user's current areas of interest based on the user's survey results. For example, the preparation unit may analyze the user's survey results and filter the event preparation alert based on the areas of interest. The preparation unit may also identify and filter the user's current areas of interest based on the user's past browsing history. For example, the preparation unit may analyze the user's past browsing history and filter the event preparation alert based on the areas of interest. Furthermore, the preparation unit may use AI to identify and filter the user's current areas of interest. For example, the preparation unit may input a prompt to the AI ​​saying, "Please identify the user's current areas of interest," and the AI ​​may automatically identify and filter the areas of interest. This allows the user to provide more relevant information by filtering information based on the user's areas of interest. Some or all of the above-described processing in the preparation unit may be performed using, for example, AI, or may be performed without using AI. For example, the preparation unit may input a prompt to the AI ​​such as "Please identify the user's current areas of interest," and the AI ​​may automatically identify the areas of interest and perform filtering.

[0073] The preparation unit can estimate the user's emotions and prioritize alerts based on the estimated user emotions. For example, if the user is feeling stressed, the preparation unit postpones alerts with lower importance. Determining the priority of alerts includes, but is not limited to, prioritization based on importance evaluation criteria and user emotions. For example, the preparation unit can evaluate the importance of alerts and prioritize them based on the user's emotions. For example, the preparation unit can evaluate the importance of alerts and estimate the user's emotions using AI to determine the priorities. The preparation unit can also prioritize alerts with higher importance based on the user's emotions. For example, the preparation unit can estimate the user's emotions and prioritize alerts with higher importance. Furthermore, the preparation unit can estimate the user's emotions using AI and prioritize alerts based on the emotions. For example, the preparation unit can input a prompt to the AI ​​saying, "Please estimate the user's emotions," and the AI ​​can automatically estimate the emotions and prioritize the alerts. This allows important information to be provided at the appropriate time by prioritizing alerts based on the user's emotions. Some or all of the above-described processing in the preparation unit may be performed using, for example, AI, or may be performed without using AI. For example, the preparation unit may input a prompt to the AI ​​such as "Please estimate the user's emotion," and the AI ​​may automatically estimate the emotion and determine the priority of the alert.

[0074] When issuing an event preparation alert, the preparation unit can prioritize alerting of highly relevant information by taking into account the user's geographical location information. For example, the preparation unit prioritizes alerting of event preparation information related to the user's current location. Use of the geographical location information includes, but is not limited to, for example, a method for acquiring location information and a criterion for evaluating relevance. For example, the preparation unit can acquire the user's location information and prioritize alerting of highly relevant information. For example, the preparation unit can acquire the user's location information and use AI to identify and alert of highly relevant information. The preparation unit can also prioritize alerting of nearby event preparation information based on the user's geographical location information. For example, the preparation unit can identify and alert of nearby event preparation information based on the user's geographical location information. Furthermore, the preparation unit can prioritize alerting of highly urgent event preparation information by taking into account the user's geographical location information. For example, the preparation unit can identify and alert of highly urgent event preparation information based on the user's geographical location information. This improves the information reception rate by alerting of highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the preparation unit may be performed using, for example, AI, or may be performed without using AI. For example, the preparation unit may input a prompt to the AI ​​such as "Please identify relevant information based on the user's geographical location information," and the AI ​​may automatically identify the information and issue an alert.

[0075] When issuing an event preparation alert, the preparation unit may analyze the user's social media activity and alert the user to related information. For example, the preparation unit may prioritize alerting the user to event preparation information in which the user has expressed interest on social media. Analysis of social media activity may include, but is not limited to, the data source and analysis algorithm used. For example, the preparation unit may use the user's social media activity as a data source and identify and alert the user to related information using an analysis algorithm. For example, the preparation unit may analyze the user's social media activity and prioritize alerting the user to event preparation information in which the user has expressed interest. The preparation unit may also identify and alert the user to event preparation information that the user is likely to be interested in from the user's social media activity. For example, the preparation unit may analyze the user's social media activity and identify and alert the user to event preparation information that the user is likely to be interested in. Furthermore, the preparation unit may alert the user to event preparation information based on the user's social media activity. For example, the preparation unit may analyze the user's social media activity time and select the optimal alert time. This improves the reception rate of the information by alerting the user to related information based on the user's social media activity. Some or all of the above-described processing in the preparation unit may be performed using, for example, AI, or may be performed without using AI. For example, the preparation department can input a prompt to the AI ​​such as "Please analyze the user's social media activity," and the AI ​​can automatically analyze, identify relevant information, and provide alerts. === Hard Collateral 1-1 === Each of the multiple elements including the distribution unit, tallying unit, and preparation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the distribution unit is realized by the control unit 46A of the smart device 14 and creates an electronic circular notice and distributes it by email or push notification. The tallying unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically tallies the amount of neighborhood association fees and sends reminders to those who have not paid. The preparation unit is realized, for example, by the control unit 46A of the smart device 14 and supports tasks related to event preparation, lists necessary preparations, and notifies the person in charge. === Hard Collateral 1-2 === Each of the multiple elements, including the distribution unit, tallying unit, and preparation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the distribution unit is realized by the control unit 46A of the smart glasses 214 and creates an electronic circular and distributes it by email or push notification. The tallying unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically tallies the amount of neighborhood association fees and sends reminders to those who have not paid. The preparation unit is realized, for example, by the control unit 46A of the smart glasses 214 and supports tasks related to event preparation, lists necessary preparations, and notifies the person in charge. === Hard Collateral 1-3 === Each of the multiple elements including the distribution unit, tallying unit, and preparation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the distribution unit is realized by the control unit 46A of the headset type terminal 314 and creates an electronic circular notice and distributes it by email or push notification. The tallying unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically tallies the amount of neighborhood association fees and sends reminders to those who have not paid. The preparation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and supports tasks related to event preparation, lists necessary preparations, and notifies the person in charge. === Hard Collateral 1-4 === Each of the multiple elements including the distribution unit, tallying unit, and preparation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the distribution unit is realized by the control unit 46A of the robot 414, and creates an electronic circular notice and distributes it by email or push notification. The tallying unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically tallies the amount of neighborhood association fees and sends reminders to those who have not paid. The preparation unit is realized, for example, by the control unit 46A of the robot 414, and supports tasks related to event preparation, lists necessary preparations, and notifies the person in charge.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The distribution unit can estimate the user's emotions and customize the contents of the circular based on the estimated user's emotions. For example, if the user is feeling stressed, the distribution unit can omit less important information and keep the content concise. If the user is relaxed, the distribution unit can distribute a circular containing detailed information. Furthermore, if the user is excited, the distribution unit can distribute a circular written in a positive tone. This makes it possible to provide optimal information according to the user's emotions.

[0078] When aggregating membership fees, the aggregation unit can analyze the user's past payment patterns and determine the optimal timing for payment reminders. For example, if the user has made late payments in the past, it can send reminders earlier. Also, if the user makes payments regularly, it can reduce the frequency of reminders. Furthermore, if the user tends to make payments on specific days of the week or at specific times of the day, it can send reminders at those times. This allows for smooth payment of membership fees.

[0079] When supporting tasks related to event preparation, the preparation unit can estimate the user's emotions and adjust task priorities based on the estimated emotions. For example, if the user is feeling stressed, it can postpone less important tasks. Also, if the user is relaxed, it can prioritize detailed tasks. Furthermore, if the user is excited, it can provide a task list that includes positive feedback. This enables optimal task management according to the user's emotions.

[0080] When distributing circulars, the distribution unit can prioritize distribution of area-specific information based on the user's geographical location information. For example, if the user is in a specific area, event information and important notices for that area can be distributed with priority. Also, if the user is traveling, information about the area of ​​the user's travel destination can be provided. Furthermore, if the user is in a disaster area, emergency information can be distributed with priority. This makes it possible to provide the most appropriate information according to the user's current location.

[0081] The tallying unit can estimate the user's emotions when tallying the membership fees and suggest payment methods based on the estimated emotions. For example, if the user is feeling stressed, an easy payment method can be suggested. If the user is relaxed, detailed payment options can be provided. Furthermore, if the user is busy, a quick payment method can be suggested. In this way, the optimal payment method according to the user's emotions can be provided.

[0082] When supporting tasks related to event preparation, the preparation unit can analyze the user's past event participation history and suggest the optimal preparation method. For example, it can suggest a similar preparation method based on the preparation method used by the user for events he or she has previously participated in. Also, if the user prefers a specific preparation method, it can preferentially suggest that method. Furthermore, it can make suggestions to avoid preparation methods that the user has had problems with in the past. This allows for efficient event preparation.

[0083] The distribution unit can estimate the user's emotions and adjust the distribution method of the circular based on the estimated emotions. For example, if the user is feeling stressed, the circular can be distributed in a simple email format. If the user is feeling relaxed, the circular can be distributed in a PDF format containing detailed information. Furthermore, if the user is excited, the circular can be distributed in the form of an SNS notification written in a positive tone. This provides the optimal distribution method according to the user's emotions.

[0084] When calculating the membership fees, the calculation unit can propose an optimal payment plan taking into account the user's current financial situation. For example, if the user's income is unstable, the calculation unit can offer an installment payment option. Also, if the user's expenses are high, the calculation unit can offer an extension of the payment deadline. Furthermore, if the user's financial situation is good, the calculation unit can offer a lump-sum payment option. In this way, the optimal payment plan is offered according to the user's financial situation.

[0085] The preparation unit can analyze the user's social media activities and provide relevant information when supporting tasks related to event preparation. For example, it can provide preparation information related to events in which the user has expressed interest on social media. It can also provide information on event organizers that the user follows on social media with priority. It can also provide preparation information related to events that the user has shared on social media. This allows optimal preparation support to be provided based on the user's social media activities.

[0086] The distribution unit can estimate the user's emotions and adjust the frequency of distribution of the circular notice based on the estimated emotions. For example, if the user is feeling stressed, the distribution frequency can be reduced and only important information can be distributed. Also, if the user is relaxed, information can be provided at a normal distribution frequency. Furthermore, if the user is excited, the distribution frequency can be increased and more positive information can be provided. In this way, an optimal distribution frequency can be provided according to the user's emotions.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The distribution department distributes the digitized circulars simultaneously. Digitized circulars can be in PDF, HTML, text, and other formats. The distribution department can create electronic circulars and distribute them simultaneously via email, push notification, social media, and other methods. The distribution department can also create electronic circulars using templates and design input forms. It is also possible to use AI to automatically generate the content; for example, by entering a prompt such as "Please create an important announcement for the neighborhood association," the AI ​​will automatically generate the content of the announcement. Step 2: The calculation unit automatically calculates the amount of neighborhood association fees and automatically bills those who have not paid. The calculation unit can automatically calculate the amount of fees using a calculation algorithm and send reminders to those who have not paid. Furthermore, when the due date for payment of fees approaches, it automatically sends reminders to those who have not paid. It is also possible to use AI to create a list of non-payers and automatically generate invoices; for example, by entering a prompt such as "Please create a list of non-payers," the AI ​​will automatically generate the list. Step 3: The Preparation Department supports tasks related to event preparation. As the event date approaches, the Preparation Department can list the necessary preparations and notify the person in charge. In addition, it is possible to use AI to automatically create event information materials. For example, by entering a prompt such as "Please create event information materials," the AI ​​will automatically generate the materials.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0091] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

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

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0136] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

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

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0160] [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A distribution department that distributes electronic circulars simultaneously, A counting unit that automatically tallies the amount of neighborhood association fees and automatically bills those who have not paid; Have a preparation department to support tasks related to preparing for the event A system characterized by:

2. The distribution unit Create an electronic circular 2. The system of claim 1.

3. The aggregation unit Send automatic reminders to delinquent payers 2. The system of claim 1.

4. The preparation unit As the event date approaches, create a list of necessary preparations and notify the person in charge.

2. The system of claim 1.

5. The distribution unit Estimates user emotions and adjusts the timing of circular notifications based on the estimated user emotions.

2. The system of claim 1.

6. The distribution unit When distributing circulars, analyze the user's past browsing history and select the appropriate distribution method.

2. The system of claim 1.

7. The distribution unit Filtering circulars based on the user's current interests 2. The system of claim 1.

8. The distribution unit Estimate the user's emotions and determine the priority of the circulars to be sent based on the estimated user emotions.

2. The system of claim 1.

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