50 results about "Language translation" patented technology

Disclosed are a method, system, and apparatus of a biometric sensor of a tactical gear to monitor health and stress of a wearer. In one embodiment, personal protective equipment includes a language translator module integrated in a tactical gear, a biometric sensor, and a responsive device integrated in the tactical gear. A wearer bi-directionally communicates with an individual in an ambient environment using any language other than a primary language spoken by the wearer when the language translator module is activated. The biometric sensor detects the health condition of the wearer. The responsive device vibrates to notify the wearer when the biometric sensor detects an elevated stress level of the wearer of the tactical gear. The biometric sensor is detachable from the tactical gear and placeable on an injured person nearby to the wearer through an armband extendable from the biometric sensor when it is removed from the tactical gear.

This application discloses a method and system for multilingual enhancement based on a large language model using model ensemble. The system includes: a pre-trained multilingual translation model, a semantic representation mapping module, and a large language model. The multilingual translation model is used for multilingual semantic modeling and language generation, including a multilingual encoder module and a multilingual decoder module. The semantic representation mapping module is used to transform the latent space representations of different models into an interactive unified semantic space based on a cross-model representation mapping mechanism. The output of the multilingual encoder is mapped to the unified semantic representation space of the large language model, and the mapped semantics are input into the large language model to perform language-independent instruction understanding. The intermediate semantic representation output by the large language model is mapped and transformed to a cross-attention representation space, generating the final output text under the target language distribution. The system of this application outperforms existing technologies in terms of efficiency, stability, and generation quality in multilingual capability extension.

A mobile electronic code device includes a personal electronic device carried by a recipient user. The personal electronic device has a transceiver which receives a communication signal from an extrinsic communication network. An electronic memory is integrated into the personal electronic device which stores a language translation program. The language translation program translates a message in the communication signal into a coded audio signal to audibly communicate the message to the recipient user in a code that only the recipient user can understand thereby enhancing privacy for the recipient user. A speaker is integrated into the personal electronic device to audibly emit the coded audio signal to the recipient user through the plurality of sound holes.

This invention belongs to the field of natural language processing and neural machine translation technology, and discloses a low-resource neural machine translation method that integrates a shared semantic space and bidirectional iterative generative optimization (BIGO). The method includes constructing a shared semantic space module and a bidirectional iterative generative optimization module. First, this invention utilizes singular value decomposition and entropy regularization for optimal transport to map the source language and the target low-resource language into a unified semantic space, achieving high consistency between the two languages ​​at the representation level, thereby significantly improving the model's cross-language generalization ability under conditions of insufficient data. Subsequently, this invention continuously improves translation quality through alternating training of forward and backward Transformer models, in a loop of generating pseudo-bilingual corpora, backward reconstruction, and joint forward and backward optimization. This allows the model to correct errors generated in the previous iteration and enhance semantic consistency in each iteration. This invention can stably achieve dual convergence of model parameters and semantic alignment matrix, significantly improving the accuracy, robustness, and long sentence consistency of low-resource language translation, and effectively reducing training instability and overfitting.

We provide the system. [Solution] A means of entering user information and creating a travel itinerary, Means for performing multilingual translation, Means of providing information on local rules and customs, A means of providing information on the nearest support organization in an emergency, A means of dynamically suggesting sightseeing routes, local attractions, and events based on the user's current location and interests, A system that includes a means of providing a quiz function about local culture and customs.

The present application relates to a large model Lao translation method and system based on agent cooperation and cross-language semantic alignment, belonging to the field of natural language processing and machine translation. In the independent multilingual translation generation stage, the target Lao translation corresponding to the source sentence, the reference language translation and the linguistically similar language translation are generated respectively; cross-language semantic alignment and correction are carried out, the English translation is taken as the semantic alignment reference, the Thai translation provides morphological and syntactic guidance, and through the iterative information interaction between agents, the Lao translation is continuously evaluated and corrected; in the semantic clarification and strengthening stage, the key semantic fragments of the source sentence are extracted by analyzing the agent, the overall structure is simplified, and the Lao agent is assisted to generate the final high-fidelity translation result. The present application significantly improves the semantic accuracy and structural rationality of the Lao translation result; effectively solves the semantic illusion problem easily produced by the large language model in the Lao machine translation scene due to data scarcity.

This application proposes a smart wearable device and a multi-party calling method and system, belonging to the field of multi-party calling technology. The multi-party calling method involves a smart wearable master device uniformly collecting user voice signals from itself and each smart wearable slave device, identifying the target language of each device, and uniformly interacting with the cloud. The cloud performs targeted cross-language translation based on the target language of each device. The smart wearable master device then performs speech synthesis and enables local playback on the master device and targeted transmission and playback on the slave devices. Through accurate device-specific language translation in the cloud and targeted synthesis and playback on the master device, accurate translation and efficient transmission of each user's voice to the corresponding target language of all other users in a multi-party call can be achieved. This allows users speaking different languages ​​to complete barrier-free multi-party voice calls through smart wearable devices, significantly improving the user experience and adaptability of smart wearable devices in multi-party cross-language calling scenarios.

1. The name of the design product: translation machine (MT-Z10). 2. The use of the design product: for language translation. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

This application provides an image forming apparatus, a document processing method, and an image forming system. The image forming apparatus includes: an acquisition module for acquiring a target document; a language detection module for detecting whether the language of the target document is a first language; an output module for outputting a prompt message when the language of the target document is not the first language, the prompt message indicating to the user whether to translate the target document; and an execution module for translating the language of the target document into the first language when the user selects to translate the target document. This application can automatically identify whether a user has a translation need and provide translation services to the user.

The application provides a large model low-resource language translation method and system based on reasoning and retrieval fusion, which comprises the following steps: obtaining enhancement information, which contains retrieved similar parallel sentence pairs and extracted auxiliary knowledge; performing first-stage implicit correction, constructing a comparative demonstration context containing retrieved example source sentences, auxiliary knowledge, initial translation and standard reference translation, inputting the large model with the to-be-translated source sentence and initial translation, and generating first-stage corrected translation; performing second-stage explicit correction, performing error detection and labeling on the first-stage corrected translation based on the large model according to multi-dimensional quality measurement standards, generating structured feedback, and inputting the large model again to generate second-stage corrected translation. The application effectively improves the accuracy and robustness of low-resource language translation.

This invention discloses a sign language translation glove and a sign language recognition method, including a glove body, a curvature sensor disposed on the fingers, an inertial sensor disposed on the back of the hand, a data processing module, and an output module. The data processing module extracts hand shape features based on finger bending data collected by the curvature sensor, extracts motion features based on hand motion data collected by the inertial sensor, and extracts coupling features based on the relative temporal relationship between hand shape features and motion features. Then, based on the hand shape prototype, motion prototype, and coupling prototype, a joint decision is made to output the sign language recognition result. This invention can improve the ability to distinguish similar gestures and supports the rapid expansion of new sign language words.

A sign language translation method, system, electronic device and storage medium relate to the field of video natural language generation, and alleviate the problems that existing sign language translation technology cannot consider gesture dynamic change and semantic information. A sign language translation method comprises a video processing step, uniformly sampling an RGB sign language video to be recognized to obtain a visual image sequence; a visual feature extraction step, obtaining visual features by passing the visual image sequence through a visual encoder; a language feature extraction step, encoding a plurality of predetermined sign language labels to obtain a plurality of corresponding text semantic features; a semantic alignment step, aligning the text semantic features and the visual features to obtain aligned text semantic features and visual features; and a translation step, completing the translation of the RGB sign language video to be recognized. The present application is suitable for the field of sign language recognition and translation, and is especially suitable for the field of semantic understanding of continuous and natural sign language videos.

1. Name of the product in this design: Translator (F02). 2. Purpose of this design: To translate a received language into other languages. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The present application relates to the technical field of natural language processing, and provides a translation method and device, electronic equipment and a storage medium, wherein the method comprises: determining to-be-translated data and a scene label corresponding to the to-be-translated data; performing dependency syntax analysis on the to-be-translated data to obtain syntax tree data representing the grammatical dependency relationship between words in the to-be-translated data; and translating the to-be-translated data based on the scene label and the syntax tree data to obtain target translation data corresponding to the to-be-translated data, which not only effectively solves the problems of part-of-speech misjudgment of polysemous words and structural confusion in translation from an agglutinative language to a fusional language, but also takes into account the grammatical accuracy of the language structure and the business professionalism of the specific field expression, greatly improving the translation accuracy and the final text quality of machine translation in the vertical field and complex conversational scenarios.

The application discloses an intelligent real-time language translation system and method and a terminal, and belongs to the technical field of language translation, and comprises a multi-modal acquisition module, a voiceprint feature extraction module, an emotional and semantic fusion module, a dynamic context module, a cross-language translation engine, a self-defined language rule engine, a real-time voiceprint cloning engine, an emotional migration module, a culture adaptation module and a multi-modal output module. The system supports multi-person dialogue scenes and can be continuously iteratively improved through an adaptive optimization module. The system effectively solves the problems of loss of emotional information, single tone, lack of personalized customization and cultural inadaptation in the prior art, and realizes natural, accurate and personalized intelligent cross-language communication.

This invention provides a sign language recognition and interaction system based on edge-cloud collaboration, belonging to the field of artificial intelligence technology. It includes an image acquisition module for acquiring images of gestures from deaf and mute individuals and extracting keyframe images from these images; a client processing module, wirelessly connected to the image acquisition module, for receiving keyframe images, extracting key hand points from the keyframe images, and transmitting the extracted key hand point sequence to the cloud; a cloud processing module for processing the transmitted key hand point sequence using a sign language recognition model, recognizing the gestures as text based on the key hand point sequence; the cloud processing module also transmits the text to the client processing module, which in turn performs voice playback of the text and simultaneously acquires the voice response from a hearing person, converting the voice response into text for display. This system improves the portability and accuracy of sign language translation, reduces production costs, and improves the quality of life for deaf and mute individuals.

The application provides a language translation model training method, an electronic device and a storage medium, and relates to the technical field of machine learning. The method inputs a pseudo-parallel training sample set equivalent to the second training sample set into a forward first translation model, that is, a high-resource translation model, while training a second translation model, that is, a low-resource translation model, using the second training sample set, and guides the training of the initial second model according to the prediction result of the pseudo-parallel source language of the forward first translation model. Since the translation performance of the high-resource translation model is better, the translation prediction result of the high-resource translation model is used to guide the translation prediction result of the low-resource translation model, so that the information migration of the high-resource translation model is more comprehensive. Moreover, the loss information is calculated by combining the prediction results of the low-resource translation model and the high-resource translation model, the amount of information contained in the loss information is richer, and the problem of overfitting in the training process of the low-resource translation model is alleviated.

Various embodiments of the present disclosure are directed to an inflight entertainment computer server. The IFE computer server includes at least one network interface, at least one processor connected to communicate through the at least one network interface with video display units and / or personal electronic devices, and at least one memory. The at least one memory including a language conversion module operative to translate an announcement including digitized speech and / or text from a first language to an available set of translated languages; a passenger preferred language module operative to identify a set of passenger preferred languages based on information obtained for passengers onboard the aircraft; and an announcement distribution module operative to receive the announcement in the first language generated by a crew member or by a controller, and process the announcement in the first language through the language conversion module to output a set of translated announcements.

The present disclosure provides a geometric question translator training data construction method, system, device and medium, the training data construction method comprising: obtaining current input data; inputting the current input data into a pre-trained language translation model to output a current formal language corresponding to a to-be-translated geometric question; performing a syntax verification operation on the current formal language; in response to the current formal language passing the syntax verification operation, obtaining a current natural language corresponding to the current formal language; performing a semantic verification operation on the current natural language; and in response to the current natural language passing the semantic verification operation, constructing training data for the geometric question translator based on the current formal language. The present disclosure realizes the automatic generation of the current formal language, guarantees the grammatical accuracy of the current formal language and the semantic accuracy of the current natural language, makes the training data of the geometric question translator have very high accuracy and credibility, and lays a foundation for the accuracy of the geometric question translator.

This disclosure relates to dynamic rear-view display feedback. Overall, this disclosure relates to user interfaces and techniques for displaying content using a rear-view display of a computer system. In some embodiments, the rear-view display is used to provide camera feedback. In some embodiments, the rear-view display is used to provide contextual display of digital decorations. In some embodiments, the rear-view display is used to provide status information based on the usage context of the computer system. In some embodiments, the rear-view display is used to provide language translation.

1. Name of the product subject to this design: Translator (F01-01). 2. Purpose of this design: To translate a received language into other languages. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This invention discloses a method for accurate translation of legal terminology based on neural networks, belonging to the field of natural language processing technology. Specifically, it includes: first, identifying legal terminology units and article citation markers in the source language legal text, and constructing logical dependency chains by parsing the citations; then, fusing each terminology unit with its local context and the remote article content associated with the dependency chain to form an extended context sequence; this sequence is input into a neural network translation model integrating a logical constraint prediction head; during decoding and generation, the prediction head determines whether word generation should be subject to logical constraints, and if constrained, extracts target language constraints from the dependency chain, injecting them into the probability distribution of the decoder in the form of a dynamic bias vector, thereby guiding the generation; finally, it outputs the target language translation and the association annotations between the translated words and the source language logical elements; this invention achieves the preservation and accurate transmission of the cross-article logical structure of legal text.

Language Translation Device

A computer-implemented method is disclosed, comprising: receiving, by one or more large language models, LLMs, one or more natural language system prompts which, when processed, causes the one or more LLMs to perform a system method. The system method comprises: detecting the source language of a source text, wherein the source text is provided in natural language; determining, based on a user prompt, a target language; generating, a first translation by translating the source text from the source language to the target language; generating, a back translation by translating the first translation from the target language to the source language; comparing, the back translation with the source text to determine one or more inconsistencies; determining whether the one or more inconsistencies exceed an error threshold; based on the one or more inconsistencies exceeding the error threshold, generating, based on the one or more inconsistencies, a second translation by translating the source text from the source language to the target language; and based on the one or more inconsistencies not exceeding the error threshold, outputting, to a user, the first translation; processing, by the one or more LLMs, the one or more system prompts; receiving, by the one or more LLMs, via a user device, a user prompt comprising the source text; and providing, by the one or more LLMs, using the system method, the first translation or the second translation.

1. The name of the design product: translation machine (01). 2. The use of the design product: for running programs, data processing, language translation, and text transcription. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

1. The name of the design product: dictionary pen (six-in-one). 2. The use of the design product: the design product is used as a dictionary pen for language translation, inquiry, and voice reading. 3. The design points of the design product: the overall shape of the product. 4. The picture or photo that best indicates the design points: perspective view 1.

This disclosure provides a method, system, device, and medium for constructing training data for a geometry problem translator. The training data construction method includes: acquiring current input data; inputting the current input data into a pre-trained language translation model, outputting the current formal language corresponding to the geometry problem to be translated; performing grammatical verification on the current formal language; in response to the current formal language passing the grammatical verification, acquiring the current natural language corresponding to the current formal language; performing semantic verification on the current natural language; and in response to the current natural language passing the semantic verification, constructing training data for the geometry problem translator based on the current formal language. This disclosure achieves automated generation of the current formal language, ensuring the grammatical accuracy of the current formal language and the semantic accuracy of the current natural language, making the training data for the geometry problem translator highly accurate and reliable, laying the foundation for the accuracy of the geometry problem translator.