system

Abstract

The system according to this embodiment aims to easily record daily events and convert them into diary-style text or videos. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a video generation unit. The reception unit receives a bulleted list of events that occurred during the day from the user. The generation unit analyzes the information received by the reception unit and generates a diary-style text. The video generation unit creates a video using photos registered by the user, based on the diary-style text generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to easily record daily events and convert them into diary-style texts or videos.

[0005] The system according to the embodiment aims to easily record daily events and convert them into diary-style texts or videos.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a video generation unit. The reception unit receives a bulleted list of events that occurred during the day from the user. The generation unit analyzes the information received by the reception unit and generates a diary-style text. The video generation unit creates a video using photos registered by the user, based on the diary-style text generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can easily record daily events and convert them into diary-style text or videos. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The diary generation system according to an embodiment of the present invention is a system that automatically generates diary-style text simply by having the user record the events of the day in a simple bulleted list, and further creates a video when the user registers photos from that day. The diary generation system works by having the user input the events of the day in a bulleted list. For example, the user inputs things like, "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner." This information is input to the AI. Next, the AI ​​analyzes the input information and generates diary-style text. For example, it might generate text like, "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." In this way, the user can obtain diary-style text simply by inputting a simple bulleted list. Furthermore, when the user registers photos from that day, the AI ​​creates a video using those photos. For example, if the user registers "a photo of breakfast pancakes," "a photo of the movie theater," and "a photo of dinner curry," the AI ​​combines these photos to create a video. Since this video also includes the diary-style text, the user can look back on the events of the day as a video. In this way, the user can easily record the events of each day, 365 days a year, and save their memories as videos. For example, it allows users to easily record events from special days, such as travel memories or anniversaries, and look back on them later. Furthermore, even those who find writing a diary tedious can easily generate diary-style entries with simple bullet point input, making it user-friendly even for those without a diary-writing habit. Additionally, since AI automatically creates videos, users can save their memories as videos without any effort. In short, this diary generation system allows users to easily generate diary-style entries and create videos using photos.

[0029] The diary generation system according to this embodiment comprises a reception unit, a generation unit, and a video generation unit. The reception unit allows the user to input events that happened that day in bullet points. For example, the user can input things like "I ate pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner." The reception unit provides, for example, a text input interface to allow the user to easily input bullet points. The reception unit also provides a voice input interface, allowing the user to input events by voice. For example, if the user inputs "I ate pancakes for breakfast today" by voice, the reception unit converts the voice into text and accepts it as bullet points. Furthermore, the reception unit provides an interface for the user to register photos. For example, the user can upload "a photo of the pancakes for breakfast," "a photo of the movie theater," and "a photo of the curry for dinner." The generation unit analyzes the information received by the reception unit and generates diary-style text. The generation unit analyzes the bullet points and generates text using, for example, natural language processing technology. For example, the generation unit generates diary-style sentences such as "Today I ate pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner," based on bulleted list information such as "I ate pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner." Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can generate sentences using a generation AI model that takes bulleted list information as input and outputs diary-style sentences. The video generation unit creates a video using photos registered by the user based on the diary-style sentences generated by the generation unit. The video generation unit can, for example, display the photos in a slideshow format and incorporate the diary-style sentences as narration. For example, the video generation unit can display a photo of "pancakes for breakfast" while playing narration such as "Today I ate pancakes for breakfast." The video generation unit can also create videos with added music. For example, the video generation unit can create a video combining photos and diary-style sentences while playing music selected by the user as background music.As a result, the diary generation system according to this embodiment allows users to easily generate diary-style text and create videos using photographs.

[0030] The reception desk allows users to input events that happened during the day in a bulleted list. For example, a user can input things like "I ate pancakes for breakfast," "I went to see a movie with a friend," or "I made curry for dinner." The reception desk provides a text input interface, for example, to make it easy for users to input bulleted information. Specifically, the text entered by the user is displayed in real time, making it easy to correct typos and add information. The reception desk also provides a voice input interface, allowing users to input events by voice. For example, if a user voice-inputs "I ate pancakes for breakfast today," the reception desk converts the voice into text and accepts it as bulleted information. This voice input uses speech recognition technology, enabling accurate transcription of the user's speech into text. Furthermore, the reception desk provides an interface for users to register photos. For example, a user can upload "a photo of my breakfast pancakes," "a photo of the movie theater," or "a photo of my dinner curry." Photo uploads are done via drag-and-drop or a file selection dialog, designed for intuitive operation. This allows the reception desk to centrally receive information in various formats, such as text, audio, and photos, enabling users to easily and quickly record the events of the day.

[0031] The generation unit analyzes the information received by the reception unit and generates diary-style text. The generation unit analyzes bulleted information using, for example, natural language processing technology and generates text. Specifically, the generation unit analyzes the bulleted information entered by the user based on the context and generates natural-sounding text by inserting appropriate conjunctions and particles. For example, based on bulleted information such as "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner," it generates diary-style text such as "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. When a generative AI is used, the generation unit can generate text using a generative AI model that takes bulleted information as input and outputs diary-style text. This generative AI model has learned from a large amount of text data and has the ability to generate grammatically accurate and natural-sounding text. For example, based on the user's input, "I had pancakes for breakfast," the generation AI will generate a specific sentence such as, "I had pancakes for breakfast today." In this way, the generation unit can automatically generate natural and easy-to-read diary-style sentences based on the information the user inputs.

[0032] The video generation unit creates videos using user-registered photos based on diary-style text generated by the unit. For example, the video generation unit can display photos in a slideshow format and incorporate the diary-style text as narration. Specifically, the video generation unit arranges the user-uploaded photos chronologically and adds diary-style text corresponding to each photo as narration. For example, the video generation unit might display a photo of "pancakes for breakfast" while playing narration such as, "I had pancakes for breakfast today." The video generation unit can also create videos with added music. For example, the video generation unit can create a video combining photos and diary-style text while playing music selected by the user as background music. Users can choose from multiple genres and tempos according to their preferences, further enhancing the atmosphere of the video. In addition, the video generation unit can create visually appealing videos by adding text animations and transition effects. For example, it can use fade-in / fade-out effects when photos switch, or add text scrolling animations. This allows the video generation unit to easily generate diary-style text and create engaging videos using photos.

[0033] The generation unit can analyze bulleted information entered by the user and generate diary-style text. The generation unit can analyze bulleted information and generate text using, for example, natural language processing technology. For example, based on bulleted information such as "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner," the generation unit can generate diary-style text such as "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the generation unit can generate text using a generative AI model that takes bulleted information as input and outputs diary-style text. This allows for the generation of diary-style text based on bulleted information entered by the user.

[0034] The video generation unit can create videos using photos registered by the user. For example, the video generation unit can display photos in a slideshow format and incorporate diary-style text as narration. For example, the video generation unit can display a photo of "pancakes for breakfast" while playing narration such as "I had pancakes for breakfast today." The video generation unit can also create videos with added music. For example, the video generation unit can create a video combining photos and diary-style text while playing music selected by the user as background music. Some or all of the above processing in the video generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the video generation unit can create videos using a generative AI model that takes photos registered by the user as input and outputs a video. This allows for the creation of videos using photos registered by the user.

[0035] The video generation unit can incorporate the generated diary-style text into the video. For example, the video generation unit can display photos in a slideshow format and incorporate the diary-style text as narration. For example, the video generation unit can display a photo of "pancakes for breakfast" while playing narration such as "I had pancakes for breakfast today." The video generation unit can also display the generated diary-style text as text in the video. For example, the video generation unit can display the diary-style text below a photo to provide information visually. Some or all of the above processing in the video generation unit may be performed using, for example, a generation AI, or not. For example, the video generation unit can take the generated diary-style text as input and perform the process of incorporating it into the video using a generation AI model. This allows the generated diary-style text to be incorporated into the video.

[0036] The reception desk allows users to input events that happened during the day in a bulleted list. The reception desk provides, for example, a text input interface to allow users to easily input bulleted information. The reception desk also provides a voice input interface, allowing users to input events by voice. For example, if a user voice-inputs "I had pancakes for breakfast today," the reception desk converts the voice into text and accepts it as bulleted information. Furthermore, the reception desk provides an interface for users to register photos. For example, a user can upload "a photo of breakfast pancakes," "a photo of a movie theater," or "a photo of dinner curry." Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input voice-input information into a generative AI and have the generative AI perform the process of converting it into text. This allows users to input events that happened during the day in a bulleted list.

[0037] The generation unit can construct diary-style text based on information entered by the user. For example, the generation unit can use natural language processing technology to analyze bulleted information and generate text. For example, based on bulleted information such as "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner," the generation unit can generate diary-style text such as "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can generate text using a generative AI model that takes bulleted information as input and outputs diary-style text. This allows the generation unit to construct diary-style text based on information entered by the user.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions events that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest events that the user will enter during a specific time period based on their past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method. This allows the reception desk to suggest the optimal input method based on the user's past input history.

[0039] The reception system can automatically complete input content based on the user's current activity status during input. For example, if the user is on the move, the reception system will automatically complete events at that location based on location information. For example, if the user is participating in a specific event, the reception system will automatically complete events related to that event. For example, the reception system will automatically complete input content based on the user's daily activities during a specific time period. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the user's current activity data into a generating AI and have the generating AI perform automatic completion of the input content. This allows the system to automatically complete input content based on the user's current activity status.

[0040] The generation unit can adjust the level of detail in the text based on the importance of the input events during generation. For example, the generation unit can add detailed descriptions to important events and generate text. For example, the generation unit can add concise descriptions to everyday events and generate text. For example, the generation unit can add emotional expressions to special events and generate text. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the importance of the input events to the generation AI and have the generation AI adjust the level of detail in the text. This allows the level of detail in the text to be adjusted based on the importance of the input events.

[0041] The generation unit can apply different text generation algorithms depending on the category of the input event during generation. For example, for events related to meals, the generation unit adds detailed descriptions of ingredients and cooking methods. For events related to travel, the generation unit adds detailed descriptions of places visited and activities experienced. For events related to work, the generation unit adds detailed descriptions of project progress and results achieved. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the category of the input event into the generation AI and cause the generation AI to apply different text generation algorithms. This allows different text generation algorithms to be applied depending on the category of the input event.

[0042] The generation unit can determine the priority of sentences based on the timing of the input events during generation. For example, the generation unit may prioritize and describe recent events in detail. For example, the generation unit may summarize past events concisely. For example, the generation unit may prioritize and describe events related to a specific period. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the timing of the input events into the generation AI and have the generation AI determine the priority of sentences. This allows the priority of sentences to be determined based on the timing of the input events.

[0043] The generation unit can adjust the order of sentences based on the relationships between the input events during generation. For example, the generation unit may group related events together. For example, the generation unit may describe events in chronological order. For example, the generation unit may describe important events first and other events later. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the relationships between the input events to the generation AI and have the generation AI perform the adjustment of the sentence order. This allows the order of sentences to be adjusted based on the relationships between the input events.

[0044] The video generation unit can adjust the video's structure based on the importance of the registered photos during video generation. For example, the video generation unit can compose the video focusing on important photos. For example, it can display everyday photos concisely. For example, it can compose the video by adding detailed explanations to photos of special events. Some or all of the above processing in the video generation unit may be performed using a generation AI, or not. For example, the video generation unit can input the importance of the registered photos into the generation AI and have the generation AI adjust the video's structure. This allows the video's structure to be adjusted based on the importance of the registered photos.

[0045] The video generation unit can apply different video editing algorithms depending on the category of the registered photos when generating videos. For example, the video generation unit can add detailed descriptions of ingredients and cooking methods to photos of food. For example, the video generation unit can add detailed descriptions of places visited and activities experienced to photos of travel. For example, the video generation unit can add detailed descriptions of project progress and results achieved to photos of work. Some or all of the above processing in the video generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the video generation unit can input the category of the registered photos into the generation AI and cause the generation AI to apply different video editing algorithms. This allows different video editing algorithms to be applied depending on the category of the registered photos.

[0046] The video generation unit can determine the priority of videos based on the shooting dates of the registered photos when generating videos. For example, the video generation unit may prioritize displaying the most recent photos. For example, the video generation unit may display older photos concisely. For example, the video generation unit may prioritize displaying photos related to a specific period. Some or all of the above processing in the video generation unit may be performed using a generation AI, or not. For example, the video generation unit can input the shooting dates of the registered photos into the generation AI and have the generation AI determine the priority of the videos. This allows the priority of videos to be determined based on the shooting dates of the registered photos.

[0047] The video generation unit can adjust the order of videos based on the relationships between registered photos during video generation. For example, the video generation unit can group related photos together. For example, the video generation unit can display photos in chronological order. For example, the video generation unit can display important photos first and other photos later. Some or all of the above processing in the video generation unit may be performed using a generation AI, or not. For example, the video generation unit can input the relationships between registered photos into the generation AI and have the generation AI perform the adjustment of the video order. This allows the order of videos to be adjusted based on the relationships between registered photos.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The reception desk allows users to input events that happened during the day in a bulleted list. The reception desk provides, for example, a text input interface to allow users to easily input bulleted information. The reception desk also provides a voice input interface, allowing users to input events by voice. For example, if a user voice-inputs "I had pancakes for breakfast today," the reception desk converts the voice into text and accepts it as bulleted information. Furthermore, the reception desk provides an interface for users to register photos. For example, a user can upload "a photo of breakfast pancakes," "a photo of a movie theater," or "a photo of dinner curry." Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input voice-input information into a generative AI and have the generative AI perform the process of converting it into text. This allows users to input events that happened during the day in a bulleted list.

[0050] The generation unit can analyze bulleted information entered by the user and generate diary-style text. The generation unit can analyze bulleted information and generate text using, for example, natural language processing technology. For example, based on bulleted information such as "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner," the generation unit can generate diary-style text such as "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the generation unit can generate text using a generative AI model that takes bulleted information as input and outputs diary-style text. This allows for the generation of diary-style text based on bulleted information entered by the user.

[0051] The video generation unit can create videos using photos registered by the user. For example, the video generation unit can display photos in a slideshow format and incorporate diary-style text as narration. For example, the video generation unit can display a photo of "pancakes for breakfast" while playing narration such as "I had pancakes for breakfast today." The video generation unit can also create videos with added music. For example, the video generation unit can create a video combining photos and diary-style text while playing music selected by the user as background music. Some or all of the above processing in the video generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the video generation unit can create videos using a generative AI model that takes photos registered by the user as input and outputs a video. This allows for the creation of videos using photos registered by the user.

[0052] The video generation unit can incorporate the generated diary-style text into the video. For example, the video generation unit can display photos in a slideshow format and incorporate the diary-style text as narration. For example, the video generation unit can display a photo of "pancakes for breakfast" while playing narration such as "I had pancakes for breakfast today." The video generation unit can also display the generated diary-style text as text in the video. For example, the video generation unit can display the diary-style text below a photo to provide information visually. Some or all of the above processing in the video generation unit may be performed using, for example, a generation AI, or not. For example, the video generation unit can take the generated diary-style text as input and perform the process of incorporating it into the video using a generation AI model. This allows the generated diary-style text to be incorporated into the video.

[0053] The generation unit can adjust the level of detail in the text based on the importance of the input events during generation. For example, the generation unit can add detailed descriptions to important events and generate text. For example, the generation unit can add concise descriptions to everyday events and generate text. For example, the generation unit can add emotional expressions to special events and generate text. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the importance of the input events to the generation AI and have the generation AI adjust the level of detail in the text. This allows the level of detail in the text to be adjusted based on the importance of the input events.

[0054] The generation unit can apply different text generation algorithms depending on the category of the input event during generation. For example, for events related to meals, the generation unit adds detailed descriptions of ingredients and cooking methods. For events related to travel, the generation unit adds detailed descriptions of places visited and activities experienced. For events related to work, the generation unit adds detailed descriptions of project progress and results achieved. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the category of the input event into the generation AI and cause the generation AI to apply different text generation algorithms. This allows different text generation algorithms to be applied depending on the category of the input event.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk allows users to input a bulleted list of events that happened that day. For example, a user might input things like "I had pancakes for breakfast," "I went to see a movie with a friend," or "I made curry for dinner." The reception desk provides text input and voice input interfaces to make it easy for users to input bulleted information. The reception desk also provides an interface for users to register photos. Step 2: The generation unit analyzes the information received by the reception unit and generates diary-style text. The generation unit uses natural language processing technology and generative AI to analyze bulleted information and generate text. For example, based on bulleted information such as "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner," it generates diary-style text such as "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." Step 3: The video generation unit creates a video using photos registered by the user, based on the diary-style text generated by the unit. The video generation unit displays the photos in a slideshow format and incorporates the diary-style text as narration. For example, it might display a photo of "pancakes for breakfast" while playing narration such as "I had pancakes for breakfast today." The video generation unit can also add music to create the video.

[0057] (Example of form 2) The diary generation system according to an embodiment of the present invention is a system that automatically generates diary-style text simply by having the user record the events of the day in a simple bulleted list, and further creates a video when the user registers photos from that day. The diary generation system works by having the user input the events of the day in a bulleted list. For example, the user inputs things like, "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner." This information is input to the AI. Next, the AI ​​analyzes the input information and generates diary-style text. For example, it might generate text like, "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." In this way, the user can obtain diary-style text simply by inputting a simple bulleted list. Furthermore, when the user registers photos from that day, the AI ​​creates a video using those photos. For example, if the user registers "a photo of breakfast pancakes," "a photo of the movie theater," and "a photo of dinner curry," the AI ​​combines these photos to create a video. Since this video also includes the diary-style text, the user can look back on the events of the day as a video. In this way, the user can easily record the events of each day, 365 days a year, and save their memories as videos. For example, it allows users to easily record events from special days, such as travel memories or anniversaries, and look back on them later. Furthermore, even those who find writing a diary tedious can easily generate diary-style entries with simple bullet point input, making it user-friendly even for those without a diary-writing habit. Additionally, since AI automatically creates videos, users can save their memories as videos without any effort. In short, this diary generation system allows users to easily generate diary-style entries and create videos using photos.

[0058] The diary generation system according to this embodiment comprises a reception unit, a generation unit, and a video generation unit. The reception unit allows the user to input events that happened that day in bullet points. For example, the user can input things like "I ate pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner." The reception unit provides, for example, a text input interface to allow the user to easily input bullet points. The reception unit also provides a voice input interface, allowing the user to input events by voice. For example, if the user inputs "I ate pancakes for breakfast today" by voice, the reception unit converts the voice into text and accepts it as bullet points. Furthermore, the reception unit provides an interface for the user to register photos. For example, the user can upload "a photo of the pancakes for breakfast," "a photo of the movie theater," and "a photo of the curry for dinner." The generation unit analyzes the information received by the reception unit and generates diary-style text. The generation unit analyzes the bullet points and generates text using, for example, natural language processing technology. For example, the generation unit generates diary-style sentences such as "Today I ate pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner," based on bulleted list information such as "I ate pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner." Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can generate sentences using a generation AI model that takes bulleted list information as input and outputs diary-style sentences. The video generation unit creates a video using photos registered by the user based on the diary-style sentences generated by the generation unit. The video generation unit can, for example, display the photos in a slideshow format and incorporate the diary-style sentences as narration. For example, the video generation unit can display a photo of "pancakes for breakfast" while playing narration such as "Today I ate pancakes for breakfast." The video generation unit can also create videos with added music. For example, the video generation unit can create a video combining photos and diary-style sentences while playing music selected by the user as background music.As a result, the diary generation system according to this embodiment allows users to easily generate diary-style text and create videos using photographs.

[0059] The reception desk allows users to input events that happened during the day in a bulleted list. For example, a user can input things like "I ate pancakes for breakfast," "I went to see a movie with a friend," or "I made curry for dinner." The reception desk provides a text input interface, for example, to make it easy for users to input bulleted information. Specifically, the text entered by the user is displayed in real time, making it easy to correct typos and add information. The reception desk also provides a voice input interface, allowing users to input events by voice. For example, if a user voice-inputs "I ate pancakes for breakfast today," the reception desk converts the voice into text and accepts it as bulleted information. This voice input uses speech recognition technology, enabling accurate transcription of the user's speech into text. Furthermore, the reception desk provides an interface for users to register photos. For example, a user can upload "a photo of my breakfast pancakes," "a photo of the movie theater," or "a photo of my dinner curry." Photo uploads are done via drag-and-drop or a file selection dialog, designed for intuitive operation. This allows the reception desk to centrally receive information in various formats, such as text, audio, and photos, enabling users to easily and quickly record the events of the day.

[0060] The generation unit analyzes the information received by the reception unit and generates diary-style text. The generation unit analyzes bulleted information using, for example, natural language processing technology and generates text. Specifically, the generation unit analyzes the bulleted information entered by the user based on the context and generates natural-sounding text by inserting appropriate conjunctions and particles. For example, based on bulleted information such as "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner," it generates diary-style text such as "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. When a generative AI is used, the generation unit can generate text using a generative AI model that takes bulleted information as input and outputs diary-style text. This generative AI model has learned from a large amount of text data and has the ability to generate grammatically accurate and natural-sounding text. For example, based on the user's input, "I had pancakes for breakfast," the generation AI will generate a specific sentence such as, "I had pancakes for breakfast today." In this way, the generation unit can automatically generate natural and easy-to-read diary-style sentences based on the information the user inputs.

[0061] The video generation unit creates videos using user-registered photos based on diary-style text generated by the unit. For example, the video generation unit can display photos in a slideshow format and incorporate the diary-style text as narration. Specifically, the video generation unit arranges the user-uploaded photos chronologically and adds diary-style text corresponding to each photo as narration. For example, the video generation unit might display a photo of "pancakes for breakfast" while playing narration such as, "I had pancakes for breakfast today." The video generation unit can also create videos with added music. For example, the video generation unit can create a video combining photos and diary-style text while playing music selected by the user as background music. Users can choose from multiple genres and tempos according to their preferences, further enhancing the atmosphere of the video. In addition, the video generation unit can create visually appealing videos by adding text animations and transition effects. For example, it can use fade-in / fade-out effects when photos switch, or add text scrolling animations. This allows the video generation unit to easily generate diary-style text and create engaging videos using photos.

[0062] The generation unit can analyze bulleted information entered by the user and generate diary-style text. The generation unit can analyze bulleted information and generate text using, for example, natural language processing technology. For example, based on bulleted information such as "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner," the generation unit can generate diary-style text such as "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the generation unit can generate text using a generative AI model that takes bulleted information as input and outputs diary-style text. This allows for the generation of diary-style text based on bulleted information entered by the user.

[0063] The video generation unit can create videos using photos registered by the user. For example, the video generation unit can display photos in a slideshow format and incorporate diary-style text as narration. For example, the video generation unit can display a photo of "pancakes for breakfast" while playing narration such as "I had pancakes for breakfast today." The video generation unit can also create videos with added music. For example, the video generation unit can create a video combining photos and diary-style text while playing music selected by the user as background music. Some or all of the above processing in the video generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the video generation unit can create videos using a generative AI model that takes photos registered by the user as input and outputs a video. This allows for the creation of videos using photos registered by the user.

[0064] The video generation unit can incorporate the generated diary-style text into the video. For example, the video generation unit can display photos in a slideshow format and incorporate the diary-style text as narration. For example, the video generation unit can display a photo of "pancakes for breakfast" while playing narration such as "I had pancakes for breakfast today." The video generation unit can also display the generated diary-style text as text in the video. For example, the video generation unit can display the diary-style text below a photo to provide information visually. Some or all of the above processing in the video generation unit may be performed using, for example, a generation AI, or not. For example, the video generation unit can take the generated diary-style text as input and perform the process of incorporating it into the video using a generation AI model. This allows the generated diary-style text to be incorporated into the video.

[0065] The reception desk allows users to input events that happened during the day in a bulleted list. The reception desk provides, for example, a text input interface to allow users to easily input bulleted information. The reception desk also provides a voice input interface, allowing users to input events by voice. For example, if a user voice-inputs "I had pancakes for breakfast today," the reception desk converts the voice into text and accepts it as bulleted information. Furthermore, the reception desk provides an interface for users to register photos. For example, a user can upload "a photo of breakfast pancakes," "a photo of a movie theater," or "a photo of dinner curry." Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input voice-input information into a generative AI and have the generative AI perform the process of converting it into text. This allows users to input events that happened during the day in a bulleted list.

[0066] The generation unit can construct diary-style text based on information entered by the user. For example, the generation unit can use natural language processing technology to analyze bulleted information and generate text. For example, based on bulleted information such as "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner," the generation unit can generate diary-style text such as "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can generate text using a generative AI model that takes bulleted information as input and outputs diary-style text. This allows the generation unit to construct diary-style text based on information entered by the user.

[0067] The reception unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple and intuitive interface and minimize the input steps. For example, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception unit can prioritize voice input to allow for quick input of events. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation. This allows the design of the input interface to be changed according to the user's emotions.

[0068] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions events that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest events that the user will enter during a specific time period based on their past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method. This allows the reception desk to suggest the optimal input method based on the user's past input history.

[0069] The reception system can automatically complete input content based on the user's current activity status during input. For example, if the user is on the move, the reception system will automatically complete events at that location based on location information. For example, if the user is participating in a specific event, the reception system will automatically complete events related to that event. For example, the reception system will automatically complete input content based on the user's daily activities during a specific time period. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the user's current activity data into a generating AI and have the generating AI perform automatic completion of the input content. This allows the system to automatically complete input content based on the user's current activity status.

[0070] The generation unit can estimate the user's emotions and adjust the tone and style of the text based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate text in a soft tone. If the user is in a hurry, the generation unit will generate concise and to-the-point text. If the user is excited, the generation unit will generate text in a lively tone. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the tone and style of the text. This allows the tone and style of the text to be adjusted according to the user's emotions.

[0071] The generation unit can adjust the level of detail in the text based on the importance of the input events during generation. For example, the generation unit can add detailed descriptions to important events and generate text. For example, the generation unit can add concise descriptions to everyday events and generate text. For example, the generation unit can add emotional expressions to special events and generate text. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the importance of the input events to the generation AI and have the generation AI adjust the level of detail in the text. This allows the level of detail in the text to be adjusted based on the importance of the input events.

[0072] The generation unit can apply different text generation algorithms depending on the category of the input event during generation. For example, for events related to meals, the generation unit adds detailed descriptions of ingredients and cooking methods. For events related to travel, the generation unit adds detailed descriptions of places visited and activities experienced. For events related to work, the generation unit adds detailed descriptions of project progress and results achieved. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the category of the input event into the generation AI and cause the generation AI to apply different text generation algorithms. This allows different text generation algorithms to be applied depending on the category of the input event.

[0073] The generation unit can estimate the user's emotions and adjust the length of the text based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a longer text with detailed explanations. If the user is in a hurry, the generation unit will generate a short, concise text. If the user is excited, the generation unit will generate text with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the text. This allows the length of the text to be adjusted according to the user's emotions.

[0074] The generation unit can determine the priority of sentences based on the timing of the input events during generation. For example, the generation unit may prioritize and describe recent events in detail. For example, the generation unit may summarize past events concisely. For example, the generation unit may prioritize and describe events related to a specific period. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the timing of the input events into the generation AI and have the generation AI determine the priority of sentences. This allows the priority of sentences to be determined based on the timing of the input events.

[0075] The generation unit can adjust the order of sentences based on the relationships between the input events during generation. For example, the generation unit may group related events together. For example, the generation unit may describe events in chronological order. For example, the generation unit may describe important events first and other events later. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the relationships between the input events to the generation AI and have the generation AI perform the adjustment of the sentence order. This allows the order of sentences to be adjusted based on the relationships between the input events.

[0076] The video generation unit can estimate the user's emotions and adjust the video editing style based on the estimated emotions. For example, if the user is relaxed, the video generation unit will generate a video that progresses at a leisurely pace. For example, if the user is in a hurry, the video generation unit will generate a video that emphasizes the shortest route. For example, if the user is excited, the video generation unit will generate a video with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input user emotion data into the generative AI and have the generative AI adjust the video editing style. This allows the video editing style to be adjusted according to the user's emotions.

[0077] The video generation unit can adjust the video's structure based on the importance of the registered photos during video generation. For example, the video generation unit can compose the video focusing on important photos. For example, it can display everyday photos concisely. For example, it can compose the video by adding detailed explanations to photos of special events. Some or all of the above processing in the video generation unit may be performed using a generation AI, or not. For example, the video generation unit can input the importance of the registered photos into the generation AI and have the generation AI adjust the video's structure. This allows the video's structure to be adjusted based on the importance of the registered photos.

[0078] The video generation unit can apply different video editing algorithms depending on the category of the registered photos when generating videos. For example, the video generation unit can add detailed descriptions of ingredients and cooking methods to photos of food. For example, the video generation unit can add detailed descriptions of places visited and activities experienced to photos of travel. For example, the video generation unit can add detailed descriptions of project progress and results achieved to photos of work. Some or all of the above processing in the video generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the video generation unit can input the category of the registered photos into the generation AI and cause the generation AI to apply different video editing algorithms. This allows different video editing algorithms to be applied depending on the category of the registered photos.

[0079] The video generation unit can estimate the user's emotions and adjust the video length based on the estimated emotions. For example, if the user is relaxed, the video generation unit will generate a longer video with detailed explanations. For example, if the user is in a hurry, the video generation unit will generate a short, concise video. For example, if the user is excited, the video generation unit will generate a video with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input user emotion data into the generative AI and have the generative AI adjust the video length. This allows the video length to be adjusted according to the user's emotions.

[0080] The video generation unit can determine the priority of videos based on the shooting dates of the registered photos when generating videos. For example, the video generation unit may prioritize displaying the most recent photos. For example, the video generation unit may display older photos concisely. For example, the video generation unit may prioritize displaying photos related to a specific period. Some or all of the above processing in the video generation unit may be performed using a generation AI, or not. For example, the video generation unit can input the shooting dates of the registered photos into the generation AI and have the generation AI determine the priority of the videos. This allows the priority of videos to be determined based on the shooting dates of the registered photos.

[0081] The video generation unit can adjust the order of videos based on the relationships between registered photos during video generation. For example, the video generation unit can group related photos together. For example, the video generation unit can display photos in chronological order. For example, the video generation unit can display important photos first and other photos later. Some or all of the above processing in the video generation unit may be performed using a generation AI, or not. For example, the video generation unit can input the relationships between registered photos into the generation AI and have the generation AI perform the adjustment of the video order. This allows the order of videos to be adjusted based on the relationships between registered photos.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The reception desk allows users to input events that happened during the day in a bulleted list. The reception desk provides, for example, a text input interface to allow users to easily input bulleted information. The reception desk also provides a voice input interface, allowing users to input events by voice. For example, if a user voice-inputs "I had pancakes for breakfast today," the reception desk converts the voice into text and accepts it as bulleted information. Furthermore, the reception desk provides an interface for users to register photos. For example, a user can upload "a photo of breakfast pancakes," "a photo of a movie theater," or "a photo of dinner curry." Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input voice-input information into a generative AI and have the generative AI perform the process of converting it into text. This allows users to input events that happened during the day in a bulleted list.

[0084] The generation unit can analyze bulleted information entered by the user and generate diary-style text. The generation unit can analyze bulleted information and generate text using, for example, natural language processing technology. For example, based on bulleted information such as "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner," the generation unit can generate diary-style text such as "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the generation unit can generate text using a generative AI model that takes bulleted information as input and outputs diary-style text. This allows for the generation of diary-style text based on bulleted information entered by the user.

[0085] The video generation unit can create videos using photos registered by the user. For example, the video generation unit can display photos in a slideshow format and incorporate diary-style text as narration. For example, the video generation unit can display a photo of "pancakes for breakfast" while playing narration such as "I had pancakes for breakfast today." The video generation unit can also create videos with added music. For example, the video generation unit can create a video combining photos and diary-style text while playing music selected by the user as background music. Some or all of the above processing in the video generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the video generation unit can create videos using a generative AI model that takes photos registered by the user as input and outputs a video. This allows for the creation of videos using photos registered by the user.

[0086] The video generation unit can incorporate the generated diary-style text into the video. For example, the video generation unit can display photos in a slideshow format and incorporate the diary-style text as narration. For example, the video generation unit can display a photo of "pancakes for breakfast" while playing narration such as "I had pancakes for breakfast today." The video generation unit can also display the generated diary-style text as text in the video. For example, the video generation unit can display the diary-style text below a photo to provide information visually. Some or all of the above processing in the video generation unit may be performed using, for example, a generation AI, or not. For example, the video generation unit can take the generated diary-style text as input and perform the process of incorporating it into the video using a generation AI model. This allows the generated diary-style text to be incorporated into the video.

[0087] The reception unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple and intuitive interface and minimize the input steps. For example, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception unit can prioritize voice input to allow for quick input of events. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation. This allows the design of the input interface to be changed according to the user's emotions.

[0088] The generation unit can estimate the user's emotions and adjust the tone and style of the text based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate text in a soft tone. If the user is in a hurry, the generation unit will generate concise and to-the-point text. If the user is excited, the generation unit will generate text in a lively tone. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the tone and style of the text. This allows the tone and style of the text to be adjusted according to the user's emotions.

[0089] The generation unit can adjust the level of detail in the text based on the importance of the input events during generation. For example, the generation unit can add detailed descriptions to important events and generate text. For example, the generation unit can add concise descriptions to everyday events and generate text. For example, the generation unit can add emotional expressions to special events and generate text. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the importance of the input events to the generation AI and have the generation AI adjust the level of detail in the text. This allows the level of detail in the text to be adjusted based on the importance of the input events.

[0090] The generation unit can apply different text generation algorithms depending on the category of the input event during generation. For example, for events related to meals, the generation unit adds detailed descriptions of ingredients and cooking methods. For events related to travel, the generation unit adds detailed descriptions of places visited and activities experienced. For events related to work, the generation unit adds detailed descriptions of project progress and results achieved. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the category of the input event into the generation AI and cause the generation AI to apply different text generation algorithms. This allows different text generation algorithms to be applied depending on the category of the input event.

[0091] The generation unit can estimate the user's emotions and adjust the length of the text based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a longer text with detailed explanations. If the user is in a hurry, the generation unit will generate a short, concise text. If the user is excited, the generation unit will generate text with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the text. This allows the length of the text to be adjusted according to the user's emotions.

[0092] The video generation unit can estimate the user's emotions and adjust the video editing style based on the estimated emotions. For example, if the user is relaxed, the video generation unit will generate a video that progresses at a leisurely pace. For example, if the user is in a hurry, the video generation unit will generate a video that emphasizes the shortest route. For example, if the user is excited, the video generation unit will generate a video with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the video generation unit may be performed using AI or not. For example, the video generation unit can input user emotion data into the generative AI and have the generative AI adjust the video editing style. This allows the video editing style to be adjusted according to the user's emotions.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The reception desk allows users to input a bulleted list of events that happened that day. For example, a user might input things like "I had pancakes for breakfast," "I went to see a movie with a friend," or "I made curry for dinner." The reception desk provides text input and voice input interfaces to make it easy for users to input bulleted information. The reception desk also provides an interface for users to register photos. Step 2: The generation unit analyzes the information received by the reception unit and generates diary-style text. The generation unit uses natural language processing technology and generative AI to analyze bulleted information and generate text. For example, based on bulleted information such as "I had pancakes for breakfast," "I went to see a movie with a friend," and "I made curry for dinner," it generates diary-style text such as "Today I had pancakes for breakfast. Afterwards, I went to see a movie with a friend. I made curry for dinner." Step 3: The video generation unit creates a video using photos registered by the user, based on the diary-style text generated by the unit. The video generation unit displays the photos in a slideshow format and incorporates the diary-style text as narration. For example, it might display a photo of "pancakes for breakfast" while playing narration such as "I had pancakes for breakfast today." The video generation unit can also add music to create the video.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0097] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] Each of the multiple elements described above, including the reception unit, generation unit, and video generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input events that happened that day in bullet points. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, analyzing the input information to generate a diary-style text. The video generation unit is implemented by the output device 40 of the smart device 14, creating a video using photographs based on the generated diary-style text. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0099] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0100] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0105] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0106] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0107] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0108] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0109] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the reception unit, generation unit, and video generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input events that happened that day by voice. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the input information and generates a diary-style text. The video generation unit is implemented by the speaker 240 of the smart glasses 214, which creates a video using photographs based on the generated diary-style text. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, generation unit, and video generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input events that happened that day by voice. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and generates a diary-style text. The video generation unit is implemented by the display 343 of the headset terminal 314, which creates a video using photographs based on the generated diary-style text. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0139] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the reception unit, generation unit, and video generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input events that happened that day by voice. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the input information and generates a diary-style text. The video generation unit is implemented by, for example, the speaker 240 of the robot 414, which creates a video using photographs based on the generated diary-style text. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0150] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0151] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0152] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0156] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0157] 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.

[0158] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0159] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0160] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0161] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0163] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0164] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0165] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0166] (Note 1) The reception desk allows users to input a bulleted list of events that happened that day, A generation unit analyzes the information received by the reception unit and generates diary-style text, The system includes a video generation unit that creates a video using photos registered by the user, based on diary-style text generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is It analyzes bulleted information entered by the user and generates diary-style text. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned video generation unit, Create a video using photos registered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned video generation unit, The generated diary-style text is incorporated into the video. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The user enters a bulleted list of events that happened that day. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is When generating diary-style entries, the text is constructed based on the information entered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system automatically completes input based on the user's current activity status during input. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the tone and style of the text based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, the level of detail in the text is adjusted based on the importance of the events entered. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, different text generation algorithms are applied depending on the category of the input event. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the length of the text based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the text priority is determined based on the timing of the events entered. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the order of sentences is adjusted based on the relevance of the input events. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned video generation unit, It estimates the user's emotions and adjusts the video editing style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned video generation unit, When generating a video, the video's structure is adjusted based on the importance of the registered photos. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned video generation unit, When generating a video, different video editing algorithms are applied depending on the category of the registered photos. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned video generation unit, It estimates the user's emotions and adjusts the video length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned video generation unit, When generating videos, the priority of the videos is determined based on when the registered photos were taken. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned video generation unit, When generating a video, the order of the videos is adjusted based on the relevance of the registered photos. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk allows users to input a bulleted list of events that happened that day, A generation unit analyzes the information received by the reception unit and generates diary-style text, The system includes a video generation unit that creates a video using photos registered by the user, based on diary-style text generated by the generation unit. A system characterized by the following features.

2. The generating unit is It analyzes bulleted information entered by the user and generates diary-style text. The system according to feature 1.

3. The aforementioned video generation unit, Create a video using photos registered by the user. The system according to feature 1.

4. The aforementioned video generation unit, The generated diary-style text is incorporated into the video. The system according to feature 1.

5. The aforementioned reception unit is The user enters a bulleted list of events that happened that day. The system according to feature 1.

6. The generating unit is When generating diary-style entries, the text is constructed based on the information entered by the user. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is The system automatically completes input based on the user's current activity status during input. The system according to feature 1.

10. The generating unit is It estimates the user's emotions and adjusts the tone and style of the text based on those estimated emotions. The system according to feature 1.

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