system

Abstract

The system according to the embodiment aims to efficiently recognize small characters, read them aloud, and insert them into a database for inventory management. [Solution] A system according to an embodiment includes a photographing unit, a character recognition unit, an audio output unit, a data insertion unit, and an inventory management unit. The photographing unit photographs characters of a specific size with a camera. The character recognition unit analyzes the image photographed by the photographing unit and recognizes the characters. The audio output unit reads out loud the characters recognized by the character recognition unit. The data insertion unit inserts the character data recognized by the character recognition unit into a database. The inventory management unit manages inventory information based on the data inserted by the data insertion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently recognize small characters, read them aloud, or insert them into a database for inventory management.

[0005] The system according to the embodiment aims to efficiently recognize small characters, read them aloud, and insert them into a database for inventory management. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, a character recognition unit, an audio output unit, a data insertion unit, and an inventory management unit. The photographing unit photographs characters of a specific size with a camera. The character recognition unit analyzes the image photographed by the photographing unit and recognizes the characters. The audio output unit reads out loud the characters recognized by the character recognition unit. The data insertion unit inserts the character data recognized by the character recognition unit into a database. The inventory management unit manages inventory information based on the data inserted by the data insertion unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently recognize small characters, read them aloud, and insert them into a database for inventory control. [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) An AI system according to an embodiment of the present invention recognizes small characters, reads them aloud, inserts data, and manages inventory. This AI system uses a camera to capture small characters, and AI analyzes the captured image to recognize the characters. The recognized characters are read aloud using speech synthesis technology and automatically inserted into a database. The inserted character data is reflected in an inventory management system, enabling real-time inventory status monitoring. For example, text on product labels or packaging is captured with a high-resolution camera, and AI analyzes the image to recognize the characters. The recognized text can be used to provide information to visually impaired individuals via voice using speech synthesis technology. The recognized text data is also registered in a database as product inventory and price information and reflected in the inventory management system. This allows efficient inventory management when checking product inventory in a warehouse by having AI recognize product labels and read the inventory information aloud. Furthermore, when visually impaired individuals check product information, AI can provide the information by reading the text aloud. This allows the AI ​​system to recognize small characters, read them aloud, insert data, and manage inventory.

[0029] The AI ​​system according to the embodiment includes a photographing unit, a character recognition unit, a voice output unit, a data insertion unit, and an inventory management unit. The photographing unit photographs characters of a specific size using a camera. The photographing unit photographs small characters using, for example, a high-resolution camera. The high-resolution camera is, for example, a camera with a resolution of 12 megapixels or more, which clearly captures characters. The character recognition unit analyzes the photographed image using AI and recognizes characters. The AI ​​recognizes characters using, for example, deep learning technology. Deep learning technology learns from large amounts of data and achieves highly accurate character recognition. The voice output unit reads out the recognized characters using voice synthesis technology. The voice synthesis technology converts characters into voice using, for example, text-to-speech synthesis (TTS) technology. TTS technology can read out characters in a natural voice. The data insertion unit inserts the recognized character data into a database. The data insertion unit inserts data into the database using, for example, an API. The API streamlines communication with the database and automates data insertion. The inventory management unit manages inventory information based on the data inserted by the data insertion unit. The inventory management unit synchronizes inventory information in real time, for example. Real-time synchronization keeps inventory information up to date and enables accurate inventory management. This allows the AI ​​system according to the embodiment to recognize small characters, read them aloud, insert data, and manage inventory.

[0030] The photographing unit can photograph characters of a specific size using a camera with a specific resolution. Specific resolutions include, but are not limited to, a resolution of 12 megapixels or more. The photographing unit, for example, photographs small characters using a high-resolution camera. The high-resolution camera is, for example, a camera with a resolution of 12 megapixels or more, and clearly photographs characters. As a result, using a high-resolution camera clearly photographs characters. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input image data photographed by the camera to a generation AI and have the generation AI recognize characters from the image data.

[0031] The character recognition unit can analyze images and recognize characters using AI. Examples of AI include, but are not limited to, deep learning technology and neural network technology. The character recognition unit can analyze images and recognize characters using, for example, deep learning technology. Deep learning technology learns large amounts of data and achieves highly accurate character recognition. The character recognition unit can also recognize characters using neural network technology. Neural network technology is an algorithm that mimics the structure of the human brain and has advanced pattern recognition capabilities. This allows the use of AI to improve the accuracy of character recognition. Some or all of the above-mentioned processing in the character recognition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the character recognition unit can input image data to a generation AI and have the generation AI perform character recognition.

[0032] The voice output unit can read out the recognized characters using voice synthesis technology. Voice synthesis technology includes, but is not limited to, text-to-speech (TTS) technology and a voice synthesis engine. The voice output unit can read out the recognized characters using, for example, text-to-speech (TTS) technology. TTS technology can read out the characters in a natural voice. The voice output unit can also convert the characters into voice using a voice synthesis engine. The voice synthesis engine generates high-quality voice and clearly reads out the recognized characters. Thus, by using voice synthesis technology, the recognized characters can be provided by voice. Some or all of the above-described processing in the voice output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the voice output unit can input the recognized character data to a generation AI and have the generation AI generate the voice.

[0033] The data insertion unit can insert data into the database using an API. Examples of APIs include, but are not limited to, a RESTful API and a SOAP API. The data insertion unit can insert data into the database using, for example, a RESTful API. The RESTful API uses the HTTP protocol to send and receive data, streamlining communication with the database. The data insertion unit can also insert data using a SOAP API. The SOAP API is an XML-based protocol that communicates while maintaining data integrity. As a result, using the API can streamline data insertion into the database. Some or all of the above-described processing in the data insertion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the data insertion unit can input recognized character data into a generation AI and have the generation AI insert the data into the database.

[0034] The inventory management unit can synchronize inventory information in real time. Real time includes, but is not limited to, updates every second or every millisecond. The inventory management unit synchronizes inventory information, for example, every second. Synchronization every second updates inventory information almost in real time, allowing the latest inventory status to be grasped. The inventory management unit can also synchronize inventory information every millisecond. Synchronization every millisecond updates inventory information with extremely high accuracy, enabling real-time inventory management. By synchronizing inventory information in real time, the latest inventory status can be grasped. Some or all of the above-described processing in the inventory management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the inventory management unit can input inventory information obtained from a database into the generation AI and have the generation AI perform real-time synchronization.

[0035] The photographing unit can take multiple photographs from different angles and select the clearest image. Different angles include, but are not limited to, angles such as 30 degrees, 45 degrees, and 60 degrees. The photographing unit can take multiple photographs from angles such as 30 degrees, 45 degrees, and 60 degrees and select the clearest image. This allows the clearest image to be selected by photographing from different angles. Some or all of the above-described processing in the photographing unit can be performed using, or without, the generation AI. For example, the photographing unit can input image data photographed from different angles into the generation AI and have the generation AI select the clearest image.

[0036] The image capture unit can detect variations in ambient light and automatically adjust exposure and white balance. Examples of variations in ambient light include, but are not limited to, changes in light intensity and color temperature. The image capture unit can, for example, detect variations in light intensity and automatically adjust exposure. By detecting variations in light intensity in real time and automatically adjusting exposure, optimal images can be captured. The image capture unit can also detect variations in color temperature and automatically adjust white balance. By detecting variations in color temperature in real time and automatically adjusting white balance, accurate color reproduction can be achieved. This allows optimal images to be captured by adjusting exposure and white balance according to variations in ambient light. Some or all of the above-described processing in the image capture unit can be performed, for example, using or without the generation AI. For example, the image capture unit can input ambient light variation data into the generation AI and have the generation AI adjust the exposure and white balance.

[0037] The image capture unit can detect camera shake and automatically perform image stabilization. Examples of camera shake include, but are not limited to, camera shake and user hand movement. The image capture unit can, for example, detect camera shake and automatically apply electronic image stabilization. Electronic image stabilization uses image processing technology to correct camera shake and generate clear images. The image capture unit can also automatically apply optical image stabilization. Optical image stabilization corrects camera shake by controlling the movement of the lens or sensor. Furthermore, the image capture unit can also correct camera shake by combining multiple images. By combining multiple images, a clear image is generated with the effects of camera shake minimized. This allows for camera shake correction, resulting in a clear image. Some or all of the above-described processing in the image capture unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the image capture unit can input camera shake data to a generation AI and have the generation AI perform image stabilization.

[0038] The image capturing unit can measure the distance to an object and automatically set an appropriate focal length. The distance to an object can range from a few centimeters to a few meters, but is not limited to such examples. The image capturing unit can measure the distance to an object using, for example, laser ranging technology and automatically set an optimal focal length. Laser ranging technology uses laser light to measure the distance to an object with high accuracy. The image capturing unit can also measure the distance to an object using an ultrasonic sensor and automatically set the focal length. The ultrasonic sensor measures the distance to an object using ultrasonic waves. The image capturing unit can also measure the distance to an object using image analysis technology and automatically set the focal length. Image analysis technology analyzes the position and size of an object in an image to estimate the distance. This allows the focal length to be automatically set according to the distance to the object, thereby enabling an image to be captured with an optimal focus. Some or all of the above-described processing in the image capturing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the image capturing unit can input distance data of the object to the generation AI and have the generation AI set the focal length.

[0039] The character recognition unit can apply an algorithm for identifying different fonts or handwritten characters. Examples of different fonts or handwritten characters include, but are not limited to, Arial, Times New Roman, and handwritten characters. The character recognition unit can identify different fonts using, for example, a deep learning algorithm. The deep learning algorithm learns a large amount of font data to achieve highly accurate font identification. The character recognition unit can also identify handwritten characters using a machine learning algorithm. The machine learning algorithm learns the characteristics of handwritten characters to achieve highly accurate handwritten character identification. The character recognition unit can also identify fonts in different languages ​​using a multilingual support algorithm. The multilingual support algorithm learns font data for multiple languages ​​to achieve highly accurate multilingual font identification. This improves the accuracy of character recognition by identifying different fonts or handwritten characters. Some or all of the above-described processing in the character recognition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the character recognition unit can input font data to a generation AI and have the generation AI perform font identification.

[0040] The character recognition unit can remove noise from an image to improve recognition accuracy. Examples of noise include, but are not limited to, blurred images and pixel distortion. The character recognition unit can remove noise from an image using, for example, filtering technology. Filtering technology removes unnecessary pixels from an image to generate a clear image. The character recognition unit can also remove noise using edge detection technology. Edge detection technology emphasizes edges in an image to remove noise. Furthermore, the character recognition unit can also remove noise using contrast adjustment technology. Contrast adjustment technology adjusts the brightness of an image to remove noise. This removes noise from an image, thereby improving character recognition accuracy. Some or all of the above-described processing in the character recognition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the character recognition unit can input image data for noise removal to a generation AI and have the generation AI perform noise removal.

[0041] The character recognition unit can be added with a function to simultaneously recognize multiple languages. Examples of multiple languages ​​include, but are not limited to, English, Japanese, and Chinese. The character recognition unit simultaneously recognizes multiple languages ​​using, for example, a multilingual support algorithm. The multilingual support algorithm learns character data in multiple languages ​​to achieve highly accurate multilingual recognition. The character recognition unit can also simultaneously recognize characters in different languages ​​and convert them into text data. This enables multilingual support by simultaneously recognizing multiple languages. Some or all of the above-described processing in the character recognition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character recognition unit can input character data in multiple languages ​​to a generation AI and have the generation AI perform the recognition.

[0042] The character recognition unit can also simultaneously recognize shapes and icons in an image and convert them into text data. Examples of shapes and icons include, but are not limited to, circles, triangles, arrows, and information icons. The character recognition unit, for example, uses a shape recognition algorithm to recognize shapes in an image and convert them into text data. The shape recognition algorithm detects specific shapes in an image and converts them into text data. The character recognition unit can also recognize icons in an image and convert them into text data using a feature extraction algorithm. The feature extraction algorithm extracts specific features in an image and converts them into text data. This allows for the recognition of shapes and icons in an image, thereby converting more information into text data. Some or all of the above-described processing in the character recognition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character recognition unit can input data of shapes and icons into a generation AI and have the generation AI perform the recognition.

[0043] The voice output unit may be added with a function that allows selection of different voice qualities and accents. Examples of voice qualities and accents include, but are not limited to, male voices, female voices, and regional accents. The voice output unit may select different voice qualities using, for example, a voice library. The voice library contains multiple voice qualities and outputs voice according to the user's selection. The voice output unit may also select different accents using voice synthesis technology. Voice synthesis technology generates voice with a specific accent and realizes voice output according to the user's preferences. This allows voice output according to the user's preferences by selecting different voice qualities and accents. Some or all of the above-described processing in the voice output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the voice output unit may input voice quality and accent data into a generation AI and have the generation AI generate the voice.

[0044] The audio output unit can detect background sound and automatically adjust the volume. Background sound includes, but is not limited to, ambient noise and other sounds. The audio output unit can detect background sound and automatically adjust the volume, for example, using noise canceling technology. The noise canceling technology detects ambient noise and optimizes the volume of the audio output. The audio output unit can also automatically increase or decrease the volume according to the level of the background sound using a volume adjustment algorithm. The volume adjustment algorithm detects the level of the background sound in real time and adjusts the volume of the audio output. This improves the audibility of the audio output by adjusting the volume according to the background sound. Some or all of the above-described processing in the audio output unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the audio output unit can input background sound data to a generation AI and have the generation AI adjust the volume.

[0045] The audio output unit can be added with a function to simultaneously read aloud in multiple languages. Examples of multiple languages ​​include, but are not limited to, English, Japanese, and Chinese. The audio output unit simultaneously reads aloud in multiple languages ​​using, for example, a speech synthesis engine. The speech synthesis engine can generate speech data in multiple languages ​​and simultaneously read aloud. The audio output unit can also alternately output speech in different languages. This enables multilingual support by simultaneously reading aloud in multiple languages. Some or all of the above-described processing in the audio output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the audio output unit can input speech data in multiple languages ​​to a generation AI and have the generation AI generate the speech.

[0046] The audio output unit may be added with a function to emphasize and read specific keywords. Examples of specific keywords include, but are not limited to, important information and keywords specified by the user. The audio output unit may, for example, use audio enhancement technology to emphasize and read specific keywords. Audio enhancement technology adjusts the volume and tone of specific keywords and emphasizes and reads them. The audio output unit may also emphasize and read keywords specified by the user. This allows important information to be emphasized by emphasizing and reading specific keywords. Some or all of the above-described processing in the audio output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the audio output unit may input specific keyword data to the generation AI and have the generation AI execute the voice enhancement.

[0047] The data insertion unit may be added with a function to check the consistency of data and automatically correct it. Data consistency includes, but is not limited to, data consistency and accuracy. The data insertion unit checks the consistency of data, for example, using a database consistency check algorithm. The consistency check algorithm verifies the consistency and accuracy of data and detects inconsistencies. The data insertion unit may also automatically correct data using a data correction algorithm. The data correction algorithm automatically corrects data that detects inconsistencies and maintains accurate data. This improves data accuracy by checking the consistency of data and automatically correcting it. Some or all of the above-described processing in the data insertion unit may be performed, for example, using or without the generation AI. For example, the data insertion unit may input data consistency check data to the generation AI and have the generation AI perform consistency checks and corrections.

[0048] The data insertion unit can add a conversion function to support different database formats. Examples of different database formats include, but are not limited to, SQL, NoSQL, and XML databases. The data insertion unit supports different database formats using, for example, a data conversion algorithm. The data conversion algorithm converts data into different formats to maintain compatibility. The data insertion unit can also support multiple database formats and automatically convert data. This improves data compatibility by supporting different database formats. Some or all of the above-described processing in the data insertion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data insertion unit can input data conversion data to a generation AI and have the generation AI convert the data.

[0049] The data insertion unit can add a function to automatically create a backup of data when inserting data. Data backups include, but are not limited to, periodic backups and real-time backups, for example. The data insertion unit, for example, creates a backup of data periodically. Periodic backups save data at regular intervals to ensure data safety. The data insertion unit can also create a backup of data in real time. Real-time backups create a backup every time a data change occurs, keeping the latest data. This improves data safety by automatically creating a backup of data. Some or all of the above-described processing in the data insertion unit may be performed using, or without, the generation AI, for example. For example, the data insertion unit can input backup data to the generation AI and have the generation AI create a backup.

[0050] The data insertion unit can encrypt data when inserting the data to enhance security. Data encryption includes, but is not limited to, encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The data insertion unit encrypts data using, for example, AES. AES provides high security and protects the confidentiality of data. The data insertion unit can also encrypt data using RSA. RSA uses public key cryptography to achieve secure data communication. This enhances security by encrypting the data. Some or all of the above-described processing in the data insertion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the data insertion unit can input encrypted data to the generation AI and have the generation AI perform the encryption processing.

[0051] The inventory management unit can be added with a function for performing demand forecasting by referencing past inventory data. Past inventory data includes, for example, past sales data and inventory fluctuation data, but is not limited to these examples. For example, the inventory management unit analyzes past sales data to perform demand forecasting. Based on past sales data, future demand is predicted and inventory is optimized. The inventory management unit can also perform demand forecasting based on seasons and events. Demand fluctuations are predicted taking into account the effects of seasons and events. By doing so, the accuracy of demand forecasting is improved by referring to past inventory data. Some or all of the above-described processing in the inventory management unit may be performed using, or without, a generation AI. For example, the inventory management unit can input past inventory data into the generation AI and have the generation AI perform a demand forecast.

[0052] The inventory management unit can add a function to automatically streamline inventory transfers between different warehouses. Examples of inventory transfers between different warehouses include, but are not limited to, detecting inventory imbalances and creating optimal transfer plans. For example, the inventory management unit detects inventory imbalances and creates optimal transfer plans. It efficiently transfers imbalanced inventory to maintain inventory balance. The inventory management unit can also monitor inventory status in real time and perform optimal transfers. It grasps inventory status in real time and transfers inventory as needed. This optimizes inventory transfers between different warehouses, improving the efficiency of inventory management. Some or all of the above-described processing in the inventory management unit may be performed using, or without, a generating AI. For example, the inventory management unit can input inventory transfer data into a generating AI and have the generating AI perform transfer optimization.

[0053] The inventory management unit can be added with a function to manage inventory taking into account product expiration dates and use-by dates. Product expiration dates and use-by dates include, but are not limited to, expiration dates for food and medicine. The inventory management unit, for example, manages inventory taking into account product expiration dates. Products with upcoming expiration dates are prioritized for shipping to prevent inventory loss. The inventory management unit can also manage inventory taking into account product expiration dates. Products with upcoming expiration dates are prioritized for shipping to maintain inventory quality. This improves the accuracy of inventory management by taking product expiration dates and use-by dates into account. Some or all of the above-described processing in the inventory management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the inventory management unit can input data on expiration dates and use-by dates into the generation AI and have the generation AI perform inventory management.

[0054] The inventory management unit can add a function to track product location information and support efficient picking. Product location information includes, but is not limited to, GPS and RFID tags. The inventory management unit tracks product location information using, for example, GPS. GPS determines the exact location of products in real time and supports efficient picking. The inventory management unit can also track product location information using RFID tags. RFID tags automatically detect the location of products and improve picking efficiency. Tracking product location information thereby enables efficient picking. Some or all of the above-described processing in the inventory management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the inventory management unit can input product location information data into the generation AI and have the generation AI perform picking assistance.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The character recognition unit can apply algorithms to distinguish between different fonts and handwritten characters. For example, different fonts such as Arial and Times New Roman can be distinguished. It can also apply algorithms to distinguish between handwritten characters. Furthermore, it can use multilingual algorithms to distinguish between fonts in different languages. This improves the accuracy of character recognition by distinguishing between different fonts and handwritten characters.

[0057] The data insertion unit can add functions to check data consistency and automatically correct it. For example, it can use a database consistency check algorithm to verify the consistency and accuracy of data and detect inconsistencies. It can also automatically correct data using a data correction algorithm. It can also monitor data consistency in real time and make immediate corrections if a problem occurs. This improves data accuracy.

[0058] The inventory management department can add a function to perform demand forecasting by referencing past inventory data. For example, it can analyze past sales data and predict future demand. It can also perform demand forecasting based on seasons and events. Furthermore, it can optimize inventory based on past inventory fluctuation data. This improves the accuracy of demand forecasting and improves the efficiency of inventory management.

[0059] The camera can take multiple photos from different angles and select the clearest image. For example, it can take photos from angles of 30 degrees, 45 degrees, and 60 degrees and select the clearest image. It can also take photos under different lighting conditions and select the most appropriate image. It can also take photos at different focal lengths and select the clearest image. This improves the accuracy of character recognition by selecting the clearest image.

[0060] The voice output unit can be equipped with a function that allows users to select different voice qualities and accents. For example, a male voice or a female voice can be selected. Voices with regional accents can also be selected. Furthermore, the tone and speed of the voice can be adjusted according to the user's preferences. This allows voice output to be tailored to the user's preferences.

[0061] The inventory management section can add a function that manages inventory taking into account product expiration dates and use-by dates. For example, products with an approaching expiration date can be prioritized for shipping. Products with an approaching use-by date can also be prioritized for shipping. Furthermore, an alert can be displayed for products with an approaching expiration date to prevent stock loss. This improves the accuracy of inventory management while maintaining product quality.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The camera captures text of a specific size. For example, a high-resolution camera is used to capture small text. A high-resolution camera has a resolution of 12 megapixels or more, allowing the text to be captured clearly. Step 2: The character recognition unit uses AI to analyze the captured image and recognize characters. Deep learning technology is used to learn from large amounts of data, achieving highly accurate character recognition. Step 3: The voice output unit uses speech synthesis technology to read out the recognized characters. Text-to-speech (TTS) technology is used to convert the characters into speech and read the characters out in a natural voice. Step 4: The data insertion part inserts the recognized character data into the database. The data insertion into the database is automated by using an API. Step 5: The inventory management unit manages inventory information based on the data inserted by the data insertion unit. By synchronizing inventory information in real time and keeping it up to date, accurate inventory management is achieved.

[0064] (Example 2) An AI system according to an embodiment of the present invention recognizes small characters, reads them aloud, inserts data, and manages inventory. This AI system uses a camera to capture small characters, and AI analyzes the captured image to recognize the characters. The recognized characters are read aloud using speech synthesis technology and automatically inserted into a database. The inserted character data is reflected in an inventory management system, enabling real-time inventory status monitoring. For example, text on product labels or packaging is captured with a high-resolution camera, and AI analyzes the image to recognize the characters. The recognized text can be used to provide information to visually impaired individuals via voice using speech synthesis technology. The recognized text data is also registered in a database as product inventory and price information and reflected in the inventory management system. This allows efficient inventory management when checking product inventory in a warehouse by having AI recognize product labels and read the inventory information aloud. Furthermore, when visually impaired individuals check product information, AI can provide the information by reading the text aloud. This allows the AI ​​system to recognize small characters, read them aloud, insert data, and manage inventory.

[0065] The AI ​​system according to the embodiment includes a photographing unit, a character recognition unit, a voice output unit, a data insertion unit, and an inventory management unit. The photographing unit photographs characters of a specific size using a camera. The photographing unit photographs small characters using, for example, a high-resolution camera. The high-resolution camera is, for example, a camera with a resolution of 12 megapixels or more, which clearly captures characters. The character recognition unit analyzes the photographed image using AI and recognizes characters. The AI ​​recognizes characters using, for example, deep learning technology. Deep learning technology learns from large amounts of data and achieves highly accurate character recognition. The voice output unit reads out the recognized characters using voice synthesis technology. The voice synthesis technology converts characters into voice using, for example, text-to-speech synthesis (TTS) technology. TTS technology can read out characters in a natural voice. The data insertion unit inserts the recognized character data into a database. The data insertion unit inserts data into the database using, for example, an API. The API streamlines communication with the database and automates data insertion. The inventory management unit manages inventory information based on the data inserted by the data insertion unit. The inventory management unit synchronizes inventory information in real time, for example. Real-time synchronization keeps inventory information up to date and enables accurate inventory management. This allows the AI ​​system according to the embodiment to recognize small characters, read them aloud, insert data, and manage inventory.

[0066] The photographing unit can photograph characters of a specific size using a camera with a specific resolution. Specific resolutions include, but are not limited to, a resolution of 12 megapixels or more. The photographing unit, for example, photographs small characters using a high-resolution camera. The high-resolution camera is, for example, a camera with a resolution of 12 megapixels or more, and clearly photographs characters. As a result, using a high-resolution camera clearly photographs characters. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input image data photographed by the camera to a generation AI and have the generation AI recognize characters from the image data.

[0067] The character recognition unit can analyze images and recognize characters using AI. Examples of AI include, but are not limited to, deep learning technology and neural network technology. The character recognition unit can analyze images and recognize characters using, for example, deep learning technology. Deep learning technology learns large amounts of data and achieves highly accurate character recognition. The character recognition unit can also recognize characters using neural network technology. Neural network technology is an algorithm that mimics the structure of the human brain and has advanced pattern recognition capabilities. This allows the use of AI to improve the accuracy of character recognition. Some or all of the above-mentioned processing in the character recognition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the character recognition unit can input image data to a generation AI and have the generation AI perform character recognition.

[0068] The voice output unit can read out the recognized characters using voice synthesis technology. Voice synthesis technology includes, but is not limited to, text-to-speech (TTS) technology and a voice synthesis engine. The voice output unit can read out the recognized characters using, for example, text-to-speech (TTS) technology. TTS technology can read out the characters in a natural voice. The voice output unit can also convert the characters into voice using a voice synthesis engine. The voice synthesis engine generates high-quality voice and clearly reads out the recognized characters. Thus, by using voice synthesis technology, the recognized characters can be provided by voice. Some or all of the above-described processing in the voice output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the voice output unit can input the recognized character data to a generation AI and have the generation AI generate the voice.

[0069] The data insertion unit can insert data into the database using an API. Examples of APIs include, but are not limited to, a RESTful API and a SOAP API. The data insertion unit can insert data into the database using, for example, a RESTful API. The RESTful API uses the HTTP protocol to send and receive data, streamlining communication with the database. The data insertion unit can also insert data using a SOAP API. The SOAP API is an XML-based protocol that communicates while maintaining data integrity. As a result, using the API can streamline data insertion into the database. Some or all of the above-described processing in the data insertion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the data insertion unit can input recognized character data into a generation AI and have the generation AI insert the data into the database.

[0070] The inventory management unit can synchronize inventory information in real time. Real time includes, but is not limited to, updates every second or every millisecond. The inventory management unit synchronizes inventory information, for example, every second. Synchronization every second updates inventory information almost in real time, allowing the latest inventory status to be grasped. The inventory management unit can also synchronize inventory information every millisecond. Synchronization every millisecond updates inventory information with extremely high accuracy, enabling real-time inventory management. By synchronizing inventory information in real time, the latest inventory status can be grasped. Some or all of the above-described processing in the inventory management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the inventory management unit can input inventory information obtained from a database into the generation AI and have the generation AI perform real-time synchronization.

[0071] The image capture unit can estimate the user's emotion using a specific algorithm and adjust the timing of capturing images based on the estimated user emotion. The specific algorithm includes, but is not limited to, an emotion recognition algorithm or a machine learning model. The image capture unit can estimate the user's emotion using, for example, an emotion recognition algorithm. The emotion recognition algorithm analyzes the user's facial expressions and voice to estimate the emotion. The image capture unit can also estimate the user's emotion using a machine learning model. The machine learning model learns from large amounts of data to achieve highly accurate emotion estimation. This allows the image capture to be performed at the optimal timing by adjusting the timing of capturing images according to the user's emotion. Some or all of the above-described processing in the image capture unit can be performed using, for example, a generation AI, or without using a generation AI. For example, the image capture unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0072] The photographing unit can take multiple photographs from different angles and select the clearest image. Different angles include, but are not limited to, angles such as 30 degrees, 45 degrees, and 60 degrees. The photographing unit can take multiple photographs from angles such as 30 degrees, 45 degrees, and 60 degrees and select the clearest image. This allows the clearest image to be selected by photographing from different angles. Some or all of the above-described processing in the photographing unit can be performed using, or without, the generation AI. For example, the photographing unit can input image data photographed from different angles into the generation AI and have the generation AI select the clearest image.

[0073] The image capture unit can detect variations in ambient light and automatically adjust exposure and white balance. Examples of variations in ambient light include, but are not limited to, changes in light intensity and color temperature. The image capture unit can, for example, detect variations in light intensity and automatically adjust exposure. By detecting variations in light intensity in real time and automatically adjusting exposure, optimal images can be captured. The image capture unit can also detect variations in color temperature and automatically adjust white balance. By detecting variations in color temperature in real time and automatically adjusting white balance, accurate color reproduction can be achieved. This allows optimal images to be captured by adjusting exposure and white balance according to variations in ambient light. Some or all of the above-described processing in the image capture unit can be performed, for example, using or without the generation AI. For example, the image capture unit can input ambient light variation data into the generation AI and have the generation AI adjust the exposure and white balance.

[0074] The image capture unit can estimate the user's emotions using a specific algorithm and prioritize the subjects to be captured based on the estimated user emotions. Examples of specific algorithms include, but are not limited to, emotion recognition algorithms and machine learning models. The image capture unit can estimate the user's emotions using, for example, an emotion recognition algorithm. The emotion recognition algorithm analyzes the user's facial expressions and voice to estimate the emotions. The image capture unit can also estimate the user's emotions using a machine learning model. The machine learning model learns from large amounts of data and achieves highly accurate emotion estimation. This allows important information to be captured preferentially by prioritizing the subjects to be captured based on the user's emotions. Some or all of the above-described processing in the image capture unit can be performed using, for example, a generation AI, or without a generation AI. For example, the image capture unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0075] The image capture unit can detect camera shake and automatically perform image stabilization. Examples of camera shake include, but are not limited to, camera shake and user hand movement. The image capture unit can, for example, detect camera shake and automatically apply electronic image stabilization. Electronic image stabilization uses image processing technology to correct camera shake and generate clear images. The image capture unit can also automatically apply optical image stabilization. Optical image stabilization corrects camera shake by controlling the movement of the lens or sensor. Furthermore, the image capture unit can also correct camera shake by combining multiple images. By combining multiple images, a clear image is generated with the effects of camera shake minimized. This allows for camera shake correction, resulting in a clear image. Some or all of the above-described processing in the image capture unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the image capture unit can input camera shake data to a generation AI and have the generation AI perform image stabilization.

[0076] The image capturing unit can measure the distance to an object and automatically set an appropriate focal length. The distance to an object can range from a few centimeters to a few meters, but is not limited to such examples. The image capturing unit can measure the distance to an object using, for example, laser ranging technology and automatically set an optimal focal length. Laser ranging technology uses laser light to measure the distance to an object with high accuracy. The image capturing unit can also measure the distance to an object using an ultrasonic sensor and automatically set the focal length. The ultrasonic sensor measures the distance to an object using ultrasonic waves. The image capturing unit can also measure the distance to an object using image analysis technology and automatically set the focal length. Image analysis technology analyzes the position and size of an object in an image to estimate the distance. This allows the focal length to be automatically set according to the distance to the object, thereby enabling an image to be captured with an optimal focus. Some or all of the above-described processing in the image capturing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the image capturing unit can input distance data of the object to the generation AI and have the generation AI set the focal length.

[0077] The character recognition unit can estimate the user's emotion using a specific algorithm and adjust the accuracy of character recognition based on the estimated user's emotion. Specific algorithms include, but are not limited to, emotion recognition algorithms and machine learning models. The character recognition unit can estimate the user's emotion using, for example, an emotion recognition algorithm. The emotion recognition algorithm analyzes the user's facial expressions and voice to estimate the emotion. The character recognition unit can also estimate the user's emotion using a machine learning model. The machine learning model learns large amounts of data to achieve highly accurate emotion estimation. This allows the accuracy of character recognition to be adjusted according to the user's emotion, thereby providing optimal recognition results. Some or all of the above-mentioned processing in the character recognition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the character recognition unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0078] The character recognition unit can apply an algorithm for identifying different fonts or handwritten characters. Examples of different fonts or handwritten characters include, but are not limited to, Arial, Times New Roman, and handwritten characters. The character recognition unit can identify different fonts using, for example, a deep learning algorithm. The deep learning algorithm learns a large amount of font data to achieve highly accurate font identification. The character recognition unit can also identify handwritten characters using a machine learning algorithm. The machine learning algorithm learns the characteristics of handwritten characters to achieve highly accurate handwritten character identification. The character recognition unit can also identify fonts in different languages ​​using a multilingual support algorithm. The multilingual support algorithm learns font data for multiple languages ​​to achieve highly accurate multilingual font identification. This improves the accuracy of character recognition by identifying different fonts or handwritten characters. Some or all of the above-described processing in the character recognition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the character recognition unit can input font data to a generation AI and have the generation AI perform font identification.

[0079] The character recognition unit can remove noise from an image to improve recognition accuracy. Examples of noise include, but are not limited to, blurred images and pixel distortion. The character recognition unit can remove noise from an image using, for example, filtering technology. Filtering technology removes unnecessary pixels from an image to generate a clear image. The character recognition unit can also remove noise using edge detection technology. Edge detection technology emphasizes edges in an image to remove noise. Furthermore, the character recognition unit can also remove noise using contrast adjustment technology. Contrast adjustment technology adjusts the brightness of an image to remove noise. This removes noise from an image, thereby improving character recognition accuracy. Some or all of the above-described processing in the character recognition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the character recognition unit can input image data for noise removal to a generation AI and have the generation AI perform noise removal.

[0080] The character recognition unit can estimate the user's emotion using a specific algorithm and adjust the display method of the recognition result based on the estimated user emotion. Specific algorithms include, but are not limited to, emotion recognition algorithms and machine learning models. The character recognition unit can estimate the user's emotion using, for example, an emotion recognition algorithm. The emotion recognition algorithm analyzes the user's facial expressions and voice to estimate the emotion. The character recognition unit can also estimate the user's emotion using a machine learning model. The machine learning model learns from large amounts of data to achieve highly accurate emotion estimation. This improves visibility by adjusting the display method of the recognition result according to the user's emotion. Some or all of the above-mentioned processing in the character recognition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the character recognition unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] The character recognition unit can be added with a function to simultaneously recognize multiple languages. Examples of multiple languages ​​include, but are not limited to, English, Japanese, and Chinese. The character recognition unit simultaneously recognizes multiple languages ​​using, for example, a multilingual support algorithm. The multilingual support algorithm learns character data in multiple languages ​​to achieve highly accurate multilingual recognition. The character recognition unit can also simultaneously recognize characters in different languages ​​and convert them into text data. This enables multilingual support by simultaneously recognizing multiple languages. Some or all of the above-described processing in the character recognition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character recognition unit can input character data in multiple languages ​​to a generation AI and have the generation AI perform the recognition.

[0082] The character recognition unit can also simultaneously recognize shapes and icons in an image and convert them into text data. Examples of shapes and icons include, but are not limited to, circles, triangles, arrows, and information icons. The character recognition unit, for example, uses a shape recognition algorithm to recognize shapes in an image and convert them into text data. The shape recognition algorithm detects specific shapes in an image and converts them into text data. The character recognition unit can also recognize icons in an image and convert them into text data using a feature extraction algorithm. The feature extraction algorithm extracts specific features in an image and converts them into text data. This allows for the recognition of shapes and icons in an image, thereby converting more information into text data. Some or all of the above-described processing in the character recognition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character recognition unit can input data of shapes and icons into a generation AI and have the generation AI perform the recognition.

[0083] The audio output unit can estimate the user's emotion using a specific algorithm and adjust the tone and speed of the voice based on the estimated user's emotion. Examples of specific algorithms include, but are not limited to, emotion recognition algorithms and machine learning models. The audio output unit can estimate the user's emotion using, for example, an emotion recognition algorithm. The emotion recognition algorithm analyzes the user's facial expressions and voice to estimate the emotion. The audio output unit can also estimate the user's emotion using a machine learning model. The machine learning model learns large amounts of data and achieves highly accurate emotion estimation. This enables more appropriate audio output by adjusting the tone and speed of the voice according to the user's emotion. Some or all of the above-described processing in the audio output unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the audio output unit can input the user's emotion data into a generation AI and have the generation AI adjust the tone and speed of the voice.

[0084] The voice output unit may be added with a function that allows selection of different voice qualities and accents. Examples of voice qualities and accents include, but are not limited to, male voices, female voices, and regional accents. The voice output unit may select different voice qualities using, for example, a voice library. The voice library contains multiple voice qualities and outputs voice according to the user's selection. The voice output unit may also select different accents using voice synthesis technology. Voice synthesis technology generates voice with a specific accent and realizes voice output according to the user's preferences. This allows voice output according to the user's preferences by selecting different voice qualities and accents. Some or all of the above-described processing in the voice output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the voice output unit may input voice quality and accent data into a generation AI and have the generation AI generate the voice.

[0085] The audio output unit can detect background sound and automatically adjust the volume. Background sound includes, but is not limited to, ambient noise and other sounds. The audio output unit can detect background sound and automatically adjust the volume, for example, using noise canceling technology. The noise canceling technology detects ambient noise and optimizes the volume of the audio output. The audio output unit can also automatically increase or decrease the volume according to the level of the background sound using a volume adjustment algorithm. The volume adjustment algorithm detects the level of the background sound in real time and adjusts the volume of the audio output. This improves the audibility of the audio output by adjusting the volume according to the background sound. Some or all of the above-described processing in the audio output unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the audio output unit can input background sound data to a generation AI and have the generation AI adjust the volume.

[0086] The audio output unit can estimate a user's emotion using a specific algorithm and customize the content of the audio based on the estimated user emotion. Examples of specific algorithms include, but are not limited to, emotion recognition algorithms and machine learning models. The audio output unit can estimate a user's emotion using, for example, an emotion recognition algorithm. The emotion recognition algorithm analyzes the user's facial expressions and voice to estimate the emotion. The audio output unit can also estimate a user's emotion using a machine learning model. The machine learning model learns large amounts of data and achieves highly accurate emotion estimation. This enables more appropriate information to be provided by customizing the content of the audio according to the user's emotion. Some or all of the above-described processing in the audio output unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the audio output unit can input user emotion data into a generation AI and have the generation AI customize the content of the audio.

[0087] The audio output unit can be added with a function to simultaneously read aloud in multiple languages. Examples of multiple languages ​​include, but are not limited to, English, Japanese, and Chinese. The audio output unit simultaneously reads aloud in multiple languages ​​using, for example, a speech synthesis engine. The speech synthesis engine can generate speech data in multiple languages ​​and simultaneously read aloud. The audio output unit can also alternately output speech in different languages. This enables multilingual support by simultaneously reading aloud in multiple languages. Some or all of the above-described processing in the audio output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the audio output unit can input speech data in multiple languages ​​to a generation AI and have the generation AI generate the speech.

[0088] The audio output unit may be added with a function to emphasize and read specific keywords. Examples of specific keywords include, but are not limited to, important information and keywords specified by the user. The audio output unit may, for example, use audio enhancement technology to emphasize and read specific keywords. Audio enhancement technology adjusts the volume and tone of specific keywords and emphasizes and reads them. The audio output unit may also emphasize and read keywords specified by the user. This allows important information to be emphasized by emphasizing and reading specific keywords. Some or all of the above-described processing in the audio output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the audio output unit may input specific keyword data to the generation AI and have the generation AI execute the voice enhancement.

[0089] The data insertion unit can estimate the user's emotion using a specific algorithm and adjust the timing of data insertion based on the estimated user's emotion. The specific algorithm includes, but is not limited to, an emotion recognition algorithm or a machine learning model. The data insertion unit can estimate the user's emotion using, for example, an emotion recognition algorithm. The emotion recognition algorithm analyzes the user's facial expressions and voice to estimate the emotion. The data insertion unit can also estimate the user's emotion using a machine learning model. The machine learning model learns from a large amount of data and achieves highly accurate emotion estimation. This allows the data to be inserted at the optimal timing by adjusting the timing of data insertion according to the user's emotion. Some or all of the above-described processing in the data insertion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the data insertion unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data insertion.

[0090] The data insertion unit may be added with a function to check the consistency of data and automatically correct it. Data consistency includes, but is not limited to, data consistency and accuracy. The data insertion unit checks the consistency of data, for example, using a database consistency check algorithm. The consistency check algorithm verifies the consistency and accuracy of data and detects inconsistencies. The data insertion unit may also automatically correct data using a data correction algorithm. The data correction algorithm automatically corrects data that detects inconsistencies and maintains accurate data. This improves data accuracy by checking the consistency of data and automatically correcting it. Some or all of the above-described processing in the data insertion unit may be performed, for example, using or without the generation AI. For example, the data insertion unit may input data consistency check data to the generation AI and have the generation AI perform consistency checks and corrections.

[0091] The data insertion unit can add a conversion function to support different database formats. Examples of different database formats include, but are not limited to, SQL, NoSQL, and XML databases. The data insertion unit supports different database formats using, for example, a data conversion algorithm. The data conversion algorithm converts data into different formats to maintain compatibility. The data insertion unit can also support multiple database formats and automatically convert data. This improves data compatibility by supporting different database formats. Some or all of the above-described processing in the data insertion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data insertion unit can input data conversion data to a generation AI and have the generation AI convert the data.

[0092] The data insertion unit can estimate the user's emotion using a specific algorithm and prioritize data based on the estimated user's emotion. Examples of specific algorithms include, but are not limited to, emotion recognition algorithms and machine learning models. The data insertion unit can estimate the user's emotion using, for example, an emotion recognition algorithm. The emotion recognition algorithm analyzes the user's facial expressions and voice to estimate the emotion. The data insertion unit can also estimate the user's emotion using a machine learning model. The machine learning model learns from large amounts of data and achieves highly accurate emotion estimation. This allows important data to be preferentially inserted by prioritizing data based on the user's emotion. Some or all of the above-described processing in the data insertion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the data insertion unit can input the user's emotion data into the generation AI and have the generation AI determine the data priority.

[0093] The data insertion unit can add a function to automatically create a backup of data when inserting data. Data backups include, but are not limited to, periodic backups and real-time backups, for example. The data insertion unit, for example, creates a backup of data periodically. Periodic backups save data at regular intervals to ensure data safety. The data insertion unit can also create a backup of data in real time. Real-time backups create a backup every time a data change occurs, keeping the latest data. This improves data safety by automatically creating a backup of data. Some or all of the above-described processing in the data insertion unit may be performed using, or without, the generation AI, for example. For example, the data insertion unit can input backup data to the generation AI and have the generation AI create a backup.

[0094] The data insertion unit can encrypt data when inserting the data to enhance security. Data encryption includes, but is not limited to, encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The data insertion unit encrypts data using, for example, AES. AES provides high security and protects the confidentiality of data. The data insertion unit can also encrypt data using RSA. RSA uses public key cryptography to achieve secure data communication. This enhances security by encrypting the data. Some or all of the above-described processing in the data insertion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the data insertion unit can input encrypted data to the generation AI and have the generation AI perform the encryption processing.

[0095] The inventory management unit can estimate a user's emotion using a specific algorithm and adjust the display method of inventory information based on the estimated user's emotion. Specific algorithms include, but are not limited to, emotion recognition algorithms and machine learning models. The inventory management unit can estimate a user's emotion using, for example, an emotion recognition algorithm. The emotion recognition algorithm analyzes the user's facial expressions and voice to estimate the emotion. The inventory management unit can also estimate a user's emotion using a machine learning model. The machine learning model learns from large amounts of data to achieve highly accurate emotion estimation. This improves visibility by adjusting the display method of inventory information according to the user's emotion. Some or all of the above-described processing in the inventory management unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the inventory management unit can input user emotion data into a generation AI and have the generation AI adjust the display method.

[0096] The inventory management unit can be added with a function for performing demand forecasting by referencing past inventory data. Past inventory data includes, for example, past sales data and inventory fluctuation data, but is not limited to these examples. For example, the inventory management unit analyzes past sales data to perform demand forecasting. Based on past sales data, future demand is predicted and inventory is optimized. The inventory management unit can also perform demand forecasting based on seasons and events. Demand fluctuations are predicted taking into account the effects of seasons and events. By doing so, the accuracy of demand forecasting is improved by referring to past inventory data. Some or all of the above-described processing in the inventory management unit may be performed using, or without, a generation AI. For example, the inventory management unit can input past inventory data into the generation AI and have the generation AI perform a demand forecast.

[0097] The inventory management unit can add a function to automatically streamline inventory transfers between different warehouses. Examples of inventory transfers between different warehouses include, but are not limited to, detecting inventory imbalances and creating optimal transfer plans. For example, the inventory management unit detects inventory imbalances and creates optimal transfer plans. It efficiently transfers imbalanced inventory to maintain inventory balance. The inventory management unit can also monitor inventory status in real time and perform optimal transfers. It grasps inventory status in real time and transfers inventory as needed. This optimizes inventory transfers between different warehouses, improving the efficiency of inventory management. Some or all of the above-described processing in the inventory management unit may be performed using, or without, a generating AI. For example, the inventory management unit can input inventory transfer data into a generating AI and have the generating AI perform transfer optimization.

[0098] The inventory management unit can estimate a user's emotions using a specific algorithm and adjust the update frequency of the inventory information based on the estimated user's emotions. Specific algorithms include, but are not limited to, emotion recognition algorithms and machine learning models. The inventory management unit can estimate a user's emotions using, for example, an emotion recognition algorithm. The emotion recognition algorithm analyzes the user's facial expressions and voice to estimate emotions. The inventory management unit can also estimate a user's emotions using a machine learning model. The machine learning model learns from large amounts of data to achieve highly accurate emotion estimation. This allows the latest information to be provided by adjusting the update frequency of the inventory information according to the user's emotions. Some or all of the above-described processing in the inventory management unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the inventory management unit can input user emotion data into the generation AI and have the generation AI adjust the update frequency.

[0099] The inventory management unit can be added with a function to manage inventory taking into account product expiration dates and use-by dates. Product expiration dates and use-by dates include, but are not limited to, expiration dates for food and medicine. The inventory management unit, for example, manages inventory taking into account product expiration dates. Products with upcoming expiration dates are prioritized for shipping to prevent inventory loss. The inventory management unit can also manage inventory taking into account product expiration dates. Products with upcoming expiration dates are prioritized for shipping to maintain inventory quality. This improves the accuracy of inventory management by taking product expiration dates and use-by dates into account. Some or all of the above-described processing in the inventory management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the inventory management unit can input data on expiration dates and use-by dates into the generation AI and have the generation AI perform inventory management.

[0100] The inventory management unit can add a function to track product location information and support efficient picking. Product location information includes, but is not limited to, GPS and RFID tags. The inventory management unit tracks product location information using, for example, GPS. GPS determines the exact location of products in real time and supports efficient picking. The inventory management unit can also track product location information using RFID tags. RFID tags automatically detect the location of products and improve picking efficiency. Tracking product location information thereby enables efficient picking. Some or all of the above-described processing in the inventory management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the inventory management unit can input product location information data into the generation AI and have the generation AI perform picking assistance. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned photographing unit, character recognition unit, audio output unit, data insertion unit, and inventory management unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart device 14. The character recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The audio output unit is realized, for example, by the speaker 40B of the smart device 14. The data insertion unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The inventory management unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned photographing unit, character recognition unit, audio output unit, data insertion unit, and inventory management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart glasses 214. The character recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The audio output unit is realized, for example, by the speaker 240 of the smart glasses 214. The data insertion unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The inventory management unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned photographing unit, character recognition unit, audio output unit, data insertion unit, and inventory management unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the headset type terminal 314. The character recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The audio output unit is realized, for example, by the speaker 240 of the headset type terminal 314. The data insertion unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The inventory management unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned photographing unit, character recognition unit, audio output unit, data insertion unit, and inventory management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the robot 414. The character recognition unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The audio output unit is realized, for example, by the speaker 240 of the robot 414. The data insertion unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The inventory management unit is realized, for example, by the specific processing unit 290 of the data processing device 12.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The camera unit can estimate the user's emotions using a specific algorithm and adjust the timing of shooting based on the estimated user emotions. For example, if the user is nervous, the camera can delay shooting until they relax. Alternatively, if the user is concentrating, the camera can take a photo immediately. Furthermore, if the user is tired, the camera can display a message encouraging the user to take a break. This allows the camera to take a photo at the optimal timing according to the user's emotions.

[0103] The character recognition unit can apply algorithms to distinguish between different fonts and handwritten characters. For example, different fonts such as Arial and Times New Roman can be distinguished. It can also apply algorithms to distinguish between handwritten characters. Furthermore, it can use multilingual algorithms to distinguish between fonts in different languages. This improves the accuracy of character recognition by distinguishing between different fonts and handwritten characters.

[0104] The audio output unit can estimate the user's emotions using a specific algorithm and adjust the tone and speed of the audio based on the estimated user's emotions. For example, if the user is excited, the audio tone can be made calmer. Also, if the user is tired, the audio speed can be slowed down. Furthermore, if the user is concentrating, the audio tone can be made clearer. This makes it possible to output optimal audio according to the user's emotions.

[0105] The data insertion unit can add functions to check data consistency and automatically correct it. For example, it can use a database consistency check algorithm to verify the consistency and accuracy of data and detect inconsistencies. It can also automatically correct data using a data correction algorithm. It can also monitor data consistency in real time and make immediate corrections if a problem occurs. This improves data accuracy.

[0106] The inventory management department can add a function to perform demand forecasting by referencing past inventory data. For example, it can analyze past sales data and predict future demand. It can also perform demand forecasting based on seasons and events. Furthermore, it can optimize inventory based on past inventory fluctuation data. This improves the accuracy of demand forecasting and improves the efficiency of inventory management.

[0107] The camera can take multiple photos from different angles and select the clearest image. For example, it can take photos from angles of 30 degrees, 45 degrees, and 60 degrees and select the clearest image. It can also take photos under different lighting conditions and select the most appropriate image. It can also take photos at different focal lengths and select the clearest image. This improves the accuracy of character recognition by selecting the clearest image.

[0108] The character recognition unit can estimate the user's emotions using a specific algorithm and adjust the accuracy of character recognition based on the estimated user emotions. For example, if the user is in a hurry, additional analysis can be performed to improve recognition accuracy. If the user is relaxed, processing can be performed with normal recognition accuracy. Furthermore, if the user is tired, the time for checking the recognition results can be extended. This enables optimal character recognition according to the user's emotions.

[0109] The voice output unit can be equipped with a function that allows users to select different voice qualities and accents. For example, a male voice or a female voice can be selected. Voices with regional accents can also be selected. Furthermore, the tone and speed of the voice can be adjusted according to the user's preferences. This allows voice output to be tailored to the user's preferences.

[0110] The data insertion unit can estimate the user's emotions using a specific algorithm and adjust the timing of data insertion based on the estimated user emotions. For example, if the user is impatient, data insertion can be delayed. Alternatively, if the user is relaxed, data insertion can be performed immediately. Furthermore, if the user is tired, data insertion can be paused and a message urging the user to take a break can be displayed. This allows data insertion to be performed at the optimal timing according to the user's emotions.

[0111] The inventory management section can add a function that manages inventory taking into account product expiration dates and use-by dates. For example, products with an approaching expiration date can be prioritized for shipping. Products with an approaching use-by date can also be prioritized for shipping. Furthermore, an alert can be displayed for products with an approaching expiration date to prevent stock loss. This improves the accuracy of inventory management while maintaining product quality.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The camera captures text of a specific size. For example, a high-resolution camera is used to capture small text. A high-resolution camera has a resolution of 12 megapixels or more, allowing the text to be captured clearly. Step 2: The character recognition unit uses AI to analyze the captured image and recognize characters. Deep learning technology is used to learn from large amounts of data, achieving highly accurate character recognition. Step 3: The voice output unit uses speech synthesis technology to read out the recognized characters. Text-to-speech (TTS) technology is used to convert the characters into speech and read the characters out in a natural voice. Step 4: The data insertion part inserts the recognized character data into the database. The data insertion into the database is automated by using an API. Step 5: The inventory management unit manages inventory information based on the data inserted by the data insertion unit. By synchronizing inventory information in real time and keeping it up to date, accurate inventory management is achieved.

[0114] 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.

[0115] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 camera unit that takes pictures of characters of a specific size; a character recognition unit that analyzes the image captured by the image capturing unit and recognizes characters; a voice output unit that reads out the characters recognized by the character recognition unit; a data insertion unit that inserts the character data recognized by the character recognition unit into a database; an inventory management unit that manages inventory information based on the data inserted by the data insertion unit; A system characterized by:

2. The imaging unit is Taking a photo of text of a specific size using a camera with a specific resolution 2. The system of claim 1.

3. The character recognition unit Uses AI to analyze images and recognize characters 2. The system of claim 1.

4. The audio output unit Uses speech synthesis technology to read out the recognized characters 2. The system of claim 1.

5. The data insertion unit Inserting data into the database using the API 2. The system of claim 1.

6. The inventory management unit Sync inventory information in real time 2. The system of claim 1.

7. The imaging unit is The system estimates the user's emotions using a specific algorithm and adjusts the timing of the photo shoot based on the estimated user emotions.

2. The system of claim 1.

8. The imaging unit is Take multiple shots from different angles and select the clearest image 2. The system of claim 1.

9. The imaging unit is Detects changes in ambient light and automatically adjusts exposure and white balance 2. The system of claim 1.

10. The imaging unit is The system estimates the user's emotions using a specific algorithm and prioritizes the subjects to be photographed based on the estimated user emotions.

2. The system of claim 1.

Images