EP2577495A1 - Recherche utilisant une taxinomie - Google Patents

Recherche utilisant une taxinomie

Info

Publication number
EP2577495A1
EP2577495A1 EP10852294.7A EP10852294A EP2577495A1 EP 2577495 A1 EP2577495 A1 EP 2577495A1 EP 10852294 A EP10852294 A EP 10852294A EP 2577495 A1 EP2577495 A1 EP 2577495A1
Authority
EP
European Patent Office
Prior art keywords
class
relevant documents
classification
primary
relevant
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP10852294.7A
Other languages
German (de)
English (en)
Other versions
EP2577495A4 (fr
Inventor
Randy W. Lacasse
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
CPA Global Patent Research Ltd
Original Assignee
CPA Global Patent Research Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by CPA Global Patent Research Ltd filed Critical CPA Global Patent Research Ltd
Publication of EP2577495A1 publication Critical patent/EP2577495A1/fr
Publication of EP2577495A4 publication Critical patent/EP2577495A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35Clustering; Classification
    • G06F16/358Browsing; Visualisation therefor

Definitions

  • Patent applications submitted for examination before the U.S. Patent and Trademark Office must meet certain requirements in order to issue as patents.
  • the subject matter claimed in the patent applications must be deemed new, useful, and non-obvious. Similar standards are applied in patent offices of most, if not all, foreign patent offices.
  • To more effectively prepare a patent application for examination it is useful to have knowledge of prior art, including prior patent documents (e.g., patents and published patent applications) in related areas of technology since only one patent may be granted per invention.
  • Conducting a patent search can be one way in which prior art can be ascertained. The results of the patent search can help the drafter of a patent application to focus on aspects that appear to be patentable subject matter and aid in developing a reasonable strategy for achieving the goals of the inventor or owner of the patent rights.
  • This relates to a search platform that can facilitate efficient and intuitive perusal and analysis of search results. Additionally, the search platform can enable the user to easily narrow a result set of documents to focus on more relevant documents.
  • a method for processing search results.
  • the method provides for executing a search based on a user input entered via a graphical user interface using a processor, identifying relevant documents based on the search, and obtaining a standard classification for each relevant document.
  • the standard classification is a classification within a standard classification system.
  • the method also provides for reclassifying each relevant document, based on the relevant document's standard classification, into an interpretive classification within an interpretive classification system.
  • the interpretive classification comprises at least a primary class and a secondary class.
  • the method further provides for grouping the relevant documents into each relevant document's primary class and secondary class, and displaying the primary classes of the relevant documents and a number of relevant documents grouped in each displayed primary class via the graphical user interface on a display device.
  • a system for processing search results.
  • the system includes a classifier configured to obtain a standard classification for each document of a plurality of documents and to classify each document, based on the document's standard classification, into an interpretive classification within an interpretive classification system.
  • the standard classification is a classification within a standard classification system while the interpretive classification comprises at least a primary class and a secondary class.
  • the system further includes a search engine configured to search the plurality of documents based on a user input and to identify relevant documents, a processor configured to group the relevant documents into each relevant document's primary class and secondary class, and a display device configured to display the primary classes of the relevant documents and a number of relevant documents grouped in each displayed primary class.
  • FIG. 1 illustrates an example of search platform architecture.
  • FIG. 2 illustrates an example of a process for conducting a search and displaying search results.
  • FIG. 3 illustrates an example of a process for searching a patent collection.
  • FIG. 4 illustrates an example of a user interface.
  • FIG. 5 illustrates an example of a computing device.
  • This relates to a search platform that can facilitate efficient and intuitive perusal and analysis of search results. Additionally, the search platform can enable the user to easily narrow a result set of documents to focus on more relevant documents.
  • FIG. 1 illustrates an embodiment of exemplary search platform architecture.
  • client 100 can access server 1 10 across network 105.
  • Server 1 10 can deploy search engine 120 and classifier 150, which can be associated with patent collection 130 and metadata 140.
  • Patent collection 130 can include one or more databases storing patent documents, such as patents and/or patent publications for example, associated with one or more national patent offices.
  • Metadata 140 can include one or more databases storing data associated with the patent documents. The data can include bibliographic information, document vectors, classification information, summaries or abstracts, titles, claim terms, etc. , related to the documents in the collection. The data can be organized in an index including a record for each document.
  • patent collection 130 and metadata 140 are shown as distinct databases in the embodiment illustrated in FIG. 1 , in other embodiments the data embodied in patent collection 130 and metadata 140 can be stored together in one or more databases or other suitable storage medium.
  • Search engine 120 can be based on any of numerous commercially available search engines.
  • search engine 120 can be based on an enterprise search platform, such as the Fast Enterprise Search Platform by Microsoft Corp.
  • a search engine can be programmed by one of ordinary skill in the art based on numerous search techniques. For example, a document vector search technique is discussed with respect to FIG. 3.
  • Classifier 150 can be used to analyze documents in patent collection 130 and to extract and/or create metadata 140. Classifier 150 can be a standalone unit or part of a larger unit with additional functionality. Classifier 150 can parse documents in patent collection 130 using known parsing techniques and extract or identify from the documents a standard classification.
  • a standard classification is a predetermined classification based on a standard system of classification.
  • a standard system of classification is a system of classification that is accepted by at least some in a field of endeavor.
  • the standard system of classification can be a classification system established by a governmental agency or a standard-setting organization, for example.
  • two examples of standard systems of classification are the International Patent Classification (I PC) system and the U.S. Patent Classification (USPC) system.
  • the extracted/identified classification can be stored in metadata 140.
  • Classifier 150 can reclassify documents in patent collection 130 into an interpretive classification.
  • An interpretive classification is a classification that is based on an interpretive system of classification.
  • An interpretive system of classification can include more or fewer classifications than a standard system of classification.
  • An interpretive classification includes at least one class and one subclass.
  • An interpretive system of classification can consist of a larger or smaller hierarchy of classes and subclasses (i.e. class levels) than a standard system of classification.
  • the number of classes at each level in the hierarchy can vary to provide the most user-friendly, intuitive hierarchy for enabling an ordinary searcher to quickly process and understand the breakdown of the hierarchy. Such a structure can allow the searcher to quickly narrow a large number of documents returned in a search to focus on the most relevant documents to the searcher.
  • the names of classes and subclasses within an interpretive classification can be simpler, shorter, and/or more descriptive. Thus, an interpretive system of classification can be more user-friendly than a standard system of
  • An interpretive system of classification can be designed to exploit the nature and characteristics of electronic searching and electronic display of relevant documents and their classifications.
  • graphical user interfaces provide various capabilities for providing an intuitive, user-friendly display of a class hierarchy through the use of tree elements, expansion buttons, and scroll buttons, for example.
  • links, information bubbles, and the like can be used to quickly and easily provide additional information regarding a class or subclass.
  • the interpretive classification system can aid the searcher in ways that standard classification systems do not.
  • Classifier 150 can implement many different techniques for reclassifying documents into interpretive classifications. Classifier 150 can reclassify documents in patent collection 130 based on the standard classification of the documents. For instance, classifier 150 can consult a mapping between classifications in the standard system of classification and classifications in the interpretive system of classification. In an embodiment, classifier 150 can access other information regarding the documents from metadata 140, such as the title and claim terms, to aid in reclassification. In a further embodiment, classifier 150 can access document vectors of the documents to aid in reclassification. [0024] In an embodiment, classifier 150 can reclassify a given document into multiple interpretive classifications.
  • classifier 150 can select an interpretive classification that is mapped to the extracted standard classification but then could also select one or more other classifications based on terms in the document vector of the document. Weights of the terms, as discussed below, can be taken into consideration.
  • FIG. 2 illustrates an exemplary embodiment for conducting a search and displaying search results.
  • a search can be executed (block 200).
  • the search can be based on an input entered by a user via an input element of a graphical user interface, for example.
  • the search can be executed by search engine 120 over patent collection 130.
  • the ways in which search engine 120 can search a document collection can be myriad.
  • FIG. 3 illustrates an embodiment in which search engine 120 can employ a vector based search methodology.
  • search engine 120 upon receiving a query (block 300) search engine 120 can create (block 310) a document vector for the query.
  • the document vector can be a weighted list of words and phrases, such as:
  • search engine 120 can compare (block 320) the query document vector with document vectors retrieved from patent collection 130 that have been previously created for each of the patent documents in patent collection 130.
  • the document vectors can also be stored in metadata 140, such as in a record in the index corresponding to each document in patent collection 130.
  • the comparison can include, for example, multiplying the weights of any common terms among the query document vector and the retrieved document vector, and adding the results to obtain a similarity ranking.
  • query document vector [table, 1 ][chair, 0.5][plate, 0.2]
  • search engine 120 can consider the patent document associated with the retrieved document vector to be a match. In other embodiments, rather than using a vector based search methodology, search engine 120 can utilize less dynamic search methodologies that do not involve the creation of document vectors for the patent documents.
  • each patent document stored in patent collection 130 can be associated with one or more document vectors.
  • a distinct document vector can be created for various sections or combinations of sections of a patent document, enabling search engine 120 to tailor a search on specific sections of the patent document.
  • the document vectors can be adjusted to remove non- relevant words or phrases to yield a smaller and more concise document vector, which can improve efficiency of query processing due to time not spent by search engine 120 to process the removed strings.
  • one or more documents can be identified as relevant to the input (block 200).
  • the result set can be empty if no documents are deemed relevant to the input.
  • a standard classification of each relevant document can be obtained (block 210).
  • the standard classification can be an IPC or USPC classification, as discussed previously.
  • the standard classification can be obtained by classifier 150, for example, by processing the document on-the-fly. Alternatively, the standard classification can be obtained by consulting metadata 140 if the document has already been processed by classifier 150.
  • Each document can be reclassified into an interpretive classification (block 220).
  • the interpretive classification can be a classification in an interpretive classification system and can comprise a hierarchical structure including at least a primary class and a secondary class, but can further include additional subsidiary classes.
  • the reclassification can occur on-the-fly after the search has been executed or it could have already been performed before the search was executed, and thus the interpretive classification can be stored in, and thus accessed from, metadata 140, for example.
  • the functions of blocks 210 and 220 can be performed during database creation or updating.
  • classifier 150 can determine the standard classification of each document in patent collection 130 and store the classification in metadata 140.
  • Classifier 150 can also classify the documents into an interpretive classification at database creation time or another time.
  • the interpretive classification of each document can also be stored in metadata 140.
  • Database creation includes adding additional documents to an already created database.
  • the relevant documents can be grouped according to their interpretive classifications (block 230).
  • each document can be grouped into each class and subclass that comprises the document's classification.
  • a grouping for a primary class COMPUTER could consist of all documents grouped in all of its subsidiary classes.
  • a grouping denotes a stored association or relationship between a document and a class. The location of the document in a memory of a computer may not change as a result of the grouping. The number of documents in each grouping can be stored as well.
  • a document is reclassified into multiple interpretive classifications
  • that document can be grouped into the classes and subclasses of each of its interpretive classifications.
  • FIG. 4 depicts an exemplary graphical user interface 400 for displaying the relevant documents and the classes.
  • User interface 400 can include a query section 410, a classification section 420, and a result section 430.
  • Query section 410 can include a text box 41 1 for entering an input and search button 412 for requesting execution of a search.
  • search term "DISC” has been entered into text box 41 1 and a search performed.
  • Classification section 420 can display the hierarchy of the interpretive classification system.
  • the classes displayed correspond to classes of relevant documents identified by the search.
  • the search term "disc” could refer to a computer disc, a disc brake in a car, or a disc in the body.
  • the primary classes displayed in this example are COMPUTER,
  • the number of documents grouped in each class can be listed next to the class.
  • the classes on a particular level of the hierarchy can be arranged in descending order with respect to the number of documents grouped in the class such that the class with the highest number of documents appears first. In a case where two or more classes on the same level have the same number of grouped documents, those classes can be displayed alphabetically. In the case of a large number of classes, a scroll button can be provided to permit a user to scroll through the classes.
  • the hierarchy of the classes and checkboxes 421 and 422 are discussed below.
  • Displaying the primary classes of the relevant documents in this way allows a searcher to easily and quickly view the types of documents in the result set and their relationship to the original input, in this case "DISC". If a searcher is interested in computer discs, the searcher can select the COMPUTER class, as discussed below, and thus reduce the number of relevant documents. In this case, if the documents each have only one interpretive classification, then the relevant documents would be reduced by more than half by selecting the COMPUTER class. In addition, further winnowing of the relevant documents can be performed by selecting subclasses.
  • Result section 430 can display document references of relevant documents.
  • the document references can be displayed as a list 431 and can include relevant text of the document underneath the reference to enable a user to further ascertain the content of the document.
  • the document references can be displayed in descending order of relevancy, as determined by the search engine.
  • additional pages of document references can be displayed on subsequent pages of result section 430 as indicated by buttons 432.
  • a desired page can be selected via the buttons 432. In this example, there are five pages, as indicated by the five buttons.
  • a document reference can be a link.
  • the document reference can link to a copy of the document stored in patent collection 130 of server 1 10.
  • the document reference can also link to a copy of the document stored elsewhere in the world, such as a server of a patent office or a server local to client 100.
  • the document reference can link to a copy of the document stored on a local memory of client 100.
  • a copy of the document can be transmitted to the client along with the result set.
  • the document can be immediately available to a user upon viewing the result set. The time and processing power often required to reconnect to a server to retrieve a document specified in a result set can thus be eliminated.
  • a primary class can be selected (block 250), as discussed previously.
  • a user can select one or more primary classes via classification section 420.
  • Each class listed in classification section 420 has a selection checkbox (located to the left) and a deselection checkbox (located to the right).
  • Selection and deselection boxes 421 correspond to primary class COMPUTER.
  • the secondary classes of documents grouped into primary class COMPUTER can be displayed (block 260).
  • the secondary classes include MEMORY, PROCESSOR, and SOFTWARE.
  • selection box 422 corresponding to MEMORY
  • the tertiary classes of documents grouped into secondary class MEMORY can be displayed (in this example, DISK and MAIN).
  • the documents grouped into the selected class can be exclusively displayed (block 260).
  • Result section 430 can thus be updated to display only the documents grouped into the selected class.
  • Result section 430 can be updated accordingly.
  • a deselection can have an effect on just the display of the documents within the deselected category.
  • a selection of a class can have the effect of deselecting all other classes at that level.
  • classification section 420 is updated to display an 'X' in the checkboxes of each of the automatically deselected classes.
  • a user can later choose to select a deselected class.
  • a selection or deselection can be reversed by clicking on the selection or deselection checkbox.
  • the classification section 420 can be updated to collapse the subclasses (if any) of the now unselected class and the result section 430 can be updated to display the appropriate documents.
  • result section 430 can be updated to display all documents grouped in the COMPUTER class.
  • result section 430 can be updated to display, in addition to the already displayed documents, the documents grouped in the deselected class.
  • multiple classes at the same level in the class hierarchy can be selected at one time.
  • COMPUTER the COMPUTER and COMPUTER
  • AUTOMOTIVE primary classes can be selected by the user.
  • classification section 420 can display the secondary classes of each selected primary class.
  • result section 430 can display the documents grouped in each selected primary class. This feature can be useful, for example, if a searcher is interested in a teaching or feature that may be applicable to multiple technical fields.
  • a display-only feature can be provided when multiple classes and/or subclasses are selected at the same levels.
  • a user can select display-only for a specific class and result section 430 can update to display only documents grouped in that class.
  • the display-only feature can be a separate graphical user interface input element or can be instructed through some combination of a mouse or keyboard input, along with the selection checkbox of the desired class, for example.
  • Such a feature can be useful if a searcher has selected multiple classes on the same level, especially at different levels of the class hierarchy, but desires to quickly view the documents grouped in only one specific class-subclass chain to see if a highly relevant document can be located.
  • the hierarchy displayed in classification section has subclasses indented with respect to immediately preceding classes.
  • the relationship between class and subclass can also be reflected using different colors, font sizes, text sizes, etc.
  • the checkboxes 421 , 422 could be replaced with other graphical user interface elements. For example, the mere action clicking on a class with a mouse pointer could expand the class and thus serve as a selection.
  • FIG. 5 shows a block diagram of an example of a computing device, which may generally correspond to client 100 and server 1 10.
  • the form of computing device 500 may be widely varied.
  • computing device 500 can be a personal computer, workstation, server, handheld computing device, or any other suitable type of microprocessor-based device.
  • Computing device 500 can include, for example, one or more components including processor 510, input device 520, output device 530, storage 540, and communication device 560. These components may be widely varied, and can be connected to each other in any suitable manner, such as via a physical bus, network line or wirelessly for example.
  • input device 520 may include a keyboard, mouse, touch screen or monitor, voice-recognition device, or any other suitable device that provides input.
  • Output device 530 may include, for example, a monitor or other display, printer, disk drive, speakers, or any other suitable device that provides output.
  • Storage 540 may include volatile and/or nonvolatile data storage, such as one or more electrical, magnetic or optical memories such as a RAM, cache, hard drive, CD-ROM drive, tape drive or removable storage disk for example.
  • Communication device 560 may include, for example, a network interface card, modem or any other suitable device capable of transmitting and receiving signals over a network.
  • Network 105 may include any suitable interconnected communication system, such as a local area network (LAN) or wide area network (WAN) for example.
  • Network 105 may implement any suitable communications protocol and may be secured by any suitable security protocol.
  • the corresponding network links may include, for example, telephone lines, DSL, cable networks, T1 or T3 lines, wireless network connections, or any other suitable arrangement that implements the transmission and reception of network signals.
  • Software 550 can be stored in storage 540 and executed by processor 510, and may include, for example, programming that embodies the functionality described in the various embodiments of the present disclosure.
  • the programming may take any suitable form.
  • programming embodying the patent collection search functionality of search engine 120 can be based on an enterprise search platform, such as the Fast Enterprise Search Platform by Microsoft Corp. for example.
  • Software 550 can also be stored and/or transported within any computer- readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as computing device 500 for example, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a computer-readable storage medium can be any medium, such as storage 540 for example, that can contain or store programming for use by or in connection with an instruction execution system, apparatus, or device.
  • Software 550 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as computing device 500 for example, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device.
  • the transport readable medium can include, but is not limited to, an electronic, magnetic, optical,

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

L'invention porte sur une technique de traitement de résultats de recherche. La technique consiste à exécuter une recherche sur la base d'une entrée d'utilisateur saisie par l'intermédiaire d'une interface utilisateur graphique à l'aide d'un processeur, à identifier des documents pertinents sur la base de la recherche, et à obtenir une classification standard pour chaque document pertinent. La classification standard est une classification dans un système de classification standard. La technique consiste également à re-classifier chaque document pertinent, sur la base de la classification standard du document pertinent, dans une classification interprétative dans un système de classification interprétative. La classification interprétative comporte au moins une classe primaire et une classe secondaire. La technique consiste en outre à regrouper les documents pertinents dans chaque classe primaire et secondaire de documents pertinents, et à afficher les classes primaires des documents pertinents et un certain nombre de documents pertinents regroupés dans chaque classe primaire affichée par l'intermédiaire de l'interface utilisateur graphique sur un dispositif d'affichage.
EP10852294.7A 2010-05-26 2010-05-26 Recherche utilisant une taxinomie Withdrawn EP2577495A4 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2010/036159 WO2011149454A1 (fr) 2010-05-26 2010-05-26 Recherche utilisant une taxinomie

Publications (2)

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EP2577495A1 true EP2577495A1 (fr) 2013-04-10
EP2577495A4 EP2577495A4 (fr) 2015-09-16

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CN (1) CN102947822A (fr)
WO (1) WO2011149454A1 (fr)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130212093A1 (en) 2012-02-15 2013-08-15 International Business Machines Corporation Generating visualizations of a display group of tags representing content instances in objects satisfying a search criteria
US9360982B2 (en) 2012-05-01 2016-06-07 International Business Machines Corporation Generating visualizations of facet values for facets defined over a collection of objects
CN105528356B (zh) * 2014-09-29 2019-01-18 阿里巴巴集团控股有限公司 结构化标签生成方法、使用方法及装置
CN114022934B (zh) * 2021-11-04 2023-06-27 清华大学 一种基于多数原则的实时人像聚档方法、系统和介质

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Publication number Priority date Publication date Assignee Title
WO2001075728A1 (fr) * 2000-03-30 2001-10-11 I411, Inc. Procedes et systemes permettant la recuperation efficace de donnees a partir de collections de donnees
US20080134060A1 (en) * 2005-04-01 2008-06-05 Paul Albrecht System for creating a graphical visualization of data with a browser
TW200407736A (en) * 2002-11-08 2004-05-16 Hon Hai Prec Ind Co Ltd System and method for classifying patents and displaying patent classification
CN1517912A (zh) * 2003-01-16 2004-08-04 财团法人资讯工业策进会 专利文献资料检索的方法
US20050010559A1 (en) * 2003-07-10 2005-01-13 Joseph Du Methods for information search and citation search
GB0502259D0 (en) * 2005-02-03 2005-03-09 British Telecomm Document searching tool and method
KR100869545B1 (ko) * 2008-04-28 2008-11-19 한국생명공학연구원 검색 히스토리를 생성하는 되풀이 검색시스템
US20100125566A1 (en) * 2008-11-18 2010-05-20 Patentcafe.Com, Inc. System and method for conducting a patent search

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Publication number Publication date
EP2577495A4 (fr) 2015-09-16
CN102947822A (zh) 2013-02-27
WO2011149454A1 (fr) 2011-12-01

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