27 results about "Character (computing)" patented technology

Techniques and apparatuses for enhanced text suggestions are described. An example method includes detecting a user gesture performed by a user of the computing system based on data from one or more neuromuscular sensors and identifying a set of text characters corresponding to the user gesture. The method further includes causing display of the set of text terms in a user interface and determining whether a cognitive load of the user meets one or more criteria. The method also includes providing a text suggestion to the user based on the set of text characters in accordance with a determination that the cognitive load of the user meets the one or more criteria, and forgoing providing the text suggestion to the user based on the set of text characters, in accordance with a determination that the cognitive load of the user does not meet the one or more criteria.

A system includes a computing device that includes a memory configured to store instructions. The system also includes a processor to execute the instructions to perform operations that include receiving data representing characters of a font present in an electronic communication. The data includes an integer code for each character of the font. Operations also include applying an operator to the integer code of each character of the font to produce an encrypted integer code. The respective character of the font is assigned to the encrypted integer code. Operations also include sending an encrypted font file comprising data representing each character of the font and the respective encrypted integer code, the encrypted font file being sent to one or more recipient computing devices for rendering the electronic communication using the encrypted font file.

An example method includes outputting a graphical user interface including: a graphical keyboard comprising a plurality of character keys, the plurality of character keys including two to eight character keys; a text-editing region; and a word-suggestion region. The method also includes detecting a first user input at a location of the presence-sensitive display associated with a particular character key from the plurality of character keys. The method further includes, responsive to detecting the first user input, determining a first character associated with the particular character key. The method additionally includes outputting the first character for display within the text-editing region and a set of suggested words for display within the word-suggestion region. The method also includes, responsive to receiving a transition input via an input device of the computing device, transitioning a focus region from a location of the text-editing region to a location of the word-suggestion region.

This invention discloses a method, system, and device for text segmentation highlighting based on speech character position mapping, applied in the field of data processing technology. It achieves text segmentation highlighting through scrolling character position mapping. First, the text content, scrolling sequence, speed, and font layout are preprocessed and uniformly converted into character coordinates and progress percentage format. Character indices and highlight intervals are extracted through position mapping and segmentation detection to form display baseline information. Based on the scrolling-driven calculation module, text features are modulated and non-viewport content is filtered. A highlighting following effect is generated after pixel restoration and synchronization calibration. The highlighting module is directly connected to a fixed-logic prompting engine, and synchronization calibration of scrolling and chapter modes is only performed at the front-end mapping layer, achieving parameter optimization. Seamless adaptation of the rendering framework is achieved through dimensional alignment. Using the prompting engine as a timing discriminator, a complete technical solution for low-computing-power, high-precision, and pluggable scrolling text synchronization and segmentation highlighting is ultimately formed.

A method and a computing device for displaying patent text based on large language model output, wherein the method is performed by the computing device, and the method comprises receiving patent text output by a large language model, wherein the patent text comprises at least one placeholder, each placeholder comprises a general symbol and symbol information, and the symbol information corresponds to a special symbol, backfilling the special symbol having a mapping relationship with the placeholder at the position of the placeholder in the patent text, and displaying the backfilled patent text. The method can realize accurate display of formulas, chemical equations and other special characters.

One embodiment provides a computer-implemented method that includes comparing, by a computing device, an input character signal with one or more prestored character templates for determining an estimated difference measure. The computing device, based on the estimated difference measure, determines one or more mixing weights between a stored character patch buffer and a current input character patch for determining an output mixing patch. The computing device further updates the character patch buffer based on the output mixing patch. The computing device additionally substitutes a designated area using the output mixing patch to produce a final output.

The invention relates to a system (1) for recognizing online handwriting, comprising: - a handwriting instrument (2) comprising a main body (3) extending longitudinally between a first end (4) and a second end (5), the first end (4) having a writing tip (6) capable of writing on a support, the handwriting instrument (2) further comprising a module (17) comprising at least one movement sensor (7) configured to acquire movement data relating to the user's handwriting as the user is writing a sequence of characters using the handwriting instrument (2), - a computing unit (8) in communication with the at least one movement sensor (7) and configured to analyze the movement data by means of a machine learning model trained in a multitask manner so that it is capable of simultaneously performing at least two tasks, the machine learning model being configured to deliver as output the sequence of characters written by the user using the handwriting instrument.

The invention belongs to the technical field of digital image processing and ancient text information processing, particularly relates to an automatic oracle annotation method, system and equipment for digital literatures, and aims to solve the problem of automatic identification and annotation of oracle and Chinese character mixed literatures. The method comprises the following steps: receiving a document image, and obtaining respective coordinate areas of a plurality of characters in the image; obtaining each character image according to the coordinate area, and classifying the character images into an oracle character type or a non-oracle character type; performing differentiation recognition based on a classification result, determining codes of the oracle characters by adopting an image matching model, and determining texts of the non-oracle characters by adopting a character recognition model; and finally, associating an identification result with the coordinate region to generate a structured annotation file. According to the method, the technical bottlenecks of failure of a general OCR (Optical Character Recognition) technology, poor adaptability of a complex format and high computing power dependence are solved, and high-precision and automatic labeling of the oracle in the mixed literature in a low computing power environment is realized.

A computing system includes a memory; and processing circuitry in communication with the memory. The processing circuitry is configured to: receive a paraphrase comprising a paraphrase text sample corresponding to an original text sample; and calculate a paraphrase metric value corresponding to the paraphrase, wherein the paraphrase metric value is calculated based on an adequacy score, a novelty score, and a fluency score of the paraphrase, the adequacy score indicating an extent to which the paraphrase text sample preserves a meaning of the original text sample, the novelty score indicating a level of difference between words and characters of the paraphrase text sample and words and characters of the original text sample, and the fluency score indicating an extent to which the paraphrase text sample is devoid of repetition, spelling, and grammatical mistakes.

Systems and methods for detecting a typographical error in a domain name, including: receiving a domain name comprising Unicode characters; encoding each character, where the encoding includes: computing an integer index of the Unicode characters; converting the integer index into a binary representation; and multiplying the binary representation by a dense matrix to obtain a floating-point vector; and comparing the floating-point vector to a reference floating-point vector of a known domain name using a model to determine if the domain name contains the typographical error.

The invention belongs to the technical field of computers, and discloses a general character string expression based calculation component and method.The general character string expression based calculation component comprises a starting module and a visual expression configurator, and the visual expression configurator sets general expression configuration items, calculation task configuration items and calculation source configuration items to be used for selecting data fields; the AI auxiliary configuration module is used for analyzing selected data fields and historical configuration records to recommend a visual expression template, an AI code auxiliary module is arranged in the editor, a script is automatically complemented according to input part script content loading grammar rules and a historical script library, and an optimization structure is recommended. According to the method, the visual expression configurator or editor is combined with the AI auxiliary configuration module, complex conditional relations are presented through visual interface elements, expression of complex nesting rules can be clearer through real-time analysis and suggestion of AI, and the method is easy to implement. The problems that in the prior art, an expression mode is not clear, complex calculation is difficult to deal with and reliability is poor are solved.

The invention discloses an entity identification method and device for an ultrasonic report and a storage medium. The method comprises the following steps: firstly, determining a plurality of text segments in a report text, and then determining text segment codes of the text segments according to semantic codes and position codes of characters in the corresponding text segments. Particularly, the text fragment codes of the corresponding text fragments comprise absolute position difference codes, and the absolute position difference codes can represent the absolute position difference of the positions of the two ends of the text fragments in the original report text. The computing device may then perform entity recognition on the corresponding text segment according to the text segment code, for example, determine whether the corresponding text segment belongs to an entity and what entity type the corresponding text segment belongs to according to the text segment code. According to the method, absolute position difference codes are introduced into entity recognition, so that the accuracy of entity recognition can be improved through the entity recognition method provided by the embodiment in a scene similar to a scene of performing entity recognition on an ultrasonic report.

The invention relates to a Chinese language input keyboard enhancement scheme, and belongs to the technical field of Chinese input software and hardware of computing equipment and the like. In order to solve the problems that when Chinese and English punctuation marks are switched in Chinese input at present, full-angle and half-angle states need to be switched frequently, then input is tedious and prone to making mistakes, a standard keyboard is not compatible with input of Chinese pinyin characters with tones to enhance the Chinese pinyin input efficiency, and the like, the Chinese pinyin input efficiency is improved based on a standard QWERTY keyboard, and the Chinese pinyin input efficiency is improved. The full-angle Chinese punctuation mark keys and / or the Chinese pinyin tone combination keys and other keys are expanded, and full-angle Chinese punctuation marks and / or Chinese pinyin characters with tones can be input more quickly. The convenience of Chinese input can be effectively enhanced, and a user can learn and use the method easily.

Techniques are disclosed relating to determining whether input data is authentic. A system detects input data, that includes text data and typing data, at a computing device. The system may generate, using a string model, a string-level prediction for the input data, where the string model is trained to increase a similarity between embeddings of authentic text data and corresponding sequences of typing data. Using a character model, the system may generate a character-level prediction for the set of input data, where the character-level model predicts an intended sequence of characters based on the text data and a sequence of typing actions included in the input data. Using machine learning, the system determines, based on the string-level prediction and the character-level prediction, whether the input data is authentic input. The system transmits, to the device, a decision that is generated based on determining whether the input data is authentic.

A computing system includes a memory; and processing circuitry in communication with the memory. The processing circuitry is configured to: receive a paraphrase comprising a paraphrase text sample corresponding to an original text sample; and calculate a paraphrase metric value corresponding to the paraphrase, wherein the paraphrase metric value is calculated based on an adequacy score, a novelty score, and a fluency score of the paraphrase, the adequacy score indicating an extent to which the paraphrase text sample preserves a meaning of the original text sample, the novelty score indicating a level of difference between words and characters of the paraphrase text sample and words and characters of the original text sample, and the fluency score indicating an extent to which the paraphrase text sample is devoid of repetition, spelling, and grammatical mistakes.

A computer-implemented method of detecting account codes and displaying the detected account codes on a graphical user interface comprising receiving, by a recommendation engine of a recommendation system, invoice data comprising supplier-customer information that corresponds to a supplier-customer transaction, wherein the invoice data comprises invoice descriptions and invoice characters, wherein the invoice descriptions and the invoice characters define contexts and patterns; determining, by the recommendation engine, that an amount of the invoice characters is not more than a preset threshold number of characters; in response to determining that the amount of the invoice characters is not more than the preset threshold number of characters, filtering, by the recommendation engine, the invoice descriptions of the invoice data based on predetermined constraints to extract filtered invoice data comprising filtered description lines and to generate a training corpus for a pre-trained Natural Language Processing (NLP) model; identifying, by classifying the filtered description lines with the pre-trained NLP model, one or more categories associated with each of the filtered description lines of the filtered invoice data; matching, by the recommendation engine, each of the identified one or more categories with one or more predefined historical categories, wherein the contexts and patterns associated with the filtered description lines are matched with predefined contexts and patterns of predefined historical invoice data that corresponds to the same supplier-customer information; generating, by the recommendation engine, a feature vector for the invoice data based on the matching; computing, by the recommendation engine, a categorical similarity score for each of the identified one or more categories based on the feature vector and an additional feature vector, wherein the additional feature vector is based on the predefined historical invoice data; and displaying, by the recommendation engine on the graphical user interface, a recommendation including an account code based on the computed categorical similarity score of each of the one or more categories to map the account code to the invoice data.

The embodiment of the invention provides a data processing method and device, computing equipment and a storage medium, and the data processing method comprises the steps: responding to a starting instruction for a target object, and running the target object based on a target analyzer; according to translation configuration information carried in a target code of the target parser, a character to be translated in a target script of the target object is translated, a target character is obtained, and the target code is obtained by adding the translation configuration information into an initial code of the target parser; and rendering the target character in a display interface corresponding to the target object. The data processing method can be widely applied to the fields of virtual reality processing software, digital culture product manufacturing software, digital culture creative software and the like in the digital creative technology.

Techniques are disclosed relating to determining whether input data is authentic. A system detects input data, that includes text data and typing data, at a computing device. The system may generate, using a string model, a string-level prediction for the input data, where the string model is trained to increase a similarity between embeddings of authentic text data and corresponding sequences of typing data. Using a character model, the system may generate a character-level prediction for the set of input data, where the character-level model predicts an intended sequence of characters based on the text data and a sequence of typing actions included in the input data. Using machine learning, the system determines, based on the string-level prediction and the character-level prediction, whether the input data is authentic input. The system transmits, to the device, a decision that is generated based on determining whether the input data is authentic.

Techniques and apparatuses for enhanced text suggestions are described. An example method includes detecting a user gesture performed by a user of the computing system based on data from one or more sensors and identifying a set of text characters corresponding to the user gesture. The method further includes determining whether a cognitive load of the user meets one or more criteria. The method also includes providing a text suggestion to the user based on the set of text characters in accordance with a determination that the cognitive load of the user meets the one or more criteria, and forgoing providing the text suggestion to the user based on the set of text characters, in accordance with a determination that the cognitive load of the user does not meet the one or more criteria.

The present disclosure provides a method and device for generating training samples of rare characters, and relates to the technical field of image recognition. The specific implementation of the method comprises: in response to a service request sent by a client, issuing an information verification instruction to the client; obtaining a correction character input by a user through the client according to a verification result of a to-be-verified character returned by the client, and constructing a rare dictionary library by taking the correction character as a rare character; collecting a text corpus corresponding to the rare dictionary library, selecting a background image, a font format and a font color to render each text segment of the text corpus, and generating a training image; taking each text segment as an image label of the training image, and combining the training image and the image label to generate a training sample. The implementation can reduce the labor cost, learning cost, computing resource and development cost, the training data amount is sufficient, the recognition accuracy of the trained model is high, the convenience and efficiency of model training and use are improved, and the expansibility is strong.

The invention provides a mass high-quality data screening method, which relates to the technical field of large language models, and comprises the following steps: collecting a mass data set, and screening the data set to obtain a high-quality text; calculating the proportion of the token length of the high-quality text to the character length of the original text so as to divide the high-quality text into a plurality of uniform intervals; sampling the same number of text data from each interval to construct an initial training set, and distilling the initial training set to generate annotation data containing text quality scores; constructing a small parameter model, and carrying out joint training on the small parameter model through mathematical calculation of the thinking chain data and the annotation data; a small parameter model is used for screening mass data, and a large model is called again for distillation and iterative optimization until the screening accuracy of the small parameter model meets a preset threshold value; final data screening is completed through the small parameter models with the screening accuracy meeting a preset threshold value; the large model reasoning speed can be increased, and the computing power resource cost is remarkably reduced.

The present invention relates to a computing system for classifying readability of Chinese content into a plurality of difficulty levels based on Hong Kong local linguistics, comprising: an input module for retrieving one or more corpora comprising Traditional Chinese and Cantonese vocabulary; a pre-processing unit for generating four levels of linguistic features for each document in the one or more corpora, wherein the four levels of linguistic features comprise: character (C), word (W), syntax (S) and discourse (D) levels; and a local processing unit having a local artificial intelligence (AI) engine for training two machine learning algorithms to classify readability based on the four levels of linguistic features, wherein the two machine learning algorithms comprise a random forest (RF) algorithm and a support vector machine (SVM); wherein the difficulty levels have 24 difficulty levels based on 12 grades, and each grade comprises 2 form levels according to the Hong Kong education system.

Methods, systems, and apparatuses are described herein for efficiently identifying and processing sensitive data. A computing device may receive text content, such as one or more words. The computing device may select characters of that text content by identifying, using regular expressions corresponding to sensitive data categories, matches and select various characters including and around such matches. The computing device may provide the selected characters as input to a machine learning model trained to identify sensitive data, and that trained machine learning model may output information about a sensitivity of the characters. Such output might be used to modify all or portions of the text content.

Embodiments of the present disclosure provide a text recognition method, medium, device and computing device, the method comprising: obtaining to-be-recognized data; obtaining character features of to-be-recognized characters through a target text recognition model, the target text recognition model being obtained by training based on sample character features of sample characters in sample data; determining a text corresponding to the to-be-recognized characters according to the character features of the to-be-recognized characters; and determining a target text corresponding to the to-be-recognized data according to the text corresponding to the to-be-recognized characters. In this scheme, the text corresponding to the characters is obtained through the character features and a preset codebook, the conventional classification layer in the target text recognition model can be eliminated, the parameter amount of the preset codebook used is relatively small, the package size of the target text recognition model can be reduced while ensuring the accuracy of the text recognition result, and the target text recognition model can be flexibly applied to various computing devices.

The embodiment of the invention discloses a multi-dimensional constraint-based intelligent calculation method and system for semantic path consistency of an examination document. The method comprises the following steps of: firstly, obtaining a semantic path which is generated by an examination document text and comprises characters, behaviors, events, time and place elements; then, for any two semantic paths, based on a constraint system comprising at least multiple dimensions in semantics, behaviors, roles, time, places and event chains, calculating consistency scores of the dimensions respectively; then, the scores of all the dimensions are fused through a weighted summation model, and a comprehensive consistency score between the paths is obtained; and finally, outputting a consistency judgment result according to a scoring threshold value, and marking a conflict type. According to the method, multi-dimensional and structured logic consistency analysis is carried out from a path level, the defects that manual comparison is low in efficiency and a traditional natural language processing technology is difficult to meet specific logic judgment requirements in the field are effectively overcome, and the automation degree and accuracy of examination document analysis are remarkably improved.

Techniques are disclosed relating to determining whether input data is authentic. A system detects input data, that includes text data and typing data, at a computing device. The system may generate, using a string model, a string-level prediction for the input data, where the string model is trained to increase a similarity between embeddings of authentic text data and corresponding sequences of typing data. Using a character model, the system may generate a character-level prediction for the set of input data, where the character-level model predicts an intended sequence of characters based on the text data and a sequence of typing actions included in the input data. Using machine learning, the system determines, based on the string-level prediction and the character-level prediction, whether the input data is authentic input. The system transmits, to the device, a decision that is generated based on determining whether the input data is authentic.

Systems and methods for detecting a typographical error in a domain name, including: receiving a domain name comprising Unicode characters; encoding each character, where the encoding includes: computing an integer index of the Unicode characters; converting the integer index into a binary representation; and multiplying the binary representation by a dense matrix to obtain a floating-point vector; and comparing the floating-point vector to a reference floating¬ point vector of a known domain name using a model to determine if the domain name contains the typographical error.