Abstract: A system is described for determining the quality of query suggestions using a network of users and advertisers. The system may include a memory and a processor. The memory may store a historical dataset, a residual value, a query-advertisement link value, a query suggestion value, and a data representing a network. The network may comprise a plurality of query items linked to a plurality of advertisement items via a plurality of query-advertisement link items. The processor may generate data representing the network and may identify a query-advertisement link item in the network. The processor may calculate the residual value of the query suggestion system represented by the match type of the query-advertisement link item. The processor may calculate the query-advertisement link value. The processor may add the residual value to the query-advertisement link value to determine a query suggestion value and may store the query suggestion value in the memory.

Abstract: A system is described for optimizing the performance of online advertisements using a network of users and advertisers. The system may include a memory, an interface, and a processor. The memory may store a data representing a network comprised of queries linked to advertisements, a search query, a relevance value for each query, and a predicted weight for each advertisement. The interface may communicate with a plurality of users. The processor may be operatively connected to the memory and interface and may identify the network, and receive a query from a user, wherein the query exists in the network. The processor may calculate relevance values for the queries and use the queries with the highest relevance values to calculate a weight for each advertisement, the weight representing the relevance of the advertisement to the search query. The processor may then serve the advertisements with the highest weights to the user.

Abstract: A system is described for determining the quality of query suggestion systems using a network of users and advertisers. The system may include a memory and a processor. The memory may store data representing a plurality of query suggestion systems, a historical dataset, a plurality of residual values, and a data representing a network. The network may comprise a plurality of query items representing queries linked to a plurality of advertisement items representing advertisements via a plurality of query-advertisement link items. The processor may retrieve the historical dataset and generate the data representing the network. The processor may calculate the residual values for each query suggestion system in the plurality of query suggestion systems. The residual value may represent a value attributable to the query suggestions generated by a given query suggestion system. The processor may store the residual value of each query suggestion in the memory.

Abstract: A system is described for generating query suggestions using a network of users and advertisers. The system may include a memory, an interface, and a processor. The memory may store a data representing a network comprising query items linked to advertisement items via link items, wherein each link item comprises a weight representing the strength of the relationship between each query item and advertisement item, a search query item, and a relevance value for each query item. The processor may be operatively connected to the memory and the interface and may identify the data representing the network and receive a search query item. The processor may calculate a relevance value for each additional query item in the network based on its relationship to the received search query item. The processor may then suggest the query items with the highest relevance values to the user via the interface.

Abstract: A system is described for building a data structure representing a network of advertisers and users. The system may include a memory and a processor. The memory may be operatively connected to the processor and may store a historical dataset comprising of a plurality of query items and advertisement items, a plurality of query-advertisement link items, a weight, a data structure and a condition. The processor may identify the historical dataset, and link the query items to the advertisement items to generate query-advertisement link items. The processor may determine the weight of each query-advertisement link item and may store the query-advertisement link items and the weight in the data structure if the query-advertisement link item satisfies the condition.

Abstract: A method used in an electronic equipment (100) generates a set of joint multimodal interpretations (125) from a set of multimodal interpretations (115) generated by one or more modalities (105) during a turn, generates a set of integrated multimodal interpretations (135) including an integrated multimodal interpretation formed from each joint multimodal interpretation by unifying the type feature structure of each multimodal interpretation in the joint multimodal interpretation, and generates a multilevel confidence score for each integrated multimodal interpretation based on at least one of a context score, a content score, and a confidence score of the integrated multimodal interpretation. The method classifies the multimodal interpretations and generates a set of joint multimodal interpretations that comprises essentially all possible joint multimodal interpretations.

Abstract: A system (30) or method (100) of updating a calling party's phonebook (32) can include tracking (102) location information of at least a party that is an entry in the calling party's phonebook, storing (104) the location information at a server (15), and checking (106) the server for an updated contact number for a called party before completing a call setup to a the called party. The method can further include completing (108) the call to the updated contact number. The method can further include updating (110) the calling party's phonebook at a mobile device (13) or at fixed network equipment (15 or 16) by using the information from the server. The method can further include detecting (112) a context of a called party and routing calls to the called party based on a called party preference.

Abstract: A method and apparatus for selective distributed speech recognition includes an embedded speech recognition engine (104) and a dialog manager (102), such as a browser, coupled to the embedded speech recognition engine (104). The method and apparatus further includes the dialog manager (102) being operably couple able to at least one external speech recognition engine (106), such as a WLAN speech recognition engine (108) or a network speech recognition engine (110). The method and apparatus further includes preference information (114), environment information (112) and a speech input (116) all provided to the dialog manager (102). The dialog manager (102), in response to the preference information (114) and the environment information (112), provides the speech input (116) to the embedded speech recognition engine (104), the WLAN speech recognition engine (108) or the network speech recognition engine (110).

Abstract: An apparatus and method for selective distributed speech recognition includes a dialog manager (104) that is capable of receiving a grammar type indicator (170). The dialog manager (104) is capable of being coupled to an external speech recognition engine (108), which may be disposed on a communication network (142). The apparatus and method further includes an audio receiver (102) coupled to the dialog manager (104) wherein the audio receiver (104) receives a speech input (110) and provides an encoded audio input (112) to the dialog manager (104). The method and apparatus also includes an embedded speech recognition engine (106) coupled to the dialog manager (104), such that the dialog manager (104) selects to distribute the encoded audio input (112) to either the embedded speech recognition engine (106) or the external speech recognition engine (108) based on the corresponding grammar type indicator (170).

Abstract: A method and a system for integrating multimodal interpretations (MMIs) in user inputs to a data processing system (100) are provided. The method includes generating (603) a set of MMIs, based on the user inputs collected during a turn. The set of MMIs includes at least semantic relationship function, the semantic relationship function denoting a semantic relationship between values determined from the user inputs entered through one or more of the at least one input modality, the semantic relationship function comprising at least one semantic operator. The method further includes generating (604) sets of joint MMIs. Each set of joint MMIs includes MMIs of semantically compatible types. The method further includes resolving (606) the reference values of the references of each set of joint MMIs to generate corresponding sets of reference resolved MMIs. The method further includes generating (608) an integrated MMI for each set of reference-resolved MMIs.

Abstract: A method used in an electronic equipment (100) generates a set of joint multimodal interpretations (125) from a set of multimodal interpretations (115) generated by one or more modalities (105) during a turn, generates a set of integrated multimodal interpretations (135) including an integrated multimodal interpretation formed from each joint multimodal interpretation by unifying the type feature structure of each multimodal interpretation in the joint multimodal interpretation, and generates a multilevel confidence score for each integrated multimodal interpretation based on at least one of a context score, a content score, and a confidence score of the integrated multimodal interpretation. The method classifies the multimodal interpretations and generates a set of joint multimodal interpretations that comprises essentially all possible joint multimodal interpretations.

Abstract: A multi-modal input fusion (MMIF) (200) is made scalable based on the resources available. When system resources are low, the MMIF module will limit the number of elements in each set of related interpretations. Additionally, the number of sets generated can be increased or reduced based on an amount of system resources available. In order to accommodate the scalable MMIF module, a resource profile (205) is provided to the MMIF describing the amount of resources (memory, processing power, etc.) available, and/or an amount of resources the MMIF module can utilize. Based on the amount of resources the MMIF module calculates threshold values that are used to adjust the number of sets produced and the number of elements included within each set.

Abstract: In a system (200) where a user's input is received by a user interface (201), users are free to use available input modalities in any order and at any time. In order to ensure that all inputs are collected before inferring the user's intent, an multi-modal input fusion (MMIF) module (204) receives the user input and attempts to fill available MMI templates (contained within a database (206)) with the user's input. The MMIF module (204) will wait for further modality inputs if no MMI template is filled. However, if any MMI template within the database (206) is filled completely, the MMIF module (204) will generate a semantic representation of the user's input with the current collection of user inputs. Additionally, if after a predetermined time no MMIF template has been filled, the MMIF module (204) will generate a semantic representation of the current user's input and output this representation.

Abstract: A method and apparatus for selective distributed speech recognition includes an embedded speech recognition engine (104) and a dialog manager (102), such as a browser, coupled to the embedded speech recognition engine (104). The method and apparatus further includes the dialog manager (102) being operably couple able to at least one external speech recognition engine (106), such as a WLAN speech recognition engine (108) or a network speech recognition engine (110). The method and apparatus further includes preference information (114), environment information (112) and a speech input (116) all provided to the dialog manager (102). The dialog manager (102), in response to the preference information (114) and the environment information (112), provides the speech input (116) to the embedded speech recognition engine (104), the WLAN speech recognition engine (108) or the network speech recognition engine (110).

Abstract: An apparatus and method for selective distributed speech recognition includes a dialog manager (104) that is capable of receiving a grammar type indicator (170). The dialog manager (104) is capable of being coupled to an external speech recognition engine (108), which may be disposed on a communication network (142). The apparatus and method further includes an audio receiver (102) coupled to the dialog manager (104) wherein the audio receiver (104) receives a speech input (110) and provides an encoded audio input (112) to the dialog manager (104). The method and apparatus also includes an embedded speech recognition engine (106) coupled to the dialog manager (104), such that the dialog manager (104) selects to distribute the encoded audio input (112) to either the embedded speech recognition engine (106) or the external speech recognition engine (108) based on the corresponding grammar type indicator (170).