System

Abstract

An object of the system according to the embodiment is to easily create a video invitation reflecting a special event or an episode of love of a couple.SOLUTION: A system includes a reception unit, a generation unit, and a provision unit. The reception unit inputs a special event or an episode of love of a couple. The generation unit analyzes the information input by the reception unit and generates a video invitation. The providing unit provides the video invitation generated by the generating unit.SELECTED DRAWING: Figure 1

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 technologies make it difficult to easily create video invitations that reflect special events or love stories of couples, and there is room for improvement.

[0005] The system according to the embodiment aims to easily create video invitations that reflect special events or love stories of couples. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs a special event or love story of the couple. The generation unit analyzes the information input by the reception unit and generates a video invitation. The provision unit provides the video invitation generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows couples to easily create video invitations that reflect special events and love stories. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A video invitation generation system according to an embodiment of the present invention automatically inputs a couple's special moments and love story, analyzes them using a generation AI, and generates and provides a video invitation. In the video invitation generation system, a couple inputs their special moments and love story, and the generation AI analyzes the information to generate a video invitation. This video invitation beautifully expresses the couple's special moments and love story, making it creative and unique. For example, the video invitation generation system allows a couple to input their special moments and love story. For example, the couple can input information such as photos, videos, and text. The video invitation generation system then uses a generation AI to analyze the input information. The generation AI then understands the couple's special moments and love story and generates a video invitation based on that information. For example, the generation AI combines the couple's photos and videos and adds inspiring music and effects to create a creative and unique video invitation. The video invitation generation system then provides the generated video invitation. This allows couples to enjoy the wedding process while providing guests with a touching and unique invitation. For example, couples can enjoy creating a video invitation while reminiscing about their story. Guests can also share the couple's special moments and love stories, heightening their anticipation for the wedding. This allows the video invitation generation system to automatically generate and provide video invitations that beautifully express the couple's special moments and love story. For example, a couple can input their special moments and love story, and the generation AI can analyze it and generate a video invitation, quickly and accurately delivering a moving invitation. Couples can also enjoy creating a video invitation while reminiscing about their story, and guests can share the couple's special moments and love story, heightening their anticipation for the wedding.

[0029] A video invitation generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs a special event or love story of the couple. Examples of the special event of the couple include, but are not limited to, an anniversary, a marriage proposal, and a trip. For example, the reception unit allows the couple to input special moments and love stories in the form of photos, videos, text, or the like. The generation unit uses a generation AI to analyze the information input by the reception unit and generate a video invitation. For example, the generation unit analyzes the couple's special moments and love stories and generates a video invitation based on the analysis. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to combine photos and videos of the couple and add moving music and effects to create a creative and unique video invitation. The provision unit provides the video invitation generated by the generation unit. For example, the provision unit provides the generated video invitation to a user. The provision unit can provide the generated video invitation via email, social media, a download link, or other means. As a result, the video invitation generation system according to the embodiment can automatically generate and provide video invitations that beautifully express the couple's special moments and love story.

[0030] The generation unit can generate a video invitation by combining photos, videos, and text and adding emotional music or effects. For example, the generation unit generates a video invitation by combining photos and videos of a couple and adding moving music and effects. The generation unit can also analyze the couple's special moments and love story and generate a video invitation based on that. For example, the generation unit can analyze the couple's photos and videos and select moving music and effects based on that. The generation unit can also use generation AI to understand the couple's special moments and love story and generate a video invitation based on that. This makes it possible to generate a more moving video invitation by combining photos, videos, and text and adding moving music and effects.

[0031] The generation unit can analyze the couple's special events or love stories and generate a video invitation based on the analysis. For example, the generation unit analyzes the couple's special events or love stories. For example, the generation unit can analyze the couple's special events or love stories using text analysis or sentiment analysis. The generation unit can also use a generative AI to understand the couple's special events or love stories and generate a video invitation based on the analysis. For example, the generation unit can analyze the couple's special events or love stories and select moving music and effects based on the analysis. This makes it possible to generate a more personalized and moving video invitation by analyzing the couple's special moments and love story.

[0032] The providing unit may provide the generated video invitation to a user. For example, the providing unit may provide the generated video invitation to a user. The providing unit may provide the generated video invitation by means of email, social media, a download link, or the like. For example, the providing unit may send the generated video invitation by email so that the user can easily access it. The providing unit may also share the generated video invitation on social media to widely disseminate it. Furthermore, the providing unit may provide the generated video invitation as a download link so that the user can freely download it. In this way, by providing the generated video invitation to the user, couples can easily share the invitation.

[0033] The generator may select music and effects based on a particular theme or emotion. For example, the generator may select romantic music and moving effects based on a couple's special moments or love story. The generator may also use a generation AI to understand a couple's special moments or love story and select music and effects based on that understanding. For example, the generator may analyze a couple's special moments or love story and select moving music and effects based on that understanding. This allows for the generation of more moving video invitations by selecting music and effects based on a particular theme or emotion.

[0034] The reception unit can analyze the couple's past event history and suggest the optimal input method. The reception unit, for example, analyzes the couple's past event history. For example, the reception unit automatically displays special moments that the couple has input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the couple has used in the past. Furthermore, the reception unit can predict and suggest special moments that will be used during specific time periods from the couple's past event history. In this way, by analyzing the couple's past event history, it is possible to suggest a more appropriate input method.

[0035] The reception unit can filter the input content based on the couple's current interests and trends when the input is made. The reception unit filters the input content based on, for example, the couple's current interests and trends. For example, the reception unit can suggest relevant special moments based on trends in which the couple is currently interested. The reception unit can also analyze the couple's social media activity and suggest relevant special moments. Furthermore, the reception unit can filter the input content based on the couple's current interests and suggest optimal special moments. In this way, by filtering the input content based on the couple's current interests and trends, more relevant special moments can be suggested.

[0036] The reception unit can select the optimal input means depending on the input method of the couple when inputting. For example, the reception unit selects the optimal input means depending on the input method of the couple. For example, if the couple inputs a special moment by voice, the reception unit can prioritize support for voice input. Also, if the couple inputs a special moment by text, the reception unit can prioritize support for text input. Furthermore, if the couple inputs a special moment by image, the reception unit can prioritize support for image input. This allows for more efficient input by selecting the optimal input means depending on the input method of the couple.

[0037] The reception unit can, at the time of input, prioritize input of highly relevant information taking into consideration the geographical location information of the couple. The reception unit, for example, prioritizes input of highly relevant information taking into consideration the geographical location information of the couple. For example, if the couple is in a specific location, the reception unit suggests that special moments related to that location be prioritized to be input. The reception unit can also suggest that special moments related to locations close to the couple's current location be prioritized to be input. Furthermore, the reception unit can also suggest that highly relevant special moments be prioritized to be input based on the couple's past location information. In this way, by taking into consideration the geographical location information of the couple, more relevant special moments can be prioritized to be input.

[0038] The reception unit can analyze the social media activity of the couple at the time of input and input relevant information. The reception unit, for example, analyzes the social media activity of the couple and inputs relevant information. For example, the reception unit automatically inputs special moments shared by the couple on social media. The reception unit can also analyze the content of the couple's posts on social media and input relevant special moments. Furthermore, the reception unit can input relevant special moments by referring to the activities of the couple's friends on social media. In this way, more relevant special moments can be input by analyzing the couple's social media activity.

[0039] The reception unit can customize the input method by reflecting the couple's past feedback when inputting data. The reception unit customizes the input method by reflecting, for example, the couple's past feedback. For example, the reception unit suggests the optimal input method based on feedback provided by the couple in the past. The reception unit can also preferentially suggest a specific input method based on the couple's past feedback. Furthermore, the reception unit can analyze the couple's past feedback and customize the input method. In this way, a more optimal input method can be provided by reflecting the couple's past feedback.

[0040] The generation unit may adjust the level of detail of the video based on the importance of the couple's special moment during generation. For example, the generation unit may adjust the level of detail of the video based on the importance of the couple's special moment. For example, if the couple's special moment is important, the generation unit may generate a video that expresses the moment in detail. In addition, if the couple's special moment is relatively unimportant, the generation unit may generate a video that expresses the couple's special moment in a concise manner. Furthermore, the generation unit may adjust the level of detail of the video according to the importance of the couple's special moment. In this way, by adjusting the level of detail of the video based on the importance of the couple's special moment, more important moments can be expressed in detail.

[0041] The generation unit may apply different generation algorithms depending on the category of the couple's story during generation. For example, the generation unit may apply different generation algorithms depending on the category of the couple's story. For example, if the couple's story is romantic, the generation unit may apply a generation algorithm that emphasizes romantic expressions. Also, if the couple's story is humorous, the generation unit may apply a generation algorithm that emphasizes humorous expressions. Furthermore, if the couple's story is touching, the generation unit may apply a generation algorithm that emphasizes touching expressions. Thus, by applying different generation algorithms depending on the category of the couple's story, a more appropriate video invitation can be generated.

[0042] The generation unit can improve the accuracy of generation by referring to the results of the couple's past video invitations during generation. For example, the generation unit improves the accuracy of generation by referring to the results of the couple's past video invitations. For example, the generation unit improves the accuracy of generation based on feedback from the couple's past video invitations. The generation unit can also improve the accuracy of generation by referring to successful examples of the couple's past video invitations. Furthermore, the generation unit can also improve the accuracy of generation by referring to unsuccessful examples of the couple's past video invitations. In this way, the accuracy of generation can be improved by referring to the results of the couple's past video invitations.

[0043] At the time of generation, the generation unit can determine the priority of the videos based on the time of submission of the couple's special moment. For example, the generation unit determines the priority of the videos based on the time of submission of the couple's special moment. For example, the generation unit determines the priority of the videos based on the time of submission of the couple's special moment. The generation unit can also increase the priority of the video if the couple's special moment is submitted early. Furthermore, the generation unit can also decrease the priority of the video if the couple's special moment is submitted late. In this way, by determining the priority of the videos based on the time of submission of the couple's special moment, more important moments can be preferentially represented.

[0044] The generation unit can adjust the order of the videos based on the relevance of the couple's special moment during generation. The generation unit, for example, adjusts the order of the videos based on the relevance of the couple's special moment. For example, if the couple's special moment is highly relevant, the generation unit can generate a video that depicts that moment first. Also, if the couple's special moment is less relevant, the generation unit can generate a video that depicts that moment later. Furthermore, the generation unit can adjust the order of the videos according to the relevance of the couple's special moment. In this way, by adjusting the order of the videos based on the relevance of the couple's special moment, more consistent video invitations can be generated.

[0045] The generation unit may adjust the use of technical terms in the video according to the expertise level of the couple during generation. For example, the generation unit may adjust the use of technical terms in the video according to the expertise level of the couple. For example, if the expertise level of the couple is high, the generation unit may generate a video that uses a lot of technical terms. Also, if the expertise level of the couple is low, the generation unit may generate a video that uses less technical terms. Furthermore, the generation unit may adjust the use of technical terms in the video according to the expertise level of the couple. In this way, by adjusting the use of technical terms in the video according to the expertise level of the couple, it is possible to generate a video invitation that is easier to understand.

[0046] At the time of provision, the providing unit can select the optimal providing method by referring to the couple's past provision history. The providing unit, for example, selects the optimal providing method by referring to the couple's past provision history. For example, the providing unit selects the optimal providing method based on the couple's past provision history. The providing unit can also preferentially select a specific providing method from the couple's past provision history. Furthermore, the providing unit can analyze the couple's past provision history and customize the providing method. In this way, a more appropriate providing method can be selected by referring to the couple's past provision history.

[0047] The providing unit can customize the provided content based on the current situation of the couple at the time of providing. The providing unit customizes the provided content based on, for example, the current situation of the couple. For example, if the current situation of the couple is special, the providing unit customizes the provided content to suit that situation. The providing unit can also acquire the current situation of the couple in real time and customize the provided content based on that. Furthermore, the providing unit can also customize the provided content based on the current situation of the couple. In this way, by customizing the provided content based on the current situation of the couple, more appropriate provided content can be provided.

[0048] The providing unit can improve the providing method by reflecting the couple's feedback when providing the service. For example, the providing unit improves the providing method by reflecting the couple's feedback. For example, the providing unit improves the providing method based on the couple's feedback. The providing unit can also prioritize improving a specific providing method based on the couple's feedback. Furthermore, the providing unit can analyze the couple's feedback and customize the providing method. In this way, a more appropriate providing method can be provided by reflecting the couple's feedback.

[0049] The providing unit can select the optimal providing method by taking into consideration the geographical location information of the couple at the time of providing. The providing unit selects the optimal providing method by taking into consideration, for example, the geographical location information of the couple. For example, the providing unit selects the optimal providing method based on the couple's current location. The providing unit can also provide related special moments based on the geographical location information of the couple. Furthermore, the providing unit can select the optimal providing method by referring to the couple's past location information. In this way, a more appropriate providing method can be selected by taking into consideration the couple's geographical location information.

[0050] At the time of provision, the providing unit can analyze the social media activity of the couple and suggest a means of provision. For example, the providing unit analyzes the social media activity of the couple and suggests a means of provision. For example, the providing unit analyzes the couple's social media activity and suggests the optimal means of provision. The providing unit can also provide related special moments based on the content of the couple's social media posts. Furthermore, the providing unit can also suggest the optimal means of provision by taking into account the activities of the couple's friends on social media. In this way, by analyzing the couple's social media activity, it is possible to suggest a more appropriate means of provision.

[0051] The providing unit can customize the providing method by reflecting the couple's past feedback when providing the information. The providing unit, for example, customizes the providing method by reflecting the couple's past feedback. For example, the providing unit customizes the providing method based on the couple's past feedback. The providing unit can also preferentially customize a specific providing method based on the couple's past feedback. Furthermore, the providing unit can analyze the couple's past feedback and optimize the providing method. In this way, a more appropriate providing method can be provided by reflecting the couple's past feedback.

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

[0053] When a couple inputs special events or love stories, the reception unit can automatically analyze the couple's past social media posts and suggest related photos and videos. For example, it can automatically detect travel photos or marriage proposal videos shared by the couple in the past and display them as input candidates. The reception unit can also suggest related special moments using tagging information from the couple's social media friends. Furthermore, the reception unit can suggest the optimal input method based on the frequency of the couple's social media activity and the content of their posts. This allows couples to easily input their past memories and generate richer video invitations.

[0054] When analyzing the couple's special events and love stories, the generation unit can improve the accuracy of generation by referring to the couple's past feedback on video invitations. For example, the generation algorithm can be adjusted based on the couple's past feedback. The generation unit can also improve the accuracy of generation by referring to examples of successful video invitations from the couple's past. Furthermore, the generation unit can improve the accuracy of generation by referring to examples of unsuccessful video invitations from the couple's past. In this way, higher quality video invitations can be generated by reflecting the couple's past feedback.

[0055] The generation unit may take into consideration the couple's past musical preferences when selecting music and effects based on a particular theme or emotion. For example, music suitable for the video invitation may be selected based on music the couple liked to listen to in the past. Music suitable for the video invitation may also be selected based on music used in the couple's past events. Furthermore, music suitable for the video invitation may also be selected based on music playlists shared on the couple's social media. By selecting music and effects based on the couple's musical preferences, a more moving video invitation may be generated.

[0056] The reception unit can analyze the couple's past event history and suggest the optimal input method. For example, special moments that the couple have input in the past can be automatically displayed as candidates. It can also prioritize suggestions of input methods (voice, text, etc.) that the couple have used in the past. Furthermore, it can predict and suggest special moments that will be used during specific time periods from the couple's past event history. In this way, by analyzing the couple's past event history, it is possible to suggest more appropriate input methods.

[0057] The reception unit can filter the input content based on the couple's current interests and trends when the input is made. For example, relevant special moments can be suggested based on trends that the couple is currently interested in. The reception unit can also analyze the couple's social media activity and suggest relevant special moments. Furthermore, the reception unit can filter the input content and suggest optimal special moments based on the couple's current interests. This makes it possible to suggest more relevant special moments by filtering the input content based on the couple's current interests and trends.

[0058] At the time of provision, the provision unit can select the optimal provision method by referring to the couple's past provision history. For example, the optimal provision method is selected based on the couple's past provision history. Also, a specific provision method can be preferentially selected from the couple's past provision history. Furthermore, the couple's past provision history can be analyzed and the provision method can be customized. In this way, a more appropriate provision method can be selected by referring to the couple's past provision history.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The receptionist inputs the couple's special events or love stories. Couples can input special moments and love stories such as anniversaries, proposals, and trips in the form of photos, videos, text, etc. Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and generate a video invitation. The generation unit analyzes the couple's special moments and love story and generates a video invitation based on that. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to combine the couple's photos and videos and add inspiring music and effects to create a creative and unique video invitation. Step 3: The providing unit provides the video invitation generated by the generating unit. The providing unit can provide the generated video invitation to the user by means of email, social media, a download link, or the like.

[0061] (Example 2) A video invitation generation system according to an embodiment of the present invention automatically inputs a couple's special moments and love story, analyzes them using a generation AI, and generates and provides a video invitation. In the video invitation generation system, a couple inputs their special moments and love story, and the generation AI analyzes the information to generate a video invitation. This video invitation beautifully expresses the couple's special moments and love story, making it creative and unique. For example, the video invitation generation system allows a couple to input their special moments and love story. For example, the couple can input information such as photos, videos, and text. The video invitation generation system then uses a generation AI to analyze the input information. The generation AI then understands the couple's special moments and love story and generates a video invitation based on that information. For example, the generation AI combines the couple's photos and videos and adds inspiring music and effects to create a creative and unique video invitation. The video invitation generation system then provides the generated video invitation. This allows couples to enjoy the wedding process while providing guests with a touching and unique invitation. For example, couples can enjoy creating a video invitation while reminiscing about their story. Guests can also share the couple's special moments and love stories, heightening their anticipation for the wedding. This allows the video invitation generation system to automatically generate and provide video invitations that beautifully express the couple's special moments and love story. For example, a couple can input their special moments and love story, and the generation AI can analyze it and generate a video invitation, quickly and accurately delivering a moving invitation. Couples can also enjoy creating a video invitation while reminiscing about their story, and guests can share the couple's special moments and love story, heightening their anticipation for the wedding.

[0062] A video invitation generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs a special event or love story of the couple. Examples of the special event of the couple include, but are not limited to, an anniversary, a marriage proposal, and a trip. For example, the reception unit allows the couple to input special moments and love stories in the form of photos, videos, text, or the like. The generation unit uses a generation AI to analyze the information input by the reception unit and generate a video invitation. For example, the generation unit analyzes the couple's special moments and love stories and generates a video invitation based on the analysis. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to combine photos and videos of the couple and add moving music and effects to create a creative and unique video invitation. The provision unit provides the video invitation generated by the generation unit. For example, the provision unit provides the generated video invitation to a user. The provision unit can provide the generated video invitation via email, social media, a download link, or other means. As a result, the video invitation generation system according to the embodiment can automatically generate and provide video invitations that beautifully express the couple's special moments and love story.

[0063] The generation unit can generate a video invitation by combining photos, videos, and text and adding emotional music or effects. For example, the generation unit generates a video invitation by combining photos and videos of a couple and adding moving music and effects. The generation unit can also analyze the couple's special moments and love story and generate a video invitation based on that. For example, the generation unit can analyze the couple's photos and videos and select moving music and effects based on that. The generation unit can also use generation AI to understand the couple's special moments and love story and generate a video invitation based on that. This makes it possible to generate a more moving video invitation by combining photos, videos, and text and adding moving music and effects.

[0064] The generation unit can analyze the couple's special events or love stories and generate a video invitation based on the analysis. For example, the generation unit analyzes the couple's special events or love stories. For example, the generation unit can analyze the couple's special events or love stories using text analysis or sentiment analysis. The generation unit can also use a generative AI to understand the couple's special events or love stories and generate a video invitation based on the analysis. For example, the generation unit can analyze the couple's special events or love stories and select moving music and effects based on the analysis. This makes it possible to generate a more personalized and moving video invitation by analyzing the couple's special moments and love story.

[0065] The providing unit may provide the generated video invitation to a user. For example, the providing unit may provide the generated video invitation to a user. The providing unit may provide the generated video invitation by means of email, social media, a download link, or the like. For example, the providing unit may send the generated video invitation by email so that the user can easily access it. The providing unit may also share the generated video invitation on social media to widely disseminate it. Furthermore, the providing unit may provide the generated video invitation as a download link so that the user can freely download it. In this way, by providing the generated video invitation to the user, couples can easily share the invitation.

[0066] The generator may select music and effects based on a particular theme or emotion. For example, the generator may select romantic music and moving effects based on a couple's special moments or love story. The generator may also use a generation AI to understand a couple's special moments or love story and select music and effects based on that understanding. For example, the generator may analyze a couple's special moments or love story and select moving music and effects based on that understanding. This allows for the generation of more moving video invitations by selecting music and effects based on a particular theme or emotion.

[0067] The reception unit can estimate the emotional state of the couple and adjust the design of the input interface based on the estimated emotional state of the couple. The reception unit, for example, estimates the emotional state of the couple. For example, the reception unit analyzes the couple's facial expressions and voice to estimate the emotional state. The reception unit can also estimate the couple's emotional state using generative AI. For example, the reception unit analyzes the couple's facial expressions and voice and estimates the emotional state based on the analysis. Next, the reception unit adjusts the design of the input interface based on the estimated emotional state of the couple. For example, if the couple is nervous, the reception unit provides an interface with calm colors to reduce visual stress. Also, if the couple is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the couple is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This makes it possible to provide a more comfortable input experience by adjusting the design of the input interface according to the couple's emotions.

[0068] The reception unit can analyze the couple's past event history and suggest the optimal input method. The reception unit, for example, analyzes the couple's past event history. For example, the reception unit automatically displays special moments that the couple has input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the couple has used in the past. Furthermore, the reception unit can predict and suggest special moments that will be used during specific time periods from the couple's past event history. In this way, by analyzing the couple's past event history, it is possible to suggest a more appropriate input method.

[0069] The reception unit can filter the input content based on the couple's current interests and trends when the input is made. The reception unit filters the input content based on, for example, the couple's current interests and trends. For example, the reception unit can suggest relevant special moments based on trends in which the couple is currently interested. The reception unit can also analyze the couple's social media activity and suggest relevant special moments. Furthermore, the reception unit can filter the input content based on the couple's current interests and suggest optimal special moments. In this way, by filtering the input content based on the couple's current interests and trends, more relevant special moments can be suggested.

[0070] The reception unit can select the optimal input means depending on the input method of the couple when inputting. For example, the reception unit selects the optimal input means depending on the input method of the couple. For example, if the couple inputs a special moment by voice, the reception unit can prioritize support for voice input. Also, if the couple inputs a special moment by text, the reception unit can prioritize support for text input. Furthermore, if the couple inputs a special moment by image, the reception unit can prioritize support for image input. This allows for more efficient input by selecting the optimal input means depending on the input method of the couple.

[0071] The reception unit can estimate the emotional state of the couple and prioritize input content based on the estimated emotional state of the couple. The reception unit, for example, estimates the emotional state of the couple. For example, the reception unit analyzes the couple's facial expressions and voice to estimate the emotional state. The reception unit can also estimate the couple's emotional state using a generative AI. For example, the reception unit analyzes the couple's facial expressions and voice and estimates the emotional state based on the analysis. Next, the reception unit prioritizes input content based on the estimated emotional state of the couple. For example, if the couple is emotional, the reception unit can suggest that emotional moments be prioritized. If the couple is having fun, the reception unit can also suggest that happy moments be prioritized. Furthermore, if the couple is nervous, the reception unit can suggest that relaxing moments be prioritized. In this way, by prioritizing input content based on the couple's emotions, more emotional moments can be prioritized.

[0072] The reception unit can, at the time of input, prioritize input of highly relevant information taking into consideration the geographical location information of the couple. The reception unit, for example, prioritizes input of highly relevant information taking into consideration the geographical location information of the couple. For example, if the couple is in a specific location, the reception unit suggests that special moments related to that location be prioritized to be input. The reception unit can also suggest that special moments related to locations close to the couple's current location be prioritized to be input. Furthermore, the reception unit can also suggest that highly relevant special moments be prioritized to be input based on the couple's past location information. In this way, by taking into consideration the geographical location information of the couple, more relevant special moments can be prioritized to be input.

[0073] The reception unit can analyze the social media activity of the couple at the time of input and input relevant information. The reception unit, for example, analyzes the social media activity of the couple and inputs relevant information. For example, the reception unit automatically inputs special moments shared by the couple on social media. The reception unit can also analyze the content of the couple's posts on social media and input relevant special moments. Furthermore, the reception unit can input relevant special moments by referring to the activities of the couple's friends on social media. In this way, more relevant special moments can be input by analyzing the couple's social media activity.

[0074] The reception unit can customize the input method by reflecting the couple's past feedback when inputting data. The reception unit customizes the input method by reflecting, for example, the couple's past feedback. For example, the reception unit suggests the optimal input method based on feedback provided by the couple in the past. The reception unit can also preferentially suggest a specific input method based on the couple's past feedback. Furthermore, the reception unit can analyze the couple's past feedback and customize the input method. In this way, a more optimal input method can be provided by reflecting the couple's past feedback.

[0075] The generation unit can estimate the emotional state of the couple and adjust the presentation of the video invitation based on the estimated emotional state of the couple. The generation unit, for example, estimates the emotional state of the couple. For example, the generation unit analyzes the couple's facial expressions and voice to estimate the emotional state. The generation unit can also estimate the couple's emotional state using a generation AI. For example, the generation unit analyzes the couple's facial expressions and voice and estimates the emotional state based on the analysis. Next, the generation unit adjusts the presentation of the video invitation based on the estimated emotional state of the couple. For example, if the couple is moved, the generation unit generates a video invitation with inspiring music and effects. If the couple is having fun, the generation unit can generate a video invitation with fun music and effects. Furthermore, if the couple is tense, the generation unit can generate a video invitation with relaxing music and effects. In this way, by adjusting the presentation of the video invitation based on the couple's emotions, a more inspiring video invitation can be generated.

[0076] The generation unit may adjust the level of detail of the video based on the importance of the couple's special moment during generation. For example, the generation unit may adjust the level of detail of the video based on the importance of the couple's special moment. For example, if the couple's special moment is important, the generation unit may generate a video that expresses the moment in detail. In addition, if the couple's special moment is relatively unimportant, the generation unit may generate a video that expresses the couple's special moment in a concise manner. Furthermore, the generation unit may adjust the level of detail of the video according to the importance of the couple's special moment. In this way, by adjusting the level of detail of the video based on the importance of the couple's special moment, more important moments can be expressed in detail.

[0077] The generation unit may apply different generation algorithms depending on the category of the couple's story during generation. For example, the generation unit may apply different generation algorithms depending on the category of the couple's story. For example, if the couple's story is romantic, the generation unit may apply a generation algorithm that emphasizes romantic expressions. Also, if the couple's story is humorous, the generation unit may apply a generation algorithm that emphasizes humorous expressions. Furthermore, if the couple's story is touching, the generation unit may apply a generation algorithm that emphasizes touching expressions. Thus, by applying different generation algorithms depending on the category of the couple's story, a more appropriate video invitation can be generated.

[0078] The generation unit can improve the accuracy of generation by referring to the results of the couple's past video invitations during generation. For example, the generation unit improves the accuracy of generation by referring to the results of the couple's past video invitations. For example, the generation unit improves the accuracy of generation based on feedback from the couple's past video invitations. The generation unit can also improve the accuracy of generation by referring to successful examples of the couple's past video invitations. Furthermore, the generation unit can also improve the accuracy of generation by referring to unsuccessful examples of the couple's past video invitations. In this way, the accuracy of generation can be improved by referring to the results of the couple's past video invitations.

[0079] The generation unit can estimate the emotional state of the couple and adjust the length of the video based on the estimated emotional state of the couple. The generation unit, for example, estimates the emotional state of the couple. For example, the generation unit analyzes the couple's facial expressions and voice to estimate the emotional state. The generation unit can also estimate the couple's emotional state using a generation AI. For example, the generation unit analyzes the couple's facial expressions and voice and estimates the emotional state based on the analysis. Next, the generation unit adjusts the length of the video based on the estimated emotional state of the couple. For example, if the couple is moved, the generation unit can generate a video that lengthens the emotional moments. Also, if the couple is having fun, the generation unit can generate a video that lengthens the fun moments. Furthermore, if the couple is tense, the generation unit can generate a video that lengthens the relaxing moments. In this way, by adjusting the length of the video based on the couple's emotions, a more touching video invitation can be generated.

[0080] At the time of generation, the generation unit can determine the priority of the videos based on the time of submission of the couple's special moment. For example, the generation unit determines the priority of the videos based on the time of submission of the couple's special moment. For example, the generation unit determines the priority of the videos based on the time of submission of the couple's special moment. The generation unit can also increase the priority of the video if the couple's special moment is submitted early. Furthermore, the generation unit can also decrease the priority of the video if the couple's special moment is submitted late. In this way, by determining the priority of the videos based on the time of submission of the couple's special moment, more important moments can be preferentially represented.

[0081] The generation unit can adjust the order of the videos based on the relevance of the couple's special moment during generation. The generation unit, for example, adjusts the order of the videos based on the relevance of the couple's special moment. For example, if the couple's special moment is highly relevant, the generation unit can generate a video that depicts that moment first. Also, if the couple's special moment is less relevant, the generation unit can generate a video that depicts that moment later. Furthermore, the generation unit can adjust the order of the videos according to the relevance of the couple's special moment. In this way, by adjusting the order of the videos based on the relevance of the couple's special moment, more consistent video invitations can be generated.

[0082] The generation unit may adjust the use of technical terms in the video according to the expertise level of the couple during generation. For example, the generation unit may adjust the use of technical terms in the video according to the expertise level of the couple. For example, if the expertise level of the couple is high, the generation unit may generate a video that uses a lot of technical terms. Also, if the expertise level of the couple is low, the generation unit may generate a video that uses less technical terms. Furthermore, the generation unit may adjust the use of technical terms in the video according to the expertise level of the couple. In this way, by adjusting the use of technical terms in the video according to the expertise level of the couple, it is possible to generate a video invitation that is easier to understand.

[0083] The providing unit can estimate the emotional state of the couple and adjust the method of providing the video invitation based on the estimated emotional state of the couple. The providing unit, for example, estimates the emotional state of the couple. For example, the providing unit analyzes the couple's facial expressions and voice to estimate the emotional state. The providing unit can also estimate the couple's emotional state using a generation AI. For example, the providing unit analyzes the couple's facial expressions and voice and estimates the emotional state based on the analysis. Next, the providing unit adjusts the method of providing the video invitation based on the estimated emotional state of the couple. For example, if the couple is moved, the providing unit can provide the video invitation with moving music. Also, if the couple is having fun, the providing unit can provide the video invitation with happy music. Furthermore, if the couple is tense, the providing unit can provide the video invitation with relaxing music. In this way, by adjusting the method of providing the video invitation based on the couple's emotions, a more moving delivery experience can be provided.

[0084] At the time of provision, the providing unit can select the optimal providing method by referring to the couple's past provision history. The providing unit, for example, selects the optimal providing method by referring to the couple's past provision history. For example, the providing unit selects the optimal providing method based on the couple's past provision history. The providing unit can also preferentially select a specific providing method from the couple's past provision history. Furthermore, the providing unit can analyze the couple's past provision history and customize the providing method. In this way, a more appropriate providing method can be selected by referring to the couple's past provision history.

[0085] The providing unit can customize the provided content based on the current situation of the couple at the time of providing. The providing unit customizes the provided content based on, for example, the current situation of the couple. For example, if the current situation of the couple is special, the providing unit customizes the provided content to suit that situation. The providing unit can also acquire the current situation of the couple in real time and customize the provided content based on that. Furthermore, the providing unit can also customize the provided content based on the current situation of the couple. In this way, by customizing the provided content based on the current situation of the couple, more appropriate provided content can be provided.

[0086] The providing unit can improve the providing method by reflecting the couple's feedback when providing the service. For example, the providing unit improves the providing method by reflecting the couple's feedback. For example, the providing unit improves the providing method based on the couple's feedback. The providing unit can also prioritize improving a specific providing method based on the couple's feedback. Furthermore, the providing unit can analyze the couple's feedback and customize the providing method. In this way, a more appropriate providing method can be provided by reflecting the couple's feedback.

[0087] The providing unit can estimate the emotional state of the couple and determine a priority for providing video invitations based on the estimated emotional state of the couple. The providing unit, for example, estimates the emotional state of the couple. For example, the providing unit analyzes the couple's facial expressions and voice to estimate the emotional state. The providing unit can also estimate the couple's emotional state using a generation AI. For example, the providing unit analyzes the couple's facial expressions and voice and estimates the emotional state based on the analysis. Next, the providing unit determines a priority for providing video invitations based on the estimated emotional state of the couple. For example, if the couple is emotional, the providing unit can prioritize providing an emotional video invitation. Also, if the couple is having fun, the providing unit can prioritize providing a fun video invitation. Furthermore, if the couple is tense, the providing unit can prioritize providing a relaxing video invitation. In this way, by determining the priority for providing video invitations based on the couple's emotions, more emotional video invitations can be prioritized.

[0088] The providing unit can select the optimal providing method by taking into consideration the geographical location information of the couple at the time of providing. The providing unit selects the optimal providing method by taking into consideration, for example, the geographical location information of the couple. For example, the providing unit selects the optimal providing method based on the couple's current location. The providing unit can also provide related special moments based on the geographical location information of the couple. Furthermore, the providing unit can select the optimal providing method by referring to the couple's past location information. In this way, a more appropriate providing method can be selected by taking into consideration the couple's geographical location information.

[0089] At the time of provision, the providing unit can analyze the social media activity of the couple and suggest a means of provision. For example, the providing unit analyzes the social media activity of the couple and suggests a means of provision. For example, the providing unit analyzes the couple's social media activity and suggests the optimal means of provision. The providing unit can also provide related special moments based on the content of the couple's social media posts. Furthermore, the providing unit can also suggest the optimal means of provision by taking into account the activities of the couple's friends on social media. In this way, by analyzing the couple's social media activity, it is possible to suggest a more appropriate means of provision.

[0090] The providing unit can customize the providing method by reflecting the couple's past feedback when providing the information. The providing unit, for example, customizes the providing method by reflecting the couple's past feedback. For example, the providing unit customizes the providing method based on the couple's past feedback. The providing unit can also preferentially customize a specific providing method based on the couple's past feedback. Furthermore, the providing unit can analyze the couple's past feedback and optimize the providing method. In this way, a more appropriate providing method can be provided by reflecting the couple's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, generation unit, and provision 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 can input a couple's special moments or love story using the reception device 38 of the smart device 14. For example, the generation unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze the input information using a generation AI to generate a video invitation. For example, the provision unit can provide the generated video invitation using the output device 40 of the smart device 14. Furthermore, the provision unit can also be realized by the specific processing unit 290 of the data processing device 12 and provide the generated video invitation via email or social media. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, and provision 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 can input a couple's special moments or love story using the microphone 238 of the smart glasses 214. For example, the generation unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze the input information using a generation AI to generate a video invitation. For example, the provision unit can provide the generated video invitation using the speaker 240 of the smart glasses 214. Furthermore, the provision unit can also be realized by the specific processing unit 290 of the data processing device 12 and provide the generated video invitation via email or social media. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, generation unit, and provision 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 can input the couple's special moments or love story using the microphone 238 of the headset-type terminal 314. For example, the generation unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze the input information using a generation AI to generate a video invitation. For example, the provision unit can provide the generated video invitation using the display 343 of the headset-type terminal 314. The provision unit can also be realized by the specific processing unit 290 of the data processing device 12 and provide the generated video invitation via email or social media. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the couple's special moments or love story using the microphone 238 of the robot 414. For example, the generation unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze the input information using a generation AI to generate a video invitation. For example, the provision unit can provide the generated video invitation using the speaker 240 of the robot 414. Furthermore, the provision unit can also be realized by the specific processing unit 290 of the data processing device 12 and provide the generated video invitation via email or social media.

[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] When a couple inputs special events or love stories, the reception unit can automatically analyze the couple's past social media posts and suggest related photos and videos. For example, it can automatically detect travel photos or marriage proposal videos shared by the couple in the past and display them as input candidates. The reception unit can also suggest related special moments using tagging information from the couple's social media friends. Furthermore, the reception unit can suggest the optimal input method based on the frequency of the couple's social media activity and the content of their posts. This allows couples to easily input their past memories and generate richer video invitations.

[0093] The generation unit can estimate the emotional state of the couple when analyzing the couple's special moments and love story, and adjust the theme of the video invitation based on the estimated emotion. For example, if the couple is emotional, a video invitation emphasizing an emotional theme can be generated. If the couple is having fun, a video invitation emphasizing a fun theme can be generated. Furthermore, if the couple is nervous, a video invitation emphasizing a relaxing theme can be generated. In this way, by adjusting the theme of the video invitation based on the couple's emotions, a more emotional and unique video invitation can be generated.

[0094] When analyzing the couple's special events and love stories, the generation unit can improve the accuracy of generation by referring to the couple's past feedback on video invitations. For example, the generation algorithm can be adjusted based on the couple's past feedback. The generation unit can also improve the accuracy of generation by referring to examples of successful video invitations from the couple's past. Furthermore, the generation unit can improve the accuracy of generation by referring to examples of unsuccessful video invitations from the couple's past. In this way, higher quality video invitations can be generated by reflecting the couple's past feedback.

[0095] When providing the generated video invitation to the user, the providing unit can estimate the emotional state of the couple and adjust the method of providing the invitation based on the estimated emotion. For example, if the couple is emotional, the video invitation can be provided with emotional music. If the couple is having fun, the video invitation can be provided with cheerful music. Furthermore, if the couple is nervous, the video invitation can be provided with relaxing music. In this way, by adjusting the method of providing the video invitation based on the couple's emotion, a more emotional delivery experience can be provided.

[0096] The generation unit may take into consideration the couple's past musical preferences when selecting music and effects based on a particular theme or emotion. For example, music suitable for the video invitation may be selected based on music the couple liked to listen to in the past. Music suitable for the video invitation may also be selected based on music used in the couple's past events. Furthermore, music suitable for the video invitation may also be selected based on music playlists shared on the couple's social media. By selecting music and effects based on the couple's musical preferences, a more moving video invitation may be generated.

[0097] The reception unit can estimate the emotional state of the couple and adjust the design of the input interface based on the estimated emotional state of the couple. For example, if the couple is nervous, a calm-colored interface can be provided to reduce visual stress. If the couple is having fun, a bright-colored interface can be provided to make input work more enjoyable. Furthermore, if the couple is tired, a simple, highly visible interface can be provided to make input work easier. In this way, by adjusting the design of the input interface according to the couple's emotions, a more comfortable input experience can be provided.

[0098] The reception unit can analyze the couple's past event history and suggest the optimal input method. For example, special moments that the couple have input in the past can be automatically displayed as candidates. It can also prioritize suggestions of input methods (voice, text, etc.) that the couple have used in the past. Furthermore, it can predict and suggest special moments that will be used during specific time periods from the couple's past event history. In this way, by analyzing the couple's past event history, it is possible to suggest more appropriate input methods.

[0099] The generation unit can estimate the emotional state of the couple and adjust the length of the video based on the estimated emotional state of the couple. For example, if the couple is emotional, a video that shows the emotional moment in a longer format can be generated. Also, if the couple is having fun, a video that shows the happy moment in a longer format can be generated. Furthermore, if the couple is nervous, a video that shows the relaxing moment in a longer format can be generated. In this way, by adjusting the length of the video based on the couple's emotions, a more touching video invitation can be generated.

[0100] The reception unit can filter the input content based on the couple's current interests and trends when the input is made. For example, relevant special moments can be suggested based on trends that the couple is currently interested in. The reception unit can also analyze the couple's social media activity and suggest relevant special moments. Furthermore, the reception unit can filter the input content and suggest optimal special moments based on the couple's current interests. This makes it possible to suggest more relevant special moments by filtering the input content based on the couple's current interests and trends.

[0101] At the time of provision, the provision unit can select the optimal provision method by referring to the couple's past provision history. For example, the optimal provision method is selected based on the couple's past provision history. Also, a specific provision method can be preferentially selected from the couple's past provision history. Furthermore, the couple's past provision history can be analyzed and the provision method can be customized. In this way, a more appropriate provision method can be selected by referring to the couple's past provision history.

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

[0103] Step 1: The receptionist inputs the couple's special events or love stories. Couples can input special moments and love stories such as anniversaries, proposals, and trips in the form of photos, videos, text, etc. Step 2: The generation unit uses generation AI to analyze the information entered by the reception unit and generate a video invitation. The generation unit analyzes the couple's special moments and love story and generates a video invitation based on that. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to combine the couple's photos and videos and add inspiring music and effects to create a creative and unique video invitation. Step 3: The providing unit provides the video invitation generated by the generating unit. The providing unit can provide the generated video invitation to the user by means of email, social media, a download link, or the like.

[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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[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 desk where couples can input their special events or love stories; a generating unit that analyzes the information input by the receiving unit and generates a video invitation; a providing unit that provides the video invitation generated by the generating unit; Equipped with A system characterized by:

2. The generation unit Create video invitations by combining photos, videos, text, and adding emotive music or effects 2. The system of claim 1.

3. The generation unit Analyze a couple's special events or love stories and generate video invitations based on them 2. The system of claim 1.

4. The providing unit Provide the generated video invitation to the user 2. The system of claim 1.

5. The generation unit Selecting music and effects based on a particular theme or emotion 2. The system of claim 1.

6. The reception unit Estimate the emotional state of a couple and adjust the design of the input interface based on the estimated emotional state of the couple.

2. The system of claim 1.

7. The reception unit Analyzes couples' past event history and suggests the best way to enter information 2. The system of claim 1.

8. The reception unit As you type, your input is filtered based on the couple's current interests and trends 2. The system of claim 1.

Images