20 results about "Human language" patented technology

A system and method is described for internationalization of web pages by extracting translatable content from extensible mark-up language (XML), or similar data-centric meta-language representations of web pages or data used to build web pages. The extracted translatable content is stored in a translation task repository (TTR) accessible by the web developer and the translator. The XML representation is then modified to include selection control logic to select the appropriate translations for insertion into the final web page. The translator accesses the TTR to translate the appropriate content and saves the translations back to the TTR associated with the original translatable data. The translations are obtained from the TTR as selection cases for the selection control logic of the XML representation. As the XML is converted into the web source code, the selection logic and translations are embedded therein facilitating building the web site in multiple different languages.

This disclosure provides a method, apparatus, electronic device, and storage medium for animal language conversion, relating to the field of artificial intelligence technology, specifically machine learning, deep learning, and natural language processing. The specific implementation involves: acquiring multimodal data related to the animal, including animal vocal data, animal behavioral data, and animal physical characteristics data; preprocessing the multimodal data to obtain fused multimodal data; identifying the animal's current emotion based on the fused multimodal data to obtain an emotion recognition result; and performing semantic mapping and language translation on the emotion recognition result to convert the animal language into human language, obtaining a language conversion result. This disclosure can accurately identify the animal's current emotional state and convert it into human language, thereby achieving deeper emotional communication and understanding between animals and humans, and improving the accuracy and efficiency of cross-species communication.

The invention relates to the field of knowledge questions and answers, in particular to a disease and insect pest questions and answers system fusing a knowledge graph and a large model. The uniqueness and accuracy of underlying facts are ensured through an automatic extraction and disambiguation mechanism driven by a large model, and the illusion phenomenon of the large model is effectively inhibited in combination with hard constraints of a knowledge graph, so that the accuracy and preciseness of disease and pest control suggestions are superior to those of a traditional data-driven method; the deep semantic understanding capability of the large language model is utilized, oral expressions, wrongly written characters and implicit intentions of peasant households are accurately recognized, compared with syntactic rule matching in the prior art, the misdiagnosis and omission ratio can be greatly reduced, and higher practicability is shown in a complex consultation scene; structured multi-source data representing expert knowledge is seamlessly fused into a large model generation process, an output result has professional constraints and conforms to human language habits, logic is smooth, the sense of speech is natural, and the interactive experience is far better than that of stiff template reply in the prior art.

The invention discloses a syntactic selection simulation method, system and equipment based on event structure and syntactic frequency interaction. The method belongs to the technical field of dynamic simulation of Chinese syntax generation, and comprises the following steps of: 1, analyzing a question element role relationship of an input event, and generating an event structure vector; 2, calling a syntactic frequency database, and obtaining the use frequency weight of the candidate sentence pattern; 3, calculating the verb semantic distance between the target event and the historical event; 4, dynamically generating a syntactic selection probability through interaction; 5, when the events are the same and verbs are multiplexed, the syntax selection probability is adjusted. Through multi-dimensional interactive modeling of the event structure, the syntactic frequency and the semantic information, more accurate and natural syntactic selection simulation conforming to human language behavior rules is realized, and the intelligent level and simulation authenticity of a language generation system are remarkably improved.

An object of the system according to the embodiment is to analyze a cry or an action of a pet and accurately convert an intention or an emotion into a human language.SOLUTION: A system includes a collection unit, an analysis unit, an estimation unit, and a conversion unit. The collection unit collects a cry or an action. The analysis unit analyzes the data collected by the collection unit. The estimation unit estimates an intention or a feeling on the basis of an analysis result obtained by the analysis unit. The conversion unit converts the intention or the feeling estimated by the estimation unit into human language.SELECTED DRAWING: Figure 1

It enables two-way communication with animals. [Solution] The system includes means for acquiring animal sound data and means for transmitting the sound data to a server. The system includes means for analyzing the audio data on a server to identify the animal's emotions or intentions, means for translating the identified emotions or intentions into human language and transmitting them to a terminal, and means for converting the human language into audio that the animal can understand and outputting it to the animal.

This invention provides a real-time voice interaction method and system based on a large-scale model. It collects multi-turn historical dialogue data from users, enabling deep understanding of the context and adjusting subsequent strategies based on the evolution of historical dialogue content. Through precise construction of dynamic context and the powerful semantic understanding capabilities of the large-scale model, the system can better understand user intent and dialogue logic, generating responses that are more in line with human language habits, significantly improving the naturalness of the interaction. Optimizing the intelligent decision-making of the large-scale model based on reinforcement learning algorithms and human feedback allows the model to continuously learn during real-time interaction, adjusting response strategies according to user feedback and dialogue progress, improving the relevance, coherence, and user satisfaction of responses. By setting up an interruption mechanism, the system can effectively handle user interruption intentions during real-time voice interaction, ensuring the effectiveness of real-time voice interaction and thus improving its accuracy and fluency.

Automatically detecting, tagging, and removing a human language stored in an audio file, including: training an application for detecting the human language using machine learning; loading each channel of the audio file into the trained application, wherein the audio file is an audio deliverable for motion picture and television; setting parameters and filtering each channel of the audio file to detect and tag the human language; and generating a list of timecodes and the corresponding human language detected.

The invention discloses a digital semantic understanding and conversion system and method based on multi-modal feature fusion, the contribution degrees of three types of features including semantics, mathematical and context in different digital type recognition are displayed through a multi-modal feature importance thermodynamic diagram, a macroscopic-microscopic double-layer classification structure is adopted, error propagation is effectively blocked, and the recognition efficiency is improved. Through three-way cooperation of a rule engine, a statistical model and a semantic engine, high-robustness decision making is realized, and an intelligent conversion routing and grammar adaptation mechanism is developed for a digital semantic conversion link. The method can effectively solve the technical bottlenecks of insufficient accuracy, poor expandability, strong training data dependence and the like of a traditional method in complex number type recognition, and is suitable for multiple application scenes such as TTS voice generation, natural language generation, financial document processing, education science and technology and the like; accurate conversion from digital character strings to semantic expressions conforming to human language habits can be achieved.

A vehicle to everything (V2X) apparatus and a method performed therein generate a message for exchanging information with at least one external object or controlling the external object, the message having the form of human language; and transmit the generated message to an external V2X apparatus equipped in the external object.

Computer-implemented systems and methods use a Large Language Model (LLM) for converting a legacy computer program in a first language to a human-language description of the legacy computer program, which description can be validated as being an accurate description of the legacy computer program. Once validated, the human-language description can be converted, again using an LLM, to a computer program in a target programming language. An LLM can also be used to generate test scripts for the new target-language program to test the performance of the target-language program in a production environment. An LLM can also be used to reconcile outputs from the legacy program to the new target program, such as on a function-by-function basis. If the differences between the outputs (if any) are sufficiently negligible, the legacy computer program can be decommissioned, and the new, target language program can be used in production.

The purpose of the present invention is to construct a system capable of improving the happiness of a rider by effectively and efficiently enhancing the sense of cooperation between a straddle-type vehicle and the rider, and to reduce the load of hardware resources constituting the system. In the system according to the present invention, character simulation presentation data is generated using data acquired by a data acquisition process and character simulation data. The hint data is provided to a large language model, and role simulation language interpretation data is obtained from the large language model. Using the language interpretation data, alternating human language information indicative of a language interpretation result and simulated by a character is output from an output device. When data for setting character simulation is input, character simulation data is updated based on the data.

The embodiments of the present disclosure relate to a test text generation method and apparatus, a device, a medium, and a program product. The method comprises determining the value of a label for an original test text, the label indicating a natural language property. The method further comprises, on the basis of the value, determining a prompt strategy for the original test text. In addition, the method further comprises generating a target test text on the basis of the original test text and the prompt strategy. By means of the present method for generating test text using a natural language property, test text that conforms to human language variations can be produced, thereby improving testing effectiveness and user experience.

Devices, systems, and methods for executable binary file description are provided. A method can include generating or identifying an execution trace for the executable binary file. The method can include operating, based on the execution trace, a transformer model that generates a human language program description of the execution trace.

Daily reports from borehole or reservoir operations can be in various languages, use various sets of activity codes, be missing activity codes, or specify incorrect activity codes. By training a natural language model on a human language and then training the model on borehole and reservoir specific operations, activities, events, and occurrences, the original daily report can be transformed into a standardized daily report. The standardized daily report can use a standard set of activity codes from the client, operator, reference, industry standards, or a combination thereof. This can reduce the time to process a received daily report from days or weeks to a few minutes of processing time. Decision makers can then use the standardized daily report to make faster and more accurate decisions regarding the operations at the borehole or reservoir, such ordering replacement parts sooner or modifying a borehole operation plan.

The invention discloses a multi-agent communication protocol self-emergence method based on a large language model, and belongs to the technical field of multi-agent communication, and the method comprises the following steps: S1, in a memory bank, retrieving and matching a corresponding sending message for a speaker according to a target object; and S2, matching a corresponding object for the listener according to the message sent by the speaker, and carrying out communication. By simulating a memory and generation mechanism in human language learning, the intelligent agent can autonomously evolve a coordinated communication protocol under the condition that a vocabulary library is not predefined, does not need to depend on large-scale pre-training language knowledge, can accept flexible natural language input, and improves generalization and mobility of the communication protocol.

The invention provides an Internet of Things semantic control method based on large language model tool calling, and the method specifically comprises the following steps: S1, defining an Internet of Things equipment control API as a tool set which can be understood by a large language model, and registering the tool set in the pre-trained large language model; and S2, calling the Internet of Things equipment control tool according to a user instruction based on the pre-trained large language model. According to the proposal of the invention, a big language model tool calling method is adopted, semantic understanding and analysis are performed on prompt words input by a user, and corresponding Internet of Things equipment is called to control APIs or functions, so that the purpose of being closer to natural interaction of human languages is achieved.

The invention provides an object detection method and system, a large language model is introduced in an object detection process, the scheme provided by the invention supports a user to input information of a detection request, the large language model detects each target object needing to be detected by the user in a target image based on the detection request of the user, and the detection efficiency is improved. Therefore, the detection result of each target object is obtained. According to the scheme provided by the invention, understanding of object detection requirements indicated by any human language vocabulary is supported, detection of any object is realized, the object detection efficiency is improved, in addition, a user can freely input own detection requirements, and the service experience of the user is improved.

Daily reports from borehole or reservoir operations can be in various languages, use various sets of activity codes, be missing activity codes, or specify incorrect activity codes. By training a natural language model on a human language and then training the model on borehole and reservoir specific operations, activities, events, and occurrences, the original daily report can be transformed into a standardized daily report. The standardized daily report can use a standard set of activity codes from the client, operator, reference, industry standards, or a combination thereof. This can reduce the time to process a received daily report from days or weeks to a few minutes of processing time. Decision makers can then use the standardized daily report to make faster and more accurate decisions regarding the operations at the borehole or reservoir, such ordering replacement parts sooner or modifying a borehole operation plan.

The present invention provides a content generation system that can use an LLM to generate more human-understandable content at high efficiency. Communicative human language information for inclusion in a content is acquired by means of an LLM using communicative human language information input via a user interface while communicative human language information for selecting visualization software capable of generating visual information for inclusion in a content is acquired by means of the LLM using communicative human language information input via the user interface. The visualization software is selected on the basis of the acquired communicative human language information, the selected visualization software is operated by means of text information obtained on the basis of LLM output in response to the communicative human language information input via the user interface, and visual information for inclusion in a content is acquired. The content including at least a portion of the acquired communicative human language information and at least a portion of the acquired visual information is generated and output.