system

Abstract

The system according to the embodiment aims to enable even inexperienced personnel to efficiently create and distribute sales announcements. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a recommendation unit, and a transmission unit. The reception unit enters the content to be disseminated and sets the date on which the information should be disseminated. The generation unit analyzes the information received by the reception unit and automatically generates a layout based on a sales announcement template. The recommendation unit evaluates the layout generated by the generation unit and recommends any missing elements. The transmission unit automatically transmits the layout evaluated by the recommendation unit on the set date.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that inexperienced staff spend a lot of time creating sales announcements.

[0005] The system according to the embodiment aims to enable even inexperienced personnel to efficiently create and distribute sales announcements. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a recommendation unit, and a transmission unit. The reception unit writes the content to be disseminated and sets the date on which the information is to be disseminated. The generation unit analyzes the information received by the reception unit and automatically generates a layout based on a sales announcement template. The recommendation unit evaluates the layout generated by the generation unit and recommends any missing elements. The transmission unit automatically transmits the layout evaluated by the recommendation unit on the set date. [Effects of the Invention]

[0007] The system according to the embodiment allows even inexperienced personnel to efficiently create and distribute sales announcements. [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 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 automatic sales announcement generation system according to an embodiment of the present invention automatically creates and distributes sales announcements. In this system, a user roughly describes the content they want to announce and sets the date they want to announce it. Next, AI automatically generates a layout based on a sales announcement template, inserts illustrations, adjusts the size and color of text, and performs other functions. Furthermore, the AI ​​compares the sales announcement with past examples and recommends additional elements if any are missing. By adding these elements accordingly, a more understandable sales announcement can be created. Finally, the announcement is automatically distributed on the specified date. For example, a user enters the release date of a new product or the start date of a campaign into the AI. Next, the AI ​​analyzes the input information and automatically generates a layout based on a sales announcement template. For example, by inserting illustrations and adjusting the size and color of text, a more visually understandable sales announcement can be created. Furthermore, the AI ​​compares the sales announcement with past examples and recommends additional elements if any are missing. For example, if important information is missing, elements that will improve the appearance can be added. This allows for the creation of a more understandable sales announcement. Finally, the announcement is automatically distributed on the specified date. This allows users to distribute sales announcements without any effort. This allows the automatic sales announcement generation system to allow even those who are not used to sending out sales announcements to easily create great sales announcements and automatically send them on a set date. Furthermore, the AI ​​takes past examples into consideration and recommends any missing elements, resulting in a well-looking, eye-catching sales announcement.

[0029] An automatic sales announcement generation system according to an embodiment includes a reception unit, a generation unit, a recommendation unit, and a transmission unit. The reception unit allows a user to enter content to be announced and set a date for the announcement. For example, the user can enter the release date of a new product or the start date of a campaign. The generation unit analyzes the information received by the reception unit and automatically generates a layout based on a sales announcement template. For example, the generation unit inserts illustrations and adjusts the size and color of text. For example, when inserting an illustration, the generation unit considers the image format and insertion position. Furthermore, when adjusting the size and color of text, the generation unit considers the selection criteria for font size and color. The recommendation unit evaluates the layout generated by the generation unit and recommends missing elements. For example, the recommendation unit can compare the layout with past cases and add elements that are missing important information or that will improve the appearance. The recommendation unit, for example, references a database of past sales announcements and considers the selection criteria for the cases. The transmission unit automatically transmits the layout evaluated by the recommendation unit on a set date. For example, the sending unit takes into consideration the timing of sending and the communication means to be used. As a result, the sales announcement automatic generation system according to the embodiment allows a user to easily create a sales announcement and automatically send it on a set date. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can adjust the timing of sending using an AI model that inputs the layout evaluated by the recommendation unit and outputs the timing of sending.

[0030] The generation unit can insert illustrations and adjust the size and color of text. For example, when inserting an illustration, the generation unit considers the image format and insertion position. For example, the generation unit inserts an image in JPEG or PNG format and places it in a specified position. Furthermore, when adjusting the size and color of text, the generation unit considers font size and color selection criteria. For example, the generation unit can adjust the font size of text from 12 points to 24 points and select the color from red, blue, green, etc. This allows for the creation of visually easy-to-understand sales announcements. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can automatically generate a layout using an AI model that inputs the insertion position of the illustration, the size of the text, and the color selection and outputs the optimal layout.

[0031] The recommendation unit can compare with past cases and recommend missing elements. The recommendation unit, for example, references a database of past sales announcements and considers case selection criteria. For example, the recommendation unit extracts and recommends effective elements from the database of past sales announcements. The recommendation unit can also analyze past cases and make recommendations based on specific patterns. For example, the recommendation unit adds elements that are missing important information or that improve the appearance based on past cases. This allows for the creation of more effective sales announcements based on past cases. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that inputs past case data and outputs missing elements.

[0032] The sending unit can automatically send a message on a set date. The sending unit considers, for example, the timing of the message and the communication means to be used. For example, the sending unit sends a message using a communication means such as email, SNS, or push notification based on the set date. The sending unit can also consider the optimal time of day and the user's activity hours when adjusting the timing of the message. For example, the sending unit determines the optimal timing of the message based on the user's activity hours. This allows the user to send business announcements without any effort. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can adjust the timing of the message using an AI model that inputs the set date and the user's activity hours and outputs the optimal timing of the message.

[0033] The reception unit allows the user to simply write the content they want to share and set the date they want to share it. The reception unit considers, for example, the design of the input form and the procedure for writing it. For example, the reception unit provides a simple interface so that the user can easily input the content they want to share. The reception unit can also consider the method and format for setting the date they want to share. For example, the reception unit provides options for setting a specific date, period, time period, etc. This allows the user to easily input the content they want to share. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can improve input efficiency by using an AI model that analyzes the user's input data and suggests the optimal input method.

[0034] The generation unit can automatically generate a layout based on a sales announcement template. The generation unit, for example, considers the content and format of the sales announcement template. For example, the generation unit selects a template based on the type of layout and the elements to be used. The generation unit can also consider the method and criteria for automatically generating a layout. For example, the generation unit automatically generates a layout based on the algorithm and generation procedure to be used. This allows for efficient layout generation based on a template. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can automatically generate a layout using an AI model that receives template selection and layout generation as input and outputs an optimal layout.

[0035] The reception unit can analyze the user's past public knowledge and suggest the optimal writing method. The reception unit, for example, references a database of past public knowledge and extracts effective elements. For example, the reception unit automatically suggests expressions and formats that the user has frequently used in the past. The reception unit can also extract and suggest effective elements from the past public knowledge. Furthermore, the reception unit can analyze the past public knowledge and suggest the optimal writing method based on a specific pattern. For example, the reception unit extracts a specific pattern based on the past public knowledge and suggests the optimal writing method based on that pattern. This makes it possible to suggest the optimal writing method based on the past public knowledge. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can suggest a writing method using an AI model that inputs past public knowledge data and outputs the optimal writing method.

[0036] When receiving the notification content, the reception unit can filter the notification content based on the user's current work situation and areas of interest. For example, the reception unit prioritizes receiving content related to the user's current project. For example, the reception unit analyzes the user's current work situation and prioritizes receiving highly relevant notification content. The reception unit can also filter highly relevant notification content based on the user's areas of interest. For example, the reception unit prioritizes receiving highly relevant notification content based on the user's areas of interest. Furthermore, the reception unit can also prioritize receiving highly important notification content based on the user's work situation. For example, the reception unit prioritizes receiving highly important notification content based on the user's work situation. This makes it possible to provide notification content that is appropriate for the user's work situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs data on the user's work situation and areas of interest and outputs optimal notification content.

[0037] When receiving public information, the reception unit can prioritize receiving highly relevant content taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving public information related to that area. For example, the reception unit analyzes the user's geographical location information and prioritizes receiving highly relevant public information. The reception unit can also prioritize receiving nearby events and information based on the user's current location. For example, the reception unit prioritizes receiving nearby events and information based on the user's current location. Furthermore, the reception unit can also prioritize receiving area-specific information based on the user's geographical location information. For example, the reception unit prioritizes receiving area-specific information based on the user's geographical location information. This makes it possible to provide optimal public information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's geographical location information and outputs optimal public information.

[0038] The reception unit may analyze the user's social media activity and receive related content when receiving notification content. For example, the reception unit may prioritize receiving notification content related to topics in which the user has shown interest on social media. For example, the reception unit may analyze the user's social media activity and prioritize receiving highly relevant notification content. The reception unit may also analyze the user's social media activity history and suggest highly relevant notification content. For example, the reception unit may suggest highly relevant notification content based on the user's social media activity history. Furthermore, the reception unit may prioritize receiving related notification content based on information about accounts the user follows. For example, the reception unit may prioritize receiving related notification content based on information about accounts the user follows. This makes it possible to provide optimal notification content based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may perform filtering using an AI model that inputs the user's social media activity data and outputs optimal notification content.

[0039] The generation unit can adjust the level of detail of the layout based on the importance of the well-known content when generating the layout. For example, the generation unit evaluates the importance of the well-known content and adjusts the level of detail of the layout. For example, the generation unit adds detailed explanations and illustrations to well-known content with high importance. The generation unit can also apply concise explanations and a simple layout to well-known content with low importance. Furthermore, the generation unit adjusts the size and color of the text according to the importance. For example, the generation unit increases the size of the text and makes the color more prominent to emphasize information with high importance. This makes it possible to provide an optimal layout according to the importance of the well-known content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the layout using an AI model that inputs importance data of the well-known content and outputs an optimal layout.

[0040] When generating a layout, the generation unit can apply different layout algorithms depending on the category of the known content. For example, the generation unit classifies the category of the known content and applies a layout algorithm according to the category. For example, the generation unit applies a layout including product images and a specification table to product information. The generation unit can also apply a layout that emphasizes the date, time, and location to event information. Furthermore, the generation unit applies a layout that emphasizes discount rates and benefits to campaign information. This makes it possible to provide an optimal layout according to the category of the known content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a layout using an AI model that inputs category data of the known content and outputs an optimal layout algorithm.

[0041] When generating a layout, the generation unit can determine the priority of the layout based on the submission date of the publicly known content. The generation unit, for example, evaluates the submission date of the publicly known content and determines the priority of the layout. For example, the generation unit prioritizes layout of publicly known content with an upcoming submission deadline. The generation unit can also emphasize information of high importance according to the submission date. Furthermore, the generation unit adjusts the order of the layout based on the submission date. For example, the generation unit places information with an upcoming submission deadline at the top to highlight information of high importance. This makes it possible to provide an optimal layout according to the submission date of the publicly known content. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can determine the priority of the layout using an AI model that inputs data on the submission date of the publicly known content and outputs the priority of the optimal layout.

[0042] The generation unit can adjust the order of the layout based on the relevance of the known content when generating the layout. The generation unit, for example, evaluates the relevance of the known content and adjusts the order of the layout. For example, the generation unit prioritizes layout of highly relevant information. The generation unit can also adjust the display order of information according to the relevance. Furthermore, the generation unit adjusts the layout arrangement to emphasize highly relevant information. For example, the generation unit arranges highly relevant information at the top to make important information stand out. This makes it possible to provide an optimal layout according to the relevance of the known content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the order of the layout using an AI model that inputs relevance data of the known content and outputs an optimal layout order.

[0043] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to past publicly known content. The recommendation unit, for example, refers to a database of past publicly known content and extracts elements that were effective. For example, the recommendation unit extracts and recommends effective elements from past publicly known content. The recommendation unit can also analyze past publicly known content and make recommendations based on specific patterns. Furthermore, the recommendation unit can recommend missing elements by referring to past publicly known content. For example, the recommendation unit extracts specific patterns based on past publicly known content and recommends optimal elements based on those patterns. This makes it possible to provide optimal recommendations based on past publicly known content. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can improve the accuracy of recommendations by using an AI model that inputs past publicly known content data and outputs optimal recommendations.

[0044] When making a recommendation, the recommendation unit can take into consideration attribute information of the submitter of the publicly known content. The recommendation unit, for example, recommends highly relevant elements based on the submitter's work content. For example, the recommendation unit analyzes the submitter's work content and recommends highly relevant elements. The recommendation unit can also recommend optimal elements by referring to the submitter's past publicly known content. Furthermore, the recommendation unit makes customized recommendations based on the submitter's attribute information. For example, the recommendation unit recommends optimal elements based on the submitter's attribute information, such as age, occupation, and interests. This makes it possible to provide optimal recommendations based on the submitter's attribute information. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that inputs the submitter's attribute information data and outputs optimal recommendations.

[0045] The recommendation unit can make recommendations taking into account the geographical distribution of well-known content. The recommendation unit, for example, evaluates the geographical distribution of well-known content and makes recommendations. For example, the recommendation unit prioritizes recommending elements related to geographically close locations. The recommendation unit can also recommend highly relevant elements based on the geographical distribution. Furthermore, the recommendation unit recommends optimal elements taking geographical factors into consideration. For example, the recommendation unit recommends optimal elements based on geographical relevance. This makes it possible to provide optimal recommendations based on the geographical distribution. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that inputs geographical distribution data of well-known content and outputs optimal recommendations.

[0046] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to related literature of well-known content. The recommendation unit, for example, recommends effective elements based on related literature. For example, the recommendation unit can recommend missing elements by referring to related literature. The recommendation unit can also analyze related literature and recommend optimal elements. Furthermore, the recommendation unit can extract specific patterns based on the related literature and recommend optimal elements based on those patterns. This makes it possible to provide optimal recommendations based on the related literature. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can improve the accuracy of the recommendation by using an AI model that inputs related literature data and outputs optimal recommendations.

[0047] When making a call, the calling unit can select the optimal calling method by referring to past calling history. The calling unit, for example, selects an effective calling method based on past calling history. For example, the calling unit selects an effective calling method from past calling history. The calling unit can also analyze past calling history and select the optimal calling timing. Furthermore, the calling unit selects the optimal calling channel by referring to past calling history. For example, the calling unit selects the optimal calling channel based on past calling history. This makes it possible to provide the optimal calling method based on past calling history. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can select the calling method using an AI model that inputs past calling history data and outputs the optimal calling method.

[0048] The transmission unit can determine the priority of transmission based on the importance of the information to be notified at the time of transmission. The transmission unit, for example, evaluates the importance of the information to be notified and determines the priority of transmission. For example, the transmission unit prioritizes transmission of information with high importance. The transmission unit can also adjust the timing of transmission according to the importance. Furthermore, the transmission unit emphasizes and transmits information with high importance. For example, the transmission unit adjusts the timing of transmission to make information with high importance stand out. This makes it possible to provide optimal transmission according to the importance of the information to be notified. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can determine the priority of transmission using an AI model that inputs importance data of the information to be notified and outputs optimal priority of transmission.

[0049] The transmission unit can determine the timing of transmission based on the submission date of the notification content at the time of transmission. The transmission unit, for example, evaluates the submission date of the notification content and determines the timing of transmission. For example, the transmission unit prioritizes transmission of notification content with an upcoming submission deadline. The transmission unit can also determine the optimal transmission timing depending on the submission date. Furthermore, the transmission unit adjusts the order of transmission based on the submission date. For example, the transmission unit prioritizes transmission of information with an upcoming submission deadline and adjusts the order of transmission to highlight important information. This makes it possible to provide the optimal transmission timing depending on the submission date. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can determine the timing of transmission using an AI model that inputs submission date data of the notification content and outputs the optimal transmission timing.

[0050] The transmission unit can adjust the transmission order based on the relevance of the publicly known content when transmitting. The transmission unit, for example, evaluates the relevance of the publicly known content and adjusts the transmission order. For example, the transmission unit prioritizes transmitting highly relevant information. The transmission unit can also adjust the transmission order of information according to the relevance. Furthermore, the transmission unit adjusts the transmission order to emphasize highly relevant information when transmitting it. For example, the transmission unit adjusts the transmission order to place highly relevant information at the top and make important information stand out. This makes it possible to provide an optimal transmission order according to the relevance. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can adjust the transmission order using an AI model that inputs relevance data of publicly known content and outputs an optimal transmission order.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit allows the user to enter the content they wish to announce and set the date they wish to announce it. For example, the user can enter the release date of a new product or the start date of a campaign. The generation unit analyzes the information received by the reception unit and automatically generates a layout based on a sales announcement template. For example, the generation unit inserts illustrations and adjusts the size and color of text. For example, when inserting an illustration, the generation unit considers the image format and insertion position. Furthermore, when adjusting the size and color of text, the generation unit considers the selection criteria for font size and color. The recommendation unit evaluates the layout generated by the generation unit and recommends missing elements. For example, the recommendation unit can compare the layout with past cases and add elements if important information is missing or that will improve the appearance. For example, the recommendation unit references a database of past sales announcements and considers the selection criteria for the cases. The transmission unit automatically transmits the layout evaluated by the recommendation unit on the set date. For example, the transmission unit considers the timing of transmission and the communication method to be used. As a result, the sales announcement automatic generation system according to the embodiment allows users to easily create sales announcements and automatically send them on a set schedule. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without AI. For example, the sending unit can adjust the timing of sending using an AI model that inputs the layout evaluated by the recommendation unit and outputs the timing of sending.

[0053] The generation unit can insert illustrations and adjust the size and color of text. For example, when inserting an illustration, the generation unit considers the image format and insertion position. For example, the generation unit inserts an image in JPEG or PNG format and places it in a specified position. Furthermore, when adjusting the size and color of text, the generation unit considers font size and color selection criteria. For example, the generation unit can adjust the font size of text from 12 points to 24 points and select the color from red, blue, green, etc. This allows for the creation of visually easy-to-understand sales announcements. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can automatically generate a layout using an AI model that inputs the insertion position of an illustration, the size of text, and the color selection and outputs the optimal layout.

[0054] The recommendation unit can compare with past cases and recommend missing elements. For example, the recommendation unit references a database of past sales announcements and considers the selection criteria for the cases. For example, the recommendation unit extracts and recommends effective elements from the database of past sales announcements. The recommendation unit can also analyze past cases and make recommendations based on specific patterns. For example, the recommendation unit adds elements that are missing important information or that improve the appearance based on past cases. This allows for the creation of more effective sales announcements based on past cases. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that inputs past case data and outputs missing elements.

[0055] The sending unit can automatically send a message on a set date. For example, the sending unit considers the timing of the message and the communication means to be used. For example, the sending unit sends a message using a communication means such as email, SNS, or push notification based on the set date. The sending unit can also consider the optimal time of day and the user's activity hours when adjusting the timing of the message. For example, the sending unit determines the optimal timing of the message based on the user's activity hours. This allows the user to send business announcements without any effort. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can adjust the timing of the message using an AI model that inputs the set date and the user's activity hours and outputs the optimal timing of the message.

[0056] The reception unit allows the user to simply write the content they want to share and set the date they want to share it. For example, the reception unit considers the design of the input form and the procedure for writing it. For example, the reception unit provides a simple interface so that the user can easily input the content they want to share. The reception unit can also consider the method and format for setting the date they want to share. For example, the reception unit provides options for setting a specific date, period, time period, etc. This allows the user to easily input the content they want to share. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can improve input efficiency by using an AI model that analyzes the user's input data and suggests the optimal input method.

[0057] When making a call, the calling unit can select the optimal calling method by referring to past calling history. For example, the calling unit selects an effective calling method based on past calling history. For example, the calling unit selects an effective calling method from past calling history. The calling unit can also analyze past calling history and select the optimal timing for making a call. Furthermore, the calling unit selects the optimal calling channel by referring to past calling history. For example, the calling unit selects the optimal calling channel based on past calling history. This makes it possible to provide the optimal calling method based on past calling history. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can select the calling method using an AI model that inputs past calling history data and outputs the optimal calling method.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit allows the user to enter the information they wish to share and set the date they wish to share it. For example, the user can enter the release date of a new product or the start date of a campaign. Step 2: The generation unit analyzes the information received by the reception unit and automatically generates a layout based on the sales announcement template. For example, the generation unit inserts illustrations and applies text size and color. The generation unit takes into account image format, insertion position, font size, and color selection criteria. Step 3: The recommendation unit evaluates the layout generated by the generation unit and recommends missing elements. For example, the recommendation unit compares it with past cases and can add elements that are missing or that will improve the appearance. The recommendation unit refers to a database of past sales announcements and considers the selection criteria for the cases. Step 4: The sending unit automatically sends the layout evaluated by the recommendation unit on a set date. For example, the sending unit takes into consideration the timing of sending and the communication means to be used. Some or all of the processing in the sending unit may be performed using AI, or may be performed without using AI.

[0060] (Example 2) The automatic sales announcement generation system according to an embodiment of the present invention automatically creates and distributes sales announcements. In this system, a user roughly describes the content they want to announce and sets the date they want to announce it. Next, AI automatically generates a layout based on a sales announcement template, inserts illustrations, adjusts the size and color of text, and performs other functions. Furthermore, the AI ​​compares the sales announcement with past examples and recommends additional elements if any are missing. By adding these elements accordingly, a more understandable sales announcement can be created. Finally, the announcement is automatically distributed on the specified date. For example, a user enters the release date of a new product or the start date of a campaign into the AI. Next, the AI ​​analyzes the input information and automatically generates a layout based on a sales announcement template. For example, by inserting illustrations and adjusting the size and color of text, a more visually understandable sales announcement can be created. Furthermore, the AI ​​compares the sales announcement with past examples and recommends additional elements if any are missing. For example, if important information is missing, elements that will improve the appearance can be added. This allows for the creation of a more understandable sales announcement. Finally, the announcement is automatically distributed on the specified date. This allows users to distribute sales announcements without any effort. This allows the automatic sales announcement generation system to allow even those who are not used to sending out sales announcements to easily create great sales announcements and automatically send them on a set date. Furthermore, the AI ​​takes past examples into consideration and recommends any missing elements, resulting in a well-looking, eye-catching sales announcement.

[0061] An automatic sales announcement generation system according to an embodiment includes a reception unit, a generation unit, a recommendation unit, and a transmission unit. The reception unit allows a user to enter content to be announced and set a date for the announcement. For example, the user can enter the release date of a new product or the start date of a campaign. The generation unit analyzes the information received by the reception unit and automatically generates a layout based on a sales announcement template. For example, the generation unit inserts illustrations and adjusts the size and color of text. For example, when inserting an illustration, the generation unit considers the image format and insertion position. Furthermore, when adjusting the size and color of text, the generation unit considers the selection criteria for font size and color. The recommendation unit evaluates the layout generated by the generation unit and recommends missing elements. For example, the recommendation unit can compare the layout with past cases and add elements that are missing important information or that will improve the appearance. The recommendation unit, for example, references a database of past sales announcements and considers the selection criteria for the cases. The transmission unit automatically transmits the layout evaluated by the recommendation unit on a set date. For example, the sending unit takes into consideration the timing of sending and the communication means to be used. As a result, the sales announcement automatic generation system according to the embodiment allows a user to easily create a sales announcement and automatically send it on a set date. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can adjust the timing of sending using an AI model that inputs the layout evaluated by the recommendation unit and outputs the timing of sending.

[0062] The generation unit can insert illustrations and adjust the size and color of text. For example, when inserting an illustration, the generation unit considers the image format and insertion position. For example, the generation unit inserts an image in JPEG or PNG format and places it in a specified position. Furthermore, when adjusting the size and color of text, the generation unit considers font size and color selection criteria. For example, the generation unit can adjust the font size of text from 12 points to 24 points and select the color from red, blue, green, etc. This allows for the creation of visually easy-to-understand sales announcements. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can automatically generate a layout using an AI model that inputs the insertion position of the illustration, the size of the text, and the color selection and outputs the optimal layout.

[0063] The recommendation unit can compare with past cases and recommend missing elements. The recommendation unit, for example, references a database of past sales announcements and considers case selection criteria. For example, the recommendation unit extracts and recommends effective elements from the database of past sales announcements. The recommendation unit can also analyze past cases and make recommendations based on specific patterns. For example, the recommendation unit adds elements that are missing important information or that improve the appearance based on past cases. This allows for the creation of more effective sales announcements based on past cases. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that inputs past case data and outputs missing elements.

[0064] The sending unit can automatically send a message on a set date. The sending unit considers, for example, the timing of the message and the communication means to be used. For example, the sending unit sends a message using a communication means such as email, SNS, or push notification based on the set date. The sending unit can also consider the optimal time of day and the user's activity hours when adjusting the timing of the message. For example, the sending unit determines the optimal timing of the message based on the user's activity hours. This allows the user to send business announcements without any effort. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can adjust the timing of the message using an AI model that inputs the set date and the user's activity hours and outputs the optimal timing of the message.

[0065] The reception unit allows the user to simply write the content they want to share and set the date they want to share it. The reception unit considers, for example, the design of the input form and the procedure for writing it. For example, the reception unit provides a simple interface so that the user can easily input the content they want to share. The reception unit can also consider the method and format for setting the date they want to share. For example, the reception unit provides options for setting a specific date, period, time period, etc. This allows the user to easily input the content they want to share. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can improve input efficiency by using an AI model that analyzes the user's input data and suggests the optimal input method.

[0066] The generation unit can automatically generate a layout based on a sales announcement template. The generation unit, for example, considers the content and format of the sales announcement template. For example, the generation unit selects a template based on the type of layout and the elements to be used. The generation unit can also consider the method and criteria for automatically generating a layout. For example, the generation unit automatically generates a layout based on the algorithm and generation procedure to be used. This allows for efficient layout generation based on a template. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can automatically generate a layout using an AI model that receives template selection and layout generation as input and outputs an optimal layout.

[0067] The reception unit can estimate the user's emotions and adjust the description method of the known content based on the estimated user emotions. The reception unit, for example, estimates the user's emotions using an emotion analysis algorithm. For example, the reception unit analyzes the user's facial expressions, voice, and text data to calculate an emotion score. The reception unit can also adjust the description method of the known content based on the estimated user emotions. For example, if the user is stressed, the reception unit provides a simple interface and minimizes input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input to enable the user to quickly enter the known content. This makes it possible to provide an optimal description method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can adjust the writing method using an AI model that takes the user's emotional data as input and outputs the optimal writing method.

[0068] The reception unit can analyze the user's past public knowledge and suggest the optimal writing method. The reception unit, for example, references a database of past public knowledge and extracts effective elements. For example, the reception unit automatically suggests expressions and formats that the user has frequently used in the past. The reception unit can also extract and suggest effective elements from the past public knowledge. Furthermore, the reception unit can analyze the past public knowledge and suggest the optimal writing method based on a specific pattern. For example, the reception unit extracts a specific pattern based on the past public knowledge and suggests the optimal writing method based on that pattern. This makes it possible to suggest the optimal writing method based on the past public knowledge. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can suggest a writing method using an AI model that inputs past public knowledge data and outputs the optimal writing method.

[0069] When receiving the notification content, the reception unit can filter the notification content based on the user's current work situation and areas of interest. For example, the reception unit prioritizes receiving content related to the user's current project. For example, the reception unit analyzes the user's current work situation and prioritizes receiving highly relevant notification content. The reception unit can also filter highly relevant notification content based on the user's areas of interest. For example, the reception unit prioritizes receiving highly relevant notification content based on the user's areas of interest. Furthermore, the reception unit can also prioritize receiving highly important notification content based on the user's work situation. For example, the reception unit prioritizes receiving highly important notification content based on the user's work situation. This makes it possible to provide notification content that is appropriate for the user's work situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs data on the user's work situation and areas of interest and outputs optimal notification content.

[0070] The reception unit can estimate the user's emotions and prioritize the public information based on the estimated user emotions. The reception unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the reception unit analyzes the user's facial expressions, voice, and text data to calculate an emotion score. The reception unit can also prioritize the public information based on the estimated user emotions. For example, if the user is stressed, the reception unit can prioritize displaying public information of high importance. Furthermore, if the user is relaxed, the reception unit can prioritize displaying detailed public information. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying public information that requires a quick response. This allows optimal prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can determine the priority order using an AI model that receives user emotion data as input and outputs optimal priorities.

[0071] When receiving public information, the reception unit can prioritize receiving highly relevant content taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving public information related to that area. For example, the reception unit analyzes the user's geographical location information and prioritizes receiving highly relevant public information. The reception unit can also prioritize receiving nearby events and information based on the user's current location. For example, the reception unit prioritizes receiving nearby events and information based on the user's current location. Furthermore, the reception unit can also prioritize receiving area-specific information based on the user's geographical location information. For example, the reception unit prioritizes receiving area-specific information based on the user's geographical location information. This makes it possible to provide optimal public information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform filtering using an AI model that inputs the user's geographical location information and outputs optimal public information.

[0072] The reception unit may analyze the user's social media activity and receive related content when receiving notification content. For example, the reception unit may prioritize receiving notification content related to topics in which the user has shown interest on social media. For example, the reception unit may analyze the user's social media activity and prioritize receiving highly relevant notification content. The reception unit may also analyze the user's social media activity history and suggest highly relevant notification content. For example, the reception unit may suggest highly relevant notification content based on the user's social media activity history. Furthermore, the reception unit may prioritize receiving related notification content based on information about accounts the user follows. For example, the reception unit may prioritize receiving related notification content based on information about accounts the user follows. This makes it possible to provide optimal notification content based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may perform filtering using an AI model that inputs the user's social media activity data and outputs optimal notification content.

[0073] The generation unit can estimate the user's emotion and adjust the layout representation method based on the estimated user's emotion. The generation unit, for example, uses an emotion analysis algorithm to estimate the user's emotion. For example, the generation unit analyzes the user's facial expressions, voice, and text data to calculate an emotion score. The generation unit can also adjust the layout representation method based on the estimated user's emotion. For example, the generation unit generates a visually calming layout when the user is relaxed. The generation unit can also generate a simple layout that highlights important information when the user is in a hurry. Furthermore, the generation unit generates a layout with a visually stimulating effect when the user is excited. This allows for providing an optimal layout according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generator can adjust the layout representation method using an AI model that takes user emotional data as input and outputs the optimal layout.

[0074] The generation unit can adjust the level of detail of the layout based on the importance of the well-known content when generating the layout. For example, the generation unit evaluates the importance of the well-known content and adjusts the level of detail of the layout. For example, the generation unit adds detailed explanations and illustrations to well-known content with high importance. The generation unit can also apply concise explanations and a simple layout to well-known content with low importance. Furthermore, the generation unit adjusts the size and color of the text according to the importance. For example, the generation unit increases the size of the text and makes the color more prominent to emphasize information with high importance. This makes it possible to provide an optimal layout according to the importance of the well-known content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the layout using an AI model that inputs importance data of the well-known content and outputs an optimal layout.

[0075] When generating a layout, the generation unit can apply different layout algorithms depending on the category of the known content. For example, the generation unit classifies the category of the known content and applies a layout algorithm according to the category. For example, the generation unit applies a layout including product images and a specification table to product information. The generation unit can also apply a layout that emphasizes the date, time, and location to event information. Furthermore, the generation unit applies a layout that emphasizes discount rates and benefits to campaign information. This makes it possible to provide an optimal layout according to the category of the known content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a layout using an AI model that inputs category data of the known content and outputs an optimal layout algorithm.

[0076] The generation unit can estimate the user's emotion and adjust the length of the layout based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, an emotion analysis algorithm. For example, the generation unit analyzes the user's facial expressions, voice, and text data to calculate an emotion score. The generation unit can also adjust the length of the layout based on the estimated user's emotion. For example, if the user is in a hurry, the generation unit generates a short, to-the-point layout. If the user is relaxed, the generation unit can generate a longer layout with detailed explanations. Furthermore, if the user is excited, the generation unit generates a layout with visually stimulating effects. This allows the optimal layout length to be provided according to the user's emotion. Emotion estimation is achieved using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generator can adjust the length of the layout using an AI model that takes user emotion data as input and outputs the optimal layout length.

[0077] When generating a layout, the generation unit can determine the priority of the layout based on the submission date of the publicly known content. The generation unit, for example, evaluates the submission date of the publicly known content and determines the priority of the layout. For example, the generation unit prioritizes layout of publicly known content with an upcoming submission deadline. The generation unit can also emphasize information of high importance according to the submission date. Furthermore, the generation unit adjusts the order of the layout based on the submission date. For example, the generation unit places information with an upcoming submission deadline at the top to highlight information of high importance. This makes it possible to provide an optimal layout according to the submission date of the publicly known content. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can determine the priority of the layout using an AI model that inputs data on the submission date of the publicly known content and outputs the priority of the optimal layout.

[0078] The generation unit can adjust the order of the layout based on the relevance of the known content when generating the layout. The generation unit, for example, evaluates the relevance of the known content and adjusts the order of the layout. For example, the generation unit prioritizes layout of highly relevant information. The generation unit can also adjust the display order of information according to the relevance. Furthermore, the generation unit adjusts the layout arrangement to emphasize highly relevant information. For example, the generation unit arranges highly relevant information at the top to make important information stand out. This makes it possible to provide an optimal layout according to the relevance of the known content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the order of the layout using an AI model that inputs relevance data of the known content and outputs an optimal layout order.

[0079] The recommendation unit can estimate a user's emotions and adjust recommendation criteria based on the estimated user emotions. The recommendation unit can estimate a user's emotions using, for example, an emotion analysis algorithm. For example, the recommendation unit can analyze the user's facial expressions, voice, and text data to calculate an emotion score. The recommendation unit can also adjust recommendation criteria based on the estimated user emotions. For example, the recommendation unit can provide detailed recommendations when the user is relaxed. The recommendation unit can also prioritize recommending important elements when the user is in a hurry. Furthermore, the recommendation unit can recommend visually stimulating elements when the user is excited. This makes it possible to provide optimal recommendation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may adjust the recommendation criteria using an AI model that inputs user emotion data and outputs optimal recommendation criteria.

[0080] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to past publicly known content. The recommendation unit, for example, refers to a database of past publicly known content and extracts elements that were effective. For example, the recommendation unit extracts and recommends effective elements from past publicly known content. The recommendation unit can also analyze past publicly known content and make recommendations based on specific patterns. Furthermore, the recommendation unit can recommend missing elements by referring to past publicly known content. For example, the recommendation unit extracts specific patterns based on past publicly known content and recommends optimal elements based on those patterns. This makes it possible to provide optimal recommendations based on past publicly known content. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can improve the accuracy of recommendations by using an AI model that inputs past publicly known content data and outputs optimal recommendations.

[0081] When making a recommendation, the recommendation unit can take into consideration attribute information of the submitter of the publicly known content. The recommendation unit, for example, recommends highly relevant elements based on the submitter's work content. For example, the recommendation unit analyzes the submitter's work content and recommends highly relevant elements. The recommendation unit can also recommend optimal elements by referring to the submitter's past publicly known content. Furthermore, the recommendation unit makes customized recommendations based on the submitter's attribute information. For example, the recommendation unit recommends optimal elements based on the submitter's attribute information, such as age, occupation, and interests. This makes it possible to provide optimal recommendations based on the submitter's attribute information. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that inputs the submitter's attribute information data and outputs optimal recommendations.

[0082] The recommendation unit can estimate the user's emotions and adjust the order in which recommendation results are displayed based on the estimated user emotions. The recommendation unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the recommendation unit analyzes the user's facial expressions, voice, and text data to calculate an emotion score. The recommendation unit can also adjust the order in which recommendation results are displayed based on the estimated user emotions. For example, the recommendation unit can prioritize detailed recommendation results when the user is relaxed. The recommendation unit can also prioritize important recommendation results when the user is in a hurry. Furthermore, the recommendation unit can prioritize visually stimulating recommendation results when the user is excited. This makes it possible to provide optimal recommendation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may adjust the order in which recommendation results are displayed using an AI model that receives user emotion data as input and outputs an optimal order of recommendation results.

[0083] The recommendation unit can make recommendations taking into account the geographical distribution of well-known content. The recommendation unit, for example, evaluates the geographical distribution of well-known content and makes recommendations. For example, the recommendation unit prioritizes recommending elements related to geographically close locations. The recommendation unit can also recommend highly relevant elements based on the geographical distribution. Furthermore, the recommendation unit recommends optimal elements taking geographical factors into consideration. For example, the recommendation unit recommends optimal elements based on geographical relevance. This makes it possible to provide optimal recommendations based on the geographical distribution. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that inputs geographical distribution data of well-known content and outputs optimal recommendations.

[0084] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to related literature of well-known content. The recommendation unit, for example, recommends effective elements based on related literature. For example, the recommendation unit can recommend missing elements by referring to related literature. The recommendation unit can also analyze related literature and recommend optimal elements. Furthermore, the recommendation unit can extract specific patterns based on the related literature and recommend optimal elements based on those patterns. This makes it possible to provide optimal recommendations based on the related literature. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can improve the accuracy of the recommendation by using an AI model that inputs related literature data and outputs optimal recommendations.

[0085] The transmission unit can estimate the user's emotion and adjust the timing of transmission based on the estimated user's emotion. The transmission unit, for example, uses an emotion analysis algorithm to estimate the user's emotion. For example, the transmission unit analyzes the user's facial expressions, voice, and text data to calculate an emotion score. The transmission unit can also adjust the timing of transmission based on the estimated user's emotion. For example, the transmission unit can transmit at the optimal timing when the user is relaxed. The transmission unit can also transmit quickly when the user is in a hurry. Furthermore, the transmission unit can transmit at a visually stimulating timing when the user is excited. This makes it possible to provide the optimal transmission timing according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or without AI. For example, the sending unit can adjust the timing of sending using an AI model that takes the user's emotional data as input and outputs the optimal timing of sending.

[0086] When making a call, the calling unit can select the optimal calling method by referring to past calling history. The calling unit, for example, selects an effective calling method based on past calling history. For example, the calling unit selects an effective calling method from past calling history. The calling unit can also analyze past calling history and select the optimal calling timing. Furthermore, the calling unit selects the optimal calling channel by referring to past calling history. For example, the calling unit selects the optimal calling channel based on past calling history. This makes it possible to provide the optimal calling method based on past calling history. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can select the calling method using an AI model that inputs past calling history data and outputs the optimal calling method.

[0087] The transmission unit can determine the priority of transmission based on the importance of the information to be notified at the time of transmission. The transmission unit, for example, evaluates the importance of the information to be notified and determines the priority of transmission. For example, the transmission unit prioritizes transmission of information with high importance. The transmission unit can also adjust the timing of transmission according to the importance. Furthermore, the transmission unit emphasizes and transmits information with high importance. For example, the transmission unit adjusts the timing of transmission to make information with high importance stand out. This makes it possible to provide optimal transmission according to the importance of the information to be notified. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can determine the priority of transmission using an AI model that inputs importance data of the information to be notified and outputs optimal priority of transmission.

[0088] The sending unit can estimate the user's emotion and adjust the sending method based on the estimated user's emotion. The sending unit, for example, uses an emotion analysis algorithm to estimate the user's emotion. For example, the sending unit analyzes the user's facial expressions, voice, and text data to calculate an emotion score. The sending unit can also adjust the sending method based on the estimated user's emotion. For example, if the user is relaxed, the sending unit sends a message in a visually calming manner. If the user is in a hurry, the sending unit can send a message in a quick and concise manner. Furthermore, if the user is excited, the sending unit sends a message in a visually stimulating manner. This makes it possible to provide an optimal sending method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or without AI. For example, the sending unit can adjust the sending method using an AI model that takes the user's emotional data as input and outputs the optimal sending method.

[0089] The transmission unit can determine the timing of transmission based on the submission date of the notification content at the time of transmission. The transmission unit, for example, evaluates the submission date of the notification content and determines the timing of transmission. For example, the transmission unit prioritizes transmission of notification content with an upcoming submission deadline. The transmission unit can also determine the optimal transmission timing depending on the submission date. Furthermore, the transmission unit adjusts the order of transmission based on the submission date. For example, the transmission unit prioritizes transmission of information with an upcoming submission deadline and adjusts the order of transmission to highlight important information. This makes it possible to provide the optimal transmission timing depending on the submission date. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can determine the timing of transmission using an AI model that inputs submission date data of the notification content and outputs the optimal transmission timing.

[0090] The transmission unit can adjust the transmission order based on the relevance of the publicly known content when transmitting. The transmission unit, for example, evaluates the relevance of the publicly known content and adjusts the transmission order. For example, the transmission unit prioritizes transmitting highly relevant information. The transmission unit can also adjust the transmission order of information according to the relevance. Furthermore, the transmission unit adjusts the transmission order to emphasize highly relevant information when transmitting it. For example, the transmission unit adjusts the transmission order to place highly relevant information at the top and make important information stand out. This makes it possible to provide an optimal transmission order according to the relevance. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can adjust the transmission order using an AI model that inputs relevance data of publicly known content and outputs an optimal transmission order. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, recommendation unit, and transmission unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user writes the content they want to make public and sets the date they want to make the announcement. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a layout based on a sales announcement template. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends missing elements by comparing with past cases. The transmission unit is realized, for example, by the control unit 46A of the smart device 14, and automatically sends the announcement on the set date. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, recommendation unit, and transmission unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user writes the content they want to make public and sets the date they want to make the announcement. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a layout based on a business announcement template. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends missing elements by comparing with past cases. The transmission unit is realized, for example, by the control unit 46A of the smart glasses 214, and automatically sends an announcement on a set date. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, recommendation unit, and transmission unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and the user writes down the content they want to make public and sets the date they want to make the announcement. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a layout based on a sales announcement template. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends missing elements by comparing with past cases. The transmission unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and automatically sends an announcement on a set date. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, recommendation unit, and transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user writes the content they want to make public and sets the date they want to make the announcement. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a layout based on a business announcement template. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends missing elements by comparing with past cases. The transmission unit is realized, for example, by the control unit 46A of the robot 414, and automatically transmits on a set date.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The reception unit allows the user to enter the content they wish to announce and set the date they wish to announce it. For example, the user can enter the release date of a new product or the start date of a campaign. The generation unit analyzes the information received by the reception unit and automatically generates a layout based on a sales announcement template. For example, the generation unit inserts illustrations and adjusts the size and color of text. For example, when inserting an illustration, the generation unit considers the image format and insertion position. Furthermore, when adjusting the size and color of text, the generation unit considers the selection criteria for font size and color. The recommendation unit evaluates the layout generated by the generation unit and recommends missing elements. For example, the recommendation unit can compare the layout with past cases and add elements if important information is missing or that will improve the appearance. For example, the recommendation unit references a database of past sales announcements and considers the selection criteria for the cases. The transmission unit automatically transmits the layout evaluated by the recommendation unit on the set date. For example, the transmission unit considers the timing of transmission and the communication method to be used. As a result, the sales announcement automatic generation system according to the embodiment allows users to easily create sales announcements and automatically send them on a set schedule. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without AI. For example, the sending unit can adjust the timing of sending using an AI model that inputs the layout evaluated by the recommendation unit and outputs the timing of sending.

[0093] The generation unit can insert illustrations and adjust the size and color of text. For example, when inserting an illustration, the generation unit considers the image format and insertion position. For example, the generation unit inserts an image in JPEG or PNG format and places it in a specified position. Furthermore, when adjusting the size and color of text, the generation unit considers font size and color selection criteria. For example, the generation unit can adjust the font size of text from 12 points to 24 points and select the color from red, blue, green, etc. This allows for the creation of visually easy-to-understand sales announcements. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can automatically generate a layout using an AI model that inputs the insertion position of an illustration, the size of text, and the color selection and outputs the optimal layout.

[0094] The recommendation unit can compare with past cases and recommend missing elements. For example, the recommendation unit references a database of past sales announcements and considers the selection criteria for the cases. For example, the recommendation unit extracts and recommends effective elements from the database of past sales announcements. The recommendation unit can also analyze past cases and make recommendations based on specific patterns. For example, the recommendation unit adds elements that are missing important information or that improve the appearance based on past cases. This allows for the creation of more effective sales announcements based on past cases. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that inputs past case data and outputs missing elements.

[0095] The sending unit can automatically send a message on a set date. For example, the sending unit considers the timing of the message and the communication means to be used. For example, the sending unit sends a message using a communication means such as email, SNS, or push notification based on the set date. The sending unit can also consider the optimal time of day and the user's activity hours when adjusting the timing of the message. For example, the sending unit determines the optimal timing of the message based on the user's activity hours. This allows the user to send business announcements without any effort. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can adjust the timing of the message using an AI model that inputs the set date and the user's activity hours and outputs the optimal timing of the message.

[0096] The reception unit allows the user to simply write the content they want to share and set the date they want to share it. For example, the reception unit considers the design of the input form and the procedure for writing it. For example, the reception unit provides a simple interface so that the user can easily input the content they want to share. The reception unit can also consider the method and format for setting the date they want to share. For example, the reception unit provides options for setting a specific date, period, time period, etc. This allows the user to easily input the content they want to share. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can improve input efficiency by using an AI model that analyzes the user's input data and suggests the optimal input method.

[0097] The reception unit can estimate the user's emotions and adjust the description method of the known content based on the estimated user emotions. For example, the reception unit estimates the user's emotions using an emotion analysis algorithm. For example, the reception unit analyzes the user's facial expressions, voice, and text data to calculate an emotion score. The reception unit can also adjust the description method of the known content based on the estimated user emotions. For example, if the user is stressed, the reception unit provides a simple interface and minimizes input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input to enable the user to quickly enter the known content. This makes it possible to provide an optimal description method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can adjust the writing method using an AI model that takes the user's emotional data as input and outputs the optimal writing method.

[0098] The generation unit can estimate the user's emotion and adjust the layout representation method based on the estimated user's emotion. For example, the generation unit estimates the user's emotion using an emotion analysis algorithm. For example, the generation unit analyzes the user's facial expressions, voice, and text data to calculate an emotion score. The generation unit can also adjust the layout representation method based on the estimated user's emotion. For example, the generation unit generates a visually calming layout when the user is relaxed. Furthermore, the generation unit can generate a simple layout that emphasizes important information when the user is in a hurry. Furthermore, the generation unit generates a layout that adds a visually stimulating effect when the user is excited. This allows for providing an optimal layout according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generator can adjust the layout representation method using an AI model that takes user emotional data as input and outputs the optimal layout.

[0099] The recommendation unit can estimate a user's emotions and adjust recommendation criteria based on the estimated user emotions. For example, the recommendation unit estimates a user's emotions using an emotion analysis algorithm. For example, the recommendation unit analyzes the user's facial expressions, voice, and text data to calculate an emotion score. The recommendation unit can also adjust recommendation criteria based on the estimated user emotions. For example, the recommendation unit can provide detailed recommendations when the user is relaxed. The recommendation unit can also prioritize recommending important elements when the user is in a hurry. Furthermore, the recommendation unit can recommend visually stimulating elements when the user is excited. This makes it possible to provide optimal recommendation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may adjust the recommendation criteria using an AI model that inputs user emotion data and outputs optimal recommendation criteria.

[0100] The transmission unit can estimate the user's emotions and adjust the timing of transmission based on the estimated user's emotions. For example, the transmission unit estimates the user's emotions using an emotion analysis algorithm. For example, the transmission unit analyzes the user's facial expressions, voice, and text data to calculate an emotion score. The transmission unit can also adjust the timing of transmission based on the estimated user's emotions. For example, if the user is relaxed, the transmission unit will transmit at the optimal timing. Also, if the user is in a hurry, the transmission unit can transmit quickly. Furthermore, if the user is excited, the transmission unit will transmit at a visually stimulating timing. This allows the transmission unit to adjust the optimal timing according to the user's emotions.

[0101] When making a call, the calling unit can select the optimal calling method by referring to past calling history. For example, the calling unit selects an effective calling method based on past calling history. For example, the calling unit selects an effective calling method from past calling history. The calling unit can also analyze past calling history and select the optimal timing for making a call. Furthermore, the calling unit selects the optimal calling channel by referring to past calling history. For example, the calling unit selects the optimal calling channel based on past calling history. This makes it possible to provide the optimal calling method based on past calling history. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can select the calling method using an AI model that inputs past calling history data and outputs the optimal calling method.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit allows the user to enter the information they wish to share and set the date they wish to share it. For example, the user can enter the release date of a new product or the start date of a campaign. Step 2: The generation unit analyzes the information received by the reception unit and automatically generates a layout based on the sales announcement template. For example, the generation unit inserts illustrations and applies text size and color. The generation unit takes into account image format, insertion position, font size, and color selection criteria. Step 3: The recommendation unit evaluates the layout generated by the generation unit and recommends missing elements. For example, the recommendation unit compares it with past cases and can add elements that are missing or that will improve the appearance. The recommendation unit refers to a database of past sales announcements and considers the selection criteria for the cases. Step 4: The sending unit automatically sends the layout evaluated by the recommendation unit on a set date. For example, the sending unit takes into consideration the timing of sending and the communication means to be used. Some or all of the processing in the sending unit may be performed using AI, or may be performed without using AI.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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, in order to avoid confusion and to 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section where the content to be announced is entered and the date on which it is to be announced is set; a generation unit that analyzes the information received by the reception unit and automatically generates a layout based on a business notice template; a recommendation unit that evaluates the layout generated by the generation unit and recommends missing elements; a transmission unit that automatically transmits the layout evaluated by the recommendation unit on a set date. A system characterized by:

2. The generation unit Insert illustrations and adjust text size and color The system of claim 1 .

3. The recommendation unit Compare with past cases and recommend missing elements The system of claim 1 .

4. The transmitting unit Automatically call on a set date The system of claim 1 .

5. The reception unit The user briefly describes the information they want to share and sets the date they want to share it. The system of claim 1 .

6. The generation unit Automatically generate layouts based on sales promotion templates The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the description method of the public information based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyzes the user's past communication content and suggests the best way to write it The system of claim 1 .

Images