Patent ID: 12229151

DETAILED DESCRIPTION

A description of example embodiments follows.

When a user requests information from a virtual system, two problems can arise. First, the user may assume that the system is aware of, or can process, implicit or contextual information. The user's assumption is based on a human's ability to perform reasoning based on contextual information. Contextual information can include environment or personal preferences. Current systems, however, are not aware of and do not consider many pieces of contextual, or implicit, information. Examples of contextual information can include, in a vehicle context, accessibility needs for a parking request, or weather based information for covered parking, or parking closer to a requested destination venue. Such contextual information can be provided by a user settings file, or systems or sensors of a car, such as systems indicating a windshield wiper is running or headlights are activated, and the like.

FIG.1is a block diagram100illustrating an example embodiment of the present invention. With reference toFIG.1, a system101receives a voice request104for processing. A person of ordinary skill in the art can recognize that the voice request104, in other embodiments, can be a text-based request, or a formatted query, but for purposes of simplicity, the description of various example embodiments herein refers to any such user request as a voice request. A person of ordinary skill in the art can understand that the voice request104can be processed by an automated speech recognition (ASR) or natural language understanding (NLU) system.

A reasoning interface layer102receives the voice request104. The reasoning interface layer is configured to perform contextual reasoning to infer implicit constraints, needs, etc. to deliver personalized results to the user issuing the voice request104. The reasoning interface layer interfaces with multiple reasoning modules, such as a contextual reasoning module106and a spatial reasoning module108to produce an updated query110. The reasoning interface layer102is described in further detail in reference toFIGS.2-4below.

Once such contextual information is determined, the virtual system has to process the information and fulfill that request. The virtual system has to determine which resource to access for a query with multiple parts. The system can have access to competing providers (e.g., sources of information or content) from which to select, or multiple providers that provide outputs that need to be combined together to create the final answer.

An intelligent knowledge layer112receives the updated query110. A person of ordinary skill in the art can recognize that the intelligent knowledge layer112can also operate on a query received directly from the user, or from another source other than the reasoning interface layer102. However, the reasoning interface layer102and intelligent knowledge layer112working in combination provide a greater benefit by updating the query and enhancing the result in the intelligent knowledge layer112.

The intelligent knowledge layer112considers explicit constraints (or hard constraints) in the received query (e.g., updated query110) to retrieve best results that fulfill the user's request. The intelligent knowledge layer112is configured to analyze the updated query110, determine a strategy for resolving the query using third party sources such as a point of interest (POI) database116a, parking database116b, fuel range resource116c, and restaurants116d. The intelligent knowledge layer further interfaces with a knowledge repository118. The intelligent knowledge layer112provides an enhanced result120to the user. The intelligent knowledge layer112is described in further detail in relation toFIGS.5-8below.

Solving these problems can be applied to multiple application areas. For example, a voice response system in a vehicle can provide directions to the most appropriate parking garage given a user's preferences and sensed information from the vehicle. For example, finding covered parking in rain/snow conditions is preferred over uncovered parking.

Another application is with devices using the Internet of Things (IOT) framework. A centralized hub can serve as the gateway between the user and the outside world, where the outside world are services/virtual systems that are accessed through the same hub. For example, a user might request to order a pizza for a Boston Red Sox game. The centralized hub can process the request to determine the time of the Boston Red Sox game, and automatically generate the pizza order to arrive at that time. However, this requires the system to determine when the Red Sox game is, what type of pizza the user likes, where the best restaurant to order from is, whether that restaurant delivers to the user's current address, and how long the restaurant will take to order. After determining this information, it can generate the request to the best restaurant for delivery to the user.

Applicant's system and method provides advantages by adding contextual reasoning non-query expressed data to an expressed query. While current systems may consider geo-location in contextual reasoning, Applicant's system adds in additional contextual information by using specialized logical reasoners.

Further, current systems, such as Apple's Siri®, can be configured to perform a hard wired search to go to a particular provider (e.g., Yelp for restaurants) no matter the query parameters. Each virtual assistant has a dedicated source to use for each type of query. However, depending on the request, it can make more sense to go to a different source particular to the request. Applicant's system employs dynamic selection of sources. Each source is correlated with a declaration of its abilities, and can therefore be matched up better with particular queries. Each declaration states what each source knows about and what each source can provide. Further, each source can be associated with dimensions that it knows about, weights, or confidence scores, of how useful each dimension is, and confidence scores corresponding to each query. For example, when there are two competing sources for a restaurant query, both sources may have information about restaurants and their opening/closing hours, but only one knows more about amenities (e.g., Wi-Fi, handicapped accessible restrooms, type of lighting, bar seating, etc.) at the restaurant. A description for sources captures the known amenities with a high confidence score. Therefore, when user generates a query about a restaurant with particular amenities, that query can be directed to that source. Another source might have a high confidence score for an availability dimension. Therefore, when a user asks about whether a table is available at a particular time, it is directed to that source. Applicant's system and method therefore has a matching and planning process that compares the information needed to resolve a query against capability descriptions of each available source. Further, a query may include information that is across multiple categories. An example of such a query is “find me a good restaurant near the marina, and directions to parking there.” The system needs to first find the location of the marina, then good restaurants (e.g., above a certain rating) near that location, and then parking near the restaurant. This pulls from a database correlating businesses to locations/addresses, another database correlating restaurants that are near a location, and then a third parking database.

In other words, when asking to park near a destination, the system resolves the first point of interest (POI) before you performing the next search of parking. Other systems may send the unresolved POI to the content source directly, however, sending “restaurant near the marina” to a parking database may be unsuccessful without pre-processing. When the user says “find parking at a good Italian restaurant by the marina,” the system needs to resolve the POI “good Italian restaurant by the marina” before sending that to the parking service, because the parking service may not answer the query correctly without a resolved POI.

The system and method further needs to have a mechanism to perform relaxation to an over-constrained constraint of a query. In other systems, an overly-constrained query can default to an Internet web search of the query, or drop recognized constraints arbitrarily until a result is found. However, each of the constraints can have an importance to the user. That importance can be derived and quantified from the user's preferences or by other contextual cues that the system can infer and assert. For example, the query “find a cheap parking handicapped parking place” may need to determine which is more important—cheap parking, or accessible parking. The system and method can determine the subset of constraints that can be satisfied that brings the most satisfaction to the user. This can be judged by a user-by-user basis. For example, a user that cannot use non-accessible parking under any circumstances is likely to pay whatever is necessary for an accessible parking spot, despite requesting cheap parking. Therefore, the system prioritizes, in this case, accessible parking over cheap parking, instead of arbitrarily choosing between the two.

FIG.2is a block diagram illustrating an example embodiment of a reasoning interface layer202. The reasoning interface layer202can be a further embodiment of the reasoning interface layer102ofFIG.1. With reference toFIG.2, the reasoning interface layer202receives a semantic query204. The semantic query204, in this one example, is based on the voice request “find parking near the stadium.” The semantic query can be logically represented as “Parking (x) & Stadium (Y) & Near (X,Y),” where (X) represents the parking location and (Y) represents the stadium location, and “Near(x,y)” resolves a location near (X) and (Y). The reasoning interface layer communicates with a reasoning layer, which can include multiple reasoning modules. In the example ofFIG.2, the reasoning layer includes a contextual reasoning module206, a spatial reasoning module208, a temporal reasoning module214, and a commonsense reasoning module212. The reasoning interface layer202can also receive additional information from sensor application programming interfaces (APIs)218of a vehicle216or other system. As such, the reasoning interface layer202is a flexible framework that allows for a wide range of reasoning techniques to be integrated and accessed via a unified interface. The reasoning interface layer202interfaces with a reasoner arbitration which automatically determines which reasoners to employ. Further, the reasoning interface layer202can interface with a consistency checker that merges inferences from multiple reasoners into one consistent solution before providing the new query210.

In this example, the reasoning interface layer202employs the spatial reasoning module208and commonsense reasoning module212. The reasoning engines support frequently occurring reasoning requirements such as spatial reasoning208, contextual reasoning206, temporal reasoning214, and common sense reasoning212. In this example, to find parking near the stadium, the reasoning interface layer interfaces with the sensor APIs218of the vehicle216to determine that there is precipitation. From that, the contextual reasoning module206determines the new query210should include a request for covered parking, and therefore adds “isCovered (x, true)” to the query, indicating that the parking should be covered. Further, the common sense reasoning module212determines that distance from the parking to the stadium should be less than a half of a mile, and adds in the Distance (x,y,z) & z<0.5 miles to the new query210. Therefore, the new query210is enhanced to better serve the user, without the user having to explicitly request these additional query elements.

FIG.3is a block diagram300illustrating another embodiment of the reasoning interface layer302. The reasoning interface layer302receives inputs304including a query (e.g., a SparQL query), contextual information, and an Artificial Intelligence (AI) Log Object. Then, the reasoning interface layer302provides an output310of an update query, a provenance, an updated context, and an updated AI Log Object. The reasoning interface layer302interfaces with a context mapper306, reasoner arbitration322, consistency checker324, and a meta reasoner326. The reasoning interface layer302further interfaces with a rule repository318as well.

The context mapper306converts incoming contextual information from JavaScript Object Notation (JSON) to an internal representation that is utilized by the different reasoners. The context mapper306validates the JSON, and if the input JSON is valid, the context mapper, using an open source library, parses the JSON to identify the different keys and their corresponding values. After identifying the keys and values, context mapper306converts the keys and values to the internal representation.

The reasoner arbitration/arbitrator322identifies the reasoners that are relevant for the given query and contextual information. A number of arbitration strategies are provided with a reasoning framework and additional arbitration strategies can be added by implementing a Java, or other language's, interface. Some of the arbitration strategies which are available as part of the reasoning framework are Broadcast, Naïve Bayes, and TF-IDF. However, the reasoning framework can further plug-in any arbitration strategy. The plug-and-play of different arbitration strategies is a novel aspect of Applicant's present disclosure, and allows for different systems to easily change their method of arbitration among different reasoners.

A Broadcast arbitration strategy sends the query and contextual information to all the reasoners plugged into the system.

A Naïve Bayes arbitration strategy converts the reasoner selection problem into a classification problem and by utilizing a Naïve Bayes classifier for identifying the relevant reasoners. The Naïve Bayes classifier is a supervised training method which receives, as input, a set of features and associated class labels. The definition of a feature depends on the problem and how a user wants to model them. For example, in a problem where the objective is to identify spam emails, the presence/absence of a word can serve as feature.

Below is an example embodiment of a rule in XML format that would be stored in the rule repository318. However, a person of ordinary skill in the art can understand that the rule can be in other formats, including formats other than XML.

<rule><id> DDISCv4_BIZ_NEED_RESTROOM </id><gloss>Prioritize coffee shops and fast food restaurants if driver needsto use restroom.</gloss><reasoner>Business_Reasoner</reasoner><priority> 4.0 </priority><active> true </active><definition>?PoB rdf:type nuan:business.generic .?PoB nuan:fulfills_need ?NEED .FILTER (?NEED = nuan:amenity.toilet)−>Assert:?PoB nuan:category ?CATEGORY .FILTER SOFT 70(?CATEGORY = nuan:business.coffee_shop ∥?CATEGORY = nuan:business.fast_food)</definition></rule>

With rules such as above, feature construction can be performed as follows: First, the reasoning framework constructs a vocabulary V, which is a set of terms utilized in the rule definition. Then, the reasoning framework constructs a vector for the rules iteratively, marking each term present in V as 0 if the term is absent from the rule. If the term is present in the rule, the term frequency is utilized instead of 0. The reasoner to which the rule is assigned to serves as the class label.

For example, in case of the rule above, the terms of the rule definition are:

?PoB rdf:type nuan:business.generic .?PoB nuan:fulfills_need ?NEED .FILTER (?NEED = nuan:amenity.toilet)−>Assert:?PoB nuan:category ?CATEGORY .FILTER SOFT 70(?CATEGORY = nuan:business.coffee_shop ∥?CATEGORY = nuan:business.fast_food)

The reasoning framework parses the rule to identify the rule head, which is denoted by the part to the left of/prior to “−>” in the rule definition. The rule head is then tokenized which results in a list, such as [?POB, rdf: type, nuan:business.generic, . . . ].

In the feature vector, the reasoning framework calculates the frequency for each of the terms present in the list. The terms which are present in V, but are not present in the list are marked “1” to account for Laplace Smoothing. The reasoners that the rule belongs to (as specified in the reasoner field—Business_Reasoner) serve as the label for this feature vector.

To summarize, at the end of the process, there are as many feature vectors as there are rules. Each vector has associated labels, which are the name of the reasoners it is associated with/belongs to.

The method train the Naïve Bayes method using the feature vectors calculated, as described above. The output is a trained model, which can be utilized on unlabeled data in the next step.

As described above, the reasoning framework receives a query, which can be a SPARQL query, and contextual information as input. As described herein, the reasoning framework can receive any type of query, but the disclosure herein may refer to SPARQL queries. A person of ordinary skill in the art can recognize that such SPARQL queries can be queries formatted in other ways, including queries represented as first-order logic, etc. The reasoning framework combines the two and converts them into a feature vector using the above described process. The generated vector V′ does not have a label or is the unlabeled data. However, the model described above can generate, as output, labels and their associated probability. The label names generated by the model are the names of the reasoners which are best equipped to process the query and the given contextual information.

The reasoning framework can employ one of the two strategies to pick the right reasoner. First, the reasoning framework can utilize the top k reasoners by score. Second, a cut off score can be provided to the reasoning framework, and reasoners above the threshold can be utilized for the purposes of a processing a query.

Term Frequency-Inverse Document Frequency (TF-IDF) is a technique utilized to identify how important a given word is to a document. The TF component indicates how many times a word appears in a given document. The IDF component normalizes the TF component, by calculating the reciprocal of the number of documents in the collection containing the term. For reasoning framework arbitration, TF-IDF arbitration is utilized as follows.

First, the collection of rules is parsed to remove stop words and identify the term frequency for each rule term (a) in a given rule (b) in the entire rule base. The reasoning framework populates three data structures H1 and H2 to store (a) and (b). H1 is a list of all the rules and a mapping of terms in the rule to their frequencies. H2 consists of all the terms and their frequency. In addition, a dictionary that maps the rule ID to the list of reasoners it belongs to is stored in a data structure D.

For a new query and contextual information, the method identifies non-stop words, and retains and stores them in a list L. To calculate TF-IDF for each term tin L, there are two components—(i) t's frequency in every rule and (ii) t's frequency across the rule base. For each term stored in list L, term frequency—tf in a given rule is calculated by using the structure H1. Term frequency across the rule base—idf is calculated using the structure H2. TF-IDF score (tf-idf) is then calculated using the two components tf and idf.

Then, the sum of the (tf-idf) for all input query and context terms is utilized to calculate the final tf-idf score for the given query and rule. After the TF-IDF score is calculated using the structure D, the final selection of reasoners can be made in one of the two ways: (1) find the top k rules ranked by score, or (2) identify rules above a specific cut off. Both strategies can be configured in the reasoning framework configuration file.

The consistency checker324employs reasoner mediation to merge the responses returned by the different reasoners. Each reasoner in the reasoning framework has an associated priority with it and this priority is utilized in strategies employed by with the reasoning framework. A first strategy is a greedy Strategy, which uses the result from the reasoner with highest priority, while other reasoners are ignored. A second strategy is a smart merge strategy, which uses the result from the reasoner with the highest priority is utilized as a starting point for the query to be returned. In other words, the smart merge strategy chooses from a descending order of the priority of reasoners to merge non conflicting components of the result from the other reasoners into the query to be returned. Conflicting components of the results are discarded.

FIG.4is a flow diagram400illustrating an example embodiment of a method employed by the present invention. The method begins (402) and determines candidate reasoners to invoke, which can be based on the arbitration strategies disclosed above, including Broadcast, Naïve Bayes, and TF-IDF (404). As described above, multiple candidate reasoners provide flexibility to query enhancement. Additional reasoners can be added with minimal change to the overall structure of the reasoning interface layer. Further, each reasoner can add new types of modular reasoning to the reasoning interface layer, where a single reasoner would have to be re-coded. If no candidate reasoners are found (406), the method ends (418).

However, upon finding candidate reasoners (406), the method selects the best reasoners to invoke (408) and invokes the reasoner via its reasoner handler/API (410). Then, the method merges conclusions from each invoked reasoner (412) and resolves inconsistencies and conflicts in the merged conclusion (414). This resolution can be based on a predefined strategy such as giving preference to a reasoner with higher confidence. Then, the method determines whether new conclusions were made (416). If so (416), the method begins another iteration. If not, (416), the method ends (418).

FIG.5is a diagram500illustrating an example embodiment of an Intelligent Knowledge512intelligent knowledge layer/core engine. The intelligent knowledge layer512receives an input510including a SparQL Query (e.g., from the reasoning interface layer102ofFIG.1) and provides an output520including a list of results and an updated log object. A person of ordinary skill in the art can recognize that other formats other than the SparQL Query can be used. However, for the purposes of simplicity, SparQL Query/Queries are referenced herein, but can be any other format of query. The intelligent knowledge layer512fulfills user requests through semantic routing capabilities and processing of complex to produce high-quality, on-target results. The intelligent knowledge layer512provides many features. First, the intelligent knowledge layer512allows for provider arbitration by analyzing each request and determining which provider to invoke to resolve each query. The intelligent knowledge layer512further provides knowledge fusion that dynamically chains providers when appropriate and merges their results. A constraint satisfaction and relaxation module of the intelligent knowledge layer512further applies implicit and explicit constraints, including logical operators, to perform constraint satisfaction, but further can relax and prioritize constraints as needed to provide constraint relaxation. For example, hard constraints can be applied to high priority portions of the received queries. The hard constraints require that the high priority portions of the received queries are resolved first, followed by lower priority portions of the received query. The constraint satisfaction and relaxation module can further relax constraints by determining portions of the queries that can be ignored in order to return a result. Constraints are relaxed when a result is not possible that satisfies all portion of the query. A data inference module528of the intelligent knowledge layer512can further infer missing values and attributes based on declarative inference rules. The intelligent knowledge layer512can also integrate new content providers using a configurable, declarative framework, using a provider description & plugin manager532, which includes instructions for interfacing with each provider plugin534a-n, and also a description of what type of data each provider plugin534a-ncan receive and output.

A provider arbitration and planning module522of the intelligent knowledge layer512can analyze the SparQL Query of the input510and can determine which providers534a-nare needed to resolve each part of the query. The provider arbitration and planning module522can determine chaining of providers as well. For example, resolving the query “find me handicapped accessible parking by the marina” can require using multiple providers, and chaining the results from each. A first provider can provide a geolocation for the marina, and a second provider can provide a list of handicapped accessible parking facilities near that geolocation. Another example query can be “find me parking near a highly-rated Italian restaurant near the stadium.” Again, a first provider returns a geolocation of a stadium. Then, a second provider returns a four- or five-star Italian restaurant near that geolocation. Then, a third provider searches for parking near the geolocation of the Italian restaurant. By analyzing the query ahead of time using the provider arbitration and planning module522, these chained requests can be executed accurately.

The query execution and knowledge fusion module524is responsible for executing the plan provided by the provider arbitration and planning module522by interfacing with the provider plugins534a-534nand retrieve the relevant third party content538a-n. As chained requests are completed, the query execution & knowledge fusion module524fuses the information together to determine the result.

FIG.6is a diagram600illustrating an example embodiment of the intelligent knowledge layer/core512. The intelligent knowledge layer512receives a SparQL Query510and outputs result rows520, as described in relation toFIG.5. In relation toFIG.6, responsive to receiving SparQL Query510, the intelligent knowledge layer/core512generates Queries640to be sent to plugins634a-n. Each plugin then access one or more providers638a-nto retrieve results642to return to the intelligent knowledge layer/core512. For example, the parking plugin634acan access Parkopedia638a, but may arbitrate between several parking providers638a, depending on the requested information in the queries640and the available information advertised by each parking provider638a. For example, one parking provider may provide information regarding handicapped accessibility, while another provides information regarding amenities at the parking location. Therefore, depending on the query, the parking plugin634amay be able to better utilize one provider638aover another provider638a. Therefore, a query requesting handicapped accessibility should be directed to the parking provider with information about handicapped accessibility. A query requesting amenities at the parking location should be directed to the provider regarding amenities at the parking location. However, a query that requests both information about handicapped accessibility and amenities at the parking location should be arbitrated to determine which information is more important to the user, and direct the query appropriately. In this case, it is likely that handicapped accessibility has a weighting of high importance to the user, and therefore the query is directed to the provider with information about handicapped accessibility.

Likewise, the location plugin634b, fuel plugin634c, and other plugins634ninterface with respective provider(s), such as a HERE geolocation provider638b, an OPIS fuel provider638c, and other providers638n.

FIG.7Ais a diagram700illustrating an example embodiment of query710representing a voice request. The voice request, in this example, is “Find disabled parking near city hall in san Francisco.” The query selects “?PID” (Place Identification), “?NAME” (place name) and “?PRICE” (price of place) from a table given the criteria of the query. The query then needs to solve for the unsolved fields given the defined variables. The query also establishes a soft filter for amenities including an elevator. The soft filter, however, is not a hard requirement of the query, but rather a way to sort results of the other hard requirements. On the other hand, the query establishes a filter for the occupant being a disabled person.

FIG.7Bis a diagram720illustrating an example embodiment of identifying candidate plugins based on the query illustrated inFIG.7A.FIG.7Billustrates that to solve for ?PID, the system uses a Parking plugin, for ?POI the system using POISearch, and for ?CITY the system uses a Location/geolocation plugin.

FIG.7Cis a diagram750illustrating an example embodiment of constructing a plan to resolve the query by resolving the location of the city. The plan recognizes that “san Francisco” is a city or town type of location (e.g., ns:location.citytown) and therefore issues a request for a ?CITYGEO variable defined by a latitude ?CITYLAT and longitude ?CITYLONG. Therefore, a provider can provide the ?CITYGEO variable and its subcomponents in response to the lexical “San Francisco.” Subsequent iterations (not shown) can search for the point of interest (?POI) of City Hall that is near San Francisco, and then from that point of interest, search for disabled/handicapped accessible parking near City Hall.

FIG.7Dis a diagram755illustrating such a subsequent iteration. With the result for the Location, the point of interest (POI) can be resolved. This is performed by a connection being stitched into the request to the POISearch, as further shown by a template illustrated inFIG.7E.

FIG.7Eis a diagram760illustrating an example embodiment of stitching variables. The stitching template762is matched against the query764. A stitching template is a template of how certain variables are related to each other. Various stitching templates can be available for searching, and in one example can be stored in a database. Each query can be compared to stitching templates to find the best match based on matching variable types and connections between the variables in the query to variable types and connections within each template, and then connections can be copied from the best matching template to the query. Since the pattern of connections and variables in the stitching template764matches the fragment of the query764starting at the variable ?POI of the query764, the extra missing connection from the variable ?POI to the variable ?CITYGEO is added. Therefore, when the POI search ofFIG.7Dresolves the search for “city hall,” it bases the location on the ?LONG and ?LAT of ?CITY.

FIG.7Fis a diagram765illustrating a third iteration of constructing the plan for the query766. With the Location of the city and the location of the POI in hand, the plan can develop a search for the final parking venue. The POI of city hall can be passed to the PID search for parking. This can be performed by stitching the connection to the GEO location of OBJ1ofFIG.7Gto the PID search by matching the template fromFIG.7Gto the query766.

FIG.7Gis a diagram770illustrating an example embodiment of stitching variables. In the stitching template772,?OBJ1, having a connection to an ?OBJ2variable which in turn connects to a ?GEO variable and ?LONG and ?LAT variables, is matched to the ?PID of the parking venue. The connection from ?OBJ1to ?GEO of the stitching template772is stitched to connect ?PID and ?POIGEO in the query774. Therefore, when the POI of the search ofFIG.7Fresolves the search for the parking venue, it bases the location on the ?LONG and ?LAT of ?POI, now connected to the variable ?PID, which in this figure represents the final parking venue.

FIG.8is a diagram800illustrating an example embodiment of a method of implementing the intelligent knowledge layer/core employed by the present invention. The method begins (802) and matches input query against provider capability descriptions (804). Then, the method composes matched providers to generate candidate query plans (806). The method then determines whether valid plans are generated, for example, by determining whether the input query is covered by matched provider's capabilities (808). If not, the method ends (826).

However, if valid plans are generated (808), the method selects a top query plan and removes plan from the list of candidates (810). The top query plan can be a plan with a lowest cost of using the providers if each provider charges, a plan expected to return a result the fastest based on speed analytics about each provider, etc. If a plan is not selected (812), the method ends (826). However, if a plan is selected (812), the method executes the selected plan, started by invoking the inner most provider of the plan, and applying those results to the outer most provider (814). The inner most provider indicates the provider that does not require results from any other provider. Once the result is provided from the inner most provider, another provider in the chain can provide an answer. If the invoked provider does not return results (816), the method selects the next top query plan and removes that plan from the candidates (810). However, if it does return results, the method then applies hard constraints and relaxes soft constraints when appropriate (818). Then, if no more providers remain in the plan to execute (820), the method returns the results (824) and ends (826). However, if more providers remain, (820), the method applies the partial results to dependent providers (822) and then continues executing the selected plan by invoking the next inner most provider, using the partial results if necessary (814).

FIG.9illustrates a computer network or similar digital processing environment in which embodiments of the present invention may be implemented.

Client computer(s)/devices50and server computer(s)60provide processing, storage, and input/output devices executing application programs and the like. The client computer(s)/devices50can also be linked through communications network70to other computing devices, including other client devices/processes50and server computer(s)60. The communications network70can be part of a remote access network, a global network (e.g., the Internet), a worldwide collection of computers, local area or wide area networks, and gateways that currently use respective protocols (TCP/IP, Bluetooth®, etc.) to communicate with one another. Other electronic device/computer network architectures are suitable.

FIG.10is a diagram of an example internal structure of a computer (e.g., client processor/device50or server computers60) in the computer system ofFIG.9. Each computer50,60contains a system bus79, where a bus is a set of hardware lines used for data transfer among the components of a computer or processing system. The system bus79is essentially a shared conduit that connects different elements of a computer system (e.g., processor, disk storage, memory, input/output ports, network ports, etc.) that enables the transfer of information between the elements. Attached to the system bus79is an I/O device interface82for connecting various input and output devices (e.g., keyboard, mouse, displays, printers, speakers, etc.) to the computer50,60. A network interface86allows the computer to connect to various other devices attached to a network (e.g., network70ofFIG.9). Memory90provides volatile storage for computer software instructions92and data94used to implement an embodiment of the present invention (e.g., reasoning interface layer, intelligent knowledge/core layer, handler modules, context mapper, reasoning arbitration, consistency checker, meta reasoner, provider plugins, provider arbitration and planning, query execution and knowledge fusion, constraint satisfaction and relaxation, data inference code detailed above). Disk storage95provides non-volatile storage for computer software instructions92and data94used to implement an embodiment of the present invention. A central processor unit84is also attached to the system bus79and provides for the execution of computer instructions.

In one embodiment, the processor routines92and data94are a computer program product (generally referenced92), including a non-transitory computer-readable medium (e.g., a removable storage medium such as one or more DVD-ROM's, CD-ROM's, diskettes, tapes, etc.) that provides at least a portion of the software instructions for the invention system. The computer program product92can be installed by any suitable software installation procedure, as is well known in the art. In another embodiment, at least a portion of the software instructions may also be downloaded over a cable communication and/or wireless connection. In other embodiments, the invention programs are a computer program propagated signal product embodied on a propagated signal on a propagation medium (e.g., a radio wave, an infrared wave, a laser wave, a sound wave, or an electrical wave propagated over a global network such as the Internet, or other network(s)). Such carrier medium or signals may be employed to provide at least a portion of the software instructions for the present invention routines/program92.

The teachings of all patents, published applications and references cited herein are incorporated by reference in their entirety.

While example embodiments have been particularly shown and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the embodiments encompassed by the appended claims.