System

Abstract

An object of a system according to an embodiment is to quickly recommend an optimal spot based on a current location of a user or a request.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an input of a user. The analysis unit analyzes the information received by the reception unit and selects a spot based on the current location of the user and the request. The generation unit generates a moving image based on the information of the spot selected by the analysis unit. The providing unit provides the video generated by the generating unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to quickly recommend optimal spots based on a user's current location and requests.

[0005] The system according to the embodiment aims to quickly recommend the most suitable spots based on the user's current location and requirements. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input from a user. The analysis unit analyzes the information received by the reception unit and selects a spot based on the user's current location and requests. The generation unit generates a video based on the information about the spot selected by the analysis unit. The provision unit provides the video generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can quickly recommend the most suitable spots based on the user's current location and requirements. [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 recommendation system according to an embodiment of the present invention quickly recommends recommended spots in the form of realistic videos based on a user's current situation and preferences. The recommendation system accepts and analyzes user input, selects spots, and generates and provides videos. For example, if a user inputs a preference such as "Is there anywhere I can go and return to in about an hour?" or "I can't imagine whether it will taste good after all," the system recommends the most suitable spots based on this. The system selects spots based on the user's current location and preferences, and generates realistic videos based on information about the selected spots. This allows the user to experience the experience as if they were actually at the place. This allows the recommendation system to easily find the most suitable spots based on the user's current situation and preferences, facilitating smooth decision-making in the city.

[0029] A recommendation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. The user input includes, but is not limited to, text input, voice input, and gesture input. The reception unit receives, for example, specific user preferences, such as "a place I can get to and return from in about an hour" or "a place where I can eat delicious food." The analysis unit analyzes the information received by the reception unit and selects a spot based on the user's current location and preferences. The analysis unit, for example, uses an algorithm to obtain the user's current location from GPS information or Wi-Fi location information, and selects an optimal spot based on the user's preferences. The generation unit generates a video based on information about the spot selected by the analysis unit. The generation unit, for example, uses a generation AI to generate a video including footage of the interior of the selected spot and footage of the food. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the video generated by the generation unit to the user. The provision unit displays the video, for example, through a web application or a mobile application. The providing unit can also receive feedback from the user, allowing the recommendation system according to the embodiment to recommend optimal spots based on the user's current situation and preferences, and to provide realistic videos to support the user's decision-making.

[0030] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit can automatically display as candidates the current location or preferences that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the current location or preferences to be used during a specific time period based on the user's past input history. This improves the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0031] The reception unit can add a function to automatically complete input content based on the user's current activity status. For example, when the user is moving, the reception unit automatically obtains the user's current location and completes the input content. Furthermore, when the user is engaged in a specific activity, the reception unit can automatically suggest preferences related to that activity. Furthermore, when the user is in a specific time period, the reception unit can automatically suggest spots suitable for that time period. This reduces the effort required for input by providing an input completion function according to the user's current activity status. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without AI.

[0032] The reception unit can diversify input means by using voice recognition or gesture recognition when a user inputs information. For example, the reception unit can automatically set the user's current location and preferences by simply inputting "a good restaurant nearby" by voice. The reception unit can also allow the user to easily set the user's current location and preferences by performing a specific gesture on the smartphone screen. The reception unit can also allow the user to set the user's current location and preferences more intuitively by combining voice input and gesture input. In this way, the use of voice recognition or gesture recognition diversifies the user's input means and improves convenience. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0033] The reception unit can present highly relevant input candidates taking into account the user's geographical location information. For example, the reception unit can prioritize displaying spots close to the user's current location as input candidates. Furthermore, if the user is in a specific area, the reception unit can also display spots related to that area as input candidates. Furthermore, if the user is taking a specific route, the reception unit can also display spots related to that route as input candidates. In this way, input candidates based on the user's geographical location information are presented, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0034] The reception unit can analyze the user's social media activity and suggest related input content. For example, the reception unit can suggest places where the user has checked in on social media as input candidates. The reception unit can also analyze the content of the user's social media posts and suggest related spots as input candidates. The reception unit can also suggest related spots as input candidates based on the activity of the user's friends on social media. This improves the user's input experience by suggesting related input content based on social media activity. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0035] The reception unit can customize the input interface by reflecting the user's past feedback. The reception unit can, for example, adjust the design of the input interface based on feedback provided by the user in the past. The reception unit can also simplify the input procedure based on feedback provided by the user in the past. The reception unit can also adjust the priority of input content based on feedback provided by the user in the past. In this way, the input interface can be customized based on past feedback, thereby improving the user's input experience. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without using AI.

[0036] The analysis unit can analyze the user's past behavior history and improve the accuracy of spot selection. The analysis unit can, for example, suggest related spots based on spots the user has visited in the past. The analysis unit can also analyze the user's past behavior history to analyze preferences and suggest optimal spots. The analysis unit can also analyze the user's past behavior history to suggest spots that avoid crowds. This improves the accuracy of spot selection based on the user's past behavior history, making it possible to suggest optimal spots for the user. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0037] The analysis unit can select spots based on the user's current weather and traffic conditions. For example, the analysis unit preferentially suggests indoor spots when it is raining. Furthermore, the analysis unit can also suggest spots that avoid traffic congestion when traffic congestion occurs. Furthermore, the analysis unit can preferentially suggest outdoor spots when it is sunny. In this way, the optimal spots can be suggested to the user by taking into account the current weather and traffic conditions. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0038] The analysis unit can customize spot selection criteria based on the user's preferences. The analysis unit can suggest restaurants based on the type of cuisine the user prefers, for example. The analysis unit can also suggest spots based on the user's preferred activities. The analysis unit can also suggest optimal spots based on the user's past selection history. This allows the analysis unit to suggest optimal spots to the user by providing spot selection criteria based on the user's preferences. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0039] The analysis unit can prioritize selecting relevant spots taking into account the user's geographical location information. For example, the analysis unit can prioritize selecting spots close to the user's current location. Furthermore, if the user is in a specific area, the analysis unit can prioritize selecting spots related to that area. Furthermore, if the user is taking a specific route, the analysis unit can prioritize selecting spots related to that route. This allows the analysis unit to suggest optimal spots to the user by providing spots based on the user's geographical location information. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without using AI.

[0040] The analysis unit can analyze the user's social media activity and select related spots. For example, the analysis unit can select places where the user has checked in on social media as spots. The analysis unit can also analyze the content of the user's social media posts and select related spots. The analysis unit can also select related spots by referring to the activities of the user's friends on social media. This makes it possible to suggest optimal spots to the user by providing related spots based on social media activity. Some or all of the above-described processing by the analysis unit can be performed, for example, using AI, or can be performed without using AI.

[0041] The analysis unit can customize the spot selection algorithm by reflecting the user's past feedback. The analysis unit can adjust the spot selection algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also adjust the priority of spots based on feedback provided by the user in the past. The analysis unit can also customize the spot selection criteria based on feedback provided by the user in the past. In this way, by customizing the spot selection algorithm based on past feedback, it is possible to suggest optimal spots to the user. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without using AI.

[0042] The generation unit can customize the content of the video based on detailed information about the selected spot. The generation unit generates a video that includes, for example, footage of the interior of the selected restaurant and the food. The generation unit can also generate a video that includes footage of the scenery and activities at the selected tourist spot. The generation unit can also generate a video that includes footage of the interior of the selected store and product introductions. In this way, by customizing the video based on detailed information about the selected spot, it is possible to provide the user with an optimal video. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0043] The generation unit can analyze the user's past viewing history and adjust the video generation algorithm. The generation unit can analyze the user's preferences based on, for example, the content of videos the user has viewed in the past, and generate an optimal video. The generation unit can also generate a video that adds visually appealing effects based on the user's past viewing history. The generation unit can also analyze the user's past viewing history and generate a video that includes the most interesting content. In this way, the video generation algorithm can be optimized based on the past viewing history, and the optimal video can be provided to the user. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0044] The generation unit can provide a function to add audio or text narration when generating a video. For example, the generation unit generates a video that uses audio narration to introduce a selected spot. The generation unit can also generate a video that displays detailed information about the selected spot using text narration. The generation unit can also generate a video that combines audio narration and text narration to provide more detailed information. By adding audio or text narration, the amount of information in the video is increased, deepening the user's understanding. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0045] When generating a video, the generation unit can customize the content of the video by taking into account the geographical information of the spot. For example, the generation unit generates a video that emphasizes the geographical features of the selected spot. The generation unit can also generate a video that introduces the surrounding environment of the selected spot. The generation unit can also generate a video that provides detailed instructions on how to access the selected spot. In this way, by providing a video that takes geographical information into consideration, it is possible to provide the user with an optimal video. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0046] When generating a video, the generation unit can adjust the content of the video to reflect the user's social media activity. For example, the generation unit generates a video introducing places where the user has checked in on social media. The generation unit can also generate a video that reflects the content posted by the user on social media. The generation unit can also generate a video that introduces related spots by referring to the activity of the user's friends on social media. In this way, by providing a video that reflects social media activity, it is possible to provide the user with the most suitable video. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0047] The generation unit can optimize the content of the video by reflecting the user's past feedback when generating the video. The generation unit adjusts the content of the video based on, for example, feedback provided by the user in the past. The generation unit can also optimize the effects of the video based on feedback provided by the user in the past. The generation unit can also adjust the length and level of detail of the video based on feedback provided by the user in the past. In this way, the content of the video can be optimized based on past feedback, thereby providing the user with an optimal video. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0048] The providing unit can analyze the user's past viewing history and select the optimal timing for providing the video. The providing unit selects the optimal timing for providing the video, for example, based on the time period in which the user previously viewed the video. The providing unit can also provide the video during a time period in which the user frequently views the video, based on the user's past viewing history. The providing unit can also analyze the user's past viewing history and select the most effective timing for providing the video. In this way, by selecting the optimal timing for providing the video based on the past viewing history, the video can be provided to the user at the optimal timing. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The providing unit can provide the video in an optimal video format taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide the video in a video format that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide the video in a video format optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide the video in a simple, highly visible video format. In this way, by providing a video format based on the device information, the optimal viewing experience can be provided to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0050] The providing unit can collect user feedback in real time and improve the providing method. For example, the providing unit can collect feedback provided by a user while watching a video in real time and adjust the providing method. The providing unit can also improve the next providing method based on feedback provided by a user after watching a video. The providing unit can also analyze user feedback in real time and propose an optimal providing method. In this way, by collecting feedback in real time, the providing method can be quickly improved and user satisfaction can be improved. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0051] The providing unit can provide highly relevant videos preferentially, taking into consideration the user's geographical location information. For example, the providing unit can provide videos of spots close to the user's current location preferentially. Furthermore, when the user is in a specific area, the providing unit can also provide videos of spots related to that area preferentially. Furthermore, when the user is taking a specific route, the providing unit can also provide videos of spots related to that route preferentially. In this way, by providing videos based on the geographical location information, it is possible to provide the most suitable video to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The providing unit can analyze the user's social media activity and provide related videos. For example, the providing unit can provide videos related to places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide videos of related spots. The providing unit can also provide videos of related spots by referring to the activity of the user's friends on social media. In this way, by providing related videos based on social media activity, it is possible to provide the most suitable videos for the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0053] The providing unit can customize the video providing method by reflecting the user's past feedback. The providing unit adjusts the video providing method, for example, based on feedback provided by the user in the past. The providing unit can also adjust the video providing order, based on feedback provided by the user in the past. The providing unit can also adjust the video providing timing, based on feedback provided by the user in the past. In this way, the providing method can be customized based on past feedback, thereby providing the optimal video to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When accepting a user's input, the reception unit can automatically generate input candidates based on the user's past behavioral history. For example, it can display related input candidates based on spots the user has visited in the past or keywords frequently searched for by the user. The reception unit can also suggest input candidates related to specific time periods or days of the week based on the user's past behavioral history. Furthermore, the reception unit can analyze the user's past behavioral history and provide optimal input candidates based on the user's preferences and tendencies. This can improve the user's input efficiency by automatically generating input candidates using the user's past behavioral history.

[0056] The analysis unit can adjust the priority of spot selection based on the user's current weather and traffic conditions. For example, when it is raining, indoor spots are given priority. In addition, when traffic congestion occurs, the analysis unit can also suggest spots that avoid the congestion. Furthermore, when it is sunny, the analysis unit can also give priority to outdoor spots. In this way, the optimal spots can be suggested to the user by taking into account the current weather and traffic conditions.

[0057] The providing unit can provide the video in the optimal video format taking into account the device information of the user. For example, if the user is using a smartphone, the video can be provided in a video format that matches the screen size. Also, if the user is using a tablet, the video can be provided in a video format optimized for a large screen. Furthermore, if the user is using a smartwatch, the video can be provided in a simple and highly visible video format. In this way, by providing a video format based on the device information, the optimal viewing experience can be provided to the user.

[0058] The analysis unit can analyze the user's social media activity and select related spots. For example, it can select places where the user has checked in on social media as spots. The analysis unit can also analyze the content of the user's social media posts to select related spots. Furthermore, the analysis unit can also select related spots by taking into account the activities of the user's friends on social media. In this way, it is possible to suggest the best spots for the user by providing related spots based on social media activity.

[0059] The generation unit can analyze the user's past viewing history and adjust the video generation algorithm. For example, the generation unit analyzes the user's preferences based on the content of videos the user has viewed in the past and generates the optimal video. The generation unit can also generate videos that add visually appealing effects based on the user's past viewing history. Furthermore, the generation unit can analyze the user's past viewing history and generate videos that include the most interesting content. In this way, the video generation algorithm can be optimized based on the user's past viewing history, making it possible to provide the user with the optimal video.

[0060] The reception unit can customize the input interface by reflecting the user's past feedback. For example, the reception unit can adjust the design of the input interface based on the user's past feedback. The reception unit can also simplify the input procedure based on the user's past feedback. Furthermore, the reception unit can adjust the priority of input contents based on the user's past feedback. In this way, the input interface can be customized based on the user's past feedback, thereby improving the input experience.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives user input. User input includes text input, voice input, gesture input, etc. For example, the reception unit receives input of specific user preferences such as "a place I can get to and return from in about an hour" or "a place where I can eat delicious food." Step 2: The analysis unit analyzes the information received by the reception unit and selects a spot based on the user's current location and preferences. The analysis unit uses an algorithm to obtain the user's current location from GPS information and Wi-Fi location information, and selects the optimal spot based on the user's preferences. Step 3: The generation unit generates a video based on the information about the spots selected by the analysis unit. The generation unit uses a generation AI to generate a video that includes footage of the interior of the selected spots and the food. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The providing unit provides the video generated by the generating unit to the user. The providing unit displays the video through a web application or a mobile application. The providing unit can also accept user feedback.

[0063] (Example 2) A recommendation system according to an embodiment of the present invention quickly recommends recommended spots in the form of realistic videos based on a user's current situation and preferences. The recommendation system accepts and analyzes user input, selects spots, and generates and provides videos. For example, if a user inputs a preference such as "Is there anywhere I can go and return to in about an hour?" or "I can't imagine whether it will taste good after all," the system recommends the most suitable spots based on this. The system selects spots based on the user's current location and preferences, and generates realistic videos based on information about the selected spots. This allows the user to experience the experience as if they were actually at the place. This allows the recommendation system to easily find the most suitable spots based on the user's current situation and preferences, facilitating smooth decision-making in the city.

[0064] A recommendation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. The user input includes, but is not limited to, text input, voice input, and gesture input. The reception unit receives, for example, specific user preferences, such as "a place I can get to and return from in about an hour" or "a place where I can eat delicious food." The analysis unit analyzes the information received by the reception unit and selects a spot based on the user's current location and preferences. The analysis unit, for example, uses an algorithm to obtain the user's current location from GPS information or Wi-Fi location information, and selects an optimal spot based on the user's preferences. The generation unit generates a video based on information about the spot selected by the analysis unit. The generation unit, for example, uses a generation AI to generate a video including footage of the interior of the selected spot and footage of the food. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the video generated by the generation unit to the user. The provision unit displays the video, for example, through a web application or a mobile application. The providing unit can also receive feedback from the user, allowing the recommendation system according to the embodiment to recommend optimal spots based on the user's current situation and preferences, and to provide realistic videos to support the user's decision-making.

[0065] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input their current location and preferences. This improves the user's input experience by providing an interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be 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 AI, for example, or without AI.

[0066] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit can automatically display as candidates the current location or preferences that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the current location or preferences to be used during a specific time period based on the user's past input history. This improves the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0067] The reception unit can add a function to automatically complete input content based on the user's current activity status. For example, when the user is moving, the reception unit automatically obtains the user's current location and completes the input content. Furthermore, when the user is engaged in a specific activity, the reception unit can automatically suggest preferences related to that activity. Furthermore, when the user is in a specific time period, the reception unit can automatically suggest spots suitable for that time period. This reduces the effort required for input by providing an input completion function according to the user's current activity status. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without AI.

[0068] The reception unit can diversify input means by using voice recognition or gesture recognition when a user inputs information. For example, the reception unit can automatically set the user's current location and preferences by simply inputting "a good restaurant nearby" by voice. The reception unit can also allow the user to easily set the user's current location and preferences by performing a specific gesture on the smartphone screen. The reception unit can also allow the user to set the user's current location and preferences more intuitively by combining voice input and gesture input. In this way, the use of voice recognition or gesture recognition diversifies the user's input means and improves convenience. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0069] The reception unit can estimate the user's emotions and prioritize input contents based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize displaying the simplest input contents. Furthermore, when the user is relaxed, the reception unit can prioritize displaying detailed input contents. Furthermore, when the user is in a hurry, the reception unit can prioritize displaying contents that can be input most quickly. This improves the user's input experience by prioritizing input contents according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI.

[0070] The reception unit can present highly relevant input candidates taking into account the user's geographical location information. For example, the reception unit can prioritize displaying spots close to the user's current location as input candidates. Furthermore, if the user is in a specific area, the reception unit can also display spots related to that area as input candidates. Furthermore, if the user is taking a specific route, the reception unit can also display spots related to that route as input candidates. In this way, input candidates based on the user's geographical location information are presented, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0071] The reception unit can analyze the user's social media activity and suggest related input content. For example, the reception unit can suggest places where the user has checked in on social media as input candidates. The reception unit can also analyze the content of the user's social media posts and suggest related spots as input candidates. The reception unit can also suggest related spots as input candidates based on the activity of the user's friends on social media. This improves the user's input experience by suggesting related input content based on social media activity. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0072] The reception unit can customize the input interface by reflecting the user's past feedback. The reception unit can, for example, adjust the design of the input interface based on feedback provided by the user in the past. The reception unit can also simplify the input procedure based on feedback provided by the user in the past. The reception unit can also adjust the priority of input content based on feedback provided by the user in the past. In this way, the input interface can be customized based on past feedback, thereby improving the user's input experience. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without using AI.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest more spots. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and suggest the most suitable spots. Furthermore, if the user is excited, the analysis unit can prioritize suggesting visually stimulating spots. This improves the accuracy of the analysis results by providing an analysis algorithm that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI.

[0074] The analysis unit can analyze the user's past behavior history and improve the accuracy of spot selection. The analysis unit can, for example, suggest related spots based on spots the user has visited in the past. The analysis unit can also analyze the user's past behavior history to analyze preferences and suggest optimal spots. The analysis unit can also analyze the user's past behavior history to suggest spots that avoid crowds. This improves the accuracy of spot selection based on the user's past behavior history, making it possible to suggest optimal spots for the user. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0075] The analysis unit can select spots based on the user's current weather and traffic conditions. For example, the analysis unit preferentially suggests indoor spots when it is raining. Furthermore, the analysis unit can also suggest spots that avoid traffic congestion when traffic congestion occurs. Furthermore, the analysis unit can preferentially suggest outdoor spots when it is sunny. In this way, the optimal spots can be suggested to the user by taking into account the current weather and traffic conditions. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0076] The analysis unit can customize spot selection criteria based on the user's preferences. The analysis unit can suggest restaurants based on the type of cuisine the user prefers, for example. The analysis unit can also suggest spots based on the user's preferred activities. The analysis unit can also suggest optimal spots based on the user's past selection history. This allows the analysis unit to suggest optimal spots to the user by providing spot selection criteria based on the user's preferences. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0077] The analysis unit can estimate the user's emotions and prioritize spots based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can prioritize relaxing spots. Furthermore, if the user is in a hurry, the analysis unit can prioritize quickly accessible spots. Furthermore, if the user is excited, the analysis unit can prioritize visually stimulating spots. This allows the optimal spot to be suggested to the user by prioritizing spots according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI.

[0078] The analysis unit can prioritize selecting relevant spots taking into account the user's geographical location information. For example, the analysis unit can prioritize selecting spots close to the user's current location. Furthermore, if the user is in a specific area, the analysis unit can prioritize selecting spots related to that area. Furthermore, if the user is taking a specific route, the analysis unit can prioritize selecting spots related to that route. This allows the analysis unit to suggest optimal spots to the user by providing spots based on the user's geographical location information. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without using AI.

[0079] The analysis unit can analyze the user's social media activity and select related spots. For example, the analysis unit can select places where the user has checked in on social media as spots. The analysis unit can also analyze the content of the user's social media posts and select related spots. The analysis unit can also select related spots by referring to the activities of the user's friends on social media. This makes it possible to suggest optimal spots to the user by providing related spots based on social media activity. Some or all of the above-described processing by the analysis unit can be performed, for example, using AI, or can be performed without using AI.

[0080] The analysis unit can customize the spot selection algorithm by reflecting the user's past feedback. The analysis unit can adjust the spot selection algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also adjust the priority of spots based on feedback provided by the user in the past. The analysis unit can also customize the spot selection criteria based on feedback provided by the user in the past. In this way, by customizing the spot selection algorithm based on past feedback, it is possible to suggest optimal spots to the user. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without using AI.

[0081] The generation unit can estimate the user's emotions and adjust the way the video is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a video that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a video that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a video that adds visually stimulating effects. This improves the user's viewing experience by providing a video presentation that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI.

[0082] The generation unit can customize the content of the video based on detailed information about the selected spot. The generation unit generates a video that includes, for example, footage of the interior of the selected restaurant and the food. The generation unit can also generate a video that includes footage of the scenery and activities at the selected tourist spot. The generation unit can also generate a video that includes footage of the interior of the selected store and product introductions. In this way, by customizing the video based on detailed information about the selected spot, it is possible to provide the user with an optimal video. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0083] The generation unit can analyze the user's past viewing history and adjust the video generation algorithm. The generation unit can analyze the user's preferences based on, for example, the content of videos the user has viewed in the past, and generate an optimal video. The generation unit can also generate a video that adds visually appealing effects based on the user's past viewing history. The generation unit can also analyze the user's past viewing history and generate a video that includes the most interesting content. In this way, the video generation algorithm can be optimized based on the past viewing history, and the optimal video can be provided to the user. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0084] The generation unit can provide a function to add audio or text narration when generating a video. For example, the generation unit generates a video that uses audio narration to introduce a selected spot. The generation unit can also generate a video that displays detailed information about the selected spot using text narration. The generation unit can also generate a video that combines audio narration and text narration to provide more detailed information. By adding audio or text narration, the amount of information in the video is increased, deepening the user's understanding. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0085] The generation unit can estimate the user's emotions and adjust the length of the video based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point video. If the user is relaxed, the generation unit can also generate a longer video with detailed explanations. If the user is excited, the generation unit can also generate a video with visually stimulating effects. This improves the user's viewing experience by providing a video length that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI.

[0086] When generating a video, the generation unit can customize the content of the video by taking into account the geographical information of the spot. For example, the generation unit generates a video that emphasizes the geographical features of the selected spot. The generation unit can also generate a video that introduces the surrounding environment of the selected spot. The generation unit can also generate a video that provides detailed instructions on how to access the selected spot. In this way, by providing a video that takes geographical information into consideration, it is possible to provide the user with an optimal video. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0087] When generating a video, the generation unit can adjust the content of the video to reflect the user's social media activity. For example, the generation unit generates a video introducing places where the user has checked in on social media. The generation unit can also generate a video that reflects the content posted by the user on social media. The generation unit can also generate a video that introduces related spots by referring to the activity of the user's friends on social media. In this way, by providing a video that reflects social media activity, it is possible to provide the user with the most suitable video. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0088] The generation unit can optimize the content of the video by reflecting the user's past feedback when generating the video. The generation unit adjusts the content of the video based on, for example, feedback provided by the user in the past. The generation unit can also optimize the effects of the video based on feedback provided by the user in the past. The generation unit can also adjust the length and level of detail of the video based on feedback provided by the user in the past. In this way, the content of the video can be optimized based on past feedback, thereby providing the user with an optimal video. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0089] The providing unit can estimate the user's emotions and adjust the video providing method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide the video at a leisurely pace. Also, if the user is in a hurry, the providing unit can provide the video quickly. Also, if the user is excited, the providing unit can provide the video with a visually stimulating effect. This improves the user's viewing experience by providing a video providing method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0090] The providing unit can analyze the user's past viewing history and select the optimal timing for providing the video. The providing unit selects the optimal timing for providing the video, for example, based on the time period in which the user previously viewed the video. The providing unit can also provide the video during a time period in which the user frequently views the video, based on the user's past viewing history. The providing unit can also analyze the user's past viewing history and select the most effective timing for providing the video. In this way, by selecting the optimal timing for providing the video based on the past viewing history, the video can be provided to the user at the optimal timing. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0091] The providing unit can provide the video in an optimal video format taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide the video in a video format that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide the video in a video format optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide the video in a simple, highly visible video format. In this way, by providing a video format based on the device information, the optimal viewing experience can be provided to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0092] The providing unit can collect user feedback in real time and improve the providing method. For example, the providing unit can collect feedback provided by a user while watching a video in real time and adjust the providing method. The providing unit can also improve the next providing method based on feedback provided by a user after watching a video. The providing unit can also analyze user feedback in real time and propose an optimal providing method. In this way, by collecting feedback in real time, the providing method can be quickly improved and user satisfaction can be improved. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0093] The providing unit can estimate the user's emotions and adjust the order in which videos are provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can prioritize providing relaxing videos. Furthermore, when the user is in a hurry, the providing unit can prioritize providing videos that can be viewed quickly. Furthermore, when the user is excited, the providing unit can prioritize providing visually stimulating videos. This improves the user's viewing experience by providing a video provision order that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0094] The providing unit can provide highly relevant videos preferentially, taking into consideration the user's geographical location information. For example, the providing unit can provide videos of spots close to the user's current location preferentially. Furthermore, when the user is in a specific area, the providing unit can also provide videos of spots related to that area preferentially. Furthermore, when the user is taking a specific route, the providing unit can also provide videos of spots related to that route preferentially. In this way, by providing videos based on the geographical location information, it is possible to provide the most suitable video to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0095] The providing unit can analyze the user's social media activity and provide related videos. For example, the providing unit can provide videos related to places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide videos of related spots. The providing unit can also provide videos of related spots by referring to the activity of the user's friends on social media. In this way, by providing related videos based on social media activity, it is possible to provide the most suitable videos for the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0096] The providing unit can customize the video providing method by reflecting the user's past feedback. The providing unit adjusts the video providing method, for example, based on feedback provided by the user in the past. The providing unit can also adjust the video providing order, based on feedback provided by the user in the past. The providing unit can also adjust the video providing timing, based on feedback provided by the user in the past. In this way, the providing method can be customized based on past feedback, thereby providing the optimal video to the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user input using the touch panel 38A or microphone 38B of the smart device 14. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and selects a spot based on the user's current location and preferences. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and generates a video based on information about the selected spot. The provision unit provides the generated video to the user using the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the smart glasses 214. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and selects a spot based on the user's current location and preferences. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and generates a video based on information about the selected spot. The provision unit provides the generated video to the user using the display and speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and selects a spot based on the user's current location and preferences. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and generates a video based on information about the selected spot. The provision unit provides the generated video to the user using the display 343 and speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the robot 414. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and selects a spot based on the user's current location and preferences. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and generates a video based on information about the selected spot. The provision unit provides the generated video to the user using the display and speaker 240 of the robot 414.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] When accepting a user's input, the reception unit can automatically generate input candidates based on the user's past behavioral history. For example, it can display related input candidates based on spots the user has visited in the past or keywords frequently searched for by the user. The reception unit can also suggest input candidates related to specific time periods or days of the week based on the user's past behavioral history. Furthermore, the reception unit can analyze the user's past behavioral history and provide optimal input candidates based on the user's preferences and tendencies. This can improve the user's input efficiency by automatically generating input candidates using the user's past behavioral history.

[0099] The analysis unit can adjust the priority of spot selection based on the user's current weather and traffic conditions. For example, when it is raining, indoor spots are given priority. In addition, when traffic congestion occurs, the analysis unit can also suggest spots that avoid the congestion. Furthermore, when it is sunny, the analysis unit can also give priority to outdoor spots. In this way, the optimal spots can be suggested to the user by taking into account the current weather and traffic conditions.

[0100] The generation unit can estimate the user's emotions and adjust the video effects based on the estimated user emotions. For example, if the user is relaxed, a video with calm music and soft color effects can be generated. If the user is excited, a video with vivid color effects and dynamic effects can be generated. Furthermore, if the user is stressed, a video with relaxing effects can be generated. This makes it possible to improve the viewing experience by providing effects according to the user's emotions.

[0101] The providing unit can provide the video in the optimal video format taking into account the device information of the user. For example, if the user is using a smartphone, the video can be provided in a video format that matches the screen size. Also, if the user is using a tablet, the video can be provided in a video format optimized for a large screen. Furthermore, if the user is using a smartwatch, the video can be provided in a simple and highly visible video format. In this way, by providing a video format based on the device information, the optimal viewing experience can be provided to the user.

[0102] The reception unit can estimate the user's emotion and adjust the color tone and design of the input interface based on the estimated user emotion. For example, if the user is relaxed, an interface with calm color tones and a simple design can be provided. If the user is excited, an interface with vivid color tones and a dynamic design can be provided. Furthermore, if the user is stressed, an interface with color tones and a design that has a relaxing effect can be provided. In this way, the input experience can be improved by providing an interface that corresponds to the user's emotion.

[0103] The analysis unit can analyze the user's social media activity and select related spots. For example, it can select places where the user has checked in on social media as spots. The analysis unit can also analyze the content of the user's social media posts to select related spots. Furthermore, the analysis unit can also select related spots by taking into account the activities of the user's friends on social media. In this way, it is possible to suggest the best spots for the user by providing related spots based on social media activity.

[0104] The generation unit can analyze the user's past viewing history and adjust the video generation algorithm. For example, the generation unit analyzes the user's preferences based on the content of videos the user has viewed in the past and generates the optimal video. The generation unit can also generate videos that add visually appealing effects based on the user's past viewing history. Furthermore, the generation unit can analyze the user's past viewing history and generate videos that include the most interesting content. In this way, the video generation algorithm can be optimized based on the user's past viewing history, making it possible to provide the user with the optimal video.

[0105] The providing unit can estimate the user's emotions and adjust the order in which videos are provided based on the estimated user's emotions. For example, if the user is relaxed, videos that are relaxing can be provided preferentially. If the user is in a hurry, videos that can be viewed quickly can be provided preferentially. Furthermore, if the user is excited, videos that are visually stimulating can be provided preferentially. In this way, the viewing experience can be improved by providing a video provision order that corresponds to the user's emotions.

[0106] The reception unit can customize the input interface by reflecting the user's past feedback. For example, the reception unit can adjust the design of the input interface based on the user's past feedback. The reception unit can also simplify the input procedure based on the user's past feedback. Furthermore, the reception unit can adjust the priority of input contents based on the user's past feedback. In this way, the input interface can be customized based on the user's past feedback, thereby improving the input experience.

[0107] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed to suggest more spots. If the user is in a hurry, a quick analysis can be performed to suggest the most suitable spots. Furthermore, if the user is excited, visually stimulating spots can be prioritized. This allows the accuracy of the analysis results to be improved by providing an analysis algorithm that corresponds to the user's emotions.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit receives user input. User input includes text input, voice input, gesture input, etc. For example, the reception unit receives input of specific user preferences such as "a place I can get to and return from in about an hour" or "a place where I can eat delicious food." Step 2: The analysis unit analyzes the information received by the reception unit and selects a spot based on the user's current location and preferences. The analysis unit uses an algorithm to obtain the user's current location from GPS information and Wi-Fi location information, and selects the optimal spot based on the user's preferences. Step 3: The generation unit generates a video based on the information about the spots selected by the analysis unit. The generation unit uses a generation AI to generate a video that includes footage of the interior of the selected spots and the food. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The providing unit provides the video generated by the generating unit to the user. The providing unit displays the video through a web application or a mobile application. The providing unit can also accept user feedback.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 input from a user; an analysis unit that analyzes the information received by the reception unit and selects spots based on the user's current location and requests; a generation unit that generates a video based on information about the spots selected by the analysis unit; a providing unit that provides the video generated by the generating unit to a user. A system characterized by:

2. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

4. The reception unit Add auto-complete functionality based on the user's current activity 2. The system of claim 1.

5. The reception unit Diversify input methods using voice and gesture recognition when users input.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Considers the user's geographic location to provide relevant suggestions 2. The system of claim 1.

8. The reception unit Analyze your social media activity and suggest relevant entries 2. The system of claim 1.

Images