System

Abstract

An object of a system according to an embodiment is to efficiently perform editing and searching of photographs.SOLUTION: A system includes a reception part, an editing part, a retrieval part, and a provision part. The reception unit receives an instruction in a natural language from a user. The editing unit edits the photograph based on the instruction received by the receiving unit. The retrieval unit retrieves a photograph on the basis of the instruction received by the reception unit. The providing unit provides a result obtained by the editing unit and the search unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that editing and searching for photos takes time and is difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently edit and search for photos. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an editing unit, a search unit, and a providing unit. The reception unit receives instructions in natural language from a user. The editing unit edits photos based on the instructions received by the reception unit. The search unit searches for photos based on the instructions received by the reception unit. The providing unit provides the results obtained by the editing unit and the search unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently edit and search for photos. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A photo editing system according to an embodiment of the present invention allows users to more easily and efficiently edit photos stored on their mobile phones and quickly provide the photos they want at any time. The photo editing system utilizes generative AI, which understands natural language and provides photos that meet the user's requests. For example, when a user issues a command in natural language, such as "change the background of this photo" or "adjust the brightness," the generative AI understands the command and automatically edits the photo. This eliminates the need for the user to perform complex editing tasks. Furthermore, when a user issues a command in natural language, such as "show me photos from last summer's trip," the generative AI analyzes the metadata and content of the photo and quickly provides the corresponding photo. This saves the user the trouble of searching for the desired photo among a large number of photos. Furthermore, when a marketer issues a command such as "create a promotional photo for a new product," the generative AI edits and provides the appropriate photo. This allows for the creation of promotional materials quickly and efficiently. This significantly reduces the user's photo editing and search efforts and allows for the rapid and efficient provision of photos. For example, even if a user does not want to spend time editing photos or does not know how to do so, the generative AI automatically performs appropriate editing. In addition, even if you don't want to spend time searching for photos, the generative AI can quickly provide relevant photos, thereby solving personal and business problems and improving user convenience.

[0029] A photo editing system according to an embodiment includes a reception unit, an editing unit, a search unit, and a providing unit. The reception unit receives natural language instructions from a user. Examples of natural language instructions include, but are not limited to, voice instructions and text instructions. The reception unit converts the user's voice instructions into text using, for example, voice recognition technology. The reception unit also provides a text input interface, allowing the user to directly input text instructions. The editing unit uses a generation AI to edit the photo based on the instructions received by the reception unit. Examples of editing include, but are not limited to, cropping, applying filters, and color adjustment. For example, the editing unit causes the generation AI to change the background of the photo based on the user's instructions. The editing unit also causes the generation AI to adjust the brightness of the photo based on the user's instructions. The editing unit also causes the generation AI to adjust the color tone of the photo based on the user's instructions. The search unit uses the generation AI to search for photos based on the instructions received by the reception unit. Examples of searches include, but are not limited to, keyword search and image recognition search. For example, the search unit allows the generation AI to analyze the metadata of photos and search for corresponding photos. The search unit can also allow the generation AI to analyze the content of photos and search for corresponding photos. The search unit can also allow the generation AI to search for photos that meet specific conditions based on user instructions. The providing unit provides the results obtained by the editing unit and the search unit to the user. Examples of providing include, but are not limited to, providing a download link or uploading to cloud storage. For example, the providing unit displays the editing and search results to the user through a web application or a mobile application. The providing unit can also send the editing and search results to the user by email. The providing unit can also upload the editing and search results to cloud storage so that the user can access them. This allows the photo editing system according to the embodiment to edit, search, and provide photos based on natural language instructions from the user.

[0030] The editorial department can change the background of a photo based on a user's instructions. To change the background, for example, the generation AI automatically detects the background portion of the photo and replaces it with a specified background. For example, the editorial department causes the generation AI to change the background of the photo to a landscape based on a user's instructions. The editorial department can also cause the generation AI to change the background of the photo to a solid color based on a user's instructions. The editorial department can also cause the generation AI to change the background of the photo to a specific image based on a user's instructions. In this way, the background of the photo can be changed based on a user's instructions. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, the generation AI. For example, the editorial department can input the background portion of the photo into the generation AI and have the generation AI change the background.

[0031] The editorial department can adjust the brightness of the photo based on a user's instructions. To adjust the brightness, for example, the generation AI automatically analyzes the brightness of the photo and adjusts it to a specified brightness. For example, the editorial department can have the generation AI increase the brightness of the photo based on a user's instructions. The editorial department can also have the generation AI decrease the brightness of the photo based on a user's instructions. The editorial department can also have the generation AI adjust the brightness of the photo to a specific level based on a user's instructions. This allows the brightness of the photo to be adjusted based on a user's instructions. Some or all of the above-mentioned processing in the editorial department can be performed using, or without, the generation AI. For example, the editorial department can input the brightness of the photo into the generation AI and have the generation AI adjust the brightness.

[0032] The search unit can analyze the metadata or content of a photo to search for a corresponding photo. To analyze the metadata, for example, the generation AI automatically analyzes metadata such as the date and time the photo was taken, location information, and camera settings. For example, the search unit searches for a corresponding photo based on the date and time the photo was taken. The search unit can also search for a corresponding photo based on the location information of the photo. The search unit can also search for a corresponding photo based on the camera settings of the photo. To analyze the content, for example, the generation AI analyzes the content using image recognition technology. For example, the search unit can analyze the content of a photo to search for photos containing a specific object. The search unit can also analyze the content of a photo to search for photos containing a specific scene. The search unit can also analyze the content of a photo to search for photos containing a specific person. In this way, it is possible to search for a corresponding photo by analyzing the metadata and content of a photo. Some or all of the above-mentioned processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit can input the metadata and content of a photo into the generation AI and have the generation AI search for the corresponding photo.

[0033] The providing unit can provide the results of the editing and search to the user. To provide the results, for example, the generation AI automatically generates the results of the editing and search and provides them to the user. For example, the providing unit displays the results of the editing and search to the user through a web application or a mobile application. The providing unit can also send the results of the editing and search to the user by email. The providing unit can also upload the results of the editing and search to cloud storage so that the user can access them. In this way, the results of the editing and search can be provided to the user. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the results of the editing and search to the generation AI and have the generation AI provide the results.

[0034] The providing unit can provide promotional photos for marketing. To provide the promotional photos, for example, the generation AI automatically generates promotional photos for marketing and provides them to the user. For example, the providing unit causes the generation AI to generate and provide promotional photos based on instructions from the user. The providing unit can also cause the generation AI to edit and provide promotional photos for marketing. The providing unit can also cause the generation AI to analyze metadata of the promotional photos and provide appropriate photos. This makes it possible to provide promotional photos for marketing. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to generate and edit promotional photos.

[0035] The reception unit can analyze the user's past instruction history and select an appropriate reception method. To analyze the instruction history, for example, the generation AI analyzes the frequency and success rate of the user's past instructions. For example, the reception unit can prioritize and suggest instruction methods (voice, text, etc.) that the generation AI has frequently used in the past. The reception unit can also predict and suggest an instruction method that the generation AI will use in a specific time period based on the user's past instruction history. The reception unit can also suggest a related instruction method based on the instructions the generation AI has given in the past. This makes it possible to select an optimal reception method based on the user's past instruction history. Some or all of the above-mentioned processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input the user's instruction history data into the generation AI and have the generation AI select a reception method.

[0036] When receiving an instruction, the reception unit can filter based on the user's current situation and areas of interest. To identify the current situation, for example, the generation AI analyzes the time of day, location, device status, etc. For example, the reception unit can cause the generation AI to preferentially receive instructions related to the user's current situation (e.g., traveling). The reception unit can also preferentially receive instructions related to the user's area of ​​interest (e.g., landscape photos). The reception unit can also preferentially receive instructions related to the user's current task (e.g., preparing a presentation). This allows instructions to be filtered based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0037] When receiving an instruction, the reception unit can select an appropriate reception means depending on the user's input method. To identify the input method, for example, the generation AI analyzes the type of input, such as voice input, text input, or gesture input. For example, if the user issues a voice instruction to the generation AI, the reception unit can prioritize receiving the voice input. Furthermore, if the user issues a text instruction to the generation AI, the reception unit can also prioritize receiving the text input. Furthermore, if the user issues an image instruction to the generation AI, the reception unit can also accept the instruction using image recognition. This allows the optimal reception means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's input method data to the generation AI and have the generation AI select the reception means.

[0038] When receiving instructions, the reception unit can prioritize receiving highly relevant instructions based on the user's geographical location information. To identify the geographical location information, for example, the generation AI analyzes GPS data, an IP address, or the like. For example, if the generation AI determines that the user is in a specific location, the reception unit can prioritize receiving instructions related to that location. Furthermore, if the generation AI determines that the user is traveling, the reception unit can prioritize receiving travel-related instructions. Furthermore, if the generation AI determines that the user is at home, the reception unit can prioritize receiving home-related instructions. This allows highly relevant instructions to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to receive highly relevant instructions.

[0039] When receiving an instruction, the reception unit can analyze the user's social media activity and receive related instructions. To analyze social media activity, for example, the generation AI analyzes the user's post content, the number of likes, the number of followers, etc. For example, the reception unit can prioritize receiving related instructions based on the content the generation AI has posted on social media. The reception unit can also have the generation AI analyze the user's social media activity history and prioritize receiving related instructions. The reception unit can also have the generation AI prioritize receiving related instructions based on the activities of the user's friends on social media. This makes it possible to receive related instructions based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to receive related instructions.

[0040] When receiving an instruction, the reception unit can customize the reception method by reflecting the user's past feedback. To obtain the feedback, for example, the generation AI analyzes the user's ratings, comments, survey results, etc. For example, the reception unit can have the generation AI suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also have the generation AI preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also have the generation AI analyze the user's past feedback and improve the reception method. This makes it possible to customize the reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the user's feedback data into the generation AI and have the generation AI customize the reception method.

[0041] During editing, the editing department can adjust the level of editing detail based on the importance of the photo. To evaluate the importance of a photo, for example, the generation AI analyzes user ratings, the number of views, etc. For example, if the generation AI determines that the photo is important, the editing department will carefully edit it down to the smallest detail. In addition, if the generation AI determines that the photo is general, the editing department can also perform basic editing. In addition, if the generation AI determines that the photo is not very important, the editing department can perform simple editing. This makes it possible to adjust the level of editing detail based on the importance of the photo. Some or all of the above-mentioned processing in the editing department may be performed using, or without, the generation AI. For example, the editing department can input photo importance data into the generation AI and have the generation AI adjust the level of editing detail.

[0042] During editing, the editorial department can apply different editing algorithms depending on the photo category. To identify the photo category, for example, the generation AI analyzes categories such as landscape photo and portrait photo. For example, if the generation AI determines that the photo is a landscape photo, the editorial department can perform editing to correct color tone and emphasize sharpness. Furthermore, if the generation AI determines that the photo is a portrait photo, the editorial department can also perform editing to adjust skin tones and blur the background. Furthermore, if the generation AI determines that the photo is a product photo, the editorial department can also perform editing to adjust brightness and contrast and emphasize product details. This allows different editing algorithms to be applied depending on the photo category. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, the generation AI. For example, the editorial department can input photo category data into the generation AI and have the generation AI apply the editing algorithm.

[0043] When editing, the editing department can improve the accuracy of editing by referring to the user's past editing results. To refer to the past editing results, for example, the generation AI analyzes the user's editing history and evaluations. For example, the editing department can suggest similar edits based on the edits the user has made in the past. The editing department can also have the generation AI analyze the user's past editing history and suggest the optimal editing method. The editing department can also improve the accuracy of editing by having the generation AI refer to the user's past editing results. In this way, the accuracy of editing can be improved by referring to the user's past editing results. Some or all of the above-mentioned processing in the editing department may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the editing department can input the user's past editing result data into the generation AI and have the generation AI improve the accuracy of editing.

[0044] During editing, the editorial department can determine the priority of editing based on when the photos were taken. To identify the time of taking the photos, for example, the generation AI analyzes the metadata of the photos. For example, the editorial department causes the generation AI to prioritize editing photos taken recently. The editorial department can also cause the generation AI to prioritize editing photos of a specific event (for example, a wedding). The editorial department can also cause the generation AI to prioritize editing photos related to the season. This makes it possible to determine the priority of editing based on when the photos were taken. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, the generation AI. For example, the editorial department can input data on when the photos were taken into the generation AI and have the generation AI determine the priority of editing.

[0045] During editing, the editing department can adjust the order of editing based on the relevance of the photos. To evaluate the relevance of the photos, for example, the generation AI analyzes photos from the same event, photos taken in the same location, etc. For example, the editing department causes the generation AI to edit photos from the same event together. The editing department can also cause the generation AI to edit photos taken in the same location together. The editing department can also cause the generation AI to edit photos with the same theme together. This makes it possible to adjust the order of editing based on the relevance of the photos. Some or all of the above-described processing in the editing department may be performed using, or without, the generation AI. For example, the editing department can input photo relevance data into the generation AI and have the generation AI adjust the order of editing.

[0046] During editing, the editorial department can adjust the use of technical terms in the editing according to the user's level of expertise. To evaluate the level of expertise, for example, the generation AI analyzes the user's occupation, past editing experience, etc. For example, the editorial department may cause the generation AI to explain the editing content to a novice user using simple terms. The editorial department may also cause the generation AI to explain the editing content to an intermediate user using general technical terms. The editorial department may also cause the generation AI to explain the editing content to an advanced user using detailed technical terms. This allows the use of technical terms in the editing to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the editorial department may be performed using, or without, the generation AI. For example, the editorial department may input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0047] The search unit can improve search accuracy by taking into account the interrelationships between photos during a search. To evaluate the interrelationships between photos, for example, the generation AI analyzes photos from the same event, photos taken in the same location, etc. For example, the search unit displays photos from the same event together in the search results. The search unit can also display photos taken in the same location together in the search results. The search unit can also display photos on the same theme together in the search results. This can improve search accuracy by taking into account the interrelationships between photos. Some or all of the above-described processing in the search unit may be performed using, or without, the generation AI. For example, the search unit can input photo interrelationship data into the generation AI and cause the generation AI to improve search accuracy.

[0048] The search unit can perform a search while taking into account the attribute information of the photographer of the photo. To identify the photographer's attribute information, for example, the generation AI analyzes the photographer's age, gender, occupation, etc. For example, the search unit can prioritize searching for photos taken by a specific photographer. The search unit can also search for related photos based on the photographer's style. The search unit can also search for related photos based on the photographer's past works. This makes it possible to perform a search while taking into account the attribute information of the photographer of the photo. Some or all of the above-mentioned processing in the search unit may be performed using, or without, the generation AI. For example, the search unit can input the photographer's attribute information data into the generation AI and have the generation AI perform a search.

[0049] During a search, the search unit can weight the search based on the frequency with which the photos were taken. To evaluate the frequency with which the photos were taken, for example, the generation AI analyzes the number of times photos were taken within a certain period of time or the number of times photos were taken at a specific event. For example, the search unit prioritizes searching for photos of places where the generation AI frequently took photos. The search unit can also prioritize searching for photos that the generation AI frequently took at a specific event. The search unit can also prioritize searching for photos of themes that the generation AI frequently took photos of. This allows the search to be weighted based on the frequency with which the photos were taken. Some or all of the above-described processing in the search unit may be performed using, or without, the generation AI. For example, the search unit can input photo frequency data into the generation AI and have the generation AI perform search weighting.

[0050] The search unit can perform a search taking into account the geographical distribution of photos. To evaluate the geographical distribution, for example, the generation AI analyzes the latitude and longitude of the photo location and the distribution by region. For example, the search unit can cause the generation AI to preferentially search for photos taken in a specific region. The search unit can also cause the generation AI to preferentially search for photos in regions close to the user's current location. The search unit can also cause the generation AI to preferentially search for photos of specific tourist spots or famous places. This allows the search to be performed taking into account the geographical distribution of photos. Some or all of the above-mentioned processing in the search unit may be performed using, or without, the generation AI. For example, the search unit can input geographical distribution data of photos into the generation AI and have the generation AI perform a search.

[0051] The search unit can improve search accuracy by referring to literature related to the photo during a search. To refer to related literature, the generation AI analyzes academic papers, news articles, blog posts, etc., for example. For example, the search unit refers to articles and blogs related to the photo to search for related photos. The search unit can also refer to academic papers related to the photo to search for related photos. The search unit can also refer to books and magazines related to the photo to search for related photos. This can improve search accuracy by referring to literature related to the photo. Some or all of the above-mentioned processing in the search unit may be performed using, or without, the generation AI. For example, the search unit can input related literature data into the generation AI and cause the generation AI to improve search accuracy.

[0052] The search unit can perform a search taking into account the market value of a photo. To evaluate market value, for example, the generation AI analyzes the selling price, level of demand, number of reviews, etc. For example, the search unit prioritizes searches for photos with high market value by the generation AI. The search unit can also prioritize searches for photos with high market value by the generation AI in a specific theme or genre. The search unit can also search for related photos based on photos with high market value by the generation AI. This allows searches to be performed taking into account the market value of the photo. Some or all of the above-described processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit can input market value data of photos into the generation AI and have the generation AI perform a search.

[0053] At the time of provision, the provision unit can select the optimal provision method by referring to the user's past provision history. To refer to the provision history, for example, the generation AI analyzes past provision content, provision date and time, user ratings, etc. For example, the provision unit preferentially selects a provision method that the generation AI has previously preferred. The provision unit can also select a provision method suitable for a specific time period from the user's past provision history. The provision unit can also select the optimal provision method by having the generation AI analyze the user's past provision history. This makes it possible to select the optimal provision method based on the user's past provision history. Some or all of the above-described processing in the provision unit may be performed using, or without, the generation AI. For example, the provision unit can input the user's provision history data into the generation AI and have the generation AI select the provision method.

[0054] The providing unit can customize the provided content according to the user's current task when providing the content. To identify the current task, for example, the generation AI analyzes the user's schedule, current work content, etc. For example, if the generation AI determines that the user is preparing a presentation, the providing unit can prioritize providing related photos. Furthermore, if the generation AI determines that the user is traveling, the providing unit can prioritize providing travel-related photos. Furthermore, if the generation AI determines that the user is engaged in marketing activities, the providing unit can prioritize providing photos suitable for promotions. This allows the provided content to be customized according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's current task data into the generation AI and cause the generation AI to customize the provided content.

[0055] The providing unit can improve the provision method by reflecting user feedback when providing the content. To obtain the feedback, for example, the generation AI analyzes user ratings, comments, survey results, etc. For example, if the user provides feedback on the provision method, the providing unit improves the provision method based on that feedback. The providing unit can also have the generation AI analyze the user's past feedback and improve the provision method. The providing unit can also have the generation AI reflect the user's feedback in real time and adjust the provision method. This makes it possible to improve the provision method based on user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input user feedback data into the generation AI and have the generation AI improve the provision method.

[0056] The providing unit can select an appropriate delivery method based on the user's geographical location information at the time of delivery. To identify the geographical location information, for example, the generation AI analyzes GPS data, IP address, etc. For example, if the generation AI determines that the user is in a specific location, the providing unit can prioritize providing content related to that location. Furthermore, if the generation AI determines that the user is traveling, the providing unit can prioritize providing content related to travel. Furthermore, if the generation AI determines that the user is at home, the providing unit can prioritize providing content related to home. This makes it possible to select the optimal delivery method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the delivery method.

[0057] The providing unit can analyze the user's social media activity and suggest content to be provided at the time of providing the content. To analyze social media activity, for example, the generation AI analyzes the user's posted content, the number of likes, the number of followers, etc. For example, the providing unit can prioritize providing related content based on the content posted by the user on social media by the generation AI. The providing unit can also analyze the user's social media activity history and prioritize providing related content by the generation AI. The providing unit can also prioritize providing related content by the generation AI based on the activity of the user's friends on social media. This makes it possible to suggest content to be provided based on the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest content to be provided.

[0058] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the content. To obtain the feedback, for example, the generation AI analyzes the user's ratings, comments, survey results, etc. For example, the providing unit can suggest the optimal delivery method based on the user's past feedback. The providing unit can also cause the generation AI to preferentially suggest a specific delivery method based on the user's past feedback. The providing unit can also cause the generation AI to analyze the user's past feedback and improve the delivery method. This makes it possible to customize the delivery method based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's feedback data into the generation AI and cause the generation AI to customize the delivery method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When accepting user instructions, the accepting unit can analyze the user's past behavioral patterns and provide an optimal interface. For example, if the user has frequently used voice instructions in the past, the accepting unit can preferentially display a voice input interface. Also, if the user frequently issues instructions during a specific time period, the accepting unit can provide an interface tailored to that time period. Furthermore, if the user uses a specific device (smartphone, tablet, etc.), the accepting unit can provide an interface optimized for that device. This makes it possible to provide an easier-to-use interface based on the user's past behavioral patterns.

[0061] When adjusting the brightness of a photo based on a user's instructions, the editorial department can automatically suggest the optimal brightness based on the content of the photo. For example, for landscape photos, the brightness can be increased to emphasize natural light. For portrait photos, the brightness can be adjusted to enhance facial expressions. Furthermore, for nightscape photos, the brightness can be fine-tuned to preserve the atmosphere. This allows the optimal brightness to be automatically suggested based on the content of the photo.

[0062] When providing the editing and search results to the user, the providing unit can customize the providing method by reflecting the user's past feedback. For example, if the user has previously preferred to receive the results by email, the providing unit can send the results by email. Also, if the user uses cloud storage, the results can be uploaded to cloud storage. Furthermore, if the user frequently uses web applications, the results can be displayed through the web applications. This makes it possible to select the optimal providing method based on the user's past feedback.

[0063] When accepting instructions from a user, the accepting unit can filter the instructions based on the user's current situation and areas of interest. For example, if the user is traveling, instructions related to the travel can be preferentially accepted. Also, if the user is preparing a presentation, instructions related to the presentation can be preferentially accepted. Furthermore, if the user is engaged in marketing activities, instructions related to marketing can be preferentially accepted. This makes it possible to filter instructions based on the user's current situation and areas of interest.

[0064] When changing the background of a photo based on user instructions, the editing department can apply different editing algorithms depending on the photo category. For example, for landscape photos, editing can be performed to correct color tone and emphasize sharpness. For portrait photos, editing can be performed to adjust skin tones and blur the background. Furthermore, for product photos, editing can be performed to adjust brightness and contrast and emphasize product details. This allows the optimal editing algorithm to be applied depending on the photo category.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives instructions in natural language from the user. Natural language instructions include voice instructions and text instructions. The reception unit converts the user's voice instructions into text using voice recognition technology. It also provides a text input interface, allowing the user to input instructions directly in text. Step 2: The editorial department uses the generation AI to edit the photo based on the instructions received by the reception department. Editing includes cropping, applying filters, adjusting colors, etc. For example, the generation AI may change the background of the photo or adjust the brightness and color tone based on the user's instructions. Step 3: The search unit uses the generation AI to search for photos based on the instructions received by the reception unit. Searches include keyword searches and image recognition searches. For example, the generation AI analyzes the metadata and content of photos to search for relevant photos. Step 4: The providing unit provides the results obtained by the editing and search units to the user. Providing includes providing a download link, uploading to cloud storage, etc. For example, the editing and search results may be displayed to the user through a web application or mobile application, or sent by email.

[0067] (Example 2) A photo editing system according to an embodiment of the present invention allows users to more easily and efficiently edit photos stored on their mobile phones and quickly provide the photos they want at any time. The photo editing system utilizes generative AI, which understands natural language and provides photos that meet the user's requests. For example, when a user issues a command in natural language, such as "change the background of this photo" or "adjust the brightness," the generative AI understands the command and automatically edits the photo. This eliminates the need for the user to perform complex editing tasks. Furthermore, when a user issues a command in natural language, such as "show me photos from last summer's trip," the generative AI analyzes the metadata and content of the photo and quickly provides the corresponding photo. This saves the user the trouble of searching for the desired photo among a large number of photos. Furthermore, when a marketer issues a command such as "create a promotional photo for a new product," the generative AI edits and provides the appropriate photo. This allows for the creation of promotional materials quickly and efficiently. This significantly reduces the user's photo editing and search efforts and allows for the rapid and efficient provision of photos. For example, even if a user does not want to spend time editing photos or does not know how to do so, the generative AI automatically performs appropriate editing. In addition, even if you don't want to spend time searching for photos, the generative AI can quickly provide relevant photos, thereby solving personal and business problems and improving user convenience.

[0068] A photo editing system according to an embodiment includes a reception unit, an editing unit, a search unit, and a providing unit. The reception unit receives natural language instructions from a user. Examples of natural language instructions include, but are not limited to, voice instructions and text instructions. The reception unit converts the user's voice instructions into text using, for example, voice recognition technology. The reception unit also provides a text input interface, allowing the user to directly input text instructions. The editing unit uses a generation AI to edit the photo based on the instructions received by the reception unit. Examples of editing include, but are not limited to, cropping, applying filters, and color adjustment. For example, the editing unit causes the generation AI to change the background of the photo based on the user's instructions. The editing unit also causes the generation AI to adjust the brightness of the photo based on the user's instructions. The editing unit also causes the generation AI to adjust the color tone of the photo based on the user's instructions. The search unit uses the generation AI to search for photos based on the instructions received by the reception unit. Examples of searches include, but are not limited to, keyword search and image recognition search. For example, the search unit allows the generation AI to analyze the metadata of photos and search for corresponding photos. The search unit can also allow the generation AI to analyze the content of photos and search for corresponding photos. The search unit can also allow the generation AI to search for photos that meet specific conditions based on user instructions. The providing unit provides the results obtained by the editing unit and the search unit to the user. Examples of providing include, but are not limited to, providing a download link or uploading to cloud storage. For example, the providing unit displays the editing and search results to the user through a web application or a mobile application. The providing unit can also send the editing and search results to the user by email. The providing unit can also upload the editing and search results to cloud storage so that the user can access them. This allows the photo editing system according to the embodiment to edit, search, and provide photos based on natural language instructions from the user.

[0069] The editorial department can change the background of a photo based on a user's instructions. To change the background, for example, the generation AI automatically detects the background portion of the photo and replaces it with a specified background. For example, the editorial department causes the generation AI to change the background of the photo to a landscape based on a user's instructions. The editorial department can also cause the generation AI to change the background of the photo to a solid color based on a user's instructions. The editorial department can also cause the generation AI to change the background of the photo to a specific image based on a user's instructions. In this way, the background of the photo can be changed based on a user's instructions. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, the generation AI. For example, the editorial department can input the background portion of the photo into the generation AI and have the generation AI change the background.

[0070] The editorial department can adjust the brightness of the photo based on a user's instructions. To adjust the brightness, for example, the generation AI automatically analyzes the brightness of the photo and adjusts it to a specified brightness. For example, the editorial department can have the generation AI increase the brightness of the photo based on a user's instructions. The editorial department can also have the generation AI decrease the brightness of the photo based on a user's instructions. The editorial department can also have the generation AI adjust the brightness of the photo to a specific level based on a user's instructions. This allows the brightness of the photo to be adjusted based on a user's instructions. Some or all of the above-mentioned processing in the editorial department can be performed using, or without, the generation AI. For example, the editorial department can input the brightness of the photo into the generation AI and have the generation AI adjust the brightness.

[0071] The search unit can analyze the metadata or content of a photo to search for a corresponding photo. To analyze the metadata, for example, the generation AI automatically analyzes metadata such as the date and time the photo was taken, location information, and camera settings. For example, the search unit searches for a corresponding photo based on the date and time the photo was taken. The search unit can also search for a corresponding photo based on the location information of the photo. The search unit can also search for a corresponding photo based on the camera settings of the photo. To analyze the content, for example, the generation AI analyzes the content using image recognition technology. For example, the search unit can analyze the content of a photo to search for photos containing a specific object. The search unit can also analyze the content of a photo to search for photos containing a specific scene. The search unit can also analyze the content of a photo to search for photos containing a specific person. In this way, it is possible to search for a corresponding photo by analyzing the metadata and content of a photo. Some or all of the above-mentioned processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit can input the metadata and content of a photo into the generation AI and have the generation AI search for the corresponding photo.

[0072] The providing unit can provide the results of the editing and search to the user. To provide the results, for example, the generation AI automatically generates the results of the editing and search and provides them to the user. For example, the providing unit displays the results of the editing and search to the user through a web application or a mobile application. The providing unit can also send the results of the editing and search to the user by email. The providing unit can also upload the results of the editing and search to cloud storage so that the user can access them. In this way, the results of the editing and search can be provided to the user. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the results of the editing and search to the generation AI and have the generation AI provide the results.

[0073] The providing unit can provide promotional photos for marketing. To provide the promotional photos, for example, the generation AI automatically generates promotional photos for marketing and provides them to the user. For example, the providing unit causes the generation AI to generate and provide promotional photos based on instructions from the user. The providing unit can also cause the generation AI to edit and provide promotional photos for marketing. The providing unit can also cause the generation AI to analyze metadata of the promotional photos and provide appropriate photos. This makes it possible to provide promotional photos for marketing. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can cause the generation AI to generate and edit promotional photos.

[0074] The reception unit can estimate the user's emotions and adjust the instruction acceptance method based on the estimated user emotions. To estimate emotions, for example, the generation AI uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the reception unit can have the generation AI analyze the user's facial expressions and estimate emotions. Alternatively, the reception unit can have the generation AI analyze the user's voice and estimate emotions. Alternatively, the reception unit can have the generation AI analyze the user's biometric data and estimate emotions. To adjust the instruction acceptance method, for example, the generation AI can change the interface based on the user's emotions. For example, if the generation AI is stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the generation AI is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Alternatively, if the generation AI is in a hurry, the reception unit can prioritize voice input and quickly accept instructions. This makes it possible to adjust the instruction acceptance method according to the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the method of receiving instructions.

[0075] The reception unit can analyze the user's past instruction history and select an appropriate reception method. To analyze the instruction history, for example, the generation AI analyzes the frequency and success rate of the user's past instructions. For example, the reception unit can prioritize and suggest instruction methods (voice, text, etc.) that the generation AI has frequently used in the past. The reception unit can also predict and suggest an instruction method that the generation AI will use in a specific time period based on the user's past instruction history. The reception unit can also suggest a related instruction method based on the instructions the generation AI has given in the past. This makes it possible to select an optimal reception method based on the user's past instruction history. Some or all of the above-mentioned processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input the user's instruction history data into the generation AI and have the generation AI select a reception method.

[0076] When receiving an instruction, the reception unit can filter based on the user's current situation and areas of interest. To identify the current situation, for example, the generation AI analyzes the time of day, location, device status, etc. For example, the reception unit can cause the generation AI to preferentially receive instructions related to the user's current situation (e.g., traveling). The reception unit can also preferentially receive instructions related to the user's area of ​​interest (e.g., landscape photos). The reception unit can also preferentially receive instructions related to the user's current task (e.g., preparing a presentation). This allows instructions to be filtered based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0077] When receiving an instruction, the reception unit can select an appropriate reception means depending on the user's input method. To identify the input method, for example, the generation AI analyzes the type of input, such as voice input, text input, or gesture input. For example, if the user issues a voice instruction to the generation AI, the reception unit can prioritize receiving the voice input. Furthermore, if the user issues a text instruction to the generation AI, the reception unit can also prioritize receiving the text input. Furthermore, if the user issues an image instruction to the generation AI, the reception unit can also accept the instruction using image recognition. This allows the optimal reception means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's input method data to the generation AI and have the generation AI select the reception means.

[0078] The reception unit can estimate the user's emotions and determine the priority of instructions to be received based on the estimated user emotions. To estimate emotions, for example, the generation AI uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the reception unit can have the generation AI analyze the user's facial expressions and estimate emotions. Alternatively, the reception unit can have the generation AI analyze the user's voice and estimate emotions. Alternatively, the reception unit can have the generation AI analyze the user's biometric data and estimate emotions. To determine the priority of instructions, for example, the generation AI can evaluate the urgency and importance of instructions based on the user's emotions. For example, if the generation AI determines that the user is stressed, the reception unit can prioritize important instructions. Alternatively, if the generation AI determines that the user is relaxed, the reception unit can prioritize detailed instructions. Alternatively, if the generation AI determines that the user is in a hurry, the reception unit can prioritize instructions that can be processed quickly. This allows the priority of instructions to be determined based on the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions and determine the priority of instructions.

[0079] When receiving instructions, the reception unit can prioritize receiving highly relevant instructions based on the user's geographical location information. To identify the geographical location information, for example, the generation AI analyzes GPS data, an IP address, or the like. For example, if the generation AI determines that the user is in a specific location, the reception unit can prioritize receiving instructions related to that location. Furthermore, if the generation AI determines that the user is traveling, the reception unit can prioritize receiving travel-related instructions. Furthermore, if the generation AI determines that the user is at home, the reception unit can prioritize receiving home-related instructions. This allows highly relevant instructions to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to receive highly relevant instructions.

[0080] When receiving an instruction, the reception unit can analyze the user's social media activity and receive related instructions. To analyze social media activity, for example, the generation AI analyzes the user's post content, the number of likes, the number of followers, etc. For example, the reception unit can prioritize receiving related instructions based on the content the generation AI has posted on social media. The reception unit can also have the generation AI analyze the user's social media activity history and prioritize receiving related instructions. The reception unit can also have the generation AI prioritize receiving related instructions based on the activities of the user's friends on social media. This makes it possible to receive related instructions based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to receive related instructions.

[0081] When receiving an instruction, the reception unit can customize the reception method by reflecting the user's past feedback. To obtain the feedback, for example, the generation AI analyzes the user's ratings, comments, survey results, etc. For example, the reception unit can have the generation AI suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also have the generation AI preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also have the generation AI analyze the user's past feedback and improve the reception method. This makes it possible to customize the reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the user's feedback data into the generation AI and have the generation AI customize the reception method.

[0082] The editorial department can estimate the user's emotions and adjust the way the editing is expressed based on the estimated user emotions. To estimate emotions, for example, the generation AI uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the editorial department has the generation AI analyze the user's facial expressions and estimate emotions. The editorial department can also have the generation AI analyze the user's voice and estimate emotions. The editorial department can also have the generation AI analyze the user's biometric data and estimate emotions. To adjust the way the editing is expressed, for example, the generation AI adjusts colors or applies filters based on the user's emotions. For example, if the generation AI determines that the user is relaxed, the editorial department can edit with softer colors. If the generation AI determines that the user is excited, the editorial department can edit with brighter colors. If the generation AI determines that the user is sad, the editorial department can edit with muted colors. This makes it possible to adjust the way the editing is expressed based on the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the editorial department may be performed using the generation AI, or may be performed without using the generation AI. For example, the editorial department may input user emotion data into the generation AI and have the generation AI estimate the emotion and adjust the editing expression method.

[0083] During editing, the editing department can adjust the level of editing detail based on the importance of the photo. To evaluate the importance of a photo, for example, the generation AI analyzes user ratings, the number of views, etc. For example, if the generation AI determines that the photo is important, the editing department will carefully edit it down to the smallest detail. In addition, if the generation AI determines that the photo is general, the editing department can also perform basic editing. In addition, if the generation AI determines that the photo is not very important, the editing department can perform simple editing. This makes it possible to adjust the level of editing detail based on the importance of the photo. Some or all of the above-mentioned processing in the editing department may be performed using, or without, the generation AI. For example, the editing department can input photo importance data into the generation AI and have the generation AI adjust the level of editing detail.

[0084] During editing, the editorial department can apply different editing algorithms depending on the photo category. To identify the photo category, for example, the generation AI analyzes categories such as landscape photo and portrait photo. For example, if the generation AI determines that the photo is a landscape photo, the editorial department can perform editing to correct color tone and emphasize sharpness. Furthermore, if the generation AI determines that the photo is a portrait photo, the editorial department can also perform editing to adjust skin tones and blur the background. Furthermore, if the generation AI determines that the photo is a product photo, the editorial department can also perform editing to adjust brightness and contrast and emphasize product details. This allows different editing algorithms to be applied depending on the photo category. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, the generation AI. For example, the editorial department can input photo category data into the generation AI and have the generation AI apply the editing algorithm.

[0085] When editing, the editing department can improve the accuracy of editing by referring to the user's past editing results. To refer to the past editing results, for example, the generation AI analyzes the user's editing history and evaluations. For example, the editing department can suggest similar edits based on the edits the user has made in the past. The editing department can also have the generation AI analyze the user's past editing history and suggest the optimal editing method. The editing department can also improve the accuracy of editing by having the generation AI refer to the user's past editing results. In this way, the accuracy of editing can be improved by referring to the user's past editing results. Some or all of the above-mentioned processing in the editing department may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the editing department can input the user's past editing result data into the generation AI and have the generation AI improve the accuracy of editing.

[0086] The editing department can estimate the user's emotions and adjust the length of the edits based on the estimated user emotions. To estimate emotions, for example, the generation AI uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the editing department has the generation AI analyze the user's facial expressions to estimate emotions. The editing department can also have the generation AI analyze the user's voice to estimate emotions. The editing department can also have the generation AI analyze the user's biometric data to estimate emotions. To adjust the length of the edits, for example, the generation AI cuts videos or crops photos based on the user's emotions. For example, the editing department can perform detailed edits if the user is relaxed. The editing department can also perform concise edits if the user is in a hurry. The editing department can also perform visually stimulating edits if the user is excited. This allows the length of the edits to be adjusted based on the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the editing department may be performed using the generation AI, or may be performed without using the generation AI. For example, the editing department may input user emotion data into the generation AI and have the generation AI estimate the emotion and adjust the length of the edit.

[0087] During editing, the editorial department can determine the priority of editing based on when the photos were taken. To identify the time of taking the photos, for example, the generation AI analyzes the metadata of the photos. For example, the editorial department causes the generation AI to prioritize editing photos taken recently. The editorial department can also cause the generation AI to prioritize editing photos of a specific event (for example, a wedding). The editorial department can also cause the generation AI to prioritize editing photos related to the season. This makes it possible to determine the priority of editing based on when the photos were taken. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, the generation AI. For example, the editorial department can input data on when the photos were taken into the generation AI and have the generation AI determine the priority of editing.

[0088] During editing, the editing department can adjust the order of editing based on the relevance of the photos. To evaluate the relevance of the photos, for example, the generation AI analyzes photos from the same event, photos taken in the same location, etc. For example, the editing department causes the generation AI to edit photos from the same event together. The editing department can also cause the generation AI to edit photos taken in the same location together. The editing department can also cause the generation AI to edit photos with the same theme together. This makes it possible to adjust the order of editing based on the relevance of the photos. Some or all of the above-described processing in the editing department may be performed using, or without, the generation AI. For example, the editing department can input photo relevance data into the generation AI and have the generation AI adjust the order of editing.

[0089] During editing, the editorial department can adjust the use of technical terms in the editing according to the user's level of expertise. To evaluate the level of expertise, for example, the generation AI analyzes the user's occupation, past editing experience, etc. For example, the editorial department may cause the generation AI to explain the editing content to a novice user using simple terms. The editorial department may also cause the generation AI to explain the editing content to an intermediate user using general technical terms. The editorial department may also cause the generation AI to explain the editing content to an advanced user using detailed technical terms. This allows the use of technical terms in the editing to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the editorial department may be performed using, or without, the generation AI. For example, the editorial department may input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0090] The search unit can estimate the user's emotions and adjust search criteria based on the estimated user emotions. To estimate emotions, for example, the generation AI uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the search unit can analyze the user's facial expressions and estimate emotions. The search unit can also analyze the user's voice and estimate emotions. The search unit can also analyze the user's biometric data and estimate emotions. To adjust search criteria, for example, the generation AI can change keywords or filter search results based on the user's emotions. For example, the search unit can provide detailed search results if the user is relaxed. The search unit can also provide concise search results if the user is in a hurry. The search unit can also provide visually stimulating search results if the user is excited. This allows the search criteria to be adjusted based on the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using, or without, the generation AI. For example, the search unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the search criteria.

[0091] The search unit can improve search accuracy by taking into account the interrelationships between photos during a search. To evaluate the interrelationships between photos, for example, the generation AI analyzes photos from the same event, photos taken in the same location, etc. For example, the search unit displays photos from the same event together in the search results. The search unit can also display photos taken in the same location together in the search results. The search unit can also display photos on the same theme together in the search results. This can improve search accuracy by taking into account the interrelationships between photos. Some or all of the above-described processing in the search unit may be performed using, or without, the generation AI. For example, the search unit can input photo interrelationship data into the generation AI and cause the generation AI to improve search accuracy.

[0092] The search unit can perform a search while taking into account the attribute information of the photographer of the photo. To identify the photographer's attribute information, for example, the generation AI analyzes the photographer's age, gender, occupation, etc. For example, the search unit can prioritize searching for photos taken by a specific photographer. The search unit can also search for related photos based on the photographer's style. The search unit can also search for related photos based on the photographer's past works. This makes it possible to perform a search while taking into account the attribute information of the photographer of the photo. Some or all of the above-mentioned processing in the search unit may be performed using, or without, the generation AI. For example, the search unit can input the photographer's attribute information data into the generation AI and have the generation AI perform a search.

[0093] During a search, the search unit can weight the search based on the frequency with which the photos were taken. To evaluate the frequency with which the photos were taken, for example, the generation AI analyzes the number of times photos were taken within a certain period of time or the number of times photos were taken at a specific event. For example, the search unit prioritizes searching for photos of places where the generation AI frequently took photos. The search unit can also prioritize searching for photos that the generation AI frequently took at a specific event. The search unit can also prioritize searching for photos of themes that the generation AI frequently took photos of. This allows the search to be weighted based on the frequency with which the photos were taken. Some or all of the above-described processing in the search unit may be performed using, or without, the generation AI. For example, the search unit can input photo frequency data into the generation AI and have the generation AI perform search weighting.

[0094] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated user emotions. To estimate emotions, for example, the generation AI uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the search unit can analyze the user's facial expressions and estimate emotions. The search unit can also analyze the user's voice and estimate emotions. The search unit can also analyze the user's biometric data and estimate emotions. To adjust the order in which search results are displayed, for example, the generation AI can change priorities or filter results based on the user's emotions. For example, if the generation AI determines that the user is relaxed, the search unit can prioritize detailed search results. If the generation AI determines that the user is in a hurry, the search unit can prioritize concise search results. If the generation AI determines that the user is excited, the search unit can prioritize visually stimulating search results. This allows the order in which search results are displayed to be adjusted based on the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using, or without, the generation AI. For example, the search unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the display order of search results.

[0095] The search unit can perform a search taking into account the geographical distribution of photos. To evaluate the geographical distribution, for example, the generation AI analyzes the latitude and longitude of the photo location and the distribution by region. For example, the search unit can cause the generation AI to preferentially search for photos taken in a specific region. The search unit can also cause the generation AI to preferentially search for photos in regions close to the user's current location. The search unit can also cause the generation AI to preferentially search for photos of specific tourist spots or famous places. This allows the search to be performed taking into account the geographical distribution of photos. Some or all of the above-mentioned processing in the search unit may be performed using, or without, the generation AI. For example, the search unit can input geographical distribution data of photos into the generation AI and have the generation AI perform a search.

[0096] The search unit can improve search accuracy by referring to literature related to the photo during a search. To refer to related literature, the generation AI analyzes academic papers, news articles, blog posts, etc., for example. For example, the search unit refers to articles and blogs related to the photo to search for related photos. The search unit can also refer to academic papers related to the photo to search for related photos. The search unit can also refer to books and magazines related to the photo to search for related photos. This can improve search accuracy by referring to literature related to the photo. Some or all of the above-mentioned processing in the search unit may be performed using, or without, the generation AI. For example, the search unit can input related literature data into the generation AI and cause the generation AI to improve search accuracy.

[0097] The search unit can perform a search taking into account the market value of a photo. To evaluate market value, for example, the generation AI analyzes the selling price, level of demand, number of reviews, etc. For example, the search unit prioritizes searches for photos with high market value by the generation AI. The search unit can also prioritize searches for photos with high market value by the generation AI in a specific theme or genre. The search unit can also search for related photos based on photos with high market value by the generation AI. This allows searches to be performed taking into account the market value of the photo. Some or all of the above-described processing in the search unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the search unit can input market value data of photos into the generation AI and have the generation AI perform a search.

[0098] The providing unit can estimate the user's emotions and adjust the presentation method based on the estimated user emotions. To estimate emotions, for example, the generation AI uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the providing unit can have the generation AI analyze the user's facial expressions and estimate emotions. The providing unit can also have the generation AI analyze the user's voice and estimate emotions. The providing unit can also have the generation AI analyze the user's biometric data and estimate emotions. To adjust the presentation method, for example, the generation AI can change the interface or provide feedback based on the user's emotions. For example, the providing unit can select a presentation method that includes detailed explanations when the user is relaxed. The providing unit can also select a presentation method that includes concise explanations when the user is in a hurry. The providing unit can also select a visually stimulating presentation method when the user is excited. This makes it possible to adjust the presentation method based on the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the providing method.

[0099] At the time of provision, the provision unit can select the optimal provision method by referring to the user's past provision history. To refer to the provision history, for example, the generation AI analyzes past provision content, provision date and time, user ratings, etc. For example, the provision unit preferentially selects a provision method that the generation AI has previously preferred. The provision unit can also select a provision method suitable for a specific time period from the user's past provision history. The provision unit can also select the optimal provision method by having the generation AI analyze the user's past provision history. This makes it possible to select the optimal provision method based on the user's past provision history. Some or all of the above-described processing in the provision unit may be performed using, or without, the generation AI. For example, the provision unit can input the user's provision history data into the generation AI and have the generation AI select the provision method.

[0100] The providing unit can customize the provided content according to the user's current task when providing the content. To identify the current task, for example, the generation AI analyzes the user's schedule, current work content, etc. For example, if the generation AI determines that the user is preparing a presentation, the providing unit can prioritize providing related photos. Furthermore, if the generation AI determines that the user is traveling, the providing unit can prioritize providing travel-related photos. Furthermore, if the generation AI determines that the user is engaged in marketing activities, the providing unit can prioritize providing photos suitable for promotions. This allows the provided content to be customized according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's current task data into the generation AI and cause the generation AI to customize the provided content.

[0101] The providing unit can improve the provision method by reflecting user feedback when providing the content. To obtain the feedback, for example, the generation AI analyzes user ratings, comments, survey results, etc. For example, if the user provides feedback on the provision method, the providing unit improves the provision method based on that feedback. The providing unit can also have the generation AI analyze the user's past feedback and improve the provision method. The providing unit can also have the generation AI reflect the user's feedback in real time and adjust the provision method. This makes it possible to improve the provision method based on user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input user feedback data into the generation AI and have the generation AI improve the provision method.

[0102] The providing unit can estimate the user's emotions and prioritize the content to be provided based on the estimated user emotions. To estimate emotions, for example, the generation AI can estimate the user's emotions using facial expression recognition technology or voice analysis technology. For example, the generation AI can analyze the user's facial expressions and estimate the emotions. The providing unit can also analyze the user's voice and estimate the emotions. The providing unit can also analyze the user's biometric data and estimate the emotions. To prioritize the content to be provided, for example, the generation AI can change the priorities or filter the content based on the user's emotions. For example, if the generation AI determines that the user is relaxed, the providing unit can prioritize providing detailed content. If the generation AI determines that the user is in a hurry, the providing unit can prioritize providing concise content. If the generation AI determines that the user is excited, the providing unit can prioritize providing visually stimulating content. This makes it possible to prioritize the content to be provided based on the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion and determine the priority of the content to be provided.

[0103] The providing unit can select an appropriate delivery method based on the user's geographical location information at the time of delivery. To identify the geographical location information, for example, the generation AI analyzes GPS data, IP address, etc. For example, if the generation AI determines that the user is in a specific location, the providing unit can prioritize providing content related to that location. Furthermore, if the generation AI determines that the user is traveling, the providing unit can prioritize providing content related to travel. Furthermore, if the generation AI determines that the user is at home, the providing unit can prioritize providing content related to home. This makes it possible to select the optimal delivery method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the delivery method.

[0104] The providing unit can analyze the user's social media activity and suggest content to be provided at the time of providing the content. To analyze social media activity, for example, the generation AI analyzes the user's posted content, the number of likes, the number of followers, etc. For example, the providing unit can prioritize providing related content based on the content posted by the user on social media by the generation AI. The providing unit can also analyze the user's social media activity history and prioritize providing related content by the generation AI. The providing unit can also prioritize providing related content by the generation AI based on the activity of the user's friends on social media. This makes it possible to suggest content to be provided based on the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest content to be provided.

[0105] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the content. To obtain the feedback, for example, the generation AI analyzes the user's ratings, comments, survey results, etc. For example, the providing unit can suggest the optimal delivery method based on the user's past feedback. The providing unit can also cause the generation AI to preferentially suggest a specific delivery method based on the user's past feedback. The providing unit can also cause the generation AI to analyze the user's past feedback and improve the delivery method. This makes it possible to customize the delivery method based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's feedback data into the generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, editing unit, search unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice instructions or text instructions from the user using the microphone 38B or touch panel 38A of the smart device 14. The editing unit edits photos using the AI ​​generated by the specific processing unit 290 of the data processing device 12. The search unit searches for photos using the AI ​​generated by the specific processing unit 290 of the data processing device 12. The providing unit provides the results of editing and searching to the user via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, editing unit, search unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's voice instruction using the microphone 238 of the smart glasses 214. The editing unit edits photos using the AI ​​generated by the specific processing unit 290 of the data processing device 12. The search unit searches for photos using the AI ​​generated by the specific processing unit 290 of the data processing device 12. The providing unit provides the editing and search results to the user through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, editing unit, search unit, and providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a user's voice instruction using the microphone 238 of the headset type terminal 314. The editing unit edits photos using the AI ​​generated by the specific processing unit 290 of the data processing device 12. The search unit searches for photos using the AI ​​generated by the specific processing unit 290 of the data processing device 12. The providing unit provides the results of editing and searching to the user via the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, editing unit, search unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's voice instruction using the microphone 238 of the robot 414. The editing unit edits photos using the generation AI by the specific processing unit 290 of the data processing device 12. The search unit searches for photos using the generation AI by the specific processing unit 290 of the data processing device 12. The providing unit provides the results of editing and searching to the user through the speaker 240 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When accepting user instructions, the accepting unit can analyze the user's past behavioral patterns and provide an optimal interface. For example, if the user has frequently used voice instructions in the past, the accepting unit can preferentially display a voice input interface. Also, if the user frequently issues instructions during a specific time period, the accepting unit can provide an interface tailored to that time period. Furthermore, if the user uses a specific device (smartphone, tablet, etc.), the accepting unit can provide an interface optimized for that device. This makes it possible to provide an easier-to-use interface based on the user's past behavioral patterns.

[0108] When changing the background of a photo based on a user's instructions, the editorial department can estimate the user's emotions and suggest background options based on the estimated emotions. For example, if the user is relaxed, a background with natural scenery or calm colors can be suggested. If the user is excited, a background with vivid colors or movement can be suggested. Furthermore, if the user is sad, a background with calm colors or a simple background can be suggested. In this way, it is possible to provide background options according to the user's emotions.

[0109] When adjusting the brightness of a photo based on a user's instructions, the editorial department can automatically suggest the optimal brightness based on the content of the photo. For example, for landscape photos, the brightness can be increased to emphasize natural light. For portrait photos, the brightness can be adjusted to enhance facial expressions. Furthermore, for nightscape photos, the brightness can be fine-tuned to preserve the atmosphere. This allows the optimal brightness to be automatically suggested based on the content of the photo.

[0110] The search unit can estimate the user's emotions when analyzing the metadata and content of photos and filter search results based on the estimated emotions. For example, if the user is relaxed, photos of calm landscapes and nature can be displayed preferentially. If the user is excited, action scenes and colorful photos can be displayed preferentially. Furthermore, if the user is sad, calm colors and simple photos can be displayed preferentially. This makes it possible to provide search results that correspond to the user's emotions.

[0111] When providing the editing and search results to the user, the providing unit can customize the providing method by reflecting the user's past feedback. For example, if the user has previously preferred to receive the results by email, the providing unit can send the results by email. Also, if the user uses cloud storage, the results can be uploaded to cloud storage. Furthermore, if the user frequently uses web applications, the results can be displayed through the web applications. This makes it possible to select the optimal providing method based on the user's past feedback.

[0112] When providing promotional photos for marketing, the providing unit can estimate the user's emotions and adjust the style of the promotional photos based on the estimated emotions. For example, if the user is relaxed, promotional photos with calm colors and a natural atmosphere can be provided. If the user is excited, promotional photos with vivid colors and movement can be provided. Furthermore, if the user is sad, promotional photos with calm colors and a simple feel can be provided. In this way, promotional photos can be provided that correspond to the user's emotions.

[0113] When accepting instructions from a user, the accepting unit can filter the instructions based on the user's current situation and areas of interest. For example, if the user is traveling, instructions related to the travel can be preferentially accepted. Also, if the user is preparing a presentation, instructions related to the presentation can be preferentially accepted. Furthermore, if the user is engaged in marketing activities, instructions related to marketing can be preferentially accepted. This makes it possible to filter instructions based on the user's current situation and areas of interest.

[0114] The reception unit can analyze the user's past instruction history, estimate the user's emotions when selecting an appropriate reception method, and adjust the reception method based on the estimated emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to quickly accept instructions. In this way, the reception method can be adjusted according to the user's emotions.

[0115] When changing the background of a photo based on user instructions, the editing department can apply different editing algorithms depending on the photo category. For example, for landscape photos, editing can be performed to correct color tone and emphasize sharpness. For portrait photos, editing can be performed to adjust skin tones and blur the background. Furthermore, for product photos, editing can be performed to adjust brightness and contrast and emphasize product details. This allows the optimal editing algorithm to be applied depending on the photo category.

[0116] The providing unit can estimate the user's emotions and determine the priority of the content to be provided based on the estimated emotions. For example, if the user is relaxed, detailed content can be provided preferentially. If the user is in a hurry, brief content can be provided preferentially. Furthermore, if the user is excited, visually stimulating content can be provided preferentially. In this way, the priority of the content to be provided can be determined based on the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives instructions in natural language from the user. Natural language instructions include voice instructions and text instructions. The reception unit converts the user's voice instructions into text using voice recognition technology. It also provides a text input interface, allowing the user to input instructions directly in text. Step 2: The editorial department uses the generation AI to edit the photo based on the instructions received by the reception department. Editing includes cropping, applying filters, adjusting colors, etc. For example, the generation AI may change the background of the photo or adjust the brightness and color tone based on the user's instructions. Step 3: The search unit uses the generation AI to search for photos based on the instructions received by the reception unit. Searches include keyword searches and image recognition searches. For example, the generation AI analyzes the metadata and content of photos to search for relevant photos. Step 4: The providing unit provides the results obtained by the editing and search units to the user. Providing includes providing a download link, uploading to cloud storage, etc. For example, the editing and search results may be displayed to the user through a web application or mobile application, or sent by email.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives instructions in natural language from a user; an editing unit that edits the photo based on the instruction received by the receiving unit; a search unit that searches for a photo based on the instruction received by the reception unit; a providing unit that provides the results obtained by the editing unit and the search unit. A system characterized by:

2. The editorial department Change the background of a photo based on user instructions 2. The system of claim 1.

3. The editorial department Adjust the brightness of a photo based on user input 2. The system of claim 1.

4. The search unit Analyzes the metadata or content of photos to find matching photos 2. The system of claim 1.

5. The providing unit Providing edit and search results to users 2. The system of claim 1.

6. The providing unit Providing promotional photos for marketing purposes 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the way instructions are received based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past instruction history and select the appropriate acceptance method 2. The system of claim 1.

9. The reception unit When receiving instructions, filter them based on the user's current situation or interests 2. The system of claim 1.

10. The reception unit When accepting an instruction, select an appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

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