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 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

In one aspect, the present invention facilitates the investigation of networks of criminals, by gathering associations between phone numbers, the names of persons reached at those phone numbers, and voice print data. In another aspect the invention automatically detects phone calls from a prison where the voiceprint of the person called matches the voiceprint of a past inmate. In another aspect the invention detects identity scams in prisons, by monitoring for known voice characteristics of likely imposters on phone calls made by prisoners. In another aspect, the invention automatically does speech-to-text conversion of phone numbers spoken within a predetermined time of detecting data indicative of a three-way call event while monitoring a phone call from a prison inmate. In another aspect, the invention automatically thwarts attempts of prison inmates to use re-dialing services. In another aspect, the invention automatically tags audio data retrieved from a database, by steganographically encoding into the audio data the identity of the official retrieving the audio data.

Multi-party conversation analyzer & logger

The patent describes a single location and application on a network where a user can organize public, group, and private/personal information and have this single, location accessible to the public. A new, ontology-driven portal that organizes all three categories of data according to various “facets” using underlying ontologies to define each “facet” and wherein any type of information can be classified and linked to other types of information is disclosed. An application that enables a user to effectively utilize and manage knowledge and data the user posses and allows other users to effectively and seamlessly benefit from the user's knowledge and data over a computer network is also disclosed. A method of processing content created by a user utilizing a semantic, ontology-driven portal on a computer network is described. The semantic portal application provides the user with a content base, such as a semantic form or meta-form, for creating a semantic posting. The semantic portal utilizes a knowledge data structure, such as a taxonomy or ontology, in preparing a semantic posting based on the information provided by the user via the content base. The semantic portal application prepares a preview of a semantic posting for evaluation by the user. The semantic posting is then either modified by the user or accepted and posted by the user for external parties to view.

Semantic web portal and platform

Methods and systems for entity recognition and disambiguation using natural language processing techniques are provided. Example embodiments provide an entity recognition and disambiguation system (ERDS) and process that, based upon input of a text segment, automatically determines which entities are being referred to by the text using both natural language processing techniques and analysis of information gleaned from contextual data in the surrounding text. In at least some embodiments, supplemental or related information that can be used to assist in the recognition and/or disambiguation process can be retrieved from knowledge repositories such as an ontology knowledge base. In one embodiment, the ERDS comprises a linguistic analysis engine, a knowledge analysis engine, and a disambiguation engine that cooperate to identify candidate entities from a knowledge repository and determine which of the candidates best matches the one or more detected entities in a text segment using context information.

Nlp-based entity recognition and disambiguation

A computer implemented method of suggesting replacement words for words of a string. In the method, an input string of input words is received [202]. The input words are then matched to subject words of a candidate table [218]. Next, candidate replacement words and scores from the candidate table corresponding to the matched subject words are extracted [220]. Each score is indicative of a probability that the input word should be replaced with the corresponding candidate replacement word. Finally, replacement of the input words with their corresponding candidate replacement words is selectively suggested based on the scores for the replacement words [226]. Another aspect of the present invention is directed to a spell checking system that is configured to implement the method.

Method and system for generating spelling suggestions

A “Natural Language Script Interface” (NLSI), provides an interface and query system for automatically interpreting natural language inputs to select, execute, and/or otherwise present one or more scripts or other code to the user for further user interaction. In other words, the NLSI manages a pool of scripts or code, available from one or more local and/or remote sources, as a function of the user's natural language inputs. The NLSI operates either as a standalone application, or as a component integrated into existing applications. Natural language inputs may be received from a variety of sources, and include, for example, computer-based text or voice input, handwriting or text recognition, or any other human or machine-readable input from one or more local or remote sources. In various embodiments, machine learning techniques are used to improve script selection and processing as a function of observed user interaction with selected scripts.

Natural language interface for driving adaptive scenarios

Provided is an adaptive semantic reasoning engine that receives a natural language query, which may contain one or more contexts. The query can be broken down into tokens or a set of tokens. A task search can be performed on the token or token set(s) to classify a particular query and/or context and retrieve one or more tasks. The token or token set(s) can be mapped into slots to retrieve one or more task result. A slot filling goodness may be determined that can include scoring each task search result and/or ranking the results in a different order than the order in which the tasks were retrieved. The token or token set(s), retrieved tasks, slot filling goodness, natural language query, context, search result score and/or result ranking can be feedback to the reasoning engine for further processing and/or machine learning.

Adaptive semantic reasoning engine

A task framework and a semantic reasoning engine are combined to provide a scalable mechanism for dealing with extremely large numbers of widgets, allowing users to both find a widget and automatically fill-in whatever functionality is available on the widget. Calling applications are employed to obtain task information from each widget. The calling application also receives user queries that can be resolved by a widget. A task reasoning process based on an adaptive semantic reasoning engine utilizes the task information to select a widget best suited to respond to a user's query. The task reasoning process can also be employed to determine “best-guess” slot filling of the selected widget. The calling application can then invoke the selected widget and, if available, fill appropriate slots with information to facilitate user interaction with the selected widget. Instances can be client- and/or server-side based.

Widget searching utilizing task framework

A method and apparatus are provided for automatically forming a grammar. Example text strings are received and N-grams are formed based on the text strings. A rule in the grammar is then generated automatically based in part on the n-grams.

William D. Ramsey

Papers

Method and system for generating spelling suggestions

Natural language interface for driving adaptive scenarios

Adaptive semantic reasoning engine

Widget searching utilizing task framework

Authoring speech grammars