40 results about "Data language" patented technology

Searches based on an incoming ticket identify quality ticket enrichment data using a vector database. Language model prompts target particular kinds of quality ticket data. The incoming quality ticket, or a search result ticket, or both, are enriched using enrichment data, such as a user intent identification, a workaround suggestion, a resolution description, a target audience description, a relevance description, an impact description, a description of missing resolution facilitation information, an association between the incoming quality ticket and the search result ticket, a user sentiment identification, a tag suggestion, or a feedback utility estimate. The enrichment reduces engineering and support burdens, and facilitates faster more effective resolution of the problem or the request that is stated or implied in the incoming quality ticket. Duplicate tickets are merged or removed. Tickets are prioritized. Missing problem resolution information is identified and requested sooner.

The invention relates to a multi-modal large model robot control method based on meta-learning fine tuning, and the method comprises the steps: carrying out the meta-learning pre-training of an OpenVLA model, and obtaining a pre-trained VLA model; performing task specific fine tuning to obtain a fine-tuned VLA model; and inputting the current visual data, the language cue word, the robot sensor data and the task target data graph into the fine-tuned VLA model, and outputting to obtain a decision control instruction for correspondingly controlling the working state of the robot. Compared with the prior art, the method has the advantages that meta-learning pre-training is carried out on diversified tasks, and universal features and strategies are extracted, so that the generalization ability of the model is improved; in addition, in a specific task, rapid fine tuning is performed through a small number of new samples, so that the model can adapt to new task requirements in a short time; and multi-modal information of vision, character cues and robot control is combined, so that the perception and decision-making capabilities of the model are improved, and the adaptability and operation efficiency of the robot in a complex environment are effectively improved.

An AI-powered system (100) for multimodal data processing, dynamic authentication and secure FinTech and IoT transactions, which includes the following: a multimodal data processing module configured to process structured, semi-structured and unstructured data, including financial transactions, IoT sensor data, voice input and image-based data; an AI-powered dynamic authentication module configured to provide behavioral biometrics, context analysis, and multi-factor authentication (MFA) for continuous user and device verification; an anomaly detection module integrated with machine learning models and configured to provide real-time fraud detection by analyzing transaction patterns, user behavior, and device interactions; a secure encryption module configured to use homomorphic encryption and secure multi-party computations to protect sensitive financial and IoT data; and a blockchain-based transaction validation module configured to create an immutable, tamper-proof ledger to ensure data integrity and transaction transparency.

Systems, methods, and computer-readable media for presenting one or more bets to a user based on a natural language query received from the user. In some embodiments, a natural language query from a user may be received. The natural language query may be translated into computer-readable data by a language processing engine. The language processing engine may use a large language model to translate the natural language query into computer-readable data. The computer-readable data may be in the form of an embedding. A bet engine may match the computer-readable data to a bet when the bet and the computer-readable data exceed a predetermined threshold with regard to similarity. The bet engine may generate a new bet corresponding to the computer-readable data. The system may then present the bet to the user.

The invention provides a method and equipment for improving Text-to-SQL (Structured Query Language) conversion accuracy, a medium and a program product. The method comprises the following steps: connecting a target database to obtain DDL (Double Data Language) information of all tables in the target database; extracting effective information of all tables in the database based on the acquired data to establish a table association relation graph, and performing near-synonym expansion on DDL information; when a user inputs a natural language question, an entity contained in the question is extracted and combined with the question to perform RAG retrieval, reachability analysis is performed based on a table association relation graph, and DDL information is used as a context to be transmitted to a large model after a correlation table is complemented, so that the large model generates an SQL (Structured Query Language); performing correctness verification on the generated SQL by using the table association relation graph; if the verification is passed, the generated SQL is returned to the user, and if the verification is not passed, automatic correction is tried, and the corrected SQL is returned to the user. According to the method and the device, the SQL generation accuracy can be improved.

The invention relates to the technical field of intelligent adjustment, and discloses an environment adjustment system with emotional intelligence, which comprises a data acquisition module used for acquiring language signal data of a user, including language data and voice signal data; the language processing module is used for performing text preprocessing on the language data to obtain language keywords; the emotion score determining module is also used for determining a language emotion score of the user according to the emotion knowledge base and the language keywords based on the optimized language model; the voice processing module is used for performing feature extraction on the voice signal data, determining feature data of the voice signal data, and determining a voice emotion score of the user according to the feature data based on a voice model; and the environment adjusting module is used for determining a comprehensive emotion score of the user according to the language emotion score and the voice emotion score, and adjusting environment light configuration of the user according to the comprehensive emotion score. According to the invention, the emotion of the user can be sensed in real time and indoor light can be automatically adjusted according to the emotion so as to better meet personalized requirements of the user.

An embodiment for improved computer-implemented methods of recommending emotion icons. The embodiment may detect text associated with user activity within a connected messaging environment. The embodiment may, in response to detecting the text associated with user activity within the connected messaging environment, determine a context for the detected text based on at least location data, environment data, language context data, and historical data. The embodiment may generate a filtered series of emotion icons based on the context. The embodiment may display the filtered series of emotion icons to a user for selection.

The invention discloses an interview method and device, relates to the technical field of online interview, and mainly aims to adjust interview problems in real time according to the state of an interviewer. According to the main technical scheme, the method comprises the steps of collecting audio and video data of an interviewer in an interview process; when an interview question is issued to the interviewer terminal every time, the collected audio and video data are analyzed to obtain emotion feature data, language behavior feature data and other behavior feature data except the language behavior feature data of the interviewer responding to the current interview question; selecting a next interview question based on the emotion feature data, the language behavior feature data and other behavior feature data, and issuing the next interview question to the interviewer terminal; and if it is determined that the interview process of the interviewer is finished, generating an interview report of the interviewer based on the corresponding emotional feature data, language behavior feature data, other behavior feature data and answers of the interviewer responding to each interview question in the interview process.

An expressive speech translation system may process source speech in a source language and output synthesized speech in a target language while retaining vocal performance characteristics such as intonation, emphasis, rhythm, style, and / or emotion. The system may receive a transcript of the source speech, translate it, and generate transcript data. To generate the synthesized speech, the system may process the transcript data with a language embedding representing language-dependent speech characteristics of the target language, a speaker embedding representing speaker-dependent voice identity characteristics of a speaker, and a performance embedding representing the vocal performance characteristics of the source speech. The system may control the duration of segments of the synthesized speech to better align with corresponding segments of the source speech for the purpose of dubbing multimedia content with synthesized speech in a language different from that of the original audio.

An online system uses a voice augmented language model to create a personalized online order using voice commands from a user of the online system. The online system generates a prompt for input into the language model, the prompt including user's voice content, user's sentiment data, other user data, and source data. The language model uses the prompt to generate a list of components and metadata for each component. Upon receiving an acknowledgement signal indicating an acknowledgement of the list of components by the user, the online system converts the list of components into a list of items and generates one or more options for servicing an order including the list of items. The online system then generates a user interface signal that causes a device associated with the user to display a user interface with the list of items and the one or more options for servicing the order.

The invention discloses a universal equipment motion controller, and belongs to the technical field of automatic control systems. The device motion controller comprises an operation control unit, a function execution algorithm model is embedded in the operation control unit; the man-machine interaction unit and the power execution terminal are in communication connection with the operation control unit. Wherein the function execution algorithm model at least comprises a manual operation model, a size measurement model, a teaching programming model and a data conversion model which can be mutually switched. According to the method, data languages and execution instructions for controlling programming are standardized and universalized, and interoperability and technical sharing mechanism construction among different systems are enhanced. Therefore, the equipment motion controller is convenient for an operator to learn and use, and the requirement of the operator for adapting to different types of controllers is greatly reduced.

The present disclosure is directed to a universal data language (UDL) translator. Specifically, the systems and methods disclosed enable input data from a variety of sources to be translated into a UDL that can be consistently analyzed and compared against other sources of data. For example, an entity may upload input data that has a plurality of data terms and definitions (e.g., header column in a spreadsheet). These terms may be duplicative and / or inaccurate with respect to the underlying data. If the entity wishes to compare and transact data within a data marketplace, the entity may not fully comprehend what data it is missing and / or what data another entity may have to offer for trade. To remedy this problem of business semantic management, the present invention discloses steps for creating a UDL and a UDL translator so that any input data can be translated to UDL.

The invention relates to a multi-modal generation type pre-training method and system and an image question and answer generation method. Visual modal data, language modal data and multi-modal data are obtained; training a visual base model on the visual modal data through a self-supervised comparative learning normal form, and storing a prediction layer weight as a visual prototype; a language base model is trained on the language modal data through a self-supervised lexical element prediction normal form, and word embedding weights are saved to serve as language prototypes; bridging the trained visual base model and language base model, and obtaining lexical element prediction loss through a multi-modal lexical element prediction normal form; cross-modal knowledge distillation and self-modal knowledge distillation are carried out on multi-modal data, and supervised pre-training is carried out. Compared with the prior art, the method has the advantages that the prior information of the visual modality and the language modality can be effectively utilized, and high-precision understanding and reasoning in a cross-modal scene are realized through unified multi-modal generation type pre-training.

The present invention discloses an artificial intelligence-based accurate management system for diabetic foot, including a medical record management subsystem, a big data language model subsystem, a medical and nursing end, and a user end; the medical record management subsystem is used to obtain the new medical record of the patient input in real time by the medical and nursing end and record the time of the new medical record, wherein the new medical record of the patient contains the words "diabetic foot"; the medical record management subsystem sends the new medical record of the patient, the medical records of other historical patients, and the matching degree instruction to the big data language model subsystem, the medical record management subsystem obtains the single patient matching result of the big data language model subsystem, and the medical record management subsystem records the most matching medical record of each other patient. The present invention realizes the automatic tracking and follow-up of diabetic foot, improves the accuracy of tracking and follow-up, and realizes the accurate management of diabetic foot.

The invention relates to a method and a device for formulating a data element standard in the field of nuclear power station equipment, a storage medium and electronic equipment. The method comprises the following steps of formulating a data element standard template; identifying key factors formulated by a data element standard; according to the key factors for formulating the data element standard, determining a principle for formulating the data element standard; key data items are identified according to a rule formulated by a data element standard; and formulating a data element standard according to the key data item and the data element standard template. By making the data element standard, a set of unified data language is provided for the equipment management service of the nuclear power station, the understanding ambiguity is eliminated, the data integration is promoted, the barrier of a data island is broken, the data operation and maintenance difficulty is reduced, the data quality is improved, and effective support is provided for the equipment management service and analysis decision. In addition, accuracy and consistency of the data can be ensured, time and resources required to be invested due to data errors and inconsistency are reduced, working efficiency is improved, and operation cost is reduced.

The present disclosure is directed to a universal data language (UDL) translator. Specifically, the systems and methods disclosed enable input data from a variety of sources to be translated into a UDL that can be consistently analyzed and compared against other sources of data. For example, an entity may upload input data that has a plurality of data terms and definitions (e.g., header column in a spreadsheet). These terms may be duplicative and / or inaccurate with respect to the underlying data. If the entity wishes to compare and transact data within a data marketplace, the entity may not fully comprehend what data it is missing and / or what data another entity may have to offer for trade. To remedy this problem of business semantic management, the present invention discloses steps for creating a UDL and a UDL translator so that any input data can be translated to UDL.

The invention relates to a data language model training method and device, equipment, a medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a standard vocabulary set, first financial training data and an initial financial data language model in the financial field; inputting the first financial training data into the initial financial data language model to obtain a prediction vocabulary; and performing model fine tuning on the initial financial data language model according to the standard vocabulary set and the predicted vocabulary to obtain a target financial data language model. According to the method and the device, the model fine tuning is performed on the initial financial data language model, and the accuracy of the model parameters of the target financial data language model can be improved through the model fine tuning, so that the data processing precision of the target financial data language model is improved.

The embodiment of the invention discloses a database processing method and device, electronic equipment and a storage medium. When a first transaction comprising at least one first data language statement is received, a first partition metadata lock is added to a target partition table corresponding to the first transaction; adding a target partition metadata lock for a first target partition corresponding to the first transaction; when the target partition metadata lock is added, releasing a first partition metadata lock corresponding to the target partition table; based on the target partition metadata lock, executing at least one first data language statement corresponding to the first transaction, so as to execute at least one second data language statement corresponding to the target partition table when a second transaction comprising at least one second data language statement for the target partition table is received in the process of executing the first transaction. And under the condition that the second target partition corresponding to the second transaction is inconsistent with the first target partition, the first transaction and the second transaction are executed in parallel, so that the concurrency of the partition table in the database is improved.

The invention relates to a dynamic data dictionary acquisition method based on Oracle incremental data. The method comprises the steps of querying a mapping relation between a table name and a table ID and a mapping relation between a table field number and a field name through a metadata table of a database, and constructing an initial data dictionary structure; carrying out Oracle incremental data acquisition through an SQL (Structured Query Language) interface of the logminer component, and extracting an incremental DDL (Double Data Language) redo statement; analyzing and processing the increment DDL redo statement by using an SQL grammar analyzer to obtain table structure change information; and issuing a structure change event according to the table structure change information, receiving the structure change event by the dictionary maintainer, and merging the original data dictionary with newly received change data to obtain a dynamically updated new data dictionary. According to the method, the defects that an online directory and an exported data dictionary file require that a data dictionary cannot be changed and a redo log file is low in performance are overcome, the limitation that the data dictionary cannot be changed can be avoided, and the performance of the data dictionary can be prevented from being greatly reduced.

We provide the system. [Solution] A means for receiving natural language data entered by a user and automatically identifying the language of the data, A means of translating from an identified language to another language, Means for presenting translated data to the user visually or aurally, A means of acquiring and displaying information about surrounding facilities based on the user's current location, A system that includes this.

Techniques are described for recognizing a spoken wake word (WW) or a spoken command for a human-machine interface using a speech recognition system that does not require WW or command-matching speech data for training. The system uses the text or grapheme representation of the WW or commands for pre-deployment training. The technique involves the system receiving a text representation of a target phrase in a target language. It includes training an acoustic model based on a speech database to distinguish speech signals according to the acoustic units of the target language. The training of the acoustic model is independent of the target phrase.It involves creating a recognition model based on the textual representation of the target phrase and the acoustic model to recognize the target phrase in speech, and processing speech from a speaker based on the acoustic model and the recognition model to detect the presence of the target phrase.

A method for constructing a unified business and technology data language includes the following steps: Step 1, specifying independent variables based on the business model and the indicator meta-model; Step 2, performing the specified independent variables as follows to ensure they conform to the domain language model of the asset management data domain: whether the independent variable corresponds to a concept or class in the asset management domain and whether it corresponds to metadata, whether the independent variable forms parent and child nodes and thus performs association calculations; Step 3, determining whether to use the independent variable or return to Step 1.

An embodiment for improved computer-implemented methods of recommending emotion icons. The embodiment may detect text associated with user activity within a connected messaging environment. The embodiment may, in response to detecting the text associated with user activity within the connected messaging environment, determine a context for the detected text based on at least location data, environment data, language context data, and historical data. The embodiment may generate a filtered series of emotion icons based on the context. The embodiment may display the filtered series of emotion icons to a user for selection.

The invention provides a cross-database data processing method and device, computer equipment and a medium, and the method comprises the steps: obtaining pre-configured language text data, the language text data comprising a language template path corresponding to a test instance; analyzing the language template path to obtain a path instance list corresponding to the language template path; based on a path instance list corresponding to the language template path, determining a variable mapping set corresponding to the test instance; performing variable assignment on each original program statement included in the path instance list based on a variable mapping set corresponding to the test instance to obtain a target program statement; and after establishing data connection with the target database based on the data source information corresponding to the current instance, generating test data corresponding to the business data in the target database by executing the target program statement. Therefore, the data generation efficiency is effectively improved.

Systems and methods in the present disclosure relate to technology for automated and personalized message generation. A user interface on a user device presents instructions for performing a therapeutic activity. During a session, one or more sensors capture activity data for a user while the user performs the therapeutic activity. The activity data is processed to track performance of the therapeutic activity and generate session data. A prompt for a language model is automatically generated using a prompt data structure that is updated in real-time based on at least one of the session data or historical data associated with the user. A personalized message, as generated in real-time by the language model, is presented to the user via the user interface or via audio hardware associated with the user device.

Searches based on an incoming ticket identify quality ticket enrichment data using a vector database. Language model prompts target particular kinds of quality ticket data. The incoming quality ticket, or a search result ticket, or both, are enriched using enrichment data, such as a user intent identification, a workaround suggestion, a resolution description, a target audience description, a relevance description, an impact description, a description of missing resolution facilitation information, an association between the incoming quality ticket and the search result ticket, a user sentiment identification, a tag suggestion, or a feedback utility estimate. The enrichment reduces engineering and support burdens, and facilitates faster more effective resolution of the problem or the request that is stated or implied in the incoming quality ticket. Duplicate tickets are merged or removed. Tickets are prioritized. Missing problem resolution information is identified and requested sooner.

The invention discloses a method and device for testing a voice analysis system based on a large model, and relates to the technical field of software testing, and the method comprises the steps: generating a voice for testing through a testing model, the training module is obtained by training a machine learning model according to the voice data and text data, language data, speech speed data, accent data and noise data corresponding to the voice data; inputting the voice for testing into the voice analysis system to obtain an analysis text; and obtaining a test result according to the analysis text. According to the embodiment of the invention, various test voices with various characteristics are generated by using the test model, so that the coverage of the test voices is wide, the acquisition difficulty of the voice data set is reduced, the voice analysis system can be comprehensively evaluated, and the accuracy, reliability and automation degree of the test are improved.

The present disclosure belongs to the technical field of Internet software development, and relates to a low-code development method and device, a readable storage medium and an electronic device. The method comprises the following steps: determining a target metadata language template, identifying a target metadata language corresponding to the target metadata language template; analyzing the target metadata language to obtain action information, identifying the action information to obtain a type identification result; and distributing the action information to a front-end page or a back-end system according to the type identification result, so as to execute an action corresponding to the action information in the front-end page or the back-end system. In the present disclosure, the target metadata language is obtained, the target metadata language is analyzed to obtain the action information, and the action information is distributed according to the type identification result obtained by identifying the action information. Compared with the prior art, the target metadata language can be developed by mixing the front end and the back end, the situation that the front end and the back end are developed separately is avoided, the development cycle is shortened, and the development efficiency is improved.

The application relates to the technical field of artificial intelligence, can be applied to a financial technology, medical health and other business system platform, and discloses a multi-modal action model strategy optimization method, device, equipment and medium, the method comprises the following steps: relationship analysis is carried out among acquired image data, language instructions and the behavior action sequence of a target user, relationship dependency is obtained, an initial multi-modal action model is constructed in combination with initial training parameters, the initial multi-modal action model is fine-tuned by using acquired task-specific data, a fine-tuned multi-modal action model is obtained, environment interaction data sets of a target environment are acquired, the environment interaction data sets are sampled one by one by using the fine-tuned multi-modal action model, a plurality of target interaction trajectories are generated, and the selection strategy in the fine-tuned multi-modal action model is optimized, and a target selection strategy is obtained. The application improves the selection strategy accuracy of the multi-modal action model when the model faces new situations or insufficient data.

The invention discloses a data processing method, system and device and a computer readable storage medium, and relates to the technical field of neural networks, in the process of processing to-be-processed data, target data to be subjected to softmax operation is acquired, and the to-be-processed data comprises one or more of image data, text data and language data; determining a maximum value in the target data; generating a target difference value between each datum in the target data and the maximum value, and performing exponential operation on the target difference value to obtain a normalized exponential value corresponding to each datum; generating a sum value of all normalized index values; determining a target fitting model corresponding to the sum value, wherein the target fitting model comprises a model obtained by fitting a reciprocal of the sum value; processing the sum value according to a target fitting model to obtain an initial value; performing Newton iteration on the initial value to obtain a reciprocal of the sum value; and generating a softmax operation result of the target data based on the reciprocal of the sum value. And the operation precision can be improved.