262 results about "Text string" patented technology

An electronic device associated with a media-providing service stores, in a vector space, a plurality of respective vector representations for respective media content items. The electronic device receives a user input, including a text string. The electronic device generates, using a neural network, a structured query based on the text string. The electronic device determines, based on the structured query, whether to generate a vector representation of a portion of the text string. When the electronic device determines to generate the vector representation of the portion of the text string, it generates the vector representation of the portion of the text string, wherein the vector representation is embedded in the vector space, and identifies a set of media items using the vector representation of the portion of the text string. And the electronic device provides one or more select media items from the set of media items to a user.

The invention discloses a character recognition method and system based on a large model and an OCR technology, and relates to the technical field of character recognition, and the method comprises the steps: extracting picture information, carrying out the unified preprocessing, generating a text detection box of a character region through a DBNet lightweight text detection model, and obtaining a text position; quickly identifying characters in the textbox by using a lightweight OCR model to obtain text content, and generating an identification information group; carrying out average value calculation on the confidence coefficient of the lightweight OCR model result, and analyzing the overall confidence coefficient; and for the result with low confidence coefficient, inputting the corresponding identification information group into the multi-modal large model, and carrying out secondary identification. According to the method, the text position set and the text character string sequence are combined into the identification information group, so that the effect of structured storage of detection and identification results is achieved, subsequent information retrieval and multi-modal fusion analysis are facilitated, and the effect of optimizing the subsequent processing efficiency and precision is achieved through the combination of the confidence coefficient screening step.

Provided is a system and method for controlling a plurality of devices. The method includes generating a command script by processing a text string with at least one model, the text string including a natural language input by a user, modifying the command script based on contextual data, the command script including a configuration for at least one device, generating at least one command signal based on the command script, and controlling at least one device based on the at least one command signal.

Approaches presented herein can provide for the performance of specific types of tasks using a large model, without a need to retrain the model. Custom endpoints can be trained for specific types of tasks, as may be indicated by the specification of one or more guidance mechanisms. A guidance mechanism can be added to or used along with a request to guide the model in performing a type of task with respect to a string of text. An endpoint receiving such a request can perform any marshalling needed to get the request in a format required by the model, and can add the guidance mechanisms to the request by, for example, prepending one or more text strings (or text prefixes) to a text-formatted request. A model receiving this string can process the text according to the guidance mechanisms. Such an approach can allow for a variety of tasks to be performed by a single model.

The invention belongs to the field of software development tools, and discloses a dynamic context-aware text string editing and verification system and method.The system comprises a text string editing user interface component, a verification engine, a predefined function storage library and a variable context manager; the text character string editing user interface component receives a text character string input by a user; the verification engine is used for verifying the text character string in real time, and sending an error result and a modification suggestion for the error result if an error is found through verification; the text character string editing user interface component receives and displays the error result and the modification suggestion, receives a correct text character string input by a user, and triggers the verification engine to perform verification again; the verification mode comprises a strict verification mode and a loose verification mode. According to the method, the context is dynamically perceived, the variable is dynamically generated, comprehensive, real-time and accurate verification on the text character string is facilitated, and the accuracy of the text character string is improved.

A method may include obtaining first audio data of a first communication session between a first and second device and during the first communication session, obtaining a first text string that is a transcription of the first audio data and training a model of an automatic speech recognition system using the first text string and the first audio data. The method may further include in response to completion of the training, deleting the first audio data and the first text string and after deleting the first audio data and the first text string, obtaining second audio data of a second communication session between a third and fourth device and during the second communication session obtaining a second text string that is a transcription of the second audio data and further training the model of the automatic speech recognition system using the second text string and the second audio data.

A device may be configured to perform spatial extrapolation based on information included in a neural-network post-filter characteristics message. In one example, a neural-network post-filter characteristics message includes a syntax element indicating a purpose of the neural-network post-filter characteristics message is spatial extrapolation. In one example, a neural-network post-filter characteristics message includes a syntax element specifying a text string prompt used for generating contents of a spatial extrapolation image area.

Methods and systems for scalable dataset content embedding for improved searchability. For example, the system may retrieve a first dataset from a first data source. The system may generate a first data profile of the first dataset. The system may generate a latent index of the first data profile based on processing the first data profile using a first embedding algorithm. The system may receive, via a user interface, a first request for a first text string. The system may generate an embedded request corresponding to the first request based on processing the first text string using the first embedding algorithm. The system may process the embedded request using the latent index. The system may generate for display, in the user interface, a result based on processing the embedded request using the latent index.

In various embodiments, systems and methods for design-aware replacement font suggestions are provided. In some embodiments, a substitute font-suggestion algorithm holistically considers how the original string of text from the original layout appears when re-rendered in a same-sized text frame using a potential replacement font. In some embodiments, the substitute font-suggestion algorithm generates a first image of a text frame including the text string using the first font and generates a plurality of second images of the text string using candidate replacement fonts. A ranking of the candidate replacement fonts is generated based on computing a score for each of the individual second images that represents similarity between the first image and the individual second images. Based on the assessed similarities, a ranked listing of substitute font suggestions is displayed.

The invention discloses a hierarchical rhythm mapping-driven short video copywriting mood automatic adjustment method, and relates to the technical field of video processing, and the method comprises the steps: 1, receiving a text character string and a language type identifier, and building an occupation column for bearing a tone mark, an accent mark and a duration mark at each level; 2, dividing each sentence into phrase segments based on the hierarchical index table, freezing boundaries by taking the phrase segments as units, presetting sentence end termination styles according to punctuations, determining kernel phrases according to semantic anchor points, initializing trends of the kernel phrases, and performing time sequence elastic alignment and hierarchical backfilling to obtain a sentence end termination pattern; and finally outputting a triple sequence which covers all syllables and is composed of tone marks, accent marks and duration marks as a target rhythm control sequence. And step 3, performing audio generation based on the target rhythm control sequence to obtain new dubbing. According to the method, the tone accuracy and expressive force of short video dubbing are improved, and the time and cost of manual adjustment are remarkably reduced.

A system for processing a radio transmission, having a device having a housing including processor, memory, speaker and one or more communication interfaces, at least one radio monitoring a plurality of wavelengths and having a radio output signal; and a power supply. The system including a signal to noise service module to detect variations in a signal-to-noise ratio for the radio output signal, to identify transmissions received. The system also provides a transcription service module to convert audio into a text string, and natural language processing (NLP) service module to identify an intent classification for the transmission. The system includes a logging service module to direct the radio output signal, text string, and data containing context information, to archive as logged entries in a memory storage database.

A method may include obtaining first audio data of a first communication session between a first and second device and during the first communication session, obtaining a first text string that is a transcription of the first audio data and training a model of an automatic speech recognition system using the first text string and the first audio data. The method may further include in response to completion of the training, deleting the first audio data and the first text string and after deleting the first audio data and the first text string, obtaining second audio data of a second communication session between a third and fourth device and during the second communication session obtaining a second text string that is a transcription of the second audio data and further training the model of the automatic speech recognition system using the second text string and the second audio data.

A method may include obtaining a text string that is a transcription of audio data and selecting a sequence of words from the text string as a first word sequence. The method may further include encrypting the first word sequence and comparing the encrypted first word sequence to multiple encrypted word sequences. Each of the multiple encrypted word sequences may be associated with a corresponding one of multiple counters. The method may also include in response to the encrypted first word sequence corresponding to one of the multiple encrypted word sequences based on the comparison, incrementing a counter of the multiple counters associated with the one of the multiple encrypted word sequences and adapting a language model of an automatic transcription system using the multiple encrypted word sequences and the multiple counters.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: performing natural language processing to process a text string of a user, wherein the text string specifies characteristics of a container image to be built; processing, with use of natural language processing, instances of text-based data that describe respective ones of a plurality of container images stored within a container image repository; selecting, in dependence on a result of the performing natural language processing, and the processing, a base image from the plurality of container images; and presenting prompting data to the user that prompts building of a new container image, wherein the prompting data references the base image.

A server uses a transformer model to identify a named entity associated with a transaction record. The server receives a transaction record including a text string that includes a non-normalized version of a name of a named entity. The server generates a first embedding of the text string using a first transformer model and identifies a set of similar transactions by comparing the first embedding to second embeddings representing the similar transactions. The server inputs the text string of the transaction record and the set of similar transactions into a second transformer model. The server receives an output from the second transformer and determines that the output indicates that the non-normalized version of the name in the transaction record is classifiable to one of the normalized named entities in the list. The server associates the transaction record with the normalized named entity to which the non-normalized named entity is classifiable.

Provided are a computer program product, system, and method for providing a repository of audio files having pronunciations for text strings to provide to a speech synthesizer. The repository has data structures for text strings in documents. A data structure for a text string indicates at least one attribute of a presentation of the text string in the document and at least one audio file providing at least one audio pronunciation of the text string. A search text string and a search attribute are received from the speech synthesizer. A determination is made of a data structure in the repository including a text string and an attribute matching the search text string and the search attribute, respectively. An audio file, indicated in the determined data structure, is returned to the speech synthesizer to output for the search text string in a document being processed by the speech synthesizer.

Systems, methods, devices, and media for anamorphosis systems to generate and cause display of anamorphic media are disclosed. In one embodiment, an anamorphosis system is configured to identify a set of features of a space, determine relative positions of the set of features, determine a perspective of the mobile device within the space based on the relative positions of the set of features, retrieve anamorphic media based on the location of the mobile device, and apply the anamorphic media to a presentation of the space at the mobile device. The anamorphic media may include media items such as images and videos, configured such that the media items are only visible from one or more specified perspectives. The anamorphic media may include a stylized text string projected onto surfaces of a space such that the stylized text string is correctly displayed when viewed through a user device from a specified perspective.

Approaches presented herein can provide for the performance of specific types of tasks using a large model, without a need to retrain the model. Custom endpoints can be trained for specific types of tasks, as may be indicated by the specification of one or more guidance mechanisms. A guidance mechanism can be added to or used along with a request to guide the model in performing a type of task with respect to a string of text. An endpoint receiving such a request can perform any marshalling needed to get the request in a format required by the model, and can add the guidance mechanisms to the request by, for example, prepending one or more text strings (or text prefixes) to a text-formatted request. A model receiving this string can process the text according to the guidance mechanisms. Such an approach can allow for a variety of tasks to be performed by a single model.

Systems, methods, and computer-readable storage media for identifying databases or resources which contain verifiable information used to create generative AI output. A system can receive a request for generation of a report, the request including a string of text. The system can then parse the string, resulting in a parsed request, and identify at least one verified data source for each piece of data within the parsed request. The system can then generate a query for each verified data source based on the parsed request, resulting in at least one query, and send those queries to the verified data sources. Those sources can respond with verified data, and the system can generate, using a generative Artificial Intelligence (AI) algorithm, the report using the verified data.

A central text repository may maintain, track, update, and modify text centrally that may then be distributed to applications to be used at runtime. The central text repository allows anyone involved in the software design processor lifecycle to edit, update, and / or correct text strings that are used in various applications. This allows updates to be rapidly pushed out to runtime applications without requiring the codes bases of those applications to be accessed at all. Instead, a change may be made centrally, and new resource bundles of text strings may be made available for runtime downloading usage by these applications. This effectively separates the storage and maintenance of text strings from the underlying applications. Hierarchies and modifiers may be used to override and inherit different text usages, languages, and so forth.

Software of devices, such as medical devices or other constrained devices configured to implement specific functions, can use content files to determine text strings or other content values to present in association with user interface elements. Devices can receive relevant content files, such as new versions of previously-stored content files or new content files containing text in different languages, from a remote content file repository. The software of the devices can accordingly update content values presented in user interfaces based on newly received content files, without the software being re-coded, the software being restarted, or the devices rebooting.

The invention relates to the field of data recognition, and particularly discloses an intelligent homework test paper reading and wrong question induction method and system and electronic equipment. The method comprises the following steps: acquiring image data of homework or test paper, preprocessing the acquired image data, detecting external polygon coordinates of characters in the preprocessed image through PANet, identifying a text line picture through CRNN, and outputting a text character string; for science categories, decomposing and scoring problem solving steps through a symbolic calculation engine, verifying a vector relationship through an image feature recognition engine, and for liberal arts categories, performing semantic scoring through an NLP deep analysis engine; and according to the reviewing result of each subject, respectively generating a wrong question set of the corresponding subject. According to the method, through task cutting, image processing and deep learning, on the premise that the marking precision is guaranteed, the full-process dependence of a large model is reduced, the system response speed is increased, and the hardware resource consumption is reduced.

In some aspects, the techniques described herein relate to a method including: receiving, at a query platform, a query text string; tokenizing, by the query platform, the query text string, wherein the tokenizing generates a query vector embedding; determining, by the query platform, a document vector embedding, wherein the document vector embedding is above a similarity threshold with respect to the query vector embedding; retrieving, by the query platform, textual document data related to the document vector embedding; sending, by the query platform, the query text string and the textual document data to a generative model engine; receiving, by the query platform and from the generative model engine, a natural language response to the query text string; and displaying, by the query platform, the natural language response via an interface.

Provided in the present application is a large-language-model-based long text generation method capable of realizing context compression. The method comprises: acquiring context text to be compressed and prompt text, and performing compression-based encoding processing, so as to obtain a corresponding compression vector and a prompt embedding vector; concatenating the compression vector and the prompt embedding vector, and performing autoregression-based decoding processing on a fused feature, which is obtained by means of concatenation, so as to obtain a plurality of corresponding token identifiers; and on the basis of a preset vocabulary, mapping the token identifiers into text character strings one by one, and combining the text character strings into compressed context text. By means of the present application, long context text processed by a large language model is compressed, thereby solving the technical problem in the prior art of a semantic model consuming a large amount of model calculation resources and data storage resources when processing long context text.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining foreground voice data of a multimedia stream that includes a video stream data and an audio stream; identifying in dependence on the examining an open time window that is absent of foreground voice data; processing, in dependence on the identifying, media stream data of multimedia stream; generating, in dependence on the processing, a text string for deployment in the open time window, wherein the text string describes content of the video stream; converting the text string into a synthesized voice segment; and adapting the audio stream data so that the synthesized voice segment is included in the audio stream and time bounded within the open time window.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating stylized translated text using attention heads from a transformer neural network. In particular, in some embodiments, the disclosed systems obtain an input text string in a first language, the input text string comprising a style formatting element. Additionally, in some embodiments, the disclosed systems generate, using a transformer neural network to process the input text string, a translated text string in a second language different from the first language. Moreover, in some embodiments, the disclosed systems determine attention head values generated by the transformer neural network for words of the input text string as part of generating the translated text string in the second language. Furthermore, in some embodiments, the disclosed systems generate a translated style formatting element for the translated text string based on the attention head values for the words of the input text string.

A system for recognizing vertically oriented alphanumeric text in images, the system including a processor configured to receive one or more images comprising vertically oriented alphanumeric text and detect one or more regions-of-interest in each image via a trained text detector. The processor is configured to execute a cropping of the detected one or more regions-of-interest encompassing vertically oriented alphanumeric text from each image to obtain one or more text crop portions and rotate the one or more text crop portions to obtain one or more orthogonally rotated text crop portions. The processor is configured to execute a trained ensemble of two different text recognition models on each of the obtained one or more text crop portions and the one or more orthogonally rotated text crop portions and generate a set of candidate recognized text strings based on the executed trained ensemble and determine a final recognized text string.

The present disclosure relates to a text similarity measurement method and apparatus, device, storage medium, and program product. The method includes: obtaining a first text string and a second text string; constructing a joint probability distribution of the first text string and the second text string, and sampling the joint probability distribution to obtain a sampling string; calculating a distance from the first text string to the sampling string to obtain a first distance matrix, and calculating a distance from the second text string to the sampling string to obtain a second distance matrix; and determining a similarity between the first text string and the second text string based on the first distance matrix and the second distance matrix.

Methods and apparatus for supporting generation of a virtual 3D object Methods (100, 300) are disclosed for supporting generation of a virtual 3D object. The methods include, at a preprocessing node, obtaining heterogeneous input data for the virtual 3D object (110), aggregating the heterogeneous input data (120), and generating a contextual prompt from the aggregated heterogeneous data (130), the contextual prompt comprising a text string conveying semantic information with respect to the virtual 3D object, which semantic information is derived from the aggregated heterogeneous input data. On detection of a trigger event, the preprocessing node provides the contextual prompt to a processing node (140). The methods further include, at a processing node, inputting the contextual prompt to an ML model (220) operable to generate a prompt for a Text-to-XR function within the processing node, and contextual metadata for the virtual 3D object. The methods further include, at the processing node, using the generated prompt to generate a representation of the virtual 3D object (230), and providing the representation and the contextual metadata for the virtual 3D object to an XR client (240).

A system for enhancing viewer engagement causes display of a short-form video hosted on the short-form video hosting service on a first portion of a user interface. The system can receive an input including a comment in response to the short-form video. The comment input can include a first text string. The system can cause display of the comment received from the particular viewer on a second portion of the user interface. The system can cause a generative artificial intelligence (AI) system to automatically create a response to the comment based on the first text string and metadata associated with the short-form video. The response can include a second text string. In response to approval from the content provider to publish the response, the system can cause display of the response proximate to the comment on the second portion of the user interface.