345 results about "Text string" patented technology

Implementations utilize a smaller LLM to generate content responsive to a user query and cause a portion of the generated content to be rendered as an immediate response to the user query. Implementations further utilize a larger LLM to generate content that starts with the portion of the generated content and that includes a refined portion succeeding the portion of the generated content. The refined portion can be rendered succeeding the portion of the generated content. In some implementations, instead of using the smaller LLM, alternatively, the portion of the generated content rendered as the immediate response can be generated based on a default text string or a template, where the template can be determined / selected from a plurality of predefined templates based on a natural language understanding of the user query.

An exemplary method for reducing bias in a training image dataset for training a machine-learning model comprises: receiving a plurality of text strings comprising at least one text string describing each image in the training image dataset; generating a plurality of embeddings based on the plurality of text strings; identifying, based on the plurality of embeddings, a plurality of visual features in the training image dataset; identifying one or more correlations between the plurality of visual features in the training image dataset; receiving a user input identifying at least one biased correlation from the one or more correlations; and training the machine-learning model at least partially by adjusting one or more data sampling weights associated with one or more training images in the training image dataset based on the user input.

Generating responses to queries with text extracts from unstructured data using AI models includes (i) extracting text from unstructured data sources to create machine-searchable documents, (ii) replacing PII and PHI with entity types and attributes, (iii) determining text extracts that indicate criteria, (iv) using a small-scale ML model to perform text searches and find conceptually associated text strings, (v) generating a custom context for a large language model (LLM), (vi) prompting the LLM to generate a response, (vii) combining the response with an extractive QA model to obtain relevant text extracts as response basis, and (viii) providing system-generated recommendations for next best actions based on responses that produce the most optimal outcomes in historical input documents for manually selected or automatically recommended resolution paths.

A method for training a text generation machine learning model for prompt generation includes receiving a plurality of training text strings; executing a text generation machine learning model using each of the plurality of training text strings to generate a plurality of training generated text strings, each of the plurality of generated text strings corresponding to generating a content item; executing a content scoring machine learning model using each of the plurality of generated text strings to generate a text performance score for each of the plurality of training generated text strings; for each of the plurality of generated text strings, adjusting one or more weights or parameters of the text generation machine learning model proportional to the text performance score for the generated text string.

An example system includes: a first large language model; a memory storing instructions and one or more processors operable to execute the instruction to: receive a natural language text string from a client device, where the natural language text string comprises a formal rule expressed in natural language; input the natural language text string and a control string into a large language model (LLM), where the control string is configured to cause the LLM to generate a formal expression representing the formal rule of the natural language text string; output, by the large language model, the formal expression to an output device; and evaluate a consistency of the formal expression using a test input.

Systems and methods for multimodal query suggestion can include obtaining image data, determining text strings associated with the image data, and providing the text strings as selectable options for performing a multimodal query with the image data. The text strings can be determined based on performing object detection and classification on the image data. The object classifications can then be leveraged for determining potential text queries a user may select for obtaining additional information about the classified object.

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.

A method for saving prompts on a page of a workspace includes receiving a first input that instantiates a prompt block configured to initiate a generative process to create in-block content. The prompt block is embedded on a first page of a workspace configured to include multiple pages. The first input includes a string of text. The string of text includes instructions that cause a generative artificial intelligence (AI) system to create the in-block content to be displayed on the page. The method includes receiving a second input on the prompt block. In response to the second input, the method includes saving the string of text as a particular prompt. The particular prompt is accessible from the multiple pages of the workspace and the particular prompt is configured to be shared with users of the workspace.

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 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.

Systems and methods recite a system that expands the available amount of data that may be used for training and / or detecting cyber fraud. In particular, as opposed to limiting training data (and / or successful matches) to patterns of text strings in electronic communications of user records, the systems and methods additionally generate training data (and / or successful matches) on the characteristics of the text strings themselves (e.g., the specific characters used, the length, the uniqueness of the text string, etc.). Moreover, the systems and methods may determine particular rules that an identified text string and / or characteristics thereof violates. The system may then use instances of the identified text string, characteristics thereof, and / or violated rule to supplement existing training data. Thus, the systems and methods mitigate the technical problem related to the limited training data.

A computer-based system and method provides feedback to a student in response to a student text for a provided lesson. An evaluation request including student text response to a lesson passage, a writing prompt, a student identifier, and a lesson identifier is received for evaluation of the student text response. An evaluation rubric is selected from rubrics indexed by the student grade level, student the lesson objective, and / or the lesson identifier. An evaluation data structure corresponding to the request for evaluation is initialized. A large language model (LLM) prompt is formulated based on the evaluation request and the evaluation rubric and provided to an LLM. The evaluation data structure is updated with the response from the LLM and converted to feedback text strings which are presented to the student in the context of the student text response.

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.

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.

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.

Systems and methods for data parsing are disclosed. In one aspect, a method of parsing raw data associated with one or more transactions involves receiving a text string including raw data for a transaction, matching the text string to a plurality of locations within a location corpus to extract location information from the text string, and identifying a candidate entity from the text string based on a similarity score with respect to a plurality of entities within an entity corpus. The method further involves in response to the similarity score of the identified candidate entity being less than a threshold score, generating entity information using the tokens indicative of entity information, and generating normalized transaction data including the extracted location information and one of the identified candidate entity or the generated entity information.

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.

There is provided a computer-implemented method for diagnosing machine learning model underperformance or failures, comprising: embedding validation data samples and text string phrase candidates in a joint embedding space to generate data embeddings and text embeddings; receiving a trained machine learning model; conducting inference with the validation dataset using the trained machine learning model to obtain numerical metrics per validation data sample; generating a spatio-temporal relationship representation for the validation data samples; determining a plurality of data slices; and generating a semantic description for each data slice based on the text embeddings. There is further provided a computing system for diagnosing machine learning model underperformance or failures, computer programs, machine-readable storage media, data carrier signals and use of the computer-implemented method.

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.

Techniques described herein provide for text string comparison for documents identified using automated near-duplicate detection. In one example, a system can receive a pair of documents. The system can extract text strings from the documents. The system can normalize the extracted text strings using a predefined normalization scheme. The system can identify boilerplate text segments in the normalized text strings. The system can remove the boilerplate text segments from the normalized text strings to generate filtered text strings. The system can divide the filtered text strings by identifying section indicators. The system can, for each section, generate groupings of text strings and determine a similarity score between each pair of corresponding groupings to identify matching groupings of text strings. The system can generate an output for display showing the visual indications of the matched groupings of text strings.

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.

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.

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.

The invention relates to the technical field of data processing, in particular to a deep learning optimization method for finance and tax text analysis and business association verification, and the method comprises the steps: calculating the vertical periodicity of a text string in a finance and tax text image; calculating the horizontal periodicity of each row of text string according to the horizontal distance between the text string and other text strings except the text string and the text length mean value of all the text strings; determining the text length deviation of the text string according to the difference between the text length of each text string and the text length mean value of all the text strings; determining the confidence degree of the text string in the table area; and in response to a comparison result of the confidence of the text string in the table area and the confidence threshold, determining the text string of the table area and the non-table area in the finance and taxation text image to obtain a finance and taxation text analysis and business association verification result, thereby effectively improving the accuracy of finance and taxation text analysis and business association verification.

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.

The present disclosure relates to a method, apparatus, device, storage medium, and program product for text similarity measurement. The method includes: obtaining a first text string and a second text string; constructing a joint probability distribution of the two based on the first text string and the second text string, and sampling the joint probability distribution to obtain a sampled string; calculating the distance from the first text string to the sampled string to obtain a first distance matrix, and calculating the distance from the second text string to the sampled string to obtain a second distance matrix; determining the similarity between the first text string and the second text string based on the first distance matrix and the second distance matrix. The embodiments of the present disclosure achieve dimensionality reduction calculation of the text similarity method, improving the calculation efficiency. In addition, since the sampled string contains information of the two text strings, information loss is reduced while dimensionality reduction is achieved.