DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 6-8, 13-15, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1, 8, and 15,
Step 1 Analysis: Claims 1, 8, and 15 are directed to a method, system and computer program, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claims 1, 8, and 15 recites,
The limitations of:
“extracting a plurality of text indicators from the text of the content object” is a mental process which can be performed by the human mind. A human can extract text from a sentence.
“identifying strong indicators from the plurality of text indicators, wherein each strong indicator corresponds to an entity attribute value that is associated with fewer than a threshold number of entities in the database” is a mental process which can be performed by the human mind. A human can identify adjective from text.
“scoring each candidate storage location based on correspondence between the strong indicators and the entity attribute values of the candidate storage location” is a mental process which can be performed by the human mind. A human can score candidates base on criterias.
“selecting a target storage location from the candidate storage locations based on the scoring” is a mental process which can be performed by the human mind. A human can select where to store selected candidates.
These limitations, as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: Claims 1, 8, and 15 recites the additional elements: “receiving a content object for filing, the content object comprising text”, “automatically filing the content object to the target storage location”, “the processor”, “memory”, “a computer readable medium”, and “a system”. The limitation of “collecting” and “providing” are an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application. “the processor”, “memory”, “a computer readable medium”, and “a system”, note that these recited additional elements are a high-level recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B Analysis: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more. With respect to the "receiving and filing the content” limitation is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more. Lastly the recitation of “the processor”, “memory”, “a computer readable medium”, and “a system” are recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f).
Claims 2-6, 9-13, and 16-20, recites limitations abstract ideas previously identified in the independent claims, that are mental process which can be performed by the human mind. This claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 7 and 14, recite limitations that are additional elements of using a computer as a tool to perform the recited step amount to no more than mere instructions to apply the abstract idea using generic computer component.
Therefore, the claims as a whole does not change this conclusion and the claims are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Aflalo et al. (US 2014/0136531 A1, hereinafter Aflalo) in view of Martinez et al. (US 2013/0218829 A1, hereinafter Martinez).
As to claims 1, 8, and 15, Aflalo teaches a computer-implemented method for automated filing of content objects, comprising:
receiving a content object for filing, the content object comprising text (Aflalo discloses the server appliance 115 comprises an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the environment 100. Note; Filing means keeping documents in a safe place or store, (see Aflalo: Para. 0044 and 0055). For example, structured data such as data cubes, unstructured data such as documents or other files, and semi-structured data such as HTML data, (see Aflalo: 0048 and 0095). For example, manufacturing efficiency data for a particular organizational unit of a business enterprise (associated with the enterprise portal workspace), (see Aflalo: Para. 0049 and 0050). For example, unstructured content documents include email messages, which are documents with text. The semantic network module 226 also includes a semantic network store (see Aflalo: Para. 0080). The data cube may also allow manipulation and/or analysis of the data stored in the cube from multiple perspectives, e.g., by dimensions, measures, and/or elements of the cube, (see Aflalo: Para. 0049). The identified subset of content items is ranked based on the match (e.g., based on scores defined by edges of the semantic network that describe semantic similarity between nodes); extracting a plurality of text indicators from the text of the content object (see Aflalo: Para. 0102 and 0103));
identifying strong indicators from the plurality of text indicators, wherein each strong indicator corresponds to an entity attribute value that is associated with fewer than a threshold number of entities in the database (Aflalo discloses querying of the semantic network (e.g., the nodes) with a contextual query (querying the knowledge graph), (see Aflalo: Para. 0095). The semantic network, in some aspects, includes nodes that store and/or reference the parsed metadata and edges between nodes that store and/or reference semantic scores between nodes (e.g., semantic scores that indicate a degree of similarity between nodes), (see Aflalo: Para. 0096). As another example, the enterprise portal workspace may reorganize a user's workspace in order to more quickly and automatically provide relevant content through the workspace, (see Aflalo: Para. 0041). For example, structured data such as data cubes, unstructured data such as documents and files, and semi-structured data such as HTML data, (see Aflalo: Para. 0048));
querying the database using the strong indicators to identify candidate storage locations having entity attribute values matching the strong indicators (Aflalo discloses querying of the semantic network (e.g., the nodes) with a contextual query (querying the knowledge graph), (see Aflalo: Para. 0095). The semantic network, in some aspects, includes nodes that store and/or reference the parsed metadata and edges between nodes that store and/or reference semantic scores between nodes (e.g., semantic scores that indicate a degree of similarity between nodes), (see Aflalo: Para. 0096). As another example, the enterprise portal workspace may reorganize a user's workspace in order to more quickly and automatically provide relevant content through the workspace, (see Aflalo: Para. 0041). For example, structured data such as data cubes, unstructured data such as documents and files, and semi-structured data such as HTML data, (see Aflalo: Para. 0048).);
scoring each candidate storage location based on correspondence between the strong indicators and the entity attribute values of the candidate storage location (Aflalo discloses a consistent schema with metadata attributes, while, with respect to unstructured data, for example, one or more basic metadata properties (e.g., outside of a constant schema) may be parsed and stored (e.g., to generate the semantic network 141). The semantic network 141 may include and/or represent sematic scores that define relationships between nodes (comprise nodes), (see Aflalo: Para. 0054). The parsed metadata attributes are then stored in step 406, for example, in a semantic network, (see Aflalo: Para. 0095 and 0096). For example, with or through the enterprise portal workspaces 160/165 or portal client/server 202/214 (distributed computing environment), (see Aflalo: Para. 0094));
selecting a target storage location from the candidate storage locations based on the scoring (Aflalo discloses the semantic network, in some aspects, includes nodes that store and/or reference the parsed metadata and edges between nodes that store and/or reference semantic scores between nodes (e.g., semantic scores that indicate a degree of similarity between nodes) and scores that define relationships between nodes, (see Aflalo: Para. 0054 and 0096). The enterprise portal workspace is identified and, based on such data/documents and ranked according to the matches. The semantic network may generally comprise nodes of metadata (e.g., contextual attributes of content items) and edges that connect the nodes and that define semantic relationships between nodes. (see Aflalo: Para. 0041 and 0054); and
automatically filing the content object to the target storage location For example, the nodes and edges of the semantic network may be stored in the semantic network store, (see aflalo: Para. 0080 and 0083).
Aflalo does not expressly teach maintaining a database comprising a plurality of storage locations, each storage location associated with an entity and having entity attributes that describe properties of the associated entity;
Martinez teaches document management system and method, see abstract, in which he teaches maintaining a database comprising a plurality of storage locations, each storage location associated with an entity and having entity attributes that describe properties of the associated entity (Martinez discloses document management systems (DMS) and/or enterprise content management systems (ECM) such as eDocs, Worksite, Worldox GX3, NetDocuments, SharePoint, Documentum, and FileNet in order to be accessed from any device over the Internet or shared with others. Note: enterprise content management systems referred to as document management, (see Martinez: Para. 0071, and 0078-0080). Agent server (0226) may include one or more databases that maintain information and tables for managing and performing collaboration functions consistent with the disclosed embodiments, (see Martinez: Para. 0095). Workspaces may be created by a client (e.g., client (0120, 0130)) via user input, (see Martinez: Para. 0084-0085). A new workspace profile window is opened and allows the creator to add relevant workspace attributes, create users and set access rights to the workspace, such as, Email Address, First Name and Last Name (see Martine: Para. 0127 and 0146).
It would have been obvious to a person having ordinary skill in the art at the time the invention was made to have modified Aflalo by the teaching of Martinez, because maintaining a database…, would enable the method to easily locate the correct information associated with the entity.
As to claims 2, 9, and 16, Aflalo as modified teaches wherein the step of selecting a target storage location further comprises:
identifying that scores for a plurality of candidate storage locations have exceeded a confidence threshold, thereby creating an ambiguity (see Aflalo, p. 96, The semantic network, in some aspects, includes nodes that store and/or reference the parsed metadata and edges between nodes that store and/or reference semantic scores between nodes (e.g., semantic scores that indicate a degree of similarity between nodes) and p. 100, In some implementations, edges of the semantic network (e.g., scores) may be queried after primary content items are identified. The scores may be then used to determine secondary content items to display.); and
resolving the ambiguity by evaluating predefined relationships between the entities associated with the plurality of candidate storage locations to select the one target storage location (see Aflalo, p. 54, “the edges of the semantic network 141 may include and/or represent sematic scores that define relationships between nodes (e.g., a contextual similarity between nodes)”).
As to claims 4, 11, and 18, Aflalo as modified teaches wherein identifying the strong indicators from the plurality of text indicators is based on the determined document type of the content object (see Martinez, p. 77, “In one embodiment, client (0120, 0130) may create, maintain, edit, modify, copy, send, receive, store, delete, and the like one or more documents. In one non-limiting example, a document may be a file, content, or information that software, processors, and/or users may use. For example, a document may be a word processing document containing content (e.g., text, graphics, links, etc.). A document may also be a spreadsheet file, a web page, a PDF file, or any other type of file that includes content that may be viewed, edited, modified, copied, shared, etc. by a user, processor, or software. A document may be included in a folder containing multiple documents.”).
As to claims 5, 12, and 19, Aflalo as modified teaches wherein scoring each candidate storage location further comprises:
detecting, in the text of the content object, mentions that match any of the entity attribute values from each candidate storage location ((see Aflalo, Abstract and P.2) is queried to match the user context data with the metadata, (see Aflalo: Para. 0041 and 0048)); and generating the score based on a weighted count of the detected mentions (see Aflalo, p. 101-102, score, semantic similarity).
As to claims 6, 13, and 20, Aflalo as modified teaches wherein extracting the plurality of text indicators comprises applying a regular expression to the text of the content object, wherein the regular expression describes a structure of an entity attribute value (Aflalo discloses the server appliance 115 comprises an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the environment 100. Note; Filing means keeping documents in a safe place or store, (see Aflalo: Para. 0044 and 0055). For example, structured data such as data cubes, unstructured data such as documents or other files, and semi-structured data such as HTML data, (see Aflalo: 0048 and 0095). For example, manufacturing efficiency data for a particular organizational unit of a business enterprise (associated with the enterprise portal workspace), (see Aflalo: Para. 0049 and 0050). For example, unstructured content documents include email messages, which are documents with text. The semantic network module 226 also includes a semantic network store (see Aflalo: Para. 0080). The data cube may also allow manipulation and/or analysis of the data stored in the cube from multiple perspectives, e.g., by dimensions, measures, and/or elements of the cube, (see Aflalo: Para. 0049). The identified subset of content items is ranked based on the match (e.g., based on scores defined by edges of the semantic network that describe semantic similarity between nodes), (see Aflalo: Para. 0102 and 0103).
As to claims 7 and 14, Aflalo as modified teaches wherein automatically filing the content object to the target storage location comprises filing the content object into a specific folder within the target storage location, wherein the specific folder is selected based on a determined document type of the content object (see Martinez, p. 77, “For example, disclosed embodiments may work with one or more folders containing many documents, such as multiple word processing files, spreadsheets, tables, graphical files, etc. A folder may also include one or more subfolders, each containing one or more documents. The above-listed examples of documents are not intended to be limiting to the disclosed embodiments” p. 82, “The CCP (0110) may also include one or more memory devices, such as local or networked memory storage media, shared memory platforms, or a combination thereof. In certain embodiments, the CCP (0110) includes memory that stores documents, folders of documents, information, content, data, etc. for transmission and viewing by clients (0120, 0130) through browser or similar type of software”).
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Aflalo and Martinez and in view of Eder (US Pub. 2012/0158633) (Eff filing date of app: 2/24/2012).
As to claims 3, 10, and 17, Aflalo as modified does not expressly teach the method further comprising classifying the content object to determine a document type.
Eder teaches knowledge graph based search system, see abstract, in which he teaches classifying the content object to determine a document type (maintains one or more individual and/or group contexts in a systematic fashion (knowledge graph). Data may also be obtained from a Complete Context™ Input Service (601) or other applications that can provide xml output. For example, newer versions of Microsoft® Office and Adobe® Acrobat® can be used to provide data input to the Medicine Service (100) of the present invention (provided by the Personalized Medicine Service, which is document type) Note: referred to as precision medicine, is a medical model that separates people into different group. Note: A Document Type describes the tree structure of a document and something about its data (knowledge graph/graph-structured data), (see Eder: Para. 0159 and 0162). The ontology table (152) to classify the word set as one or more phrases, (see Eder: Para. 0247, 0271 and 0272). Analyze measures for the subject hierarchy in order to evaluate alignment and adjust measures in order to achieve alignment in an automated fashion, (see Eder: Para.0202 and 0203). Sub-context frames contain information relevant to a subset of one or more function measure/ layer combinations. For example, a sub-context frame could include the portion of each of the context layers that was related to an entity process, (see Eder: Para. 0098). They typically store information similar to that shown below in Table 14, (see Eder: Para. 0169). This reads on the claim concepts of classifying the document by a document type, wherein evaluating the plurality of indicators to generate a subset of strong indicators in the plurality of indicators is based on the document type).
It would have been obvious to a person having ordinary skill in the
art at the time the invention was made to have modified Aflalo. by the teaching of Eder, because classifying the content object to determine a document type, would enable Entity related data are analyzed as required to develop an entity knowledge and one or more knowledge graphs. The knowledge graphs are used to support the retrieval of relevant search results, as disclosed by Eder, (see Abstract).
Conclusion
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BELIX M. ORTIZ DITREN
Primary Examiner
Art Unit 2164
/Belix M Ortiz Ditren/Primary Examiner, Art Unit 2164
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