An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

A concept-based indexing and search system indexes collections of documents with ontology-based predicate structures through automated and/or human-assisted methods. The system extracts the concepts behind user queries to return only those documents that match those concepts. The concept based search and retrieval system comprehends the intent behind a query from a user, and returns results matching that intent. The system can perform off-line searches for unanswered user queries and notify the user when a match is found.

Concept-based search and retrieval system

A data transmission system is disclosed which optimizes transfer and updates of information between systems by providing difference information between the systems. The system transmits data packages having instructions for manipulating user data. The data packages include a header identifying the respective packages, as well as transaction objects for effecting a change to user data on a device having object instructions.

Data transfer and synchronization system

A method, system and article of manufacture therefor, are disclosed for automatically generating a query from document content.

System for automatically generating queries

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

Weights are assigned to features of data objects and the weights are utilized to determine whether data objects are substantially identical or not. One application of such weights is to assign weights to terms in web page documents. The weights assigned to the terms may then be utilized to determine whether web page documents are substantially identical. A set of identicals may be generated for each web page that is indexed by the system and utilized to repair broken hyperlinks. Specifically, when a uniform resource locator (URL) associated with the hyperlink cannot be resolved or cannot be resolved in a timely fashion, one of the identicals of the desired web page documents may be returned to provide a requesting party with the desired content.

Method for providing a substitute for a requested inaccessible object by identifying substantially similar objects using weights corresponding to object features

The present invention is directed to performing information retrieval utilizing semantic representation of text. In a preferred embodiment, a tokenizer generates from an input string information retrieval tokens that characterize the semantic relationship expressed in the input string. The tokenizer first creates from the input string a primary logical form characterizing a semantic relationship between selected words in the input string. The tokenizer then identifies hypernyms that each has an 'is a' relationship with one of the selected words in the input string. The tokenizer then constructs from the primary logical form one or more altenative logical forms. The tokenizer constructs each alternative logical form by, for each of one or more of the selected words in the input string, replacing the selected word in the primary logical form with an identified hypernym of the selected word. Finally, the tokenizer generates tokens representing both the primary logical form and the alternative logical forms. The tokenizer is preferably used to generate tokens for both constructing an index representing target documents and processing a query against that index.

Information retrieval utilizing semantic representation of text

An author-oriented document summarizer for a word processor is described. The document summarizer performs a statistical analysis to generate a list of ranked sentences for consideration in the summary. The summarizer counts how frequently content words appear in a document and produces a table correlating the content words with their corresponding frequency counts. Phrase compression techniques are used to produce more accurate counts of repeatedly used phrases. A sentence score for each sentence is derived by summing the frequency counts of the content words in a sentence and dividing that tally by the number of the content words in the sentence. The sentences are then ranked in order of their sentence scores. Concurrent with the statistical analysis, during the same pass through the document the summarizer performs a cue-phrase analysis to weed out sentences with words or phrases that have been pre-identified as potential problem phrases. The cue-phrase analysis compares sentence phrases with a pre-compiled list of words and phrases and sets conditions on whether the sentences containing them can be used in the summary. Following the cue-phrase analysis, the summarizer creates a summary containing the higher ranked sentences. The summary may also include a conditioned sentence if the conditions established for inclusion of the sentence have been satisfied. The summarizer then inserts the sentence at the beginning of the document before the start of the text.

Document summarizer for word processors

The present invention is directed to performing information retrieval utilizing semantic representation of text. In a preferred embodiment, a tokenizer generates from an input string information retrieval tokens that characterize the semantic relationship expressed in the input string. The tokenizer first creates from the input string a primary logical form characterizing a semantic relationship between selected words in the input string. The tokenizer then identifies hypemyms that each have an “is a” relationship with one of the selected words in the input string. The tokenizer then constructs from the primary logical form one or more alternative logical forms. The tokenizer constructs each alternative logical form by, for each of one or more of the selected words in the input string, replacing the selected word in the primary logical form with an identified hypernym of the selected word. Finally, the tokenizer generates tokens representing both the primary logical form and the alternative logical forms. The tokenizer is preferably used to generate tokens for both constructing an index representing target documents and processing a query against that index.

Information retrieval utilizing semantic representation of text and based on constrained expansion of query words

The present invention is directed to performing information retrieval utilizing semantic representation of text. In a preferred embodiment, a tokenizer generates from an input string information retrieval tokens that characterize the semantic relationship expressed in the input string. The tokenizer first creates from the input string a primary logical form characterizing a semantic relationship between selected words in the input string. The tokenizer then identifies hypernyms that each have an "is a" relationship with one of the selected words in the input string. The tokenizer then constructs from the primary logical form one or more alternative logical forms. The tokenizer constructs each alternative logical form by, for each of one or more of the selected words in the input string, replacing the selected word in the primary logical form with an identified hypernym of the selected word. Finally, the tokenizer generates tokens representing both the primary logical form and the alternative logical forms. The tokenizer is preferably used to generate tokens for both constructing an index representing target documents and processing a query against that index.

Information retrieval utilizing semantic representation of text by identifying hypernyms and indexing multiple tokenized semantic structures to a same passage of text

John J. Messerly

Papers

Method for providing a substitute for a requested inaccessible object by identifying substantially similar objects using weights corresponding to object features

Information retrieval utilizing semantic representation of text

Document summarizer for word processors

Information retrieval utilizing semantic representation of text and based on constrained expansion of query words

Information retrieval utilizing semantic representation of text by identifying hypernyms and indexing multiple tokenized semantic structures to a same passage of text