system

Abstract

The system according to the embodiment aims to provide personalized promotional content to a target demographic of young people. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects photos, audio, and text from users. The analysis unit analyzes the data collected by the collection unit. The generation unit generates personalized promotional content based on the analysis results obtained by the analysis unit. The provision unit provides the content generated by the generation unit.

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 technology has faced challenges in effectively reaching young target audiences and capturing their sustained interest.

[0005] The system according to the embodiment aims to provide personalized promotional content to a target demographic of young people. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects photos, voice, and text of users. The analysis unit analyzes the data collected by the collection unit. The generation unit generates personalized promotional content based on the analysis results obtained by the analysis unit. The provision unit provides the content generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized promotional content to a target demographic of young people. [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 personalized promotion platform according to an embodiment of the present invention is a system that collects users' photos, voice, and text, analyzes them using an advanced multimodal AI, and generates and delivers personalized promotional content. The personalized promotion platform targets young people in their late teens and twenties, leveraging their frequent social media use and sensitivity to new trends to provide new value to both companies and users. For example, users can provide their own photos, voice, and text as input, and the advanced multimodal AI generates personalized promotional content (images and videos) based on these inputs. Content related to topics of high interest to users, such as music festivals, fashion events, and game releases, is automatically created. By leveraging the data processing capabilities of advanced multimodal AI, including the nuances and intonation of speech, a highly advanced level of customization is possible, reflecting the user's own voice and personality. This allows the personalized promotion platform to generate and deliver personalized promotional content that reflects the user's individuality and interests. This allows personalized promotion platforms to provide businesses with a means to deeply reach their target audience and improve their brand image, and users with an opportunity to express their individuality and build deeper relationships with their favorite brands and products. Furthermore, premium features can be offered for a fee to users who require special effects and advanced customization, thereby increasing revenue.

[0029] A personalized promotion platform according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user photos, audio, and text. The user photos, audio, and text include, but are not limited to, JPEG photos, WAV audio, and plain text. For example, the collection unit collects user-provided photos in JPEG format. The collection unit can also collect user-provided audio in WAV format. The collection unit can also collect user-provided text in plain text format. The analysis unit uses advanced multimodal AI to analyze the data collected by the collection unit. The analysis may include, but is not limited to, analyzing data including, for example, speech nuances and intonation. For example, the analysis unit analyzes speech waveforms and emphasizes phonemes. The analysis unit may also analyze speech tone and speaking style. The analysis unit may also analyze speech emotions. The generation unit generates personalized promotional content based on the analysis results obtained by the analysis unit. The generation may, for example, generate content that reflects the user's personality and interests, but is not limited to such examples. For example, the generation unit generates customized content based on the user's profile information. The generation unit may also generate detailed content based on the user's past behavior history. The generation unit may also generate optimal content based on the user's interests. The provision unit provides the content generated by the generation unit. The provision may, for example, be performed by means of email or app notification, but is not limited to such examples. For example, the provision unit may provide the generated content to the user by email. The provision unit may also provide the generated content to the user by app notification. The provision unit may also provide a function to share the generated content on a social networking site (SNS). As a result, the personalized promotion platform according to the embodiment can generate and provide personalized promotional content that reflects the user's personality and interests.

[0030] The collection unit can collect data based on the user's interests and concerns. For example, the collection unit collects data based on the user's interests and concerns based on the results of a user survey. For example, the collection unit collects data related to topics of interest that the user answered in the survey. The collection unit can also collect data based on the user's interests and concerns based on the user's past behavioral history. For example, the collection unit analyzes the history of web pages the user has previously viewed and collects data related to topics of interest. The collection unit can also collect data based on the user's interests and concerns based on the user's activities on social media. For example, the collection unit collects data related to topics that the user frequently mentions on social media. In this way, by collecting data based on the user's interests and concerns, more personalized content can be generated.

[0031] The analysis unit can analyze data including voice nuances and intonation. The analysis unit, for example, analyzes voice waveforms and emphasizes phonemes. For example, the analysis unit analyzes voice waveforms and emphasizes phonemes to analyze voice nuances and intonation. The analysis unit can also analyze voice tone and speaking style. For example, the analysis unit analyzes voice tone and extracts speaking style characteristics. The analysis unit can also analyze voice emotion. For example, the analysis unit analyzes voice emotion and estimates the user's emotion. In this way, by analyzing voice nuances and intonation, the user's emotion and intention can be more accurately understood.

[0032] The generation unit can generate content that reflects the user's personality and interests. The generation unit generates customized content based on, for example, the user's profile information. For example, the generation unit analyzes the user's profile information and generates content that reflects the user's personality and interests. The generation unit can also generate detailed content based on the user's past behavior history. For example, the generation unit analyzes the user's past behavior history and generates content related to topics of interest. The generation unit can also generate optimal content based on the user's interests. For example, the generation unit analyzes the user's interests and generates content based on the analysis. This makes it possible to attract the user's attention by generating content that reflects the user's personality and interests.

[0033] The providing unit can provide the generated content to the user. The providing unit, for example, provides the generated content to the user by email. For example, the providing unit sends the generated content to the user by email and provides it to the user. The providing unit can also provide the generated content to the user by an app notification. For example, the providing unit sends the generated content to the user by an app notification and provides it to the user. The providing unit can also provide a function for sharing the generated content on a social networking site. For example, the providing unit provides a function for sharing the generated content on a social networking site, allowing the user to share the content. In this way, providing the generated content to the user can attract the user's attention.

[0034] The providing unit may provide a function for sharing the generated content on an SNS. For example, the providing unit may provide a function for sharing the generated content on an SNS. For example, the providing unit may provide a button for sharing the generated content on an SNS, allowing a user to easily share the content. The providing unit may also provide a function for selecting a format of content when sharing on an SNS. For example, the providing unit may allow a user to share content in image format or video format. The providing unit may also provide privacy settings when sharing on an SNS. For example, the providing unit may allow a user to select whether to make the content public or to share it only with specific friends. This may attract the user's attention by sharing the generated content on an SNS.

[0035] The collection unit can analyze the user's past data provision history and select an optimal collection method. The collection unit, for example, prioritizes collection of data formats (photos, audio, text, etc.) that the user has frequently provided in the past. For example, the collection unit prioritizes collection of photo data that the user has provided in the past. The collection unit can also prioritize collection of audio data that the user has provided in the past. The collection unit can also prioritize collection of text data that the user has provided in the past. The collection unit can also collect data for a specific time period from the user's past data provision history. For example, the collection unit analyzes the time period in which the user provided data in the past and collects data for that time period. The collection unit can also analyze the content of data that the user has provided in the past and collect data related to a similar topic. For example, the collection unit analyzes the topic of data that the user has provided in the past and collects related data. In this way, the optimal collection method can be selected by analyzing the user's past data provision history.

[0036] The collection unit can filter data based on the user's current topics of interest when collecting data. For example, the collection unit preferentially collects data related to topics in which the user is currently interested. For example, the collection unit collects related data based on keywords recently searched by the user. The collection unit can also collect data based on topics frequently mentioned by the user on social media. For example, the collection unit collects data related to topics frequently mentioned by the user on social media. The collection unit can also filter data based on the user's current topics of interest. For example, the collection unit preferentially collects data related to topics in which the user is currently interested. In this way, by filtering data based on the user's current topics of interest, more relevant data can be collected.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit prioritizes collecting voice data. For example, if the user prefers voice input, the collection unit prioritizes collecting voice data. Furthermore, if the user prefers text input, the collection unit can also prioritize collecting text data. For example, if the user prefers text input, the collection unit prioritizes collecting text data. Furthermore, if the user prefers image input, the collection unit can also prioritize collecting image data. For example, if the user prefers image input, the collection unit prioritizes collecting image data. In this way, by selecting the optimal collection means depending on the user's input method, data can be collected efficiently.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the user. The collection unit, for example, prioritizes collecting data related to the location where the user is currently located. For example, the collection unit prioritizes collecting data related to the location where the user is currently located. The collection unit can also collect data related to places the user has visited in the past. For example, the collection unit collects data related to places the user has visited in the past. The collection unit can also collect data related to places the user plans to visit in the future. For example, the collection unit collects data related to places the user plans to visit in the future. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information of the user.

[0039] When collecting data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, collects data based on topics that the user frequently mentions on social media. For example, the collection unit collects data related to topics that the user frequently mentions on social media. The collection unit can also collect photos and videos that the user shared on social media. For example, the collection unit collects photos and videos that the user shared on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. In this way, related data can be collected by analyzing the user's social media activities.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also preferentially use a data collection method that the user previously preferred. For example, the collection unit preferentially use a data collection method that the user previously preferred. The collection unit can also determine the optimal collection timing based on the user's past feedback. For example, the collection unit determines the optimal collection timing based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. For example, the analysis unit performs a concise analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, the analysis can be performed efficiently.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies an image analysis algorithm to image data. For example, the analysis unit applies an image analysis algorithm to image data. The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a voice analysis algorithm to voice data. The analysis unit can also apply a text analysis algorithm to text data. For example, the analysis unit applies a text analysis algorithm to text data. In this way, by applying different analysis algorithms depending on the category of data, more accurate analysis results can be obtained.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also select the optimal analysis method based on the user's past analysis results. For example, the analysis unit selects the optimal analysis method based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that has been submitted earlier. For example, the analysis unit postpones data that has been submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission. For example, the analysis unit determines the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of data submission, analysis can be performed efficiently.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, analysis can be performed efficiently.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit provides an analysis result that makes extensive use of technical terms to a user with high level of expertise. For example, the analysis unit provides an analysis result that makes extensive use of technical terms to a user with high level of expertise. The analysis unit can also provide a concise and easy-to-understand analysis result to a user with low level of expertise. For example, the analysis unit provides a concise and easy-to-understand analysis result to a user with low level of expertise. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0047] The generation unit can adjust the level of detail of the content to reflect the user's personality and interests at the time of generation. The generation unit generates customized content, for example, based on the user's personality. For example, the generation unit generates customized content based on the user's personality. The generation unit can also generate detailed content based on the user's interests. For example, the generation unit generates detailed content based on the user's interests. The generation unit can also generate optimal content by referring to the user's past generation results. For example, the generation unit generates optimal content by referring to the user's past generation results. In this way, by adjusting the level of detail of the content to reflect the user's personality and interests, more personalized content can be provided.

[0048] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit can also select an optimal generation method from the user's past generation results. For example, the generation unit selects an optimal generation method from the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0049] The generation unit can customize the content to be generated based on a user's current topics of interest at the time of generation. For example, the generation unit generates content related to a topic in which the user is currently interested. For example, the generation unit generates content related to a topic in which the user is currently interested. The generation unit can also generate related content based on keywords recently searched by the user. For example, the generation unit generates related content based on keywords recently searched by the user. The generation unit can also generate content based on topics frequently mentioned by the user on social media. For example, the generation unit generates content based on topics frequently mentioned by the user on social media. In this way, by customizing the content based on the user's current topics of interest, more relevant content can be provided.

[0050] The generation unit can generate highly relevant content by taking into account the geographical location information of the user at the time of generation. The generation unit, for example, generates content related to the location where the user is currently located. For example, the generation unit generates content related to the location where the user is currently located. The generation unit can also generate content related to places the user has visited in the past. For example, the generation unit generates content related to places the user has visited in the past. The generation unit can also generate content related to places the user plans to visit in the future. For example, the generation unit generates content related to places the user plans to visit in the future. In this way, highly relevant content can be provided by taking into account the geographical location information of the user.

[0051] At the time of generation, the generation unit can analyze the user's social media activities and generate related content. The generation unit generates content based on, for example, topics that the user frequently mentions on social media. For example, the generation unit generates content based on topics that the user frequently mentions on social media. The generation unit can also generate content based on photos and videos that the user shared on social media. For example, the generation unit generates content based on photos and videos that the user shared on social media. The generation unit can also generate related content by referring to the activities of the user's friends on social media. For example, the generation unit generates related content by referring to the activities of the user's friends on social media. In this way, related content can be provided by analyzing the user's social media activities.

[0052] The generation unit can customize the generation method by reflecting the user's past feedback at the time of generation. The generation unit, for example, adjusts the generation method based on feedback provided by the user in the past. For example, the generation unit adjusts the generation method based on feedback provided by the user in the past. The generation unit can also preferentially use a generation method that the user previously preferred. For example, the generation unit preferentially uses a generation method that the user previously preferred. The generation unit can also determine the optimal generation timing based on the user's past feedback. For example, the generation unit determines the optimal generation timing based on the user's past feedback. In this way, the generation method can be customized by reflecting the user's past feedback.

[0053] At the time of providing, the providing unit can select the optimal display method by referring to the user's past operation history. The providing unit selects the optimal display method, for example, based on the user's past operation history. For example, the providing unit selects the optimal display method based on the user's past operation history. The providing unit can also preferentially use a display method that the user has previously preferred. For example, the providing unit preferentially use a display method that the user has previously preferred. The providing unit can also determine the optimal display timing from the user's past operation history. For example, the providing unit determines the optimal display timing based on the user's past operation history. In this way, the optimal display method can be selected by referring to the user's past operation history.

[0054] The providing unit can customize the display content according to the user's current task when providing the information. The providing unit, for example, prioritizes displaying information related to the task the user is currently performing. For example, the providing unit prioritizes displaying information related to the task the user is currently performing. The providing unit can also customize the display content according to the task the user is currently performing. For example, the providing unit customizes the display content according to the task the user is currently performing. The providing unit can also select an optimal display method based on the user's current task. For example, the providing unit selects an optimal display method based on the user's current task. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided.

[0055] The providing unit can improve the providing method by reflecting the user's feedback at the time of providing. The providing unit, for example, adjusts the providing method based on the feedback provided by the user. For example, the providing unit adjusts the providing method based on the feedback provided by the user. The providing unit can also preferentially use a providing method that the user has previously preferred. For example, the providing unit preferentially use a providing method that the user has previously preferred. The providing unit can also determine the optimal providing timing based on the user's feedback. For example, the providing unit determines the optimal providing timing based on the user's feedback. In this way, the providing method can be improved by reflecting the user's feedback.

[0056] When providing the display information, the providing unit can select the optimal display method by taking into consideration the user's device information. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, when the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. For example, when the user is using a tablet, the providing unit provides a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, when the user is using a smartwatch, the providing unit provides a display method that is concise and highly visible. In this way, the optimal display method can be provided by taking into consideration the user's device information.

[0057] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the providing unit automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit provides a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit provides the display content in that language when the user selects a specific language. This makes it possible to accommodate a larger number of users by making the display content multilingual according to the user's language setting.

[0058] The providing unit can analyze the user's social media activity at the time of providing the content and provide related content. The providing unit, for example, provides information about places where the user has checked in on social media. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide information about related tourist spots and stores. For example, the providing unit can analyze the content of the user's posts on social media and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. For example, the providing unit provides information about related places and events by referring to the activities of the user's friends on social media. In this way, related content can be provided by analyzing the user's social media activity.

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

[0060] The collection unit can monitor the user's device usage and determine the optimal timing for data collection. For example, the collection unit can identify time periods when the user frequently uses the smartphone and collect data during those time periods. The collection unit can also collect data by avoiding time periods when the user is not using the device. Furthermore, the collection unit can efficiently collect data based on the user's interests by collecting data when the user is using a specific application. This can improve the efficiency of data collection by determining the optimal timing for data collection based on the user's device usage.

[0061] The analysis unit can analyze a user's past behavioral patterns and predict future behavior. For example, if a user has tended to behave in a certain way during a certain time period in the past, the analysis unit can provide content related to that time period. Also, if a user has participated in a certain event in the past, the analysis unit can provide information about similar events. Furthermore, the analysis unit can analyze a user's past purchasing history and predict future purchasing behavior, thereby providing relevant promotional content. This makes it possible to provide more personalized content by predicting future behavior based on a user's past behavioral patterns.

[0062] The collection unit can monitor the user's current activity status and determine the optimal timing for data collection. For example, if the user is exercising, the collection unit can temporarily suspend data collection and collect data during the user's resting hours. Alternatively, if the user is working, the collection unit can collect data after the user has finished work. Furthermore, by collecting data during the user's relaxing hours, the collection unit can reduce the burden on the user. Thus, by determining the optimal timing for data collection based on the user's current activity status, the efficiency of data collection can be improved.

[0063] The generation unit can analyze the user's past generation results and optimize the generation algorithm. For example, the generation unit extracts features of content that the user liked in the past and generates new content based on the extracted features. The generation unit can also analyze features of content that the user avoided in the past and adjust the generation algorithm to avoid that content. Furthermore, the generation unit can improve the generation algorithm by referring to the user's past feedback. In this way, by analyzing the user's past generation results, the generation algorithm can be optimized and more personalized content can be provided.

[0064] The collection unit can monitor the remaining battery level of the user's device and determine the optimal timing for data collection. For example, if the battery of the user's device is low, the collection unit temporarily suspends data collection and resumes data collection after the battery is charged. The collection unit can also prioritize data collection when the user's device is charging. Furthermore, the collection unit can adjust the frequency of data collection according to the remaining battery level of the user's device. This can improve the efficiency of data collection by determining the optimal timing for data collection based on the remaining battery level of the user's device.

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

[0066] Step 1: The collection unit collects the user's photos, voice, and text. For example, the collection unit collects the user's photos in JPEG format, voice in WAV format, and text in plain text format. Step 2: The analyzer uses advanced multimodal AI to analyze the data collected by the collector. For example, the analyzer analyzes speech waveforms to analyze phoneme emphasis, tone, speaking style, and emotion. Step 3: The generation unit generates personalized promotional content based on the analysis results obtained by the analysis unit. For example, the generation unit generates content that reflects the user's personality and interests, and generates customized content based on the user's profile information and past behavioral history. Step 4: The providing unit provides the content generated by the generating unit. For example, the providing unit provides the generated content to the user through email, app notification, or a function for sharing on SNS.

[0067] (Example 2) A personalized promotion platform according to an embodiment of the present invention is a system that collects users' photos, voice, and text, analyzes them using an advanced multimodal AI, and generates and delivers personalized promotional content. The personalized promotion platform targets young people in their late teens and twenties, leveraging their frequent social media use and sensitivity to new trends to provide new value to both companies and users. For example, users can provide their own photos, voice, and text as input, and the advanced multimodal AI generates personalized promotional content (images and videos) based on these inputs. Content related to topics of high interest to users, such as music festivals, fashion events, and game releases, is automatically created. By leveraging the data processing capabilities of advanced multimodal AI, including the nuances and intonation of speech, a highly advanced level of customization is possible, reflecting the user's own voice and personality. This allows the personalized promotion platform to generate and deliver personalized promotional content that reflects the user's individuality and interests. This allows personalized promotion platforms to provide businesses with a means to deeply reach their target audience and improve their brand image, and users with an opportunity to express their individuality and build deeper relationships with their favorite brands and products. Furthermore, premium features can be offered for a fee to users who require special effects and advanced customization, thereby increasing revenue.

[0068] A personalized promotion platform according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user photos, audio, and text. The user photos, audio, and text include, but are not limited to, JPEG photos, WAV audio, and plain text. For example, the collection unit collects user-provided photos in JPEG format. The collection unit can also collect user-provided audio in WAV format. The collection unit can also collect user-provided text in plain text format. The analysis unit uses advanced multimodal AI to analyze the data collected by the collection unit. The analysis may include, but is not limited to, analyzing data including, for example, speech nuances and intonation. For example, the analysis unit analyzes speech waveforms and emphasizes phonemes. The analysis unit may also analyze speech tone and speaking style. The analysis unit may also analyze speech emotions. The generation unit generates personalized promotional content based on the analysis results obtained by the analysis unit. The generation may, for example, generate content that reflects the user's personality and interests, but is not limited to such examples. For example, the generation unit generates customized content based on the user's profile information. The generation unit may also generate detailed content based on the user's past behavior history. The generation unit may also generate optimal content based on the user's interests. The provision unit provides the content generated by the generation unit. The provision may, for example, be performed by means of email or app notification, but is not limited to such examples. For example, the provision unit may provide the generated content to the user by email. The provision unit may also provide the generated content to the user by app notification. The provision unit may also provide a function to share the generated content on a social networking site (SNS). As a result, the personalized promotion platform according to the embodiment can generate and provide personalized promotional content that reflects the user's personality and interests.

[0069] The collection unit can collect data based on the user's interests and concerns. For example, the collection unit collects data based on the user's interests and concerns based on the results of a user survey. For example, the collection unit collects data related to topics of interest that the user answered in the survey. The collection unit can also collect data based on the user's interests and concerns based on the user's past behavioral history. For example, the collection unit analyzes the history of web pages the user has previously viewed and collects data related to topics of interest. The collection unit can also collect data based on the user's interests and concerns based on the user's activities on social media. For example, the collection unit collects data related to topics that the user frequently mentions on social media. In this way, by collecting data based on the user's interests and concerns, more personalized content can be generated.

[0070] The analysis unit can analyze data including voice nuances and intonation. The analysis unit, for example, analyzes voice waveforms and emphasizes phonemes. For example, the analysis unit analyzes voice waveforms and emphasizes phonemes to analyze voice nuances and intonation. The analysis unit can also analyze voice tone and speaking style. For example, the analysis unit analyzes voice tone and extracts speaking style characteristics. The analysis unit can also analyze voice emotion. For example, the analysis unit analyzes voice emotion and estimates the user's emotion. In this way, by analyzing voice nuances and intonation, the user's emotion and intention can be more accurately understood.

[0071] The generation unit can generate content that reflects the user's personality and interests. The generation unit generates customized content based on, for example, the user's profile information. For example, the generation unit analyzes the user's profile information and generates content that reflects the user's personality and interests. The generation unit can also generate detailed content based on the user's past behavior history. For example, the generation unit analyzes the user's past behavior history and generates content related to topics of interest. The generation unit can also generate optimal content based on the user's interests. For example, the generation unit analyzes the user's interests and generates content based on the analysis. This makes it possible to attract the user's attention by generating content that reflects the user's personality and interests.

[0072] The providing unit can provide the generated content to the user. The providing unit, for example, provides the generated content to the user by email. For example, the providing unit sends the generated content to the user by email and provides it to the user. The providing unit can also provide the generated content to the user by an app notification. For example, the providing unit sends the generated content to the user by an app notification and provides it to the user. The providing unit can also provide a function for sharing the generated content on a social networking site. For example, the providing unit provides a function for sharing the generated content on a social networking site, allowing the user to share the content. In this way, providing the generated content to the user can attract the user's attention.

[0073] The providing unit may provide a function for sharing the generated content on an SNS. For example, the providing unit may provide a function for sharing the generated content on an SNS. For example, the providing unit may provide a button for sharing the generated content on an SNS, allowing a user to easily share the content. The providing unit may also provide a function for selecting a format of content when sharing on an SNS. For example, the providing unit may allow a user to share content in image format or video format. The providing unit may also provide privacy settings when sharing on an SNS. For example, the providing unit may allow a user to select whether to make the content public or to share it only with specific friends. This may attract the user's attention by sharing the generated content on an SNS.

[0074] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit collects data during the time period when the user is most relaxed. For example, the collection unit can analyze the user's facial expressions to identify the time period when the user is relaxed. Furthermore, if the user is feeling stressed, the collection unit can temporarily suspend data collection and wait until the user relaxes. For example, the collection unit can analyze the user's voice to determine whether the user is feeling stressed. Furthermore, if the user is excited, the collection unit can use that excitement to collect data related to topics that interest the user. For example, the collection unit can analyze the user's heart rate to determine whether the user is excited. This allows data to be collected at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The collection unit can analyze the user's past data provision history and select an optimal collection method. The collection unit, for example, prioritizes collection of data formats (photos, audio, text, etc.) that the user has frequently provided in the past. For example, the collection unit prioritizes collection of photo data that the user has provided in the past. The collection unit can also prioritize collection of audio data that the user has provided in the past. The collection unit can also prioritize collection of text data that the user has provided in the past. The collection unit can also collect data for a specific time period from the user's past data provision history. For example, the collection unit analyzes the time period in which the user provided data in the past and collects data for that time period. The collection unit can also analyze the content of data that the user has provided in the past and collect data related to a similar topic. For example, the collection unit analyzes the topic of data that the user has provided in the past and collects related data. In this way, the optimal collection method can be selected by analyzing the user's past data provision history.

[0076] The collection unit can filter data based on the user's current topics of interest when collecting data. For example, the collection unit preferentially collects data related to topics in which the user is currently interested. For example, the collection unit collects related data based on keywords recently searched by the user. The collection unit can also collect data based on topics frequently mentioned by the user on social media. For example, the collection unit collects data related to topics frequently mentioned by the user on social media. The collection unit can also filter data based on the user's current topics of interest. For example, the collection unit preferentially collects data related to topics in which the user is currently interested. In this way, by filtering data based on the user's current topics of interest, more relevant data can be collected.

[0077] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit prioritizes collecting voice data. For example, if the user prefers voice input, the collection unit prioritizes collecting voice data. Furthermore, if the user prefers text input, the collection unit can also prioritize collecting text data. For example, if the user prefers text input, the collection unit prioritizes collecting text data. Furthermore, if the user prefers image input, the collection unit can also prioritize collecting image data. For example, if the user prefers image input, the collection unit prioritizes collecting image data. In this way, by selecting the optimal collection means depending on the user's input method, data can be collected efficiently.

[0078] The collection unit can estimate the user's emotions and prioritize data to be collected based on the estimated user emotions. For example, if the user is relaxed, the collection unit collects data during the time period when the user is most relaxed. For example, the collection unit can analyze the user's facial expressions to identify the time period when the user is relaxed. Furthermore, if the user is feeling stressed, the collection unit can temporarily suspend data collection and wait until the user relaxes. For example, the collection unit can analyze the user's voice to determine whether the user is feeling stressed. Furthermore, if the user is excited, the collection unit can use that excitement to collect data related to topics that interest the user. For example, the collection unit can analyze the user's heart rate to determine whether the user is excited. This allows for more appropriate data to be collected by prioritizing data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the user. The collection unit, for example, prioritizes collecting data related to the location where the user is currently located. For example, the collection unit prioritizes collecting data related to the location where the user is currently located. The collection unit can also collect data related to places the user has visited in the past. For example, the collection unit collects data related to places the user has visited in the past. The collection unit can also collect data related to places the user plans to visit in the future. For example, the collection unit collects data related to places the user plans to visit in the future. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information of the user.

[0080] When collecting data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, collects data based on topics that the user frequently mentions on social media. For example, the collection unit collects data related to topics that the user frequently mentions on social media. The collection unit can also collect photos and videos that the user shared on social media. For example, the collection unit collects photos and videos that the user shared on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. In this way, related data can be collected by analyzing the user's social media activities.

[0081] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also preferentially use a data collection method that the user previously preferred. For example, the collection unit preferentially use a data collection method that the user previously preferred. The collection unit can also determine the optimal collection timing based on the user's past feedback. For example, the collection unit determines the optimal collection timing based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback.

[0082] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a concise analysis result if the user is stressed. For example, if the user is stressed, the analysis unit provides a concise analysis result. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows for adjusting the way the analysis is presented based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. For example, the analysis unit performs a concise analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, the analysis can be performed efficiently.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies an image analysis algorithm to image data. For example, the analysis unit applies an image analysis algorithm to image data. The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a voice analysis algorithm to voice data. The analysis unit can also apply a text analysis algorithm to text data. For example, the analysis unit applies a text analysis algorithm to text data. In this way, by applying different analysis algorithms depending on the category of data, more accurate analysis results can be obtained.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also select the optimal analysis method based on the user's past analysis results. For example, the analysis unit selects the optimal analysis method based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a concise analysis result when the user is stressed. For example, when the user is stressed, the analysis unit provides a concise analysis result. The analysis unit can also provide a visually stimulating analysis result when the user is excited. For example, when the user is excited, the analysis unit provides a visually stimulating analysis result. This allows for adjusting the length of the analysis based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that has been submitted earlier. For example, the analysis unit postpones data that has been submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission. For example, the analysis unit determines the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of data submission, analysis can be performed efficiently.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, analysis can be performed efficiently.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit provides an analysis result that makes extensive use of technical terms to a user with high level of expertise. For example, the analysis unit provides an analysis result that makes extensive use of technical terms to a user with high level of expertise. The analysis unit can also provide a concise and easy-to-understand analysis result to a user with low level of expertise. For example, the analysis unit provides a concise and easy-to-understand analysis result to a user with low level of expertise. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0090] The generation unit can estimate the user's emotions and adjust the expression method of the generated content based on the estimated user's emotions. For example, when the user is relaxed, the generation unit generates content that progresses at a leisurely pace. For example, when the user is relaxed, the generation unit generates content that progresses at a leisurely pace. Furthermore, when the user is in a hurry, the generation unit can generate content that emphasizes the shortest route. For example, when the user is in a hurry, the generation unit generates content that emphasizes the shortest route. Furthermore, when the user is excited, the generation unit can generate content that adds visually stimulating effects. For example, when the user is excited, the generation unit generates content that adds visually stimulating effects. This allows for adjusting the expression method of the generated content based on the user's emotions, thereby providing more appropriate content. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The generation unit can adjust the level of detail of the content to reflect the user's personality and interests at the time of generation. The generation unit generates customized content, for example, based on the user's personality. For example, the generation unit generates customized content based on the user's personality. The generation unit can also generate detailed content based on the user's interests. For example, the generation unit generates detailed content based on the user's interests. The generation unit can also generate optimal content by referring to the user's past generation results. For example, the generation unit generates optimal content by referring to the user's past generation results. In this way, by adjusting the level of detail of the content to reflect the user's personality and interests, more personalized content can be provided.

[0092] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit can also select an optimal generation method from the user's past generation results. For example, the generation unit selects an optimal generation method from the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0093] The generation unit can customize the content to be generated based on a user's current topics of interest at the time of generation. For example, the generation unit generates content related to a topic in which the user is currently interested. For example, the generation unit generates content related to a topic in which the user is currently interested. The generation unit can also generate related content based on keywords recently searched by the user. For example, the generation unit generates related content based on keywords recently searched by the user. The generation unit can also generate content based on topics frequently mentioned by the user on social media. For example, the generation unit generates content based on topics frequently mentioned by the user on social media. In this way, by customizing the content based on the user's current topics of interest, more relevant content can be provided.

[0094] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated user's emotions. For example, when the user is relaxed, the generation unit generates longer content. For example, when the user is relaxed, the generation unit generates longer content. Furthermore, when the user is in a hurry, the generation unit can generate short, to-the-point content. For example, when the user is in a hurry, the generation unit generates short, to-the-point content. Furthermore, when the user is excited, the generation unit can generate content with visually stimulating effects. For example, when the user is excited, the generation unit generates content with visually stimulating effects. In this way, by adjusting the length of the generated content based on the user's emotions, more appropriate content can be provided. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The generation unit can generate highly relevant content by taking into account the geographical location information of the user at the time of generation. The generation unit, for example, generates content related to the location where the user is currently located. For example, the generation unit generates content related to the location where the user is currently located. The generation unit can also generate content related to places the user has visited in the past. For example, the generation unit generates content related to places the user has visited in the past. The generation unit can also generate content related to places the user plans to visit in the future. For example, the generation unit generates content related to places the user plans to visit in the future. In this way, highly relevant content can be provided by taking into account the geographical location information of the user.

[0096] At the time of generation, the generation unit can analyze the user's social media activities and generate related content. The generation unit generates content based on, for example, topics that the user frequently mentions on social media. For example, the generation unit generates content based on topics that the user frequently mentions on social media. The generation unit can also generate content based on photos and videos that the user shared on social media. For example, the generation unit generates content based on photos and videos that the user shared on social media. The generation unit can also generate related content by referring to the activities of the user's friends on social media. For example, the generation unit generates related content by referring to the activities of the user's friends on social media. In this way, related content can be provided by analyzing the user's social media activities.

[0097] The generation unit can customize the generation method by reflecting the user's past feedback at the time of generation. The generation unit, for example, adjusts the generation method based on feedback provided by the user in the past. For example, the generation unit adjusts the generation method based on feedback provided by the user in the past. The generation unit can also preferentially use a generation method that the user previously preferred. For example, the generation unit preferentially uses a generation method that the user previously preferred. The generation unit can also determine the optimal generation timing based on the user's past feedback. For example, the generation unit determines the optimal generation timing based on the user's past feedback. In this way, the generation method can be customized by reflecting the user's past feedback.

[0098] The providing unit can estimate the user's emotions and adjust the display method of the content to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit provides a display method including detailed information. For example, when the user is relaxed, the providing unit provides a display method including detailed information. Furthermore, when the user is stressed, the providing unit can also provide a display method including concise information. For example, when the user is stressed, the providing unit provides a display method including concise information. Furthermore, when the user is excited, the providing unit can also provide a visually stimulating display method. For example, when the user is excited, the providing unit provides a visually stimulating display method. This allows the display method of the content to be provided to be adjusted based on the user's emotions, thereby providing a more appropriate display method. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] At the time of providing, the providing unit can select the optimal display method by referring to the user's past operation history. The providing unit selects the optimal display method, for example, based on the user's past operation history. For example, the providing unit selects the optimal display method based on the user's past operation history. The providing unit can also preferentially use a display method that the user has previously preferred. For example, the providing unit preferentially use a display method that the user has previously preferred. The providing unit can also determine the optimal display timing from the user's past operation history. For example, the providing unit determines the optimal display timing based on the user's past operation history. In this way, the optimal display method can be selected by referring to the user's past operation history.

[0100] The providing unit can customize the display content according to the user's current task when providing the information. The providing unit, for example, prioritizes displaying information related to the task the user is currently performing. For example, the providing unit prioritizes displaying information related to the task the user is currently performing. The providing unit can also customize the display content according to the task the user is currently performing. For example, the providing unit customizes the display content according to the task the user is currently performing. The providing unit can also select an optimal display method based on the user's current task. For example, the providing unit selects an optimal display method based on the user's current task. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided.

[0101] The providing unit can improve the providing method by reflecting the user's feedback at the time of providing. The providing unit, for example, adjusts the providing method based on the feedback provided by the user. For example, the providing unit adjusts the providing method based on the feedback provided by the user. The providing unit can also preferentially use a providing method that the user has previously preferred. For example, the providing unit preferentially use a providing method that the user has previously preferred. The providing unit can also determine the optimal providing timing based on the user's feedback. For example, the providing unit determines the optimal providing timing based on the user's feedback. In this way, the providing method can be improved by reflecting the user's feedback.

[0102] The providing unit can estimate the user's emotions and determine the priority of content to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit provides a display method including detailed information. For example, when the user is relaxed, the providing unit provides a display method including detailed information. Furthermore, when the user is stressed, the providing unit can also provide a display method including concise information. For example, when the user is stressed, the providing unit provides a display method including concise information. Furthermore, when the user is excited, the providing unit can also provide a visually stimulating display method. For example, when the user is excited, the providing unit provides a visually stimulating display method. This allows more appropriate content to be provided by determining the priority of content to be provided based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] When providing the display information, the providing unit can select the optimal display method by taking into consideration the user's device information. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, when the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. For example, when the user is using a tablet, the providing unit provides a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, when the user is using a smartwatch, the providing unit provides a display method that is concise and highly visible. In this way, the optimal display method can be provided by taking into consideration the user's device information.

[0104] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the providing unit automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit provides a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit provides the display content in that language when the user selects a specific language. This makes it possible to accommodate a larger number of users by making the display content multilingual according to the user's language setting.

[0105] The providing unit can analyze the user's social media activity at the time of providing the content and provide related content. The providing unit, for example, provides information about places where the user has checked in on social media. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide information about related tourist spots and stores. For example, the providing unit can analyze the content of the user's posts on social media and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. For example, the providing unit provides information about related places and events by referring to the activities of the user's friends on social media. In this way, related content can be provided by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user photos and voice using the camera 42 and microphone 38B of the smart device 14, and collects text data using the control unit 46A. The analysis unit analyzes the data using multimodal AI evolved by the specific processing unit 290 of the data processing device 12. The generation unit generates personalized promotional content based on the analysis results by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated content to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user photos and voice using the camera 42 and microphone 238 of the smart glasses 214, and collects text data using the control unit 46A. The analysis unit analyzes the data using multimodal AI evolved by the specific processing unit 290 of the data processing device 12. The generation unit generates personalized promotional content based on the analysis results by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated content to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection 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 collection unit collects photos and voice of the user using the camera 42 and microphone 238 of the headset type terminal 314, and collects text data by the control unit 46A. The analysis unit analyzes the data using multimodal AI evolved by the specific processing unit 290 of the data processing device 12. The generation unit generates personalized promotional content based on the analysis results by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated content to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user photos and voice using the camera 42 and microphone 238 of the robot 414, and collects text data using the control unit 46A. The analysis unit analyzes the data using multimodal AI evolved by the specific processing unit 290 of the data processing device 12. The generation unit generates personalized promotional content based on the analysis results by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated content to the user using the speaker 240 of the robot 414.

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

[0107] The collection unit can monitor the user's device usage and determine the optimal timing for data collection. For example, the collection unit can identify time periods when the user frequently uses the smartphone and collect data during those time periods. The collection unit can also collect data by avoiding time periods when the user is not using the device. Furthermore, the collection unit can efficiently collect data based on the user's interests by collecting data when the user is using a specific application. This can improve the efficiency of data collection by determining the optimal timing for data collection based on the user's device usage.

[0108] The analysis unit can analyze a user's past behavioral patterns and predict future behavior. For example, if a user has tended to behave in a certain way during a certain time period in the past, the analysis unit can provide content related to that time period. Also, if a user has participated in a certain event in the past, the analysis unit can provide information about similar events. Furthermore, the analysis unit can analyze a user's past purchasing history and predict future purchasing behavior, thereby providing relevant promotional content. This makes it possible to provide more personalized content by predicting future behavior based on a user's past behavioral patterns.

[0109] The generation unit can estimate the user's emotion and adjust the tone of the generated content based on the estimated user's emotion. For example, the generation unit can generate content with a calm tone when the user is relaxed. The generation unit can also generate content with an energetic tone when the user is excited. Furthermore, the generation unit can generate content with a comforting tone when the user is sad. In this way, by adjusting the tone of the content based on the user's emotion, more appropriate content can be provided.

[0110] The providing unit can estimate the user's emotions and adjust the format of the content to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide content in the form of a long article. If the user is busy, the providing unit can also provide content in the form of a short summary. Furthermore, if the user is seeking visual stimulation, the providing unit can also provide content in the form of images or videos. In this way, by adjusting the format of the content based on the user's emotions, more appropriate content can be provided.

[0111] The collection unit can monitor the user's current activity status and determine the optimal timing for data collection. For example, if the user is exercising, the collection unit can temporarily suspend data collection and collect data during the user's resting hours. Alternatively, if the user is working, the collection unit can collect data after the user has finished work. Furthermore, by collecting data during the user's relaxing hours, the collection unit can reduce the burden on the user. Thus, by determining the optimal timing for data collection based on the user's current activity status, the efficiency of data collection can be improved.

[0112] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated user's emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. Alternatively, the analysis unit can perform a brief analysis when the user is stressed. Furthermore, the analysis unit can provide a visually stimulating analysis result when the user is excited. In this way, by adjusting the depth of the analysis based on the user's emotions, more appropriate analysis results can be provided.

[0113] The generation unit can analyze the user's past generation results and optimize the generation algorithm. For example, the generation unit extracts features of content that the user liked in the past and generates new content based on the extracted features. The generation unit can also analyze features of content that the user avoided in the past and adjust the generation algorithm to avoid that content. Furthermore, the generation unit can improve the generation algorithm by referring to the user's past feedback. In this way, by analyzing the user's past generation results, the generation algorithm can be optimized and more personalized content can be provided.

[0114] The providing unit can estimate the user's emotions and adjust the order of content to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can first provide content containing detailed information. If the user is busy, the providing unit can also first provide content that focuses on the main points. Furthermore, if the user is excited, the providing unit can also first provide visually stimulating content. In this way, by adjusting the order of content based on the user's emotions, more appropriate content can be provided.

[0115] The collection unit can monitor the remaining battery level of the user's device and determine the optimal timing for data collection. For example, if the battery of the user's device is low, the collection unit temporarily suspends data collection and resumes data collection after the battery is charged. The collection unit can also prioritize data collection when the user's device is charging. Furthermore, the collection unit can adjust the frequency of data collection according to the remaining battery level of the user's device. This can improve the efficiency of data collection by determining the optimal timing for data collection based on the remaining battery level of the user's device.

[0116] The providing unit can estimate the user's emotions and adjust the visual design of the content to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide content with calm colors and a simple design. If the user is excited, the providing unit can also provide content with vivid colors and a dynamic design. Furthermore, if the user is stressed, the providing unit can also provide content with calm colors and a minimalist design. In this way, by adjusting the visual design of the content based on the user's emotions, more appropriate content can be provided.

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

[0118] Step 1: The collection unit collects the user's photos, voice, and text. For example, the collection unit collects the user's photos in JPEG format, voice in WAV format, and text in plain text format. Step 2: The analyzer uses advanced multimodal AI to analyze the data collected by the collector. For example, the analyzer analyzes speech waveforms to analyze phoneme emphasis, tone, speaking style, and emotion. Step 3: The generation unit generates personalized promotional content based on the analysis results obtained by the analysis unit. For example, the generation unit generates content that reflects the user's personality and interests, and generates customized content based on the user's profile information and past behavioral history. Step 4: The providing unit provides the content generated by the generating unit. For example, the providing unit provides the generated content to the user through email, app notification, or a function for sharing on SNS.

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

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

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

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

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

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0190] [Explanation of symbols]

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

Claims

1. a collection unit that collects user photos, voices, and texts; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates personalized promotional content based on the analysis results obtained by the analysis unit; a providing unit that provides the content generated by the generating unit. A system characterized by:

2. The collecting unit Collect data based on your interests 2. The system of claim 1.

3. The analysis unit Analyzing data that includes voice nuances and intonation 2. The system of claim 1.

4. The generation unit Generate content that reflects the user's personality and interests 2. The system of claim 1.

5. The providing unit Providing generated content to users 2. The system of claim 1.

6. The providing unit Provides the ability to share generated content on social media 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze users' past data provision history and select the optimal collection method 2. The system of claim 1.

9. The collecting unit At the time of data collection, filtering is performed based on the user's current topics of interest 2. The system of claim 1.

10. The collecting unit When collecting data, select the optimal collection method depending on the user's input method 2. The system of claim 1.

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