134 results about "Emoji" patented technology

The invention provides a multi-feature fusion rumor detection method, system and device based on knowledge distillation, and mainly solves the problems that an existing model is high in calculation overhead, insufficient in feature fusion and insufficient in emotion utilization. The method comprises the steps of firstly obtaining multi-dimensional data such as social media original texts and comments; extracting deep semantic representation by using a pre-training model, and analyzing comment emotion features in combination with a hybrid neural network; then, features such as semantics, emotions, emoticons and populations are input into a hierarchical gating interactive fusion network (GIFN), and weights are dynamically adjusted to achieve effective fusion of multi-granularity features; in order to reduce complexity, a knowledge distillation framework is designed: a deep GIFN is used as a teacher network to generate a soft label, and a lightweight student network (LSTM) is guided to perform training. According to the trained student model, the parameter quantity is remarkably reduced, meanwhile, good detection performance is kept, the student model can be conveniently deployed in an actual content auditing system or edge equipment, and social content rumors can be efficiently recognized and judged.

The invention belongs to the technical field of dialogue generation, and relates to a digital intelligent carrier pigeon WeChat intelligent dialogue generation method based on deep learning. Dialogue data are obtained, and semantic anomaly detection is carried out through a regular expression and a BERT model in combination with a public corpus; organizing dialogue data in a mode of constructing a tree structure and introducing a three-dimensional timestamp; for emotion information of emoticons, text and visual features of expressions are combined through a hybrid encoder, a dynamic attention mechanism is adopted, weighting and memory length adjustment are carried out according to importance of historical statements, after user portrait features are introduced, user features and dialogue codes are fused, and therefore, the emotion information of the expressions is obtained. In the generation stage, through a Transform-based model and a Beam Search algorithm, a diversity penalty term is introduced in the generation process, and generation parameters are dynamically adjusted according to a user portrait, so that the diversity and security of generated dialogues are ensured, the precision and coherence of a dialogue generation model are improved, and the technical problems of context coherence and personalized generation are effectively solved.

Techniques for transcribing and / or summarizing multimedia collaboration sessions are discussed herein. For example, users can communicate within a teleconferencing meeting associated with a channel. In some examples, a first machine learning model may be configured to receive audio-visual data and user interaction data (e.g., selected emojis, detected gestures, messages or text input by a user, a thread of messages, etc.) and output a teleconferencing meeting summary. In some examples, the teleconferencing meeting summary can include one or more highlights, action items, and / or artificial intelligence (AI) notes generated using a machine learning model(s).

Disclosed are a terminal on which a messenger application for an instant messaging service is installed and an operating method thereof. The terminal is configured to, based on whether a word input by a user to a chat window of the instant messaging service is an event keyword for an advertisement event, display an interfacing object associated with the event keyword, when the user selects the interfacing object, expose at least one piece of advertisement content of a recommended emoticon set and an event banner, in which the recommended emoticon set includes event emoticons associated with the event keyword and the event banner induces an entry to an advertisement event page associated with the event keyword, and based on whether the user participates in the advertisement event, receive a reward corresponding to the advertisement content.

The invention discloses an intelligent communication content adaptation system and method based on user intention recognition. The system integrates and processes texts, voices, emoticons and unstructured behavior data through a multi-modal input analysis module; the deep learning intention recognition engine adopts a triple attention mechanism, current input and weighted historical interaction features are fused, and multi-level intention classification including basic operation, semantic targets and emotion driving is output; the real-time emotion state analysis module fuses acoustics, semantics and physiological indexes to generate a dynamic emotion matrix; the content adaptation decision engine is based on the intention confidence and the emotional state; and the multi-channel output optimization module performs cooperative adjustment on the speech synthesis rhythm, the text abstract and the visual interface according to the speech synthesis rhythm and the text abstract. According to the method, the perception precision of the deep intention and emotion of the user in a complex scene is remarkably improved, the individuation and multi-channel adaptive optimization of the response content are realized, and the communication efficiency and the user experience are effectively improved.

The present technology provides an emoji recommendation service that measures a similarity score between a global emoji embedding space of an online community with a user embedding space generated from the global emoji embedding space and generates a ranking of top emojis based on the measured similarity score. The emoji recommendation service further measures an averaged ranking of top emojis based on the similarity score and term frequency scores in the online community. The emoji recommendation service trains a machine learning algorithm to arrange vectors representing emojis in the global emoji embedding space.

Techniques for transcribing and / or summarizing multimedia collaboration sessions are discussed herein. For example, a user may communicate within a teleconference associated with a channel. In some examples, a first machine learning model may be configured to receive audiovisual data and user interaction data (e.g., selected emoticons, detected gestures, messages or text entered by a user, threads of messages, etc.) and output a teleconference summary. In some examples, the teleconference summary may include one or more highlight points, action items, and / or artificial intelligence (AI) annotations generated using a machine learning model.

A method and a system for performing domain adaptations of social media text by using lexical data transformations are provided. The method includes: receiving a first data set that is usable for training a machine learning (ML) model that is designed to perform natural language processing tasks; training the ML model by using the first data set; receiving a second data set that relates to a social media platform; transforming a subset of the first data set into a third data set that is suitable for the social media platform; and retraining the ML model by using a combination of the first data set, the second data set, and the third data set. The transformations may include injecting emojis, emoticons, user mention indicators, hashtags, retransmission indicators, URLs, and / or inverse lexical normalizations that are often used in social media posts.

The technology provides mechanisms to set criteria for when a scheduled send of a communication, such as an email, text message, chat message, etc., should occur. The criteria can be based on the state of a user's message inbox, their communication channels, and / or the status or actions of others to be communicated with. Machine learning can be used to either generate the criteria for when or how to respond, or to generate a recommended response. The system can reformat a communication by changing the tone of a message, or rework the communication for transmission in an alternative format / medium via a different messaging app. This can involve changing from an email message to a chat or audio message, replacing text with emoji or the like, adding visual indicia to the communication, and / or adding audible information to the communication.

The embodiment of the invention provides an expression package generation method and related equipment. The emoticon generation method comprises the steps that in response to uploading of a target picture, use state information and user historical data are acquired, and the use state information comprises a current use scene, a current track and a current emotion; the historical data of the user comprises the use frequency and use scene of each emoji package by the user and emoji package data generated by the user; converting the target picture by using state information and user historical data to generate an undetermined expression package; obtaining user content adjustment information; and according to the user content adjustment information, adjusting the to-be-determined emoji package to obtain a target emoji package. According to the technical scheme provided by the embodiment of the invention, the finally generated expression package is more personalized, efficient adaptability can be kept in various use scenes, and the user experience is improved.

The embodiment of the invention discloses a personalized emoticon conversion processing method and system, equipment and a storage medium. According to the technical scheme provided by the embodiment of the invention, the real-time clipboard input information is acquired, and the target emoticon format of the clipboard input information is determined according to the type identifier of the clipboard input information; under the condition that the target emoticon format is not matched with the set general emoticon format, extracting image data in the clipboard input information frame by frame; and sequentially adding the extracted image data to the emoticon construction container corresponding to the general emoticon format, and generating the motion image emoticon corresponding to the general emoticon format based on the emoticon construction container. By adopting the technical means, the universal processing of the personalized emoticons can be realized, so that the personalized emoticons can be applied in a cross-platform, cross-version and cross-application manner, the visual consistency and the functional integrity of the personalized emoticons are kept, and the use experience of a user is improved.

1. Name of this design product: Pack (Emoji Pack). 2. Purpose of this design: To contain items. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model. 5. The rear view is shown in the changing state diagram, and is omitted.

The invention discloses an 18-key pinyin input method and device, electronic equipment and a medium, and belongs to the technical field of computer character input. An existing Chinese input method has the defects that the keys are too dense, mistaken touch is prone to occurring, the repeated code rate is high, spelling is inconvenient and the like, and input efficiency is affected. The design that the initial consonant area and the final area are separated is adopted, nine keys are arranged, left-hand and right-hand division input is achieved, conflicts are reduced, and the spelling speed is increased; the initial consonant area key corresponds to two letters, the final area key corresponds to a single letter, and a user-defined key is reserved as a delete key. The character prompt area is provided with double-row display, so that pinyin combination results can be prompted conveniently. Besides the alphabet area, functional keys such as Chinese-English / simple-complex switching, punctuation, emoji, number, space and return are further configured, the function of triggering extension by long pressing is supported, the input position can be moved by long pressing of the space key, and voice input can be performed by long pressing of the return key. By optimizing the number, distribution and interaction mode of the keys, the input efficiency and the operation comfort are both considered, the false touch rate is remarkably reduced, and the Chinese input experience on the mobile equipment is improved.

Provided in the embodiments of the present disclosure are an emoji content processing method and apparatus, and a device, a readable storage medium and a product. The method comprises: in a preset interface, displaying target content associated with second users, and a preset target control; on the basis of preset trigger operations performed by first users on the target control, displaying a preset emoji content generation interface; acquiring emoji content generated in the emoji content generation interface, and displaying the emoji content and a preset sending control in the emoji content generation interface; and in response to a trigger operation on the sending control, sending the emoji content to conversation interfaces associated with the second users. Therefore, the first users can generate emoji content that better meets actual requirements, and can quickly send the emoji content to the conversation interfaces, thereby simplifying the processes of generating and sending the emoji content.

This disclosure relates to a method, apparatus, storage medium, and electronic device for obtaining emoji combinations. The method includes: obtaining multiple candidate emoji combinations from multiple target texts, each candidate emoji combination comprising multiple emojis; for each candidate emoji combination, obtaining one or more adjacent characters from the multiple target texts adjacent to the candidate emoji combination, and obtaining the adjacent character richness of the candidate emoji combination based on the number of the one or more adjacent characters and the adjacent probability of each adjacent character with the candidate emoji combination; obtaining the dependency of the candidate emoji combination based on the probability of the multiple emojis of the candidate emoji combination appearing individually in the multiple target texts and the probability of the candidate emoji combination appearing in the multiple target texts; and determining a target emoji combination based on the adjacent character richness and dependency of each candidate emoji combination.

The invention provides a big language model red team test method and device based on emoticons, and relates to the technical field of artificial intelligence. The emoticon-based big language model red team test method comprises the following steps: acquiring an original red team test problem; identifying sensitive vocabularies from the original red team test problem; using a first emoticon representing sensitive vocabulary semantics to replace sensitive vocabularies in the original red team test question to obtain a first to-be-tested red team question; embedding a second emoticon into the first to-be-tested red team question to obtain a second to-be-tested red team question; wherein the second emoticon is used for de-attacking the theme of the original red team test problem; and utilizing the second to-be-tested red team question to perform red team test on the large language model. According to the method, the red team test can be efficiently and reliably carried out on the large language model.

A text analysis processing for detecting computer-generated text is provided. In some cases, a text-based chat interaction may be initiated and analyzed to determine whether the text-based chat generated by a communicating entity is computer-generated. The text of the chat session may be analyzed to evaluate punctuation, use of emojis, spacing, grammar, words, phrases, and the like to determine a further likelihood of whether the text is computer-generated. A duration of the chat session may be used as a scoring factor. The various probabilities and scores may be combined to provide a composite score.

The invention discloses a multi-modal sentiment analysis and understanding method for social media comments, which comprises the following steps: respectively extracting text sentiment features, expression sentiment features and picture sentiment features from an input social media comment screenshot, and fusing the sentiment features to form multi-modal data sentiment features; the classification model carries out sentiment analysis on multi-modal data sentiment features to obtain sentiment labels of the comments, multi-modal information such as texts, emoticons and pictures in the social media comments is effectively fused, joint training is carried out by utilizing a large model, the relevance among various types of data can be fully mined during sentiment analysis, and the sentiment labels of the comments are obtained. And the accuracy and comprehensiveness of sentiment analysis are improved.

Methods, systems, and storage media for providing a heuristic multilanguage tokenizer in a web browser environment are disclosed. Exemplary implementations can receive a query; generate a query set comprising at least one query token; generate a result set from a searchable phrase, wherein the result set comprises at least one phrase token; generating at least one supplemental set. Determining that any of the sets comprise at least one of: a first character-type, a second character-type, or an emoji. In response to determining the first character-type: determining a word boundary and extracting a diacritic. In response to determining that the query, result set and the supplemental set comprise the second-type or the emoji, adding a space around each second character-type or emoji in the set; and segmenting the set into a plurality of tokens.

A text analysis processing for detecting computer-generated text is provided. In some cases, a text-based chat interaction may be initiated and analyzed to determine whether the text-based chat generated by a communicating entity is computer-generated. The text of the chat session may be analyzed to evaluate punctuation, use of emojis, spacing, grammar, words, phrases, and the like to determine a further likelihood of whether the text is computer-generated. A duration of the chat session may be used as a scoring factor. The various probabilities and scores may be combined to provide a composite score.

Present disclosure relates to a method and a user equipment (UE) for enhancing multimodal input content. The UE obtains text-blocks associated with multimodal input and determines a plurality of features for each of the text-blocks using a natural language processing (NLP) model and a neural network (NN) model. Further, the UE identifies usage pattern of the plurality of features based on historic data associated with the usage of the plurality of features using NN model. Thereafter, the UE generates personalized text for each of the textblocks based on the plurality of features and usage pattern. The UE inserts personalized emoticons based on context of text-blocks and lingo of each word using user lingo database. Thus, the UE eliminates manual correction of texts and enhances the readability of message / text entered by user.

Systems and methods are described for preparing unstructured data for machine learning analysis. An example method may include: receiving data representing a plurality of processes; analyzing the data to identify, for each process of the plurality of processes, a time-ordered sequence of events that occurred during the process; generating a plurality of emoji sequences by, for each process of the plurality of processes, generating an emoji sequence, each emoji in the emoji sequence representing an event of the events that occurred during the process, and the emoji sequence ordered in accordance with the time-ordered sequence; generating a plurality of feature vectors corresponding to the respective plurality of emoji sequences; and applying a machine learning technique to the plurality of feature vectors.

1. Name of the design product: Cup (Emoji Color Blocking). 2. Use of the design product: For cups. 3. Design points of the design product: The combination of shape and pattern. 4. Picture or photograph that best shows the design points: Perspective view.

A message interaction method and apparatus, a device, a storage medium, and a program product. The method proposed herein comprises: receiving, in an interactive interface with a virtual object, a first message input by a user; presenting a second message generated by the virtual object on the basis of the first message; and providing a first emoji associated with the second message, the first emoji being determined from a preset first emoji library on the basis of the second message.

This invention provides a method, apparatus, device, and medium for updating emoji resources, relating to the field of resource updates. The method includes: a terminal device sending the version number of emoji resources in a first storage area to a server, where the first storage area is the working area; receiving latest version identification information sent by the server; comparing the version number of the emoji resources in the working area with the version number in the latest version identification information to obtain a comparison result; in response to the comparison result indicating a version difference, downloading the latest emoji resources sent by the server to a cache area; temporarily storing the latest emoji resources in the decompressed area to a second storage area; setting the second storage area as the working area and the first storage area as a backup area. For lightweight terminal devices, this method employs a process combining layered storage and atomic updates, avoiding the consumption of terminal memory resources and eliminating the need to parse the internal files of the emoji resources, thus achieving seamless updates and smooth switching.

This application provides a method, apparatus, electronic device, computer program product, and computer-readable storage medium for posting dynamic messages on a social network. The method comprises: displaying a virtual card in a human-computer interaction interface; wherein the virtual card includes a virtual container and multiple emoticons located outside the virtual container and associated with different dynamic messages; controlling the movement of the multiple emoticons within the virtual card based on gravity; and, in response to a target emoticon moving into the virtual container, sending a dynamic message associated with the target emoticon to the social network; wherein the target emoticon is any one of the multiple emoticons. This application can improve the diversity of human-computer interactions during dynamic posting.

The invention discloses a multi-modal sentiment analysis method for short texts with emoticons, which comprises the following steps of: acquiring a short text data set, and preprocessing data; constructing an emoticon sample set; performing feature vectorization; constructing an ET-BiLSTM-CNN hybrid analysis model, performing collaborative modeling of global and local semantics, and weighting the obtained emoticon vectors in combination with an attention mechanism; training the hybrid analysis model by using the training set; and performing short text sentiment classification by utilizing the trained hybrid analysis model. The invention provides an emoticon vectorization method, which makes up for the deficiency of emoticon vectorization in the existing research, provides richer semantic information for short text sentiment analysis, combines context to optimize semantic representation, introduces an attention mechanism to strengthen emoticon features, and fuses with text word vectors to improve the text sentiment analysis accuracy. And finally, sentiment analysis and classification are carried out, so that the analysis accuracy of the short text with the emotion symbols is greatly improved. The text content and the emoticon information can be synchronously obtained and fused at the same time.

An emoji preview method and apparatus, a device and a medium are disclosed according to the present disclosure. The method includes: when a first user uploads an emoji through a first page, acquiring an emoji to be added in response to an adding operation triggered by the first user on the first page; after the emoji to be added is acquired, displaying a preview, where the preview corresponds to the emoji to be added.

The invention discloses an NLP-based tourist bullet screen sentiment analysis and content generation method, and relates to the field of bullet screen sentiment analysis, and the method comprises the steps: carrying out BERT deep word segmentation on an original bullet screen text, and extracting a word sequence, an emoticon, a punctuation string sequence and a repeated character string; constructing three-channel feature vectors of vocabularies, side languages and time, and generating a fusion feature vector through a multi-head attention mechanism; calculating an emotion probability by using a generalized additive hybrid model in combination with a cubic spline basis function, and outputting an emotion classification tag; through multi-dimensional feature fusion and probability calibration, deep association of semantics, sublingual symbols and time sequence fluctuation in the bullet screen is effectively captured, the problem that a traditional method is insufficient in utilization of sublingual information and time sequence features is solved, the emotion probability calculation precision is improved, and the boundary sample misjudgment rate is reduced.