Abstract: A method is disclosed for use with at least one initial document describing a technical concept suitable for licensing, the method comprising: retrieving a set of intellectual property documents from a data warehouse; partitioning the set of intellectual property documents into a plurality of document categories; classifying the set of intellectual property documents by an industry parameter; constructing a contingency table that includes a listing of industry classifications for each of the document categories, and identifying documents within a particular one of the document categories that have different industry classifications so as to identify at least one potential new licensee industry of the technical concept described in the initial document.

Abstract: A structure and method for automatically creating a dictionary for clustering text documents performs a first pass for each of the documents to determine a frequency of each word in each of the documents, creates a Hashtable of most frequently occurring words in the documents, performs a second pass for each of the documents to determine a frequency of phrases in each of the documents that contain only words in the Hashtable, adds the most frequently occurring phrases to the Hashtable, and outputting outputs the most frequently occurring words and the most frequently occurring phrases as the dictionary.

Abstract: A patent evaluation method analyzes key words in the claims and how many patents use those words, to measure the impact of a given patent. For a group of patents in a particular field (e.g., as defined by a patent classification code), the key words can be indexed against the patents having claims in which those key words appear, and in particular with respect to that patent having the earliest reference date (e.g., a publication date such as the date on which the patent issued or any corresponding patent application was published). Output may be presented in the form of a table, which aids in quickly understanding a patent's value compared to other patents in its group. Various visualization and user interaction tools may be employed.

Abstract: A method and analytics tools for locating experts with specific sets of expertise are disclosed, the method including providing a collection of documents P0; generating categories representing fields of expertise derived from the collection of documents P0; refining the taxonomy of the categories by applying user domain knowledge; extracting structured fields from the collection of documents P0; constructing a contingency table having a first axis defined by the extracted structured fields and a second axis defined by the categories; and using the contingency table to identify a set of experts having a related expertise. The method may also include a network graph analysis that aids visualization of the relationship between people and expertise.

Abstract: A method and analytics tools for information mining incorporating domain specific knowledge and conceptual structures are disclosed, the method including: providing a first set of documents related to a first topic of interest; using a first taxonomy to categorize the first set of documents into a set of categories; providing a second set of documents related to a second topic of interest; categorizing the second set of documents according to the set of categories of the first set of documents; using an element of domain knowledge to re-categorize the first set of documents; and examining a category to identify a document of interest.

Abstract: A method and structure for clustering documents in datasets which include clustering first documents and a first dataset to produce first document classes, creating centroid seeds based on the first document classes, and clustering second documents in a second dataset using the centroid seeds, wherein the first dataset and the second dataset are related. The clustering of the first documents in the first dataset forms a first dictionary of most common words in the first dataset and generates a first vector space model by counting, for each word in the first dictionary, a number of the first documents in which the word occurs, and clusters the first documents in the first dataset based on the first vector space model, and further generates a second vector space model by counting, for each word in the first dictionary, a number of the second documents in which the word occurs.

Abstract: A method for analyzing predefined subject matter in a patent database being for use with a set of target patents, each target patent related to the predefined subject matter, the method comprising: creating a feature space based on frequently occurring terms found in the set of target patents; creating a partition taxonomy based on a clustered configuration of the feature space; editing the partition taxonomy using domain expertise to produce an edited partition taxonomy; creating a classification taxonomy based on structured features present in the edited partition taxonomy; creating a contingency table by comparing the edited partition taxonomy and the classification taxonomy to provide entries in the contingency table; and identifying all significant relationships in the contingency table to help determine the presence of any white space.

Abstract: A method and structure for clustering documents in datasets which include clustering first documents and a first dataset to produce first document classes, creating centroid seeds based on the first document classes, and clustering second documents in a second dataset using the centroid seeds, wherein the first dataset and the second dataset are related. The clustering of the first documents in the first dataset forms a first dictionary of most common words in the first dataset and generates a first vector space model by counting, for each word in the first dictionary, a number of the first documents in which the word occurs, and clusters the first documents in the first dataset based on the first vector space model, and further generates a second vector space model by counting, for each word in the first dictionary, a number of the second documents in which the word occurs.

Abstract: A method and analytics tools for information mining incorporating domain specific knowledge and conceptual structures are disclosed, the method including: providing a first set of documents related to a first topic of interest; using a first taxonomy to categorize the first set of documents into a set of categories; providing a second set of documents related to a second topic of interest; categorizing the second set of documents according to the set of categories of the first set of documents; using an element of domain knowledge to re-categorize the first set of documents; and examining a category to identify a document of interest.

Abstract: A method for analyzing predefined subject matter in a patent database being for use with a set of target patents, each target patent related to the predefined subject matter, the method comprising: creating a feature space based on frequently occurring terms found in the set of target patents; creating a partition taxonomy based on a clustered configuration of the feature space; editing the partition taxonomy using domain expertise to produce an edited partition taxonomy; creating a classification taxonomy based on structured features present in the edited partition taxonomy; creating a contingency table by comparing the edited partition taxonomy and the classification taxonomy to provide entries in the contingency table; and identifying all significant relationships in the contingency table to help determine the presence of any white space.

Abstract: A method is disclosed for use with at least one initial document describing a technical concept suitable for licensing, the method comprising: retrieving a set of intellectual property documents from a data warehouse; partitioning the set of intellectual property documents into a plurality of document categories; classifying the set of intellectual property documents by an industry parameter; constructing a contingency table that includes a listing of industry classifications for each of the document categories, and identifying documents within a particular one of the document categories that have different industry classifications so as to identify at least one potential new licensee industry of the technical concept described in the initial document.

Abstract: A method and analytics tools for locating experts with specific sets of expertise are disclosed, the method including providing a collection of documents P0; generating categories representing fields of expertise derived from the collection of documents P0; refining the taxonomy of the categories by applying user domain knowledge; extracting structured fields from the collection of documents P0; constructing a contingency table having a first axis defined by the extracted structured fields and a second axis defined by the categories; and using the contingency table to identify a set of experts having a related expertise. The method may also include a network graph analysis that aids visualization of the relationship between people and expertise.

Abstract: Disclosed is a method of identifying partnering potential by: assembling a set of target patents representative of an industry of interest; building a dictionary of text feature entries; generating a set of text feature clusters; creating one or more contingency tables for assignees; and deriving an indication of partnering potential between the first assignee and the second assignee by comparing the values for each category for each assignee in each contingency table.

Abstract: A method of searching a database of documents, wherein the method includes performing a search of the database using a query to produce query result documents; constructing a word dictionary of words within the query result documents; constructing an out-link dictionary of documents within the database that are pointed to by the query result documents; adding the query result documents to the out-link dictionary; constructing an in-link dictionary of documents within the database that point to the query result documents; and adding the query result documents to the in-link dictionary.

Abstract: A method and system for interesting relationships in text documents includes generating a dictionary of keywords in the text documents, forming categories of the text documents using the dictionary and an automated algorithm, counting occurrences of the structured variables, categories and structured variable/category combinations in the text documents, and calculating probabilities of occurrences of the structured variable/category combinations.

Abstract: A method and system is provided for integrating multiple feature spaces in a k-means clustering algorithm when analyzing data records having multiple, heterogeneous feature spaces. The method assigns different relative weights to these various features spaces. Optimal feature weights are also determined that lead to a clustering that simultaneously minimizes the average intra-cluster dispersion and maximizes the average inter-cluster dispersion along all the feature spaces. Examples are provided that empirically demonstrate the effectiveness of feature weighting in clustering using two different feature domains.

Abstract: A method and structure for performing a database search includes searching a database using a query (searching producing result items), and ranking the result items based on one or more of a frequency of an occurrence of in-links and out-links in each of the result items.

Abstract: A method and structure for discovering problem resolution in a helpdesk data set of problem tickets is based on using an enumerated set of phrases that have been identified as indicating diagnosis, instruction, or corrective action. A count of these phrases is then obtained and used along with any accompanying structured information to score each problem ticket. The tickets are then sorted so that the most useful problem tickets appear first.

Abstract: A system and method automatically identify candidate helpdesk problem categories that are most amenable to automated solutions. The system generates a dictionary wherein each word in the text data set is identified, and the number of documents containing these words is counted, and a corresponding count is generated. The documents are partitioned into clusters. For each generated cluster, the system sorts the dictionary terms in order of decreasing occurrence frequency. It then determines a search space by selecting the top dictionary terms as specified by a user defined depth of search. Next, the system chooses a set of terms from the search space as specified by a user-defined value indicating the desired level of detail.

Abstract: A method (and system) of determining a knowledge gap between a first database containing a set of problems records and a second database containing solutions documents, includes developing a set of clusters of the problems records of the first database, where each cluster has a centroid, developing a dictionary having entries based on the problems records in the first database, developing a vector space correlated to the solutions documents in the second database, where the vector space is based on the dictionary entries, developing a listing of distances between the cluster centroids and the vector space, and determining a knowledge gap for each cluster.