Systems and methods progressively or heuristically associate themes amongst plural objects in a computer setting. Objects, including files, modules, programs, data, and the like can be arranged in a population by their association with a particular theme and user-input context. Based on input search parameters and associations, the objects can be progressively matched with appropriate context and returned in more relevant searches. Themes and context for individual objects can be individually determined based on semantic input and well as meta data associated with the objects. Objects can be returned based on search criteria in rank order according to their association. Systems and methods are useable or organization of objects in Internet searches, document searches and collation, document and content visual representation, database management, polling systems, and document management systems.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A context based co-operative learning system comprising: an identifier configured to identify and index objects in accordance with pre-determined parameters of identification and indexing; a context determinator configured to define parameters of identification in order to determine context topic and/or context theme of the objects based on identifiable features of the objects; an information sources gatherer configured to gather sources of information in relation to or with reference to the identified objects; a searcher configured to search for the objects, in response to at least a user query, within the determined context topic and/or the determined context theme; a cluster data builder configured to build clusters of relevant objects and further adapted to build at least a cluster library based on pre-defined parameters of clustering the clusters; a context mapper configured to map at least a context of the query for the searcher with the clusters from the cluster library to segregate and poll the objects in response to the search query in line with at least a determined context topic and/or the determined context theme; and a co-operative learner configured to allow multiple systems to co-operatively learn from each other based on determined context topic and/or determined context theme. 2. The system of claim 1 wherein the identifier is further configured to identify and index objects in accordance with pre-determined parameters of identification and indexing, and wherein the identifier includes, a selector configured to establish contextual/thematic features for the objects, a key features identifier configured to identify key features in the objects, wherein the key features relate to a theme, and a relationship identifier configured to identify relationships among identified key thematic features for each of the objects. 3. The system of claim 1, wherein the context determinator includes at least one of, a semantic determinator configured to determine context and content based on semantic processing of the identified objects based on the identifiable features of the identified objects, a syntactic determinator configured to determine context and content based on syntactic processing of the identified objects based on the identifiable features of the identified objects, a first topic determinator configured to determine context topic based on topic-based processing of the identified objects based on the identifiable features of the identified objects, and a second topic determinator configured to determine at least a topic that is a representative context of textual content of the identified objects. 4. The system of claim 1, wherein the context determinator includes a topic determinator configured to determine a topic or theme based on at least one of, An association among key phrases that leads to a context, An occurrence of bigrams, trigrams, relationship and occurrence of key words and phrases, and a relation extraction between the occurrences. 5. The system of claim 1, wherein the context determinator includes a topic determinator configured to determine a topic based extracted parameters from a set of identified objects including at least one of, top frequency unigrams, top frequency bigrams, longer key phrases, an association among frequently occurring unigrams and frequently occurring bigrams, a corpus of frequent objects and their statistical association leading to most relevant pre-known topics, a likelihood and reinforcement learning mechanisms in order to learn a new topic if there is no pre-known relevant topic, semi-supervised learning mechanisms in order to learn a new topic, and a mapping of key phrases to a pre-known or learnt topic. 6. The system of claim 1, wherein the context determinator includes a thematic determinator configured to determine a theme based on extracted parameters from a set of identified objects, wherein the extracted parameters include at least one of, a local score of words that is computed, a global score of words, that is computed, based on similarity, a sentence score, that is computed, based on local score, global score, and normalization, and a situation representing primary context. 7. The system of claim 1, wherein the information sources gatherer is configured to gather information from at least a relation extractor, a name entity recognizer, and a situation builder to build a context. 8. The system of claim 1, wherein the cooperative leaner includes, a cluster data builder configured to build clusters of relevant objects and to build at least a cluster library based on pre-defined parameters of clustering the clusters, and a context mapper configured to map at least a context of the query for the searcher with the clusters from the cluster library to segregate and poll the objects in response to the search query in line with at least a determined topic, wherein the pre-defined parameters of clustering are associated with pre-determined factors including likelihood factors, statistical factors, and closeness factors, and wherein the pre-defined parameters of clustering further are associated with identification of multiple clusters having similar behaviour. 9. The system of claim 1, wherein the information sources gatherer is selected from a group consisting of a theme-based information sources gatherer, a topic-based information sources gatherer, a user-generated information sources gatherer, and a machine-based information sources gatherer. 10. The system of claim 1, wherein the context determinator includes contextual features determinator configured to infer contextual features for each of the objects, wherein each object is D in Di={Pi,Ti,Si,Oi} . . . 0<i<n where, n is a number of docs in a class, Ti is a set of temporal features, Si is a set of spatial features, Pi is a set of protagonist features, and Oi is a set of organizational features. 11. The system of claim 10, wherein the context determinator further includes a clusterer configured to cluster the inferred features of the objects for a class into a situation vector including, Ti={t0, t1, t2, . . . tn} for temporal features of the class i. Si={s0, s1, s2, . . . sn} for spatial features of the class i. Pi={p0, p1, p2, . . . pn} for protagonist features of the class i. Oi={o0, o1, o2, . . . n} for organizational features of the class i. wherein, the situation vectors define a theme and are generated for each class, and wherein the situation vectors form at least a situation model/thematic model for that category CS in CSj={Tj,Sj,Pj,Oj} . . . j=1 to C where, C is a number of categories, and Tj={t0, t1, t2, . . . , tk} Sj={s0, s1, s2, . . . , sk} Pj={p0, p1, p2, . . . , pk} Oj={o0, o1, o2, . . . , ok} 12. A context-based co-operative learning method, comprising: identifying and indexing objects in accordance with pre-determined parameters of identification and indexing, using an identifier; defining parameters of identification in order to determine context topic and/or context theme of the objects based on identifiable features of the objects, using a context determinator; gathering sources of information in relation to or with reference to the identified objects, using an information sources gatherer; searching for the objects, in response to at least a user query, within the determined context topic and/or the determined context theme, using a searcher; building clusters of relevant objects and further adapted to build at least a cluster library based on pre-defined parameters of clustering the clusters, using a cluster data builder; mapping at least a context of the query for the searcher with the clusters from the cluster library to segregate and poll the objects in response to the search query in line with at least a determined context topic and/or the determined context theme, using a context mapper; and allowing multiple systems to co-operatively learn from each other based on determined context topic and/or determined context theme, using a co-operative learner. 13. The method of claim 12, wherein the identifying and indexing objects includes, establishing contextual/thematic features for the objects, identifying key features in sad objects, which key features relate to a theme, and identifying relationships among identified key thematic features per object. 14. The method of claim 12, wherein the defining parameters of identification includes determining a topic with a topic determinator based on at least one of, an association among key phrases that leads to a context, occurrence of bigrams, trigrams, relationship and occurrence of key words and phrases, and relation extraction between the occurrences. 15. The method of claim 12, wherein the defining parameters of identification includes determining a topic with a topic determinator based on at least one of, top frequency unigrams, top frequency bigrams, longer key phrases, an association among frequently occurring unigrams and frequently occurring bigrams, a corpus of frequent objects and their statistical association leading to most relevant pre-known topics, a likelihood and reinforcement learning mechanisms in order to learn a new topic if there is no pre-known relevant topic, semi-supervised learning mechanisms in order to learn a new topic, and a mapping of key phrases to a pre-known or learnt topic. 16. The method of claim 12, wherein the defining parameters of identification includes defining a theme using a theme determinator based on an extracted parameter from a set of identified objects, wherein the extracted parameter includes at least one of, a local score of words that is computed, a global score of words, that is computed, based on similarity, a sentence score, that is computed, based on local score, global score, and normalization, and a situation representing primary context. 17. The method of claim 12, wherein the gathering sources of information includes gathering information from at least a relation extractor, a name entity recognizer, and a situation builder to build a context. 18. The method of claim 12, wherein the searching for the objects includes searching for the objects within the theme-clustered objects or the topic-clustered objects depending on a user query with the searcher. 19. The method of claim 12, further comprising: ranking searched the objects, the ranking being determined in accordance with reference to user context topic or context theme. 20. The method of claim 12, wherein the defining parameters of identification includes at least one of, determining context and content based on semantic processing of the identified objects based on the identifiable features of the identified objects, determining context and content based on syntactic processing of the identified objects based on the identifiable features of the identified objects, determining a context topic based on topic-based processing of the identified objects based on the identifiable features of the identified objects, determining at least a topic that is a representative context of textual content of the identified objects.