Abstract: The present invention relates to queue-theoretic models for integration of automated call routing systems with human operators. Organizations are increasingly turning to spoken dialog systems for automated call routing to reduce call center costs. To maintain quality service even in cases of failure, these systems often resort to ad-hoc rules for dispatching calls to a human operator. The present invention provides queue-theoretic methods that provide a modeling and simulation capability in support of decisions about the staffing of call-handling centers based on the frequency of incoming calls and the competency of automated dialog systems. The methods include a procedure for identifying when callers should be transferred to operators. The procedure integrates models that predict when a call is likely to fail using spoken dialog features with queuing models of call center volume and service time.

Abstract: A system and method of refining context-free grammars (CFGs). The method includes deriving back-off grammar (BOG) rules from an initially developed CFG and utilizing the initial CFG and the derived BOG rules to recognize user utterances. Based on a response of the initial CFG and the derived BOG rules to the user utterances, at least a portion of the derived BOG rules are utilized to modify the initial CFG and thereby produce a refined CFG. The above method can carried out iterativey, with each new iteration utilizing a refined CFG from preceding iterations.

Abstract: Architecture for integrating and generating back-off grammars (BOG) in a speech recognition application for recognizing out-of-grammar (OOG) utterances and updating the context-free grammars (CFG) with the results. A parsing component identifies keywords and/or slots from user utterances and a grammar generation component adds filler tags before and/or after the keywords and slots to create new grammar rules. The BOG can be generated from these new grammar rules and can be used to process the OOG user utterances. By processing the OOG user utterances through the BOG, the architecture can recognize and perform the intended task on behalf of the user.

Abstract: Architecture for integrating and generating back-off grammars (BOG) in a speech recognition application for recognizing out-of-grammar (OOG) utterances and updating the context-free grammars (CFG) with the results. A parsing component identifies keywords and/or slots from user utterances and a grammar generation component adds filler tags before and/or after the keywords and slots to create new grammar rules. The BOG can be generated from these new grammar rules and can be used to process the OOG user utterances. By processing the OOG user utterances through the BOG, the architecture can recognize and perform the intended task on behalf of the user.

Abstract: A system and method for prediction of a user goal for command/control of a personal device (e.g., mobile phone) is provided. The system employs statistical model(s) that can predict a command based, at least in part, on past user behavior (e.g., probability distribution over a set of predicates, and, optionally arguments). Further, the system can be employed with a speech recognition component to facilitate language modeling for predicting the user goal. The system can include predictive user models (e.g., predicate model and argument model) that receive a user input (e.g., utterance) and employ statistical modeling to determine the likely command without regard to the actual content of the input (e.g., utterance). The system employs features for predicting the next user goal which can be stored in a user data store. Features can capture personal idiosyncrasies or systematic patterns of usage (e.g., device-related, time-related, predicate-related, contact-specific and/or periodic features).

Abstract: An architecture is presented that leverages discrepancies between user model predictions and speech recognition results by identifying discrepancies between the predictive data and the speech recognition data and repairing the data based in part on the discrepancy. User model predictions predict what goal or action speech application users are likely to pursue based in part on past user behavior. Speech recognition results indicate what goal speech application users are likely to have spoken based in part on words spoken under specific constraints. Discrepancies between the predictive data and the speech recognition data are identified and a dialog repair is engaged for repairing these discrepancies. By engaging in repairs when there is a discrepancy between the predictive results and the speech recognition results, and utilizing feedback obtained via interaction with a user, the architecture can learn about the reliability of both user model predictions and speech recognition results for future processing.

Abstract: A generic predictive argument model that can be applied to a set of slot values to predict a target slot value is provided. The generic predictive argument model can predict whether or not a particular value or item is the intended target of the user command given various features. A prediction for each of the slot values can then be normalized to infer a distribution over all values or items. For any set of slot values (e.g., contacts), a number of binary variables are created that indicate whether or not each specific slot value was the intended target. For each slot value, a set of input features can be employed to predict the corresponding binary variable. These input features are generic properties of the contact that are “instantiated” based on properties of the contact (e.g., contact-specific features). These contact-specific features can be stored in a user data store.

Abstract: An expertise model based upon jargon usage is described. The expertise model is generated by an expertise model training system which includes a feature extractor to extract jargon-based features from a training text corpus. A model training component uses the features to generate the expertise model. The expertise model can be used for varied applications such as providing help resources in response to a user help inquiry or ranking or re-ranking query results.

Abstract: A system and method for online reinforcement learning is provided. In particular, a method for performing the explore-vs.-exploit tradeoff is provided. Although the method is heuristic, it can be applied in a principled manner while simultaneously learning the parameters and/or structure of the model (e.g., Bayesian network model). The system includes a model which receives an input (e.g., from a user) and provides a probability distribution associated with uncertainty regarding parameters of the model to a decision engine. The decision engine can determine whether to exploit the information known to it or to explore to obtain additional information based, at least in part, upon the explore-vs.-exploit tradeoff (e.g., Thompson strategy). A reinforcement learning component can obtain additional information (e.g., feedback from a user) and update parameter(s) and/or the structure of the model.

Abstract: A system for displaying information to a user is disclosed. The system comprises a grouping module that organizes data items into groups that are ranked based upon at least one attribute of the data items. Also included is a presentation module that presents the ranked groups of data items to a user along with a group indicator. Methods for using the disclosed system are additionally provided.

Abstract: A simulation environment for adapting a speech model (e.g., baseline model) to a user is provided. The user can interact with a base parametric speech model (e.g., statistical model with learnable parameters such as a Bayesian network) and give positive and/or negative feedback when the dialog system has performed what the user considers to be appropriate and/or inappropriate action(s). From the user feedback, the dialog system learns to take actions customized for the particular user. Speaker-dependent adaptation can be extended to the dialog level by performing maximum likelihood linear regression (MLLR) adaptation simultaneously with dialog personalization. Users are immediately able to observe how their feedback has caused the dialog system to adapt, and can quit training whenever they feel that the dialog system has adapted enough for current purposes.

Abstract: An online dialog system and method are provided. The dialog system receives speech input and outputs an action according to its models. After executing the action, the system receives feedback from the environment or user. The system immediately utilizes the feedback to update its models in an online fashion.

Abstract: A dialog system training environment and method using text-to-speech (TTS) are provided. The only knowledge a designer requires is a simple specification of when the dialog system has failed or succeeded, and for any state of the dialog, a list of the possible actions the system can take. The training environment simulates a user using TTS varied at adjustable levels, a dialog action model of a dialog system responds to the produced utterance by trying out all possible actions until it has failed or succeeded. From the data accumulated in the training environment it is possible for the dialog action model to learn which states to go to when it observes the appropriate speech and dialog features so as to increase the likelihood of success. The data can also be used to improve the speech model.

Abstract: The present invention relates to a system and methodology for dynamic presentation of search result information within a selected area of a display. In one aspect, a computerized interface for data presentation is provided. The system includes a lens component associated with a portion of a user interface display, wherein the lens component defines an area to display information from at least one search result. A layout component displays a detailed subset of information within the lens component based upon the search result.

Abstract: The present invention relates to queue-theoretic models for integration of automated call routing systems with human operators. Organizations are increasingly turning to spoken dialog systems for automated call routing to reduce call center costs. To maintain quality service even in cases of failure, these systems often resort to ad-hoc rules for dispatching calls to a human operator. The present invention provides queue-theoretic methods that provide a modeling and simulation capability in support of decisions about the staffing of call-handling centers based on the frequency of incoming calls and the competency of automated dialog systems. The methods include a procedure for identifying when callers should be transferred to operators. The procedure integrates models that predict when a call is likely to fail using spoken dialog features with queuing models of call center volume and service time.

Abstract: A multi-level decision-analytic approach to failure and repair within computer-user communications is disclosed. In one embodiment, a computerized system for repairing communication failure within a computer-user interaction context includes a maintenance module, an intention module, and a conversation control subsystem. The maintenance module manages uncertainty regarding signal identification and channel fidelity. The intention module is supported by the maintenance module, and manages uncertainty about the recognition of user's goals from signals. The conversation control subsystem surrounds both the modules, and manages the joint activity between the computer and the user, and one or more high-level events regarding the joint activity.