160 results about "Emoticon" patented technology

Methods and systems for providing dynamic emoticons during chat sessions across game consoles are disclosed. A dynamic emoticon may include haptic, visual, and / or auditory components. Each component may have an associated intensity level, e.g., based on a pressure with which a user of the game console presses a controller button or actuates a control input. The emoticon's auditory component may include or identify a sound effects filter or a known audio file; the emoticon's visual component may include or identify a visual effects filter or a known video or graphic file; the emoticon's haptic component may cause vibration of or force feedback on the controller. Emoticons may be sent from one voice chat participant's game console to another voice chat participant's game console across a network such as the Internet.

The present invention provides a method, and corresponding apparatus, for use of emoticons in IM applications by using sensory information captured by a device. Such information can include video, still image, and / or audio information. In one embodiment, based on a trigger to the system, multimedia input is captured, and relevant features are extracted from it. The extracted information is interpreted, and the interpreted information is mapped onto one or more specific pre-existing emoticons. These specific emoticons are then inserted into the IM communication via an IM API. In another aspect of the present invention, new emoticons are created based on the multimedia information captured. This can include generation of realistic emoticons based on the expressions on the user's face. Animated emoticons can also be created.

A system method of advertising using a multi-media application system is disclosed. The multi-media application relates to the delivery of multi-media messages using animated entities that audibly deliver messages created by a sender using text-to-speech technologies. The method provides targeted advertising based on information learned about both the sender of a multi-media message and the recipient of the multi-media message. The information may relate to an analysis of a text message created by the sender, emoticons chosen by the sender and inserted into the text of the message, the choice by the sender of an animated entity, or other parameters such as background music chosen for which template is chosen by the sender. Advertising messages may be delivered before the recipient receives the multi-media message, during the reception by the recipient of the multi-media message or following the reception of the multi-media message. A decision regarding whether to include an advertising message may be based on a text analysis or an analysis of the emoticons or other tags inserted into the text by the sender. Further, animated entities such as professionally designed face models, templates, additional emoticons, animation or sound effects may also be purchased by the sender for a limited number of multi-media messages, for limited amount of time or longer for use in creating multi-media messages. The system comprises a server to handle the reception and processing of sender multi-media messages and client software for both creating multi-media messages and receiving multi-media messages.

One or more character image elements can be detected from an identifier that includes a character image of an emoticon, the image elements including a base image identifier and character modifier identifier. After detection at least one uniform resource identifier component corresponding to said at least one character image element can be generated. After component generation a uniform resource locator (URL) incIuding said at least one uniform resource identifier component can be generated. After URL generation, a network resource can be requested from the URL.

The invention discloses a method for performing emotional tendency classification to microblog by using emoticons. The method comprises the following steps: building a neutral emotion set, a passive emotion set and a positive emotion set; building a neutral emotion Bayes classifier by using the neutral emotion set, the passive emotion set and the positive emotion set; building a polar emotion Bayes emotion classifier by using the passive emotion set and the positive emotion set; performing emotion classification to the microblog by using the neutral emotion Bayes classifier and the polar emotion Bayes classifier. According to the method for performing emotional tendency classification to microblog by using emoticons, two sections of classification is built, namely, building the neutral emotion classifier to remove the microblog with neutral emotion, and building the polar emotion classifier to divide the microblog with the polar emotion into the passive emotion and the positive emotion; the classifier is rapid in classifying speed, small in occupied space and robust; attitude of people to current hot topics or events and emotion of net citizens can be accurately understood through the microblog, so that the method for performing emotional tendency classification to microblog by using emoticons has important help for social scientific research and survey.

The present invention relates to a civil aviation public opinion emotion analysis method. The method comprises the steps of performing searching, preprocessing and word segmentation on microblog texts comprising keywords about civil aviation security public opinions in the internet; building a dictionary; grading each microblog text to obtain a microblog text emotion value; performing objective and subjective judgment on the microblog text according to the emotion value, so as to obtain a threat value of the microblog text for civil aviation security; and determining the threat level of the opinion in the microblog text for civil aviation security according to the threat value. The emotion value of the microblog text is determined by combining text semantics and microblog emoticons, the limitation of the dictionary and semantics rules are overcome, and the emotion value is determined more accurately. Due to full use of the characteristic of the microblog text, the threat level is determined more reasonably. The method differs from a machine learning method and is not trained with a large scale of tagged data, thereby being more suitable for processing of real-time data flow.