Abstract: Systems and methods for enhancing the depth and richness of content in computer-generated patent applications by providing non-explicit support for individual claim features are disclosed. Exemplary implementations may: receive a previously unseen claim feature, the previously unseen claim feature being absent from the previously received patent documents; provide one or more sentences of never-been-seen-before computer-generated text using the trained machine learning model and the previously unseen claim feature as input; and insert the one or more sentences of non-explicit support in a draft patent application proximal to explicit support for the previously unseen claim feature.

Abstract: Embodiments of the disclosure provide methods and systems for generating a customer inquiry processing model for processing customer inquiries. The method includes obtaining a conversation log comprising a plurality of conversation entries associated with a conversation between a customer and an agent. The method also includes identifying, from the conversation entries, a slot of key information and determining that the identified slot relates to an application program interface (API) call. The method also includes obtaining an API log comprising a plurality of API calls associated with the conversation, and identifying an API call from the API calls included in the API call log based on the identified slot. The method further includes associating the identified slot with the corresponding API call and generating a customer inquiry processing model for processing a customer inquiry based on information relating to the identified slot and the corresponding API call.

Abstract: Embodiments of the disclosure provide a method and system for processing a customer inquiry. The method includes obtaining multiple conversations. Each of the conversations includes multiple conversation entries associated with the conversation. The method also includes, for each of the conversations, generating a directed path from a start to an end of the historical conversation. The directed path includes multiple edges and vertices. Each of the edges represents a conversation entry or an API call associated with the conversation, and each of the vertices represents a state of the conversation. The method further includes generating a directed graph based on the generated directed paths and determining an optimized directed path based on the directed graph. The method also includes receiving a customer inquiry from a user device associated with a customer, and generating a response based on the optimized directed path.

Abstract: Systems and methods for facilitating editing of a confidential document by a non-privileged person by stripping away content and meaning from the document without human intervention such that only structural and/or grammatical information of the document are conveyed to the non-privileged person are disclosed. Exemplary implementations may: receive an electronic document including text conveying one or more confidential concepts; provide a content-stripped version of the electronic document to a human editor; receive an edited content-stripped version of the electronic document; and provide an edited electronic document based on the edited content-stripped version such that human-editor-provided changes were effectuated without the human editor ever being exposed to the content and meaning contained in the electronic document.

Abstract: Systems and methods for using machine learning and rules-based algorithms to create a patent specification based on human-provided patent claims such that the patent specification is created without human intervention are disclosed. Exemplary implementations may: obtain a claim set; obtain a first data structure representing the claim set; obtain a second data structure; obtain a third data structure; and determine one or more sections of the patent specification based on the first data structure, the second data structure, and the third data structure.

Abstract: Systems and methods for facilitating editing of a confidential document by a non-privileged person by stripping away content and meaning from the document without human intervention such that only structural and/or grammatical information of the document are conveyed to the non-privileged person are disclosed. Exemplary implementations may: receive an electronic document including text conveying one or more confidential concepts; provide a content-stripped version of the electronic document to a human editor; receive an edited content-stripped version of the electronic document; and provide an edited electronic document based on the edited content-stripped version such that human-editor-provided changes were effectuated without the human editor ever being exposed to the content and meaning contained in the electronic document.

Abstract: Systems and methods for using machine learning and rules-based algorithms to create a patent specification based on human-provided patent claims such that the patent specification is created without human intervention are disclosed. Exemplary implementations may: obtain a claim set; obtain a first data structure representing the claim set; obtain a second data structure; obtain a third data structure; and determine one or more sections of the patent specification based on the first data structure, the second data structure, and the third data structure.

Abstract: Systems and methods for using machine learning and rules-based algorithms to create a patent specification based on human-provided patent claims such that the patent specification is created without human intervention are disclosed. Exemplary implementations may: obtain a claim set; obtain a first data structure representing the claim set; obtain a second data structure; obtain a third data structure; and determine one or more sections of the patent specification based on the first data structure, the second data structure, and the third data structure.

Abstract: Systems and methods for using machine learning and rules-based algorithms to create a patent specification based on human-provided patent claims such that the patent specification is created without human intervention are disclosed. Exemplary implementations may: obtain a claim set; obtain a first data structure representing the claim set; obtain a second data structure; obtain a third data structure; and determine one or more sections of the patent specification based on the first data structure, the second data structure, and the third data structure.

Abstract: A learning system for a text-to-text application such as a machine translation system. The system has questions, and a matrix of correct answers to those questions. Any of the many different correct answers within the matrix can be considered as perfectly correct answers to the question. The system operates by displaying a question, which may be a phrase to be translated, and obtaining an answer to the question from the user. The answer is compared against the matrix and scored. Feedback may also be provided to the user.

Abstract: Systems and methods for using machine learning and rules-based algorithms to create a patent specification based on human-provided patent claims such that the patent specification is created without human intervention are disclosed. Exemplary implementations may: obtain a claim set; obtain a first data structure representing the claim set; obtain a second data structure; obtain a third data structure; and determine one or more sections of the patent specification based on the first data structure, the second data structure, and the third data structure.

Abstract: Systems and methods for creating connections for advocate advice are provided. Various embodiments of the present invention relate to identifying users as experts/advocates in a particular area and connecting the experts/advocates with users of a social network. The experts/advocates can be self-identified or identified through a selection process. For example, products owned by a particular user can be identified (e.g., manually or automatically), and the particular user can opt-in as an advocate for those products. In some embodiments, various rewards can be provided to encourage participation at different levels. The experts/advocates can provide recommendations based on subject matter selected by the user (e.g., twins, pregnancy, cancer, phone type, etc.) with or without any friendship connections. In addition, the experts/advocates can be recommended to users of the social network based one or more social signals.

Abstract: A system and method for capitalizing translated text is provided. A capitalized source text is automatically translated to a target text. The target text is capitalized according to information in the capitalized source text.

Abstract: Systems and methods for automatically modifying an annotated bilingual segment pair are provided. An annotated bilingual segment pair (“Pair”) may be modified to generate improved translation rules used in machine translation of documents from a source language to a target language. Because a single Pair may be used to translate a phrase, many Pairs are used in a machine translation system and manual correction of each model is impractical. Each Pair may be modified by re-labeling syntactic categories within the Pair, re-structuring a tree within the Pair, and/or re-aligning source words to target words within the Pair. In exemplary embodiments, many alternate Pairs (or portions thereof) are generated automatically, rule sequences corresponding to each are derived, and one or more rule sequences are selected. Using the selected rule sequence, a modified Pair is distilled.

Abstract: Training and translation using trees and/or subtrees as parts of the rules. A target language is word aligned with a source language, and at least one of the languages is parsed into trees. The trees are used for training, by aligning conversion steps, forming a manual set of information representing the conversion steps and then learning rules from that reduced set. The rules include subtrees as parts thereof, and are used for decoding, along with an n-gram language model and a syntax based language mode.

Abstract: A machine translation system may use non-parallel monolingual corpora to generate a translation lexicon. The system may identify identically spelled words in the two corpora, and use them as a seed lexicon. The system may use various clues, e.g., context and frequency, to identify and score other possible translation pairs, using the seed lexicon as a basis. An alternative system may use a small bilingual lexicon in addition to non-parallel corpora to learn translations of unknown words and to generate a parallel corpus.

Abstract: A statistical translation model (TM) may receive a parse tree in a source language as an input and separately output a string in a target language. The TM may perform channel operations on the parse tree using model parameters stored in probability tables. The channel operations may include reordering child nodes, inserting extra words at each node (e.g., NULL words) translating leaf words, and reading off leaf words to generate the string in the target language. The TM may assign a translation probability to the string in the target language.

Abstract: A training system for text to text application. The training system finds groups of documents, and identifies automatically similar documents in the groups which are similar. The automatically identified documents can then be used for training of the text to text application. The comparison uses reduced size versions of the documents in order to minimize the amount of processing.

Abstract: Tree transducers can be trained for use in probabilistic operations such as those involved in statistical based language processing. Given sample input/output pairs as training, and given a set of tree transducer rules, the information is combined to yield locally optimal weights for those rules. This combination is carried out by building a weighted derivation forest for each input/output pair and applying counting methods to those forests.

Abstract: A machine translation system may use non-parallel monolingual corpora to generate a translation lexicon. The system may identify identically spelled words in the two corporal and use them as a seed lexicon. The system may use various clues 1 e.g., context and frequency, to identify and score other possible translation pairs 1 using the seed lexicon as a basis. An alternative system may use a small bilingual lexicon in addition to non-parallel corpora to learn translations of unknown words and to generate a parallel corpus.