system

Abstract

The system according to the embodiment aims to efficiently store and share the contents written on a whiteboard as digital data. [Solution] A system according to an embodiment includes a reading unit, a conversion unit, a storage unit, and a sharing unit. The reading unit reads characters or drawings written by a user on a whiteboard. The conversion unit converts the characters or drawings read by the reading unit into digital data. The storage unit stores the data converted by the conversion unit in the cloud. The sharing unit shares the data stored by the storage unit among members when necessary.

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] With conventional technology, it was difficult to efficiently save and share the content written on a whiteboard as digital data.

[0005] The system according to the embodiment aims to efficiently store and share the contents written on a whiteboard as digital data. [Means for solving the problem]

[0006] The system according to the embodiment includes a reading unit, a conversion unit, a storage unit, and a sharing unit. The reading unit reads characters or drawings written by a user on a whiteboard. The conversion unit converts the characters or drawings read by the reading unit into digital data. The storage unit stores the data converted by the conversion unit in the cloud. The sharing unit shares the data stored by the storage unit among members when necessary. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently store and share the contents written on the whiteboard as digital data. [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 system according to an embodiment of the present invention utilizes generative AI to improve the efficiency of meetings and educational programs. This system reads text and drawings written by users on a whiteboard, converts them into digital data, and stores and shares them in the cloud. This allows the system to automatically digitize what users write on the whiteboard, store it in the cloud, and share it, thereby improving the efficiency of meetings and educational programs. For example, when a user handwrites ideas or discussion details on a whiteboard, the generative AI reads them and converts them into digital data. This data is stored in the cloud and can be shared among members as needed. This improves the speed of collaborative work by automatically generating meeting minutes, immediately sharing them, and allowing participants to move on to the next task or discussion.

[0029] A meeting support system according to an embodiment includes a reading unit, a conversion unit, a storage unit, and a sharing unit. The reading unit reads characters and drawings written by a user on a whiteboard. For example, the reading unit can recognize handwritten characters and drawings. The conversion unit converts the characters and drawings read by the reading unit into digital data. For example, the conversion unit can convert handwritten characters into text data. The conversion unit can also digitize handwritten diagrams and pictures. The storage unit stores the data converted by the conversion unit in the cloud. For example, the storage unit can store the data in the cloud. The sharing unit shares the data stored by the storage unit among members when necessary. For example, the sharing unit can share the data stored in the cloud among members. As a result, the meeting support system according to an embodiment can improve the efficiency of meetings and education by automatically digitizing content written by a user on a whiteboard and storing and sharing it in the cloud.

[0030] The reading unit can recognize handwritten characters or drawings. For example, the reading unit recognizes handwritten characters using OCR technology. The reading unit can also recognize handwritten drawings using an image recognition algorithm. For example, the reading unit can recognize handwritten characters with high accuracy and convert them into digital data. The reading unit can also analyze handwritten drawings and convert their contents into digital data. This allows for accurate recognition of handwritten characters and drawings, improving the accuracy of conversion into digital data.

[0031] The conversion unit can convert handwritten characters into text data. The conversion unit converts handwritten characters into text data using, for example, a character recognition algorithm. For example, the conversion unit recognizes handwritten characters with high accuracy and converts them into text data. The conversion unit can also use generation AI to improve the accuracy of handwritten character conversion. For example, the conversion unit uses generation AI to analyze handwritten characters and convert them into text data. In this way, converting handwritten characters into text data makes it easier to use digital data.

[0032] The conversion unit can digitize handwritten diagrams and pictures. The conversion unit digitizes handwritten diagrams and pictures using, for example, image processing technology. For example, the conversion unit recognizes handwritten diagrams and pictures with high accuracy and converts them into digital data. The conversion unit can also use generative AI to improve the accuracy of digitizing handwritten diagrams and pictures. For example, the conversion unit uses generative AI to analyze handwritten diagrams and pictures and convert them into digital data. In this way, by digitizing handwritten diagrams and pictures, visual information can also be stored and shared as digital data.

[0033] The storage unit can store data in the cloud. For example, the storage unit stores data using a cloud service. For example, the storage unit stores data in the cloud with high security. The storage unit can also select the format of the data to be stored in the cloud. For example, the storage unit stores data in PDF format, image format, text format, etc. This makes it easier to manage and share data by storing it in the cloud.

[0034] The sharing unit can share data stored in the cloud among members. For example, the sharing unit shares data stored in the cloud among members. For example, the sharing unit sets access permissions for data stored in the cloud so that only specific members can access it. The sharing unit can also share data stored in the cloud in real time. For example, the sharing unit shares data stored in the cloud among members in real time, achieving rapid information sharing. As a result, by sharing data stored in the cloud among members, information can be shared quickly and efficiently.

[0035] The reading unit can select an appropriate reading method based on the frequency of use of the whiteboard and its past usage history. For example, the reading unit collects the frequency of use of the whiteboard from log data and selects the optimal reading method. For example, if the whiteboard is used frequently, the reading unit selects the optimal reading method based on the past usage history. Also, if the whiteboard is used infrequently, the reading unit can apply a standard reading method. Furthermore, the reading unit can also select a reading method that matches the writing style of a specific user. For example, the generation AI selects a reading method that matches the writing style of a specific user from the past usage history. This makes it possible to select the optimal reading method by taking into account the frequency of use of the whiteboard and its past usage history.

[0036] The reading unit can detect the surface condition of the whiteboard and adjust the reading algorithm. The reading unit, for example, uses sensor technology to detect the surface condition of the whiteboard. For example, if there is dirt on the surface of the whiteboard, the reading unit applies a reading algorithm that ignores the dirt. Also, if the surface of the whiteboard is reflective, the reading unit can apply a filter to reduce the reflection. Furthermore, the reading unit can detect the surface condition of the whiteboard in real time and select the optimal reading algorithm. For example, the reading unit detects the surface condition of the whiteboard in real time, and the generation AI selects the optimal reading algorithm. In this way, the optimal reading algorithm can be applied by detecting the surface condition of the whiteboard.

[0037] The reading unit can detect the user's writing speed and writing pressure to improve reading accuracy. The reading unit detects the user's writing speed and writing pressure, for example, using sensor technology. For example, if the user's writing speed is fast, the reading unit can apply an algorithm that reads at high speed. Also, if the user's writing pressure is strong, the reading unit can apply an algorithm that prioritizes reading dark characters. Furthermore, the reading unit can detect the user's writing speed and writing pressure in real time and select the optimal reading algorithm. For example, the reading unit detects the user's writing speed and writing pressure in real time, and the generation AI selects the optimal reading algorithm. In this way, by detecting the user's writing speed and writing pressure, reading accuracy can be improved.

[0038] The reading unit can select an appropriate reading angle by taking into account the position information of the whiteboard. The reading unit, for example, uses a position sensor to collect position information of the whiteboard. For example, if the whiteboard is hanging on a wall, the reading unit selects a reading angle from the front. Also, if the whiteboard is installed at an angle, the reading unit can select the optimal diagonal angle. Furthermore, the reading unit can detect the position information of the whiteboard in real time and select the optimal reading angle. For example, the reading unit detects the position information of the whiteboard in real time, and the generation AI selects the optimal reading angle. This makes it possible to select the optimal reading angle by taking into account the position information of the whiteboard.

[0039] The reading unit can analyze the user's past writing patterns and customize the reading algorithm. The reading unit, for example, uses log data to collect the user's past writing patterns. For example, the reading unit analyzes the user's past writing patterns and applies the optimal reading algorithm. Also, if the user writes in a specific way, the reading unit can apply a reading algorithm that matches that pattern. Furthermore, the reading unit can analyze the user's past writing patterns in real time and select the optimal reading algorithm. For example, the reading unit analyzes the user's past writing patterns in real time, and the generation AI selects the optimal reading algorithm. In this way, the optimal reading algorithm can be applied by analyzing the user's past writing patterns.

[0040] The reading unit can detect the environment in which the whiteboard is used and adjust the reading accuracy. The reading unit detects the environment in which the whiteboard is used, for example, using sensor technology. For example, the reading unit applies an algorithm that improves reading accuracy when the lighting is bright. It can also apply an algorithm that corrects reading accuracy when the lighting is dim. Furthermore, the reading unit can detect sounds in the usage environment and select the optimal reading algorithm. For example, the reading unit detects sounds in the usage environment in real time, and the generating AI selects the optimal reading algorithm. In this way, the optimal reading accuracy can be applied by detecting the environment in which the whiteboard is used.

[0041] The conversion unit can adjust the level of conversion detail based on the importance of handwritten characters and figures. For example, the conversion unit evaluates the importance of handwritten characters and figures based on user instructions and content analysis. For example, the conversion unit prioritizes important handwritten characters and figures and converts them in detail. It can also simplify and convert handwritten characters and figures with less importance. Furthermore, the conversion unit can determine the importance of handwritten characters and figures in real time and select the optimal level of conversion detail. For example, the conversion unit determines the importance of handwritten characters and figures in real time, and the generation AI selects the optimal level of conversion detail. As a result, important information can be converted in detail by adjusting the level of conversion detail based on the importance of handwritten characters and figures.

[0042] The conversion unit can apply different conversion algorithms depending on the category of handwritten characters or figures. For example, the conversion unit classifies the categories of handwritten characters or figures into characters, figures, graphs, etc. For example, the conversion unit applies an algorithm to convert handwritten characters into text data. It can also apply an algorithm to digitize handwritten figures. It can also apply an algorithm to digitize handwritten pictures. For example, the conversion unit uses a generation AI to select the optimal conversion algorithm depending on the category of handwritten characters or figures. This improves conversion accuracy by applying the optimal conversion algorithm depending on the category of handwritten characters or figures.

[0043] The conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. The conversion unit, for example, uses log data to collect the user's past conversion results. For example, the conversion unit analyzes the user's past conversion results and applies the optimal conversion algorithm. The conversion unit can also improve the conversion accuracy based on data the user has converted in the past. Furthermore, the conversion unit can refer to the user's past conversion results in real time and select the optimal conversion algorithm. For example, the conversion unit refers to the user's past conversion results in real time, and the generation AI selects the optimal conversion algorithm. In this way, the conversion accuracy is improved by referring to the user's past conversion results.

[0044] The conversion unit can determine the conversion priority based on the submission time of handwritten characters and figures. The conversion unit, for example, evaluates the submission time of handwritten characters and figures using records of submission date and time. For example, the conversion unit prioritizes conversion of handwritten characters and figures submitted earlier. It can also convert handwritten characters and figures submitted later later. Furthermore, the conversion unit can determine the submission time in real time and select the optimal conversion priority. For example, the conversion unit determines the submission time in real time, and the generation AI selects the optimal conversion priority. In this way, by determining the conversion priority based on the submission time of handwritten characters and figures, important information can be converted quickly.

[0045] The conversion unit can adjust the conversion order based on the relevance of handwritten characters and figures. The conversion unit, for example, uses content analysis to evaluate the relevance of handwritten characters and figures. For example, the conversion unit prioritizes conversion of highly relevant handwritten characters and figures. It can also convert less relevant handwritten characters and figures later. Furthermore, the conversion unit can determine the relevance of handwritten characters and figures in real time and select the optimal conversion order. For example, the conversion unit determines the relevance of handwritten characters and figures in real time, and the generation AI selects the optimal conversion order. As a result, related information can be efficiently converted by adjusting the conversion order based on the relevance of handwritten characters and figures.

[0046] The conversion unit can adjust the use of technical terms in the conversion depending on the user's level of expertise. The conversion unit evaluates the user's level of expertise using, for example, survey results or past usage history. For example, if the user's level of expertise is high, the conversion unit can perform conversion that uses a lot of technical terms. Conversely, if the user's level of expertise is low, the conversion unit can perform conversion that avoids technical terms. Furthermore, the conversion unit can determine the user's level of expertise in real time and select the optimal use of technical terms. For example, the conversion unit determines the user's level of expertise in real time, and the generation AI selects the optimal use of technical terms. This makes it possible to generate appropriate digital data by adjusting the use of technical terms depending on the user's level of expertise.

[0047] The storage unit can optimize the storage algorithm by referencing past stored data. The storage unit collects past stored data using, for example, log data. For example, the storage unit analyzes past stored data and applies an optimal storage algorithm. The storage unit can also optimize the storage algorithm based on data previously stored by the user. Furthermore, the storage unit can also select an optimal storage algorithm by referencing past stored data in real time. For example, the storage unit refers to past stored data in real time, and the generation AI selects an optimal storage algorithm. In this way, the optimal storage algorithm can be applied by referencing past stored data.

[0048] The storage unit can update the stored data by reflecting user feedback. The storage unit collects user feedback using, for example, survey results or usage history. For example, the storage unit updates the stored data based on user feedback. The storage unit can also optimize the stored data by reflecting feedback provided by the user in real time. Furthermore, the storage unit can analyze user feedback and update the stored data. For example, the storage unit analyzes user feedback in real time, and the generation AI selects the optimal stored data. In this way, the accuracy of the stored data is improved by reflecting user feedback.

[0049] The storage unit can determine the storage priority based on the importance of the data. The storage unit evaluates the importance of the data, for example, based on user instructions or content analysis. For example, the storage unit prioritizes saving important data. It can also save less important data later. Furthermore, the storage unit can determine the importance of the data in real time and select the optimal storage priority. For example, the storage unit determines the importance of the data in real time, and the generation AI selects the optimal storage priority. In this way, by determining the storage priority based on the importance of the data, important data can be saved with priority.

[0050] The storage unit can weight the stored data based on the time of data submission. The storage unit, for example, evaluates the time of data submission using records of submission date and time. For example, the storage unit prioritizes and saves data submitted earlier. It can also postpone saving data submitted later. Furthermore, the storage unit can determine the time of submission in real time and assign optimal storage weighting. For example, the storage unit determines the time of submission in real time, and the generation AI selects the optimal storage weighting. In this way, by weighting the stored data based on the time of data submission, important data can be prioritized for storage.

[0051] The storage unit can integrate information from different data sources to enrich the stored data. For example, the storage unit collects information from different data sources using APIs or data mining technology. For example, the storage unit integrates and stores information from different data sources. It can also acquire information from different data sources in real time to enrich the stored data. Furthermore, the storage unit can analyze information from different data sources and select the optimal data to store. For example, the storage unit analyzes information from different data sources in real time, and an AI generating the optimal data to store is used to select it. This enriches the content of the stored data by integrating information from different data sources.

[0052] The storage unit can adjust the order of storage based on the relevance of the data. The storage unit, for example, evaluates the relevance of the data using content analysis. For example, the storage unit prioritizes saving highly relevant data. It can also save less relevant data later. Furthermore, the storage unit can determine the relevance of the data in real time and select the optimal storage order. For example, the storage unit determines the relevance of the data in real time, and the generation AI selects the optimal storage order. As a result, related information can be efficiently stored by adjusting the storage order based on the relevance of the data.

[0053] The sharing unit can select the optimal sharing method by referring to the user's past sharing history. The sharing unit, for example, uses log data to collect the user's past sharing history. For example, the sharing unit selects the optimal sharing method based on the user's past sharing history. The sharing unit can also preferentially suggest sharing methods that the user has used in the past. Furthermore, the sharing unit can also select the optimal sharing method by referring to the user's past sharing history in real time. For example, the sharing unit refers to the user's past sharing history in real time, and the generation AI selects the optimal sharing method. In this way, the optimal sharing method can be provided by referring to the user's past sharing history.

[0054] The sharing unit can customize the shared content according to the user's current task. The sharing unit, for example, evaluates the user's current task using the task content and progress. For example, the sharing unit prioritizes sharing information related to the user's current task. The sharing unit can also determine the user's current task in real time and select the optimal shared content. Furthermore, the sharing unit can customize the shared content according to the user's current task. For example, the sharing unit uses a generation AI to select the optimal shared content according to the user's current task. This allows appropriate information to be shared by customizing the shared content according to the user's current task.

[0055] The sharing unit can improve the sharing method by reflecting user feedback. The sharing unit collects user feedback, for example, using survey results or usage history. For example, the sharing unit improves the sharing method based on user feedback. The sharing unit can also reflect feedback provided by users in real time and optimize the sharing method. Furthermore, the sharing unit can analyze user feedback and improve the sharing method. For example, the sharing unit analyzes user feedback in real time, and the generation AI selects the optimal sharing method. In this way, the accuracy of the sharing method is improved by reflecting user feedback.

[0056] The sharing unit can select the optimal sharing method by taking into account the user's device information. The sharing unit collects the user's device information using, for example, the type of device and usage status. For example, if the user is using a smartphone, the sharing unit can provide the optimal sharing method for the smartphone. Also, if the user is using a tablet, the sharing unit can provide the optimal sharing method for the tablet. Furthermore, if the user is using a desktop, the sharing unit can provide the optimal sharing method for the desktop. For example, the sharing unit collects the user's device information in real time, and the generation AI selects the optimal sharing method. In this way, the optimal sharing method can be provided by taking into account the user's device information.

[0057] The sharing unit can make the shared content multilingual according to the user's language setting. The sharing unit, for example, collects the user's language setting using system settings or user instructions. For example, the sharing unit automatically translates the shared content based on the user's language setting. In addition, if the user uses multiple languages, the sharing unit can also provide a language switching function. Furthermore, the sharing unit can determine the user's language setting in real time and provide the shared content in the most appropriate language. For example, the sharing unit determines the user's language setting in real time, and an AI that generates the shared content in the most appropriate language selects it. This makes the shared content multilingual according to the user's language setting, allowing information to be shared in the appropriate language.

[0058] The sharing unit can customize the sharing method based on the user's occupation and lifestyle. The sharing unit evaluates the user's occupation and lifestyle, for example, using survey results and usage history. For example, the sharing unit provides the optimal sharing method according to the user's occupation. The sharing unit can also customize the sharing method based on the user's lifestyle. Furthermore, the sharing unit can determine the user's occupation and lifestyle in real time and select the optimal sharing method. For example, the sharing unit determines the user's occupation and lifestyle in real time, and the generation AI selects the optimal sharing method. This allows appropriate information to be shared by customizing the sharing method based on the user's occupation and lifestyle.

[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 conference support system can further include a speech recognition unit. The speech recognition unit can convert statements made during a conference into text data in real time and store it in the cloud. For example, the speech recognition unit can recognize statements made during a conference with high accuracy and convert them into text data. The speech recognition unit can also classify the text data by speaker and organize the content of statements. This makes it easy to automatically record statements made during a conference and refer to them later.

[0061] The reader can also be equipped with a color recognition function. This function can recognize the color of the letters and drawings written on the whiteboard and reflect them in the digital data. For example, the reader can convert red letters into red text data. It can also convert blue drawings into blue image data. This allows the contents of the whiteboard to be digitized in color, allowing the visual information to be saved and shared as is.

[0062] The conversion unit can also recognize the style of handwritten characters and drawings and reflect them in the digital data. For example, the conversion unit can recognize the thickness and italics of handwritten characters and reflect them in the digital data. It can also recognize the thickness and color of lines in handwritten drawings and reflect them in the digital data. This allows the handwritten style to be reflected as is in the digital data, preserving the original atmosphere.

[0063] The conversion unit can further analyze the content of handwritten characters and diagrams and automatically add related information. For example, the conversion unit can analyze the content of handwritten characters and diagrams, search for related web pages and literature, and add links. It can also analyze the content of handwritten diagrams and automatically generate related data and graphs. This makes it possible to automatically add information related to the handwritten content and enrich the information.

[0064] The storage unit may further include a data backup function. The backup function periodically backs up data stored in the cloud to prevent data loss. For example, the storage unit periodically backs up data stored in the cloud to another cloud service. Backups can also be made to local storage. This reduces the risk of data loss and enables safe data management.

[0065] The sharing unit can also have a function for recording data access logs. The access log function records the access history of data stored in the cloud, thereby enhancing security. For example, the sharing unit records information about users who accessed the data and the date and time of access. It can also analyze the access log to detect unauthorized access and implement security measures. This enhances data security and allows for safe sharing.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reader reads the characters and drawings written by the user on the whiteboard. For example, the reader can recognize handwritten characters and drawings. Step 2: The conversion unit converts the characters and figures read by the reading unit into digital data. For example, the conversion unit can convert handwritten characters into text data. The conversion unit can also digitize handwritten figures and pictures. Step 3: The storage unit stores the data converted by the conversion unit in the cloud. For example, the storage unit can store the data in the cloud. Step 4: The sharing unit shares the data stored by the storage unit among the members when necessary. For example, the sharing unit can share data stored in the cloud among the members.

[0068] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to improve the efficiency of meetings and educational programs. This system reads text and drawings written by users on a whiteboard, converts them into digital data, and stores and shares them in the cloud. This allows the system to automatically digitize what users write on the whiteboard, store it in the cloud, and share it, thereby improving the efficiency of meetings and educational programs. For example, when a user handwrites ideas or discussion details on a whiteboard, the generative AI reads them and converts them into digital data. This data is stored in the cloud and can be shared among members as needed. This improves the speed of collaborative work by automatically generating meeting minutes, immediately sharing them, and allowing participants to move on to the next task or discussion.

[0069] A meeting support system according to an embodiment includes a reading unit, a conversion unit, a storage unit, and a sharing unit. The reading unit reads characters and drawings written by a user on a whiteboard. For example, the reading unit can recognize handwritten characters and drawings. The conversion unit converts the characters and drawings read by the reading unit into digital data. For example, the conversion unit can convert handwritten characters into text data. The conversion unit can also digitize handwritten diagrams and pictures. The storage unit stores the data converted by the conversion unit in the cloud. For example, the storage unit can store the data in the cloud. The sharing unit shares the data stored by the storage unit among members when necessary. For example, the sharing unit can share the data stored in the cloud among members. As a result, the meeting support system according to an embodiment can improve the efficiency of meetings and education by automatically digitizing content written by a user on a whiteboard and storing and sharing it in the cloud.

[0070] The reading unit can recognize handwritten characters or drawings. For example, the reading unit recognizes handwritten characters using OCR technology. The reading unit can also recognize handwritten drawings using an image recognition algorithm. For example, the reading unit can recognize handwritten characters with high accuracy and convert them into digital data. The reading unit can also analyze handwritten drawings and convert their contents into digital data. This allows for accurate recognition of handwritten characters and drawings, improving the accuracy of conversion into digital data.

[0071] The conversion unit can convert handwritten characters into text data. The conversion unit converts handwritten characters into text data using, for example, a character recognition algorithm. For example, the conversion unit recognizes handwritten characters with high accuracy and converts them into text data. The conversion unit can also use generation AI to improve the accuracy of handwritten character conversion. For example, the conversion unit uses generation AI to analyze handwritten characters and convert them into text data. In this way, converting handwritten characters into text data makes it easier to use digital data.

[0072] The conversion unit can digitize handwritten diagrams and pictures. The conversion unit digitizes handwritten diagrams and pictures using, for example, image processing technology. For example, the conversion unit recognizes handwritten diagrams and pictures with high accuracy and converts them into digital data. The conversion unit can also use generative AI to improve the accuracy of digitizing handwritten diagrams and pictures. For example, the conversion unit uses generative AI to analyze handwritten diagrams and pictures and convert them into digital data. In this way, by digitizing handwritten diagrams and pictures, visual information can also be stored and shared as digital data.

[0073] The storage unit can store data in the cloud. For example, the storage unit stores data using a cloud service. For example, the storage unit stores data in the cloud with high security. The storage unit can also select the format of the data to be stored in the cloud. For example, the storage unit stores data in PDF format, image format, text format, etc. This makes it easier to manage and share data by storing it in the cloud.

[0074] The sharing unit can share data stored in the cloud among members. For example, the sharing unit shares data stored in the cloud among members. For example, the sharing unit sets access permissions for data stored in the cloud so that only specific members can access it. The sharing unit can also share data stored in the cloud in real time. For example, the sharing unit shares data stored in the cloud among members in real time, achieving rapid information sharing. As a result, by sharing data stored in the cloud among members, information can be shared quickly and efficiently.

[0075] The reading unit can estimate the user's emotion and adjust the reading accuracy based on the estimated user's emotion. The reading unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the reading unit analyzes the user's facial expression and calculates an emotion score. The reading unit can also estimate the user's emotion using voice analysis technology. For example, the reading unit analyzes the tone and speed of the user's voice and calculates an emotion score. The reading unit also adjusts the reading accuracy based on the estimated user's emotion. For example, if the user is stressed, the reading unit increases the reading accuracy and reduces misrecognition. On the other hand, if the user is relaxed, the reading unit sets the reading accuracy to normal and prioritizes processing speed. This allows misrecognition to be reduced by adjusting the reading accuracy according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The reading unit can select an appropriate reading method based on the frequency of use of the whiteboard and its past usage history. For example, the reading unit collects the frequency of use of the whiteboard from log data and selects the optimal reading method. For example, if the whiteboard is used frequently, the reading unit selects the optimal reading method based on the past usage history. Also, if the whiteboard is used infrequently, the reading unit can apply a standard reading method. Furthermore, the reading unit can also select a reading method that matches the writing style of a specific user. For example, the generation AI selects a reading method that matches the writing style of a specific user from the past usage history. This makes it possible to select the optimal reading method by taking into account the frequency of use of the whiteboard and its past usage history.

[0077] The reading unit can detect the surface condition of the whiteboard and adjust the reading algorithm. The reading unit, for example, uses sensor technology to detect the surface condition of the whiteboard. For example, if there is dirt on the surface of the whiteboard, the reading unit applies a reading algorithm that ignores the dirt. Also, if the surface of the whiteboard is reflective, the reading unit can apply a filter to reduce the reflection. Furthermore, the reading unit can detect the surface condition of the whiteboard in real time and select the optimal reading algorithm. For example, the reading unit detects the surface condition of the whiteboard in real time, and the generation AI selects the optimal reading algorithm. In this way, the optimal reading algorithm can be applied by detecting the surface condition of the whiteboard.

[0078] The reading unit can detect the user's writing speed and writing pressure to improve reading accuracy. The reading unit detects the user's writing speed and writing pressure, for example, using sensor technology. For example, if the user's writing speed is fast, the reading unit can apply an algorithm that reads at high speed. Also, if the user's writing pressure is strong, the reading unit can apply an algorithm that prioritizes reading dark characters. Furthermore, the reading unit can detect the user's writing speed and writing pressure in real time and select the optimal reading algorithm. For example, the reading unit detects the user's writing speed and writing pressure in real time, and the generation AI selects the optimal reading algorithm. In this way, by detecting the user's writing speed and writing pressure, reading accuracy can be improved.

[0079] The reading unit can estimate the user's emotions and determine the priority of the content to be read based on the estimated user's emotions. The reading unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the reading unit analyzes the user's facial expressions and calculates an emotion score. The reading unit can also estimate the user's emotions using voice analysis technology. For example, the reading unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the reading unit determines the priority of the content to be read based on the estimated user's emotions. For example, if the user is stressed, the reading unit prioritizes reading important content. On the other hand, if the user is relaxed, the reading unit reads all content equally. In this way, by determining the priority of the content to be read according to the user's emotions, important content can be prioritized and read. Emotion estimation is realized using an emotion estimation function using, 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.

[0080] The reading unit can select an appropriate reading angle by taking into account the position information of the whiteboard. The reading unit, for example, uses a position sensor to collect position information of the whiteboard. For example, if the whiteboard is hanging on a wall, the reading unit selects a reading angle from the front. Also, if the whiteboard is installed at an angle, the reading unit can select the optimal diagonal angle. Furthermore, the reading unit can detect the position information of the whiteboard in real time and select the optimal reading angle. For example, the reading unit detects the position information of the whiteboard in real time, and the generation AI selects the optimal reading angle. This makes it possible to select the optimal reading angle by taking into account the position information of the whiteboard.

[0081] The reading unit can analyze the user's past writing patterns and customize the reading algorithm. The reading unit, for example, uses log data to collect the user's past writing patterns. For example, the reading unit analyzes the user's past writing patterns and applies the optimal reading algorithm. Also, if the user writes in a specific way, the reading unit can apply a reading algorithm that matches that pattern. Furthermore, the reading unit can analyze the user's past writing patterns in real time and select the optimal reading algorithm. For example, the reading unit analyzes the user's past writing patterns in real time, and the generation AI selects the optimal reading algorithm. In this way, the optimal reading algorithm can be applied by analyzing the user's past writing patterns.

[0082] The reading unit can detect the environment in which the whiteboard is used and adjust the reading accuracy. The reading unit detects the environment in which the whiteboard is used, for example, using sensor technology. For example, the reading unit applies an algorithm that improves reading accuracy when the lighting is bright. It can also apply an algorithm that corrects reading accuracy when the lighting is dim. Furthermore, the reading unit can detect sounds in the usage environment and select the optimal reading algorithm. For example, the reading unit detects sounds in the usage environment in real time, and the generating AI selects the optimal reading algorithm. In this way, the optimal reading accuracy can be applied by detecting the environment in which the whiteboard is used.

[0083] The conversion unit can estimate the user's emotion and adjust the conversion expression method based on the estimated user's emotion. The conversion unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the conversion unit analyzes the user's facial expression and calculates an emotion score. The conversion unit can also estimate the user's emotion using voice analysis technology. For example, the conversion unit analyzes the tone and speed of the user's voice and calculates an emotion score. The conversion unit also adjusts the conversion expression method based on the estimated user's emotion. For example, if the user is stressed, the conversion unit applies a simple expression method. On the other hand, if the user is relaxed, the conversion unit applies a detailed expression method. In this way, by adjusting the conversion expression method according to the user's emotion, appropriate digital data can be generated. Emotion estimation is realized using an emotion estimation function using, 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.

[0084] The conversion unit can adjust the level of conversion detail based on the importance of handwritten characters and figures. For example, the conversion unit evaluates the importance of handwritten characters and figures based on user instructions and content analysis. For example, the conversion unit prioritizes important handwritten characters and figures and converts them in detail. It can also simplify and convert handwritten characters and figures with less importance. Furthermore, the conversion unit can determine the importance of handwritten characters and figures in real time and select the optimal level of conversion detail. For example, the conversion unit determines the importance of handwritten characters and figures in real time, and the generation AI selects the optimal level of conversion detail. As a result, important information can be converted in detail by adjusting the level of conversion detail based on the importance of handwritten characters and figures.

[0085] The conversion unit can apply different conversion algorithms depending on the category of handwritten characters or figures. For example, the conversion unit classifies the categories of handwritten characters or figures into characters, figures, graphs, etc. For example, the conversion unit applies an algorithm to convert handwritten characters into text data. It can also apply an algorithm to digitize handwritten figures. It can also apply an algorithm to digitize handwritten pictures. For example, the conversion unit uses a generation AI to select the optimal conversion algorithm depending on the category of handwritten characters or figures. This improves conversion accuracy by applying the optimal conversion algorithm depending on the category of handwritten characters or figures.

[0086] The conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. The conversion unit, for example, uses log data to collect the user's past conversion results. For example, the conversion unit analyzes the user's past conversion results and applies the optimal conversion algorithm. The conversion unit can also improve the conversion accuracy based on data the user has converted in the past. Furthermore, the conversion unit can refer to the user's past conversion results in real time and select the optimal conversion algorithm. For example, the conversion unit refers to the user's past conversion results in real time, and the generation AI selects the optimal conversion algorithm. In this way, the conversion accuracy is improved by referring to the user's past conversion results.

[0087] The conversion unit can estimate the user's emotion and adjust the length of the conversion based on the estimated user's emotion. The conversion unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the conversion unit analyzes the user's facial expression and calculates an emotion score. The conversion unit can also estimate the user's emotion using voice analysis technology. For example, the conversion unit analyzes the tone and speed of the user's voice and calculates an emotion score. The conversion unit also adjusts the length of the conversion based on the estimated user's emotion. For example, if the user is stressed, the conversion unit performs a short, concise conversion. On the other hand, if the user is relaxed, the conversion unit performs a detailed conversion. This allows appropriate digital data to be generated by adjusting the length of the conversion according to the user's emotion. 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.

[0088] The conversion unit can determine the conversion priority based on the submission time of handwritten characters and figures. The conversion unit, for example, evaluates the submission time of handwritten characters and figures using records of submission date and time. For example, the conversion unit prioritizes conversion of handwritten characters and figures submitted earlier. It can also convert handwritten characters and figures submitted later later. Furthermore, the conversion unit can determine the submission time in real time and select the optimal conversion priority. For example, the conversion unit determines the submission time in real time, and the generation AI selects the optimal conversion priority. In this way, by determining the conversion priority based on the submission time of handwritten characters and figures, important information can be converted quickly.

[0089] The conversion unit can adjust the conversion order based on the relevance of handwritten characters and figures. The conversion unit, for example, uses content analysis to evaluate the relevance of handwritten characters and figures. For example, the conversion unit prioritizes conversion of highly relevant handwritten characters and figures. It can also convert less relevant handwritten characters and figures later. Furthermore, the conversion unit can determine the relevance of handwritten characters and figures in real time and select the optimal conversion order. For example, the conversion unit determines the relevance of handwritten characters and figures in real time, and the generation AI selects the optimal conversion order. As a result, related information can be efficiently converted by adjusting the conversion order based on the relevance of handwritten characters and figures.

[0090] The conversion unit can adjust the use of technical terms in the conversion depending on the user's level of expertise. The conversion unit evaluates the user's level of expertise using, for example, survey results or past usage history. For example, if the user's level of expertise is high, the conversion unit can perform conversion that uses a lot of technical terms. Conversely, if the user's level of expertise is low, the conversion unit can perform conversion that avoids technical terms. Furthermore, the conversion unit can determine the user's level of expertise in real time and select the optimal use of technical terms. For example, the conversion unit determines the user's level of expertise in real time, and the generation AI selects the optimal use of technical terms. This makes it possible to generate appropriate digital data by adjusting the use of technical terms depending on the user's level of expertise.

[0091] The storage unit can estimate the user's emotion and select data to be saved based on the estimated user's emotion. The storage unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the storage unit analyzes the user's facial expression and calculates an emotion score. The storage unit can also estimate the user's emotion using voice analysis technology. For example, the storage unit analyzes the tone and speed of the user's voice and calculates an emotion score. The storage unit also selects data to be saved based on the estimated user's emotion. For example, if the user is stressed, the storage unit prioritizes saving important data. On the other hand, if the user is relaxed, the storage unit saves all data equally. In this way, by selecting data to be saved according to the user's emotion, important data can be prioritized and saved. Emotion estimation is realized using an emotion estimation function using, for example, 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.

[0092] The storage unit can optimize the storage algorithm by referencing past stored data. The storage unit collects past stored data using, for example, log data. For example, the storage unit analyzes past stored data and applies an optimal storage algorithm. The storage unit can also optimize the storage algorithm based on data previously stored by the user. Furthermore, the storage unit can also select an optimal storage algorithm by referencing past stored data in real time. For example, the storage unit refers to past stored data in real time, and the generation AI selects an optimal storage algorithm. In this way, the optimal storage algorithm can be applied by referencing past stored data.

[0093] The storage unit can update the stored data by reflecting user feedback. The storage unit collects user feedback using, for example, survey results or usage history. For example, the storage unit updates the stored data based on user feedback. The storage unit can also optimize the stored data by reflecting feedback provided by the user in real time. Furthermore, the storage unit can analyze user feedback and update the stored data. For example, the storage unit analyzes user feedback in real time, and the generation AI selects the optimal stored data. In this way, the accuracy of the stored data is improved by reflecting user feedback.

[0094] The storage unit can determine the storage priority based on the importance of the data. The storage unit evaluates the importance of the data, for example, based on user instructions or content analysis. For example, the storage unit prioritizes saving important data. It can also save less important data later. Furthermore, the storage unit can determine the importance of the data in real time and select the optimal storage priority. For example, the storage unit determines the importance of the data in real time, and the generation AI selects the optimal storage priority. In this way, by determining the storage priority based on the importance of the data, important data can be saved with priority.

[0095] The storage unit can estimate the user's emotion and adjust the frequency of saving based on the estimated user's emotion. The storage unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the storage unit analyzes the user's facial expression and calculates an emotion score. The storage unit can also estimate the user's emotion using voice analysis technology. For example, the storage unit analyzes the tone and speed of the user's voice and calculates an emotion score. The storage unit also adjusts the frequency of saving based on the estimated user's emotion. For example, if the user is stressed, the storage unit increases the saving frequency. On the other hand, if the user is relaxed, the storage unit sets the saving frequency to normal. In this way, by adjusting the saving frequency according to the user's emotion, data can be saved at an appropriate time. Emotion estimation is realized using an emotion estimation function using, 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.

[0096] The storage unit can weight the stored data based on the time of data submission. The storage unit, for example, evaluates the time of data submission using records of submission date and time. For example, the storage unit prioritizes and saves data submitted earlier. It can also postpone saving data submitted later. Furthermore, the storage unit can determine the time of submission in real time and assign optimal storage weighting. For example, the storage unit determines the time of submission in real time, and the generation AI selects the optimal storage weighting. In this way, by weighting the stored data based on the time of data submission, important data can be prioritized for storage.

[0097] The storage unit can integrate information from different data sources to enrich the stored data. For example, the storage unit collects information from different data sources using APIs or data mining technology. For example, the storage unit integrates and stores information from different data sources. It can also acquire information from different data sources in real time to enrich the stored data. Furthermore, the storage unit can analyze information from different data sources and select the optimal data to store. For example, the storage unit analyzes information from different data sources in real time, and an AI generating the optimal data to store is used to select it. This enriches the content of the stored data by integrating information from different data sources.

[0098] The storage unit can adjust the order of storage based on the relevance of the data. The storage unit, for example, evaluates the relevance of the data using content analysis. For example, the storage unit prioritizes saving highly relevant data. It can also save less relevant data later. Furthermore, the storage unit can determine the relevance of the data in real time and select the optimal storage order. For example, the storage unit determines the relevance of the data in real time, and the generation AI selects the optimal storage order. As a result, related information can be efficiently stored by adjusting the storage order based on the relevance of the data.

[0099] The sharing unit can estimate the user's emotion and adjust the sharing method based on the estimated user's emotion. The sharing unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the sharing unit analyzes the user's facial expression and calculates an emotion score. The sharing unit can also estimate the user's emotion using voice analysis technology. For example, the sharing unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the sharing unit adjusts the sharing method based on the estimated user's emotion. For example, if the user is stressed, the sharing unit provides a simple sharing method. On the other hand, if the user is relaxed, the sharing unit provides a detailed sharing method. In this way, an appropriate sharing method can be provided by adjusting the sharing method according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, 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.

[0100] The sharing unit can select the optimal sharing method by referring to the user's past sharing history. The sharing unit, for example, uses log data to collect the user's past sharing history. For example, the sharing unit selects the optimal sharing method based on the user's past sharing history. The sharing unit can also preferentially suggest sharing methods that the user has used in the past. Furthermore, the sharing unit can also select the optimal sharing method by referring to the user's past sharing history in real time. For example, the sharing unit refers to the user's past sharing history in real time, and the generation AI selects the optimal sharing method. In this way, the optimal sharing method can be provided by referring to the user's past sharing history.

[0101] The sharing unit can customize the shared content according to the user's current task. The sharing unit, for example, evaluates the user's current task using the task content and progress. For example, the sharing unit prioritizes sharing information related to the user's current task. The sharing unit can also determine the user's current task in real time and select the optimal shared content. Furthermore, the sharing unit can customize the shared content according to the user's current task. For example, the sharing unit uses a generation AI to select the optimal shared content according to the user's current task. This allows appropriate information to be shared by customizing the shared content according to the user's current task.

[0102] The sharing unit can improve the sharing method by reflecting user feedback. The sharing unit collects user feedback, for example, using survey results or usage history. For example, the sharing unit improves the sharing method based on user feedback. The sharing unit can also reflect feedback provided by users in real time and optimize the sharing method. Furthermore, the sharing unit can analyze user feedback and improve the sharing method. For example, the sharing unit analyzes user feedback in real time, and the generation AI selects the optimal sharing method. In this way, the accuracy of the sharing method is improved by reflecting user feedback.

[0103] The sharing unit can estimate the user's emotions and determine sharing priorities based on the estimated user emotions. The sharing unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the sharing unit analyzes the user's facial expressions and calculates an emotion score. The sharing unit can also estimate the user's emotions using voice analysis technology. For example, the sharing unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the sharing unit determines sharing priorities based on the estimated user emotions. For example, if the user is feeling stressed, the sharing unit prioritizes sharing important information. On the other hand, if the user is relaxed, the sharing unit shares all information equally. In this way, by determining sharing priorities according to the user's emotions, important information can be prioritized for sharing. Emotion estimation is achieved using an emotion estimation function using, 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.

[0104] The sharing unit can select the optimal sharing method by taking into account the user's device information. The sharing unit collects the user's device information using, for example, the type of device and usage status. For example, if the user is using a smartphone, the sharing unit can provide the optimal sharing method for the smartphone. Also, if the user is using a tablet, the sharing unit can provide the optimal sharing method for the tablet. Furthermore, if the user is using a desktop, the sharing unit can provide the optimal sharing method for the desktop. For example, the sharing unit collects the user's device information in real time, and the generation AI selects the optimal sharing method. In this way, the optimal sharing method can be provided by taking into account the user's device information.

[0105] The sharing unit can make the shared content multilingual according to the user's language setting. The sharing unit, for example, collects the user's language setting using system settings or user instructions. For example, the sharing unit automatically translates the shared content based on the user's language setting. In addition, if the user uses multiple languages, the sharing unit can also provide a language switching function. Furthermore, the sharing unit can determine the user's language setting in real time and provide the shared content in the most appropriate language. For example, the sharing unit determines the user's language setting in real time, and an AI that generates the shared content in the most appropriate language selects it. This makes the shared content multilingual according to the user's language setting, allowing information to be shared in the appropriate language.

[0106] The sharing unit can customize the sharing method based on the user's occupation and lifestyle. The sharing unit evaluates the user's occupation and lifestyle, for example, using survey results and usage history. For example, the sharing unit provides the optimal sharing method according to the user's occupation. The sharing unit can also customize the sharing method based on the user's lifestyle. Furthermore, the sharing unit can determine the user's occupation and lifestyle in real time and select the optimal sharing method. For example, the sharing unit determines the user's occupation and lifestyle in real time, and the generation AI selects the optimal sharing method. This allows appropriate information to be shared by customizing the sharing method based on the user's occupation and lifestyle. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reading unit, conversion unit, storage unit, and sharing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reading unit reads the contents of the whiteboard using the camera 42 of the smart device 14. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the read contents into digital data. The storage unit stores the data in the database 24 of the data processing device 12. The sharing unit shares the data stored in the cloud among members via the communication I / F 26 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reading unit, conversion unit, storage unit, and sharing unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reading unit reads the contents of the whiteboard using the camera 42 of the smart glasses 214. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the read contents into digital data. The storage unit stores the data in the database 24 of the data processing device 12. The sharing unit shares the data stored in the cloud among members via the communication I / F 26 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reading unit, conversion unit, storage unit, and sharing unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the reading unit reads the contents of the whiteboard using the camera 42 of the headset type terminal 314. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the read contents into digital data. The storage unit stores the data in the database 24 of the data processing device 12. The sharing unit shares the data stored in the cloud among members via the communication I / F 26 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reading unit, conversion unit, storage unit, and sharing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reading unit reads the contents of the whiteboard using the camera 42 of the robot 414. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the read contents into digital data. The storage unit stores the data in the database 24 of the data processing device 12. The sharing unit shares the data stored in the cloud among members via the communication I / F 26 of the data processing device 12.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The conference support system can further include a speech recognition unit. The speech recognition unit can convert statements made during a conference into text data in real time and store it in the cloud. For example, the speech recognition unit can recognize statements made during a conference with high accuracy and convert them into text data. The speech recognition unit can also classify the text data by speaker and organize the content of statements. This makes it easy to automatically record statements made during a conference and refer to them later.

[0109] The reader can also be equipped with a color recognition function. This function can recognize the color of the letters and drawings written on the whiteboard and reflect them in the digital data. For example, the reader can convert red letters into red text data. It can also convert blue drawings into blue image data. This allows the contents of the whiteboard to be digitized in color, allowing the visual information to be saved and shared as is.

[0110] The conversion unit can also recognize the style of handwritten characters and drawings and reflect them in the digital data. For example, the conversion unit can recognize the thickness and italics of handwritten characters and reflect them in the digital data. It can also recognize the thickness and color of lines in handwritten drawings and reflect them in the digital data. This allows the handwritten style to be reflected as is in the digital data, preserving the original atmosphere.

[0111] The conversion unit can further analyze the content of handwritten characters and diagrams and automatically add related information. For example, the conversion unit can analyze the content of handwritten characters and diagrams, search for related web pages and literature, and add links. It can also analyze the content of handwritten diagrams and automatically generate related data and graphs. This makes it possible to automatically add information related to the handwritten content and enrich the information.

[0112] The storage unit may further include a data backup function. The backup function periodically backs up data stored in the cloud to prevent data loss. For example, the storage unit periodically backs up data stored in the cloud to another cloud service. Backups can also be made to local storage. This reduces the risk of data loss and enables safe data management.

[0113] The sharing unit can also have a function for recording data access logs. The access log function records the access history of data stored in the cloud, thereby enhancing security. For example, the sharing unit records information about users who accessed the data and the date and time of access. It can also analyze the access log to detect unauthorized access and implement security measures. This enhances data security and allows for safe sharing.

[0114] The reading unit can estimate the user's emotions and adjust the reading speed based on the estimated user emotions. For example, if the user is anxious, the reading unit increases the reading speed to quickly generate digital data. On the other hand, if the user is relaxed, the reading unit sets the reading speed to normal and prioritizes accuracy. This allows the reading speed to be adjusted according to the user's emotions, and optimal digital data to be generated.

[0115] The reader can estimate the user's emotions and provide feedback based on the estimated user emotions. For example, if the user feels anxious, the reader can provide detailed feedback on the reading results, enhancing the user's sense of security. Alternatively, if the user feels confident, the reader can provide concise feedback, prioritizing efficiency. This allows for appropriate feedback to be provided in accordance with the user's emotions, improving user satisfaction.

[0116] The reading unit can estimate the user's emotions and adjust the reading priority based on the estimated user emotions. For example, if the user is feeling stressed, the reading unit will prioritize reading important content. On the other hand, if the user is relaxed, the reading unit will read all content equally. This allows the reading priority to be adjusted according to the user's emotions, making it possible to quickly obtain important information.

[0117] The reading unit can estimate the user's emotions and adjust the reading accuracy based on the estimated user emotions. For example, if the user is nervous, the reading unit increases the reading accuracy and reduces misrecognition. On the other hand, if the user is relaxed, the reading accuracy is set to normal and processing speed is prioritized. This allows the reading accuracy to be adjusted according to the user's emotions and optimal digital data to be generated.

[0118] The reading unit can estimate the user's emotions and customize the reading method based on the estimated user emotions. For example, if the user is excited, the reading unit applies a detailed reading method to improve accuracy. On the other hand, if the user is calm, the reading unit applies a simplified reading method to prioritize efficiency. This allows the reading method to be customized according to the user's emotions, and optimal digital data to be generated.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The reader reads the characters and drawings written by the user on the whiteboard. For example, the reader can recognize handwritten characters and drawings. Step 2: The conversion unit converts the characters and figures read by the reading unit into digital data. For example, the conversion unit can convert handwritten characters into text data. The conversion unit can also digitize handwritten figures and pictures. Step 3: The storage unit stores the data converted by the conversion unit in the cloud. For example, the storage unit can store the data in the cloud. Step 4: The sharing unit shares the data stored by the storage unit among the members when necessary. For example, the sharing unit can share data stored in the cloud among the members.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the 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.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 reading unit that reads characters or drawings written by a user on the whiteboard; a conversion unit that converts the characters or figures read by the reading unit into digital data; a storage unit that stores the data converted by the conversion unit in a cloud; a sharing unit that shares the data stored by the storage unit among members at a necessary timing; Equipped with A system characterized by:

2. The reading unit Recognize handwritten text or drawings 2. The system of claim 1.

3. The conversion unit Convert handwritten characters into text data 2. The system of claim 1.

4. The conversion unit Digitize hand-drawn diagrams and drawings 2. The system of claim 1.

5. The storage unit Store data in the cloud 2. The system of claim 1.

6. The common part is Share data stored in the cloud among members 2. The system of claim 1.

7. The reading unit Estimate the user's emotions and adjust the reading accuracy based on the estimated user emotions 2. The system of claim 1.

8. The reading unit Select the appropriate reading method based on the frequency of use of the whiteboard and its past usage history 2. The system of claim 1.

9. The reading unit Detects the surface condition of the whiteboard and adjusts the reading algorithm 2. The system of claim 1.

Images