Abstract:
An intelligent intellectual property (IP) engine (IIPE) retrieves IP-related data from public or proprietary IP databases. Public IP databases include, for example, Espacenet, USPTO, EPO and other websites. IP-related data may be, for example, patents, non-patent literature, R&amp;D information. The retrieved IP-related data is processed to structure, visualize, analyze and interpret the data in an individual context, thereby enabling users to make operational and strategic business decisions.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    1. Field of the Invention 
         [0002]    The present invention relates to handling and contextual analysis of large data sets. In particular, the present invention relates to handling and contextual analysis of large data sets involving large data sets related to intellectual property (IP). 
         [0003]    2. Discussion of the Related Art 
         [0004]    Systems that allow user access of large data sets (e.g., enterprise-wide information and content management systems and databases) are becoming more available, such as that described in  IBM Content Analytics with Enterprise Search , Version 3.0, copyrighted IBM Corporation, 2012. 
       SUMMARY 
       [0005]    According to one embodiment of the present invention, an intelligent intellectual property (IP) engine (IIPE) retrieves IP-related data from public or proprietary IP databases. Public IP databases include, for example, Espacenet, USPTO, EPO and other websites. IP-related data may be, for example, patents, trademarks, non-patent literature, R&amp;D information. The retrieved IP-related data is processed to structure, visualize, analyze and interpret the data in an individual context, thereby enabling users to make operational and strategic business decisions. 
         [0006]    The present invention is better understood upon consideration of the detailed description below in conjunction with the accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]      FIG. 1  shows a software architecture of intelligent IP engine  100 , according to one embodiment of the present invention. 
           [0008]      FIG. 2  is  FIG. 2A  and  FIG. 2B  taken together and shows one exemplary presentation of the clustered results, in accordance with one embodiment of the present invention. 
           [0009]      FIG. 3  shows a functional architecture of application program  301  supported by intelligent IP engine  100 , according to one embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0010]    According to one embodiment of the present invention, an intelligent processor of IP-related data (“intelligent IP engine” or “IIP engine”) may be implemented in a computer system using one or more conventional computers. As an example, in one implementation, such a computer may include a conventional microprocessor (e.g., an Intel Core2 Duo microprocessor with a processing speed exceeding 2.5 GHz), supported by 6 GB memory and a storage device having a storage capacity of 250 GB. The computer may run a conventional operating system (e.g., a Linux-based operating system), and may include a database management system (e.g., MySQL) and one or more web servers (e.g., Apache 2.x). A high performance and scalable implementation of the computer system would allow results to be returned with sufficient bandwidth for interactive use, suitable for cloud computing or other hyper converged infrastructure. 
         [0011]      FIG. 1  shows software architecture intelligent IP engine  100 , according to one embodiment of the present invention. As shown in  FIG. 1 , IIP engine  100  includes database  101 , topics management system  102  and analysis or “trend radar” system  103 . Database  101  may be organized, for example, as a MySQL database. The data in the database may be retrieved and presented at a higher level by topics management system  102 , implemented by a content management system, such as Drupal. The organized data in topics management system  102  may be accessed, processed and displayed by analysis system  103  using, for example, a hypertext marked-up language (e.g., an XML script) over the hypertext transport protocol (HTTP). As shown in  FIG. 1 , topics management system  102  system includes content management system  111  (e.g., Drupal), attribute module  112 , rating module  113 , reporting module  114 , tagging module  115 , workflow module  116  and access control module  117 . Attribute module  112  provides for management of data object definitions (e.g., defining the individual topics). Rating module  113  provides for association of quantitative values (e.g., statistical measures) with data objects which may be useful for data analysis. Reporting module  114  presents data for user viewing or visualization. Tagging module  115  provides for association of data objects with textual or non-textual metadata. Workflow module  116  provides for creation and maintenance of data processing procedures. Access control module  117  provides for security in accessing the data objects of topics management system  102 . 
         [0012]      FIG. 3  shows a functional architecture of application program  301  (“Contextual IP Sand Box”) supported in IIP engine  100 , in accordance with one embodiment of the present invention. As shown in  FIG. 3 , application program  301  accesses internal data sources  302  and external data sources  303 . Typically, internal data sources  302  constitute a secured database in which an enterprise stores its IP related data and information. For example, internal data sources  302  include complete records of the enterprise&#39;s patent and trademark portfolios. In addition, internal data sources  302  may also include information and data relating to the enterprise&#39;s areas of competence, technology, trade secrets, know-hows, technology roadmaps, risk areas, growth areas, innovation areas, competitive information and analyses, and other intellectual property related data, such as its strategic objectives, and focused areas of IP acquisitions. External data sources  303  may include sources providing information and data regarding, for example, patent applications, granted patents, IP available for acquisition or license, latest technology developments, patent and trademark infringement actions, and competitive actions. The data in internal data sources  302  are preferably refreshed or updated regularly (e.g., once a day). According to one embodiment of the present invention, external data sources  303  may include public databases or websites, such as Espacenet, United States Patent and Trademark Office (USPTO), and the European Patent Office (EPO). In addition, external data sources  303  may also include other multilingual sources (e.g., websites, forums, blogs and professional journals, and global IP marketplaces) that provide structured and unstructured content. 
         [0013]    Based on changes or new data in internal data sources  302  (e.g., updates on potential risk factors or corporate opportunities), application program  301  may access external data sources  303  to match the changes or new data with data from external data sources  303  to allow, for example, contextual analysis of opportunities and risks based on the changes or new data and the external data. Some changes of relevance include: competitive activities, new patent applications, acquisition or dispositions of assets in the IP portfolios, new development in technology, and potential infringement of IP rights. A contextual analysis may be based, for example, on contextual perspectives adopted by the enterprise, various quantitative measures (“metrics”), and potential actions that can be taken or potential consequences relevant to the enterprise. The results of the contextual analysis would be made available to management within the enterprise. Application program  301  implements suitable security measures, such that sensitive information is available only to those at suitable authorization levels. 
         [0014]    Application program  301  may run on computer or servers in the enterprise&#39;s internal computer network. IIP engine  100  may provide significant value to other potential users, such as IP consultants, lawyers, analysts, financial and venture capital firms, and other professionals (M &amp; A specialists). Other application programs in the IIP engine  100  may be hosted by external computer resources available to enterprises and professionals on a subscription basis. One advantage of hosting by external computer resources is to allow many enterprises to share non-proprietary information. For example, data in the external sources are made up-to-date by regular access to Espacenet, U.S. Patent and Trademark Office, Depatisnet, or other data sources, which would be available to all subscribing enterprises. 
         [0015]    Using IIP engine  100 , a user may perform, for example, a patent search of public or proprietary databases and information sources (e.g., blogs or specialist websites). For such searches, IIP engine  100  may provide semantic search interfaces that are capable of handling multiple languages and which allow the search to take advantage of built-in contextual information and proximity. Tools, such as advanced filters, are provided to further refine the search results by searching within the results (e.g., drilling down and refining context relevance), to reduce complexity. The search results may be automatically processed for use, for example, in white-spot identification and visualization, key criteria monitoring, and automated alert systems. The data retrieved by the search is further analyzed and organized by topics management system  102 . Results, such as patent biographical data, or full-text specification, may also be served for user viewing using any suitable format (e.g., TXT, XML or PDF). In one implementation, the results are presented in a table form. A user may select a hypertext link to a search result for further information or more detailed viewing. The user may also download, where appropriate, an original document uncovered by the search (e.g., in PDF format). 
         [0016]    Search queries or results may be saved and re-visited at a later time. In one embodiment, queries are stored with the search sources used and the keywords. A user may tag a search query with a comment, to allow the user to memorialize for later reference, for example, the circumstance or the purpose of the search, or any other information the user may deem useful. The integrity of the stored queries is maintained by access control system  117 , requiring administrator privilege to modify or delete a stored query. The user may limit the websites that should be included in subsequent searches. IIP engine  100  handles web pages provided in numerous languages. In one implementation, IIP engine  100  handles web pages in German, Spanish, English, Chinese, Russian and French. In addition, the user or the system may specify the number of results to be incorporated from each website category or each patent search. A website category may consist, for example, of a maximum of 30 different websites. A user may also exclude, for the purpose of a given search, one or more specific website categories. 
         [0017]    In one embodiment, the search results are processed and analyzed in analysis system  103  using a document clustering algorithm, such as Lingo. (A detailed description of the exemplary Lingo document clustering algorithm may be found, for example, at  A Concept - driven Algorithm for Clustering Search Results , by Stanilaw Osinski and Dawid Weiss, published in the  IEEE Intelligent Systems , May/June 3 (vol. 20), 2005, pp. 48-54). The clustering algorithm may incorporate as source information dictionaries, thesauri, and individual customer taxonomies and keywords. 
         [0018]    In one embodiment, the results of the clustering algorithm may be viewed by the user using one or more display methods, such as “tag cloud”, “foam tree” or “circles”. The user may select a cluster member, which triggers filtering of the results embodied in the cluster member. This filtered result may be displayed, for example, as a list, with each element of the list being shown according to attributes “title”, “link” and “executive summary.” The link attribute provides, for example, access to the document uncovered by the search. 
         [0019]      FIG. 2A  and  FIG. 2B  show one exemplary presentation of the clustered results, in accordance with one embodiment of the present invention. As shown in  FIG. 2  A and  FIG. 2B , the exemplary presentation presents clusters  201  (in the “foam tree” format) resulting from application of the document clustering algorithm on a search result. The user&#39;s selection of any of the clusters in cluster  201  results in reporting module  114  reporting filtered result  202 , which is shown to the right of clusters  201 . Filtered results  202  show each element of the list according to attributes “title”, “link” and “executive summary.” The filters may be based on multi-variable filtering techniques, as applied to research and development data. 
         [0020]    Based on the contextual analysis (discussed in further detail below) on the information retrieved, the user may be presented visualizations of complex data relationships, such as clustering, grouping, tag clouds, landscapes and other suitable techniques. 
         [0021]    Analysis system  103  may apply other contextual analytics on the IP data in database  101 , including data extracted from external databases and websites searched by IIP engine  100 . The methods that can be applied by analysis system  103  may include topic modeling, content analytics, natural language processing, principal component analysis (PCA), TRIZ 1  and reverse TRIZ. In one implementation, the contextual analysis may be performed using topics defined from a vocabulary, a chemical or physical structure or description, a field of application, a research topic, an inventor or a patent holder. An example of such contextual analysis may be, for example, the techniques described in  Probabilistic Topic Models , by David M. Blei, published in  Communications of the ACM , April 2012, vol. 55, No. 4, pp. 77-84.  1 TRIZ refers to the techniques used in a problem-solving, analysis and forecasting tool derived from the study of patterns of invention in the global patent literature by Soviet inventor Genrich Altshuller and his associates. 
         [0022]    In analysis system  103 , semantic clustering of data sets uses techniques including clustering and statistical measures. Analysis system  103  may provide integrated methods on platforms or tools to allow viewing of data subsets sorted by region, statistical criteria, topics, inventor, patent holder, and time span. The user may also be provided programmable tools to store automated workflows (which may be user-defined) in workflow module  116  that include application steps of analytics. The workflows may include steps based on supervised learning and applications of user-defined priorities and prior probabilities. The automated workflows may also perform analytics based on techniques such as semantic clustering of existing clusters, machine learning and Bayesian Modeling. In addition, the analytics may also apply user-defined cut-offs and contextual relationships among the topics. 
         [0023]    Over time, based on previous queries, analysis system  103  may adaptively learn the user&#39;s core IP content in database  101 , and will be able to provide recommendations, insights or advice needed for corporate decisions on competitive activities, IP opportunities and potential infringements. 
         [0024]    In addition, based in the core IP, IIP engine  100  will be able to (a) identify patents that disclose subject matter close to the core IP to allow competitive analysis and monitoring; (b) identify patents that relate to the subject matters of the core IP to suggest areas for innovation and growth; and (c) identify new application areas for the subject matters of the core IP. These capabilities may be achieved using keywords and strings of keywords, or applying a topic modeling algorithm or other suitable content analytic techniques over the content. The data in the content database relevant to these capabilities include technical objectives, roadmaps, existing IP portfolios, external competitive and comprehensive patents, patents that are licensed or available for purchase, latest results of research and development, and other technical analysis and information. Analysis may include matching of related data, relevance rating, and impact assessment. PCA techniques may be used also to help reduce the complexity of the IP-related data into a contextual structure of patents and IP portfolios. Using these tools, a user can perform “white spot” analysis that highlights specific areas of particular significance from both technology and IP viewpoints. 
         [0025]    In one embodiment of the present invention, relevant content is identified using TRIZ reverse in IIP  100  from a given set of patents, which includes patents from numerous jurisdictions worldwide. The TRIZ reverse technique may combine a “contradiction matrix” with content analytics, natural language processing and topic modeling techniques, as known to those of ordinary skill in the art. Using the TRIZ reverse technique, IIP  100  (a) identifies the patents that provide a potential solution for a given problem or task; (b) identifies from the patents a technology that can be applied to solve the problem or task; and (c) identifies an application of the identified technology to the problem or task. As an example, if a user would like to find a solution that would eliminate, reduce or prevent a given problem, the following provides the steps under TRIZ reverse:
       1. Defining context-relevant keywords (e.g., eliminate, reduce, prevent, erase, delete, limit . . . ) to be used in the context analysis;   2. Creating semantic clusters as an intermediate results, based on keyword proximity (e.g., applying a topic modeling technique) and frequency distribution;   3. Applying a content analytic search across the intermediate results;   4. Refining the semantic clusters based on content meaning extracted in the content analytic search;   5. Allowing the user to prioritize and select clusters for review; and   6. Reviewing the selected patents in priority order to identify potential solution.       
 
         [0032]    Workflow module  116  may include automated procedures for updating and adding of latest information to ensure real-time and dynamic performance in analysis system  103 . Such updating procedures may include, for example, inventory and mapping of public databases and customer portfolios, matching of various data sets that are used for the contextual IP analysis described above. In one embodiment, automated procedures are provided to extract specific information from the worldwide patent literature, on-line technical information sources, and non-patent literature, so as to gather IP-related knowledge from around the globe. The automated procedures may also include automated applications of TRIZ and reverse TRIZ techniques to the gathered IP-related documents, contextual analysis and generation of concrete recommendations. Such analysis may identify new technologies, new application areas, new uses, new user strategies, and new business objectives. Workflow module  116  may also cluster existing clusters in a continual focusing process. 
         [0033]    In one embodiment, analysis system  103  provides automated, pre-configured IP risk and opportunity analysis (i.e., gains and losses), based on dynamically matching internal and external data with global data that influences the client&#39;s risk and opportunity profiles. 
         [0034]    In one embodiment, analysis system  103  identifies potential infringements through discovering content relationship among keywords in patent databases and on specific websites. A content-related matching factor is measured among the keywords, and according to which the keywords will be structured, prioritized, and visualized in an “early-warning-system”. Infringement of the client&#39;s patents by others&#39; products or infringement of others&#39; patents by the client&#39;s products may be indicated in this analysis. The early-warning system may be useful in providing an alert automatically when it is detected that the client&#39;s core IP may infringe upon patents owned by known patent trolls or by others. The events of expiration, express abandonment, failure to take required action (e.g., failure to pay a maintenance or annuity fee), and publication of a monitored patent or application may also trigger an alert based on information retrieved regularly from such source as, for example, the INPADOC database). 
         [0035]    In one embodiment, the automated procedures may include generation of a “visualization dashboard” of the IP-related data (i.e., a presentation of the IP-related data in a pre-defined presentation format). 
         [0036]    In one embodiment, IIP engine  100  provides an online workflow system and infrastructure for joint IP development between two or more entities. By sharing common technical and IP-related data, joint development partners can co-develop technology and share IP rights with others right from the beginning of the project. IIP engine  100 , with its tools that allow identification and matching of potential partners, and its pre-configured Joint Development Agreements (JDAs) and Co-Working Platforms workflows and procedures, allow for global cooperation in research and development, as well as management of IP rights across companies, regions and topics. 
         [0037]    The above detailed description is provided to illustrate specific embodiments of the present invention and is not intended to be limiting. Numerous variations and modifications within the scope of the present invention are possible. The present invention is set forth in the following accompanying claims.