DETAILED ACTION
Priority
Applicant’s claim for the benefit to 17/207,435 filed March 19, 2021, PCT/US2021/021135 filed March 5, 2021 and 63/021968 filed May 8, 2020 is acknowledged. The effective filing date used for examination purposes is therefore May 8, 2020.
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 .
Information Disclosure Statement
The information disclosure statement filed November 22, 2023 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered. Copies of the NPL documents considered were found in Parent application 18/236080. No copy of the following NPL has been provided:
-NPL #3 “Populate Web forms with JSP and XML – Tech Republic”
-NPL #18 International Search Report and Written Opinion Dated August 13, 2021 for Application Num PCT/US2021/021135.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The title is generic and does not articulate the specific focus of this particular patent application.
The following title is suggested: Creating Enhanced document based on semantic meaning of content.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the following limitations from claims 1, 9 and 17 must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
Claim1: “generating the enhanced document from the content of the initial document that encodes the content in a structured form according to a defined schema based on semantic meaning of elements of the content;
determining additional content based on the semantic meaning of the elements; and
encoding the additional content in the enhanced document to generate an augmented enhanced document.”
Claim 9: “generate an enhanced document from the content of the initial document that encodes the content in a structured form according to a defined schema based on semantic meaning of elements of the content;
determine additional content based on the semantic meaning of the elements; and
encode the additional content in the enhanced document producing an augmented enhanced document.”
Claim 17: “determining one or more keywords based on the semantic meaning of the elements; and
generating an augmented enhanced document including the one or more keywords.”
The drawings are objected to because figures 2-5 contain text that is blurred and unreadable (e.g. the text of the example structured data).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regard to claims 1, 9, and 17, claim 1 recites “receiving content… wherein the content is unstructured… generating an enhanced document… that encodes the content in a structured form”. This claim limitation lacks antecedent basis.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The terms “structured” and “unstructured” are indefinite because the specification does not clearly redefine them.
Paragraph [0003] of the initial specification appears to describe DOCX and PDF formats as “unstructured” formats. Yet, Paragraphs [0023] and [0047] both describe DOCX and PDF as structured formats. It is unclear if the DOCX and PDF formats should be considered ‘structured’ or ‘unstructured’ within the invention.
Paragraph [0003]: Electronic documents are frequently stored in file formats, like Microsoft Word's DOCX format or Adobe Acrobat's PDF format, that allow for documents to be rendered in a manner that is visually appealing to a human reader. But these formats typically store information in an unstructured manner making it difficult for automated parsing software (or parsers) to interpret the electronic document accurately, resulting in misread or miscategorized data.
Paragraph [0023] In response to a user request to export the initial document 150 in a particular file format (e.g., in Microsoft Word's DOCX format or Adobe Acrobat's PDF format), the enhanced document creation system 110 may export the initial document 150 as an enhanced document 151, namely by encoding the document content according to a defined schema and embedding the structured content as non-visible metadata in the enhanced document 151.
Paragraph [0047] Rendering logic 123 may allow the enhanced document creation system 110 to render an encoded document (or desensitized or translated document, as the case may be) in a particular file format (e.g., Microsoft Word's DOCX format or Adobe Acrobat's PDF format), which in some embodiments, may be specified by a user 101. The rendering logic 123, for example, may be used to generate an intermediate file from the encoded content, where the visual appearance of the document content (i.e., the positioning and style of the document content) may be provided by a skin definition associated with initial document 150, which may be stored and retrieved from skin library 114a. The enhanced document creation system 110, for example, may generate an HTML file comprising different HTML elements, with specified CSS styles, along with a linked CSS stylesheet to control the position and style (i .e., the visual appearance) of the document content. The enhanced document creation system 110 may then convert the intermediate file into a particular file format, like Microsoft Word's DOCX format or Adobe Acrobat's PDF format. In doing so, the enhanced document creation system 110 may make use of publicly available conversion libraries, like Aspose (for HTML to DOCX conversion) or ABCpdf (for HTML to PDF conversion).
Paragraph [0026] of the original specification describes the ‘enhanced document’, which is supposed to be the structured document, as being in JSON, HTML, RTF, or TXT format. Yet one of ordinary skill in the art would typically recognize “TXT format” as being ‘unstructured’.
Paragraph [0026] “the enhanced document parsing system 130 may be able to specify the format in which the encoded content should be returned (e.g., a JSON, HTML, RTF, or TXT format).”
For examination purposes the ‘unstructured’ document has been interpreted as a basic text file as this is the one format that one of ordinary skill in the art typically identifies as being ‘unstructured’. For examination purposes the ‘structured’ document has interpreted as including DOCX, PDF, or HTML (as per Paragraph [0047]) XML or JSON (as per Paragraph [0003]) formats.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regard to claims 1, 9, and 17, claim 1 recites “generating an enhanced document from the content of the initial document that encodes the content in a structured form according to a defined schema based on semantic meaning of elements of the content;
determining one or more keywords based on the semantic meaning of the elements; and
generating an augmented enhanced document including the one or more keywords.” Claims 9 and 17 recite substantially similar limitations and are rejected based upon similar reasoning and rational.
Each unique claim element is expected to refer to a unique claim element. Within the instant specification, the only discussion of augmented content, is in Paragraph [0041] wherein the system describes analyzing the unstructured resume input by the user to generate the augmented enhanced document. One of ordinary skill in the art would reasonably read the augmented document as the enhanced document, and not as a separate document. The distinction between the “enhanced document” and the “augmented enhanced document” within the claims is unclear. It is unclear if the claimed device is generating two distinct documents or generating two distinct pieces of content within a single document.
For examination purposes this claim limitation has been read as referring to content within the enhanced document, wherein only one enhanced document is generated. It is suggested that the claims be amended to clarify the ‘augmenting’ process as detailed in Paragraph [0041]. For example, detailing the analyzing of the initial document to generate metadata and augmented metadata based on the semantic meaning of the content of the initial document; and generating the enhanced document containing the metadata and augmented metadata.
With regard to claims 2, 10 and 18, claim 2 recites “an element”. Claims 10 and 18 recite substantially similar limitations and are rejected based upon the same reasoning and rational. The parent claims recites “elements” and “the elements”. It is unclear if applicant is attempting to recite a new claim element or attempting to refer to one of the previously recited elements or all of the previously recited elements. For examination purposes this claim limitation has been construed to mean --a respective element--.
Claim Objections
Claims 1-20 are objected to because of the following informalities. Appropriate correction is required.
With regard to claims 1, 9, and 17, claim 1 recites “generating an enhanced document from the content of the initial document that encodes the content in a structured form according to a defined schema based on semantic meaning of elements of the content.” Claims 9 and 17 recite substantially similar limitations and are rejected based upon similar reasoning and rational.
It is unclear what the term “that” is referring to. The term ‘that’ may reasonably be read as referring to the enhanced document, the content, or the initial document. For examination purposes this claim limitation has been construed to mean -- generating an enhanced document from the content of the initial document, wherein the enhanced document encodes the content in a structured form according to a defined schema based on semantic meaning of elements of the content.--.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 9-11, 17, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Satyaki [Resume Parser with Natural Language Processing].
With regard to claim 1 Satyaki teaches A method of creating an enhanced document as the structured document resulting from the transformation (Satyaki, Page 4484, Section I “To design a model this can parse information from unstructured resumes and transform it to a structured JSON format”), comprising:
receiving content as reading the content (Satyaki, Page 4485-Page 4486 Section A. The Uploader: “First, the client uploads a file. The algorithm blocks any file having an extension other than '.pdf, '.doc', '.docx', '.odt', ' .ads ', '.txt'. After the client has successfully uploaded the file , the algorithm takes the file, reads the contents and writes the content into a text file before passing on the data to the parser.”) associated with creating an initial document as the text file created (Id) such as the original unstructured text resume submitted by a user (Satyaki, Page 4485 “student trying to beautify his/her unstructured text resume and convert into a beautiful pdf format. In either case, the algorithm remains same.”) with a document creation platform as a resume created using a websites particular formatting (Satyaki, Page 4484, Section III. History of Hiring: “These agencies required the applicants to upload their resumes on their websites in particular formats”), wherein the content is unstructured as the uploaded file and the generated text file are both ‘unstructured’ (Page 4485-Page 4486 Section A. The Uploader: “First, the client uploads a file. The algorithm blocks any file having an extension other than '.pdf, '.doc', '.docx', '.odt', ' .ads ', '.txt'. After the client has successfully uploaded the file , the algorithm takes the file, reads the contents and writes the content into a text file before passing on the data to the parser.”; Please note that the term “unstructured” has been read interpreted as detailed in the 112b rejection above);
generating an enhanced document as generating a JSON resume (Satyaki, Page 4487 Section VI. Expected Outcome: “Once the user confirms the result of our parser the system generates a JSON resume and stores it in the NoSQL database.”) from the content as the results of our parser (Id) of the initial document as the text file that is passed to the parser (Satyaki, Page 4485-Page 4486 Section A. The Uploader: “First, the client uploads a file. The algorithm blocks any file having an extension other than '.pdf, '.doc', '.docx', '.odt', ' .ads ', '.txt'. After the client has successfully uploaded the file , the algorithm takes the file, reads the contents and writes the content into a text file before passing on the data to the parser.”) that encodes the content in a structured form as JSON format (Satyaki, Page 4487 Section VI. Expected Outcome: “Once the user confirms the result of our parser the system generates a JSON resume and stores it in the NoSQL database.”; Please note this claim limitation has been interpreted as detailed in the 112b rejection above) according to a defined schema as the defined JSON schema (Id), based on semantic meaning of elements of the content (Satyaki, Page 4486 Section II. Semantic Analysis: “Semantic analysis can be defined as the study of semantics i.e the structure and meaning of speech.”);
determining additional content as the classification of the text, for example “Calcutta University” is used in place of “University of Calcutta” (Satyaki, Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”; In Information Retrieval research, text classification system is given the utmost focus which bounds the decisions to either relevant or non-relevant depending upon the information need of the user. It is not a hard task to get the user information need.”; Please note this claim limitation has been read in light of Paragraph [0041] of the original specification which provides the example of “money handling” or “customer relations” as characterizations of experiences extracted from the underlying resume) based on the semantic meaning of the elements (Satyaki, Page 4486 Section II. Semantic Analysis: “Semantic analysis can be defined as the study of semantics i.e the structure and meaning of speech.”); and
encoding the additional content as storing the token, structure, and semantic information in the model (Satyaki, Page 4487 “The Lexical analyzer preprocesses the data and tokenizes them The Syntactic analyzer takes the tokens and finds the structure in it. The parse tree diagrammatically represents the syntactic structure in the form of a tree. The Semantic analyzer studies the structure of the data to find their language-independent meaning.”) in the enhanced document to generate an augmented enhanced document as JSON format (Satyaki, Page 4487 Section VI. Expected Outcome: “Once the user confirms the result of our parser the system generates a JSON resume and stores it in the NoSQL database.”.
With regard to claims 2 and 10, Satyaki further teaches wherein determining the additional content comprises identifying one or more keywords as “Calcutta University” is used in place of “University of Calcutta” (Satyaki, Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”; In Information Retrieval research, text classification system is given the utmost focus which bounds the decisions to either relevant or non-relevant depending upon the information need of the user. It is not a hard task to get the user information need.”); from the semantic meaning of an element (Satyaki, Page 4486 Section II. Semantic Analysis: “Semantic analysis can be defined as the study of semantics i.e the structure and meaning of speech.”).
With regard to claim 3, 11, and 18 Satyaki further teaches wherein identifying the one or more keywords comprises identifying two or more keywords as “Calcutta University” is used in place of “University of Calcutta” (Satyaki, Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”; In Information Retrieval research, text classification system is given the utmost focus which bounds the decisions to either relevant or non-relevant depending upon the information need of the user. It is not a hard task to get the user information need.”) that characterize the semantic meaning (Satyaki, Page 4486 “Semantic analysis can be defined as the study of semantics i.e the structure and meaning of speech.”) of the element differently as “university of Calcutta” and “Calcutta University” are different elements which mean the same thing (Satyaki, Page 4486 “Person A has a resume which states he has graduated from the "University of Calcutta" and person B has a resume which says he has graduated from "Calcutta University".”).
With regard to claim 9 Satyaki teaches An enhanced document creation system as the structured document resulting from the transformation (Satyaki, Page 4484, Section I “To design a model this can parse information from unstructured resumes and transform it to a structured JSON format”), comprising at least one processor coupled to at least one memory that includes instructions that, when executed by the at least one processor as the processor of the computer performing the algorithm (Satyaki, Page 4486, Section: Natural Language Processing: “Computer”; Page 4484 Section III. History of Hiring: “To overcome all the above problems an intelligent algorithm was required which could parse information from any unstructured resumes, sort it based on the clusters and rank it finally. The model uses natural language processing to understand the resume and then parse the information from it. Once information is parsed it is stored in the database.”), cause the document creation system to:
receive content as reading the content (Satyaki, Page 4485-Page 4486 Section A. The Uploader: “First, the client uploads a file. The algorithm blocks any file having an extension other than '.pdf, '.doc', '.docx', '.odt', ' .ads ', '.txt'. After the client has successfully uploaded the file , the algorithm takes the file, reads the contents and writes the content into a text file before passing on the data to the parser.”) associated with creating an initial document as the text file created (Id) such as the original unstructured text resume submitted by a user (Satyaki, Page 4485 “student trying to beautify his/her unstructured text resume and convert into a beautiful pdf format. In either case, the algorithm remains same.”) with a document creation platform as a resume created using a websites particular formatting (Satyaki, Page 4484, Section III. History of Hiring: “These agencies required the applicants to upload their resumes on their websites in particular formats”), wherein the content is unstructured as the uploaded file and the generated text file are both ‘unstructured’ (Page 4485-Page 4486 Section A. The Uploader: “First, the client uploads a file. The algorithm blocks any file having an extension other than '.pdf, '.doc', '.docx', '.odt', ' .ads ', '.txt'. After the client has successfully uploaded the file , the algorithm takes the file, reads the contents and writes the content into a text file before passing on the data to the parser.”; Please note that the term “unstructured” has been read interpreted as detailed in the 112b rejection above);
generate an enhanced document as generating a JSON resume (Satyaki, Page 4487 Section VI. Expected Outcome: “Once the user confirms the result of our parser the system generates a JSON resume and stores it in the NoSQL database.”) from the content as the results of our parser (Id) of the initial document as the text file that is passed to the parser (Satyaki, Page 4485-Page 4486 Section A. The Uploader: “First, the client uploads a file. The algorithm blocks any file having an extension other than '.pdf, '.doc', '.docx', '.odt', ' .ads ', '.txt'. After the client has successfully uploaded the file , the algorithm takes the file, reads the contents and writes the content into a text file before passing on the data to the parser.”) that encodes the content in a structured form as JSON format (Satyaki, Page 4487 Section VI. Expected Outcome: “Once the user confirms the result of our parser the system generates a JSON resume and stores it in the NoSQL database.”; Please note this claim limitation has been interpreted as detailed in the 112b rejection above) according to a defined schema as the defined JSON schema (Id), based on semantic meaning of elements of the content (Satyaki, Page 4486 Section II. Semantic Analysis: “Semantic analysis can be defined as the study of semantics i.e the structure and meaning of speech.”);
determine additional content as the classification of the text, for example “Calcutta University” is used in place of “University of Calcutta” (Satyaki, Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”; In Information Retrieval research, text classification system is given the utmost focus which bounds the decisions to either relevant or non-relevant depending upon the information need of the user. It is not a hard task to get the user information need.”; Please note this claim limitation has been read in light of Paragraph [0041] of the original specification which provides the example of “money handling” or “customer relations” as characterizations of experiences extracted from the underlying resume) based on the semantic meaning of the elements (Satyaki, Page 4486 Section II. Semantic Analysis: “Semantic analysis can be defined as the study of semantics i.e the structure and meaning of speech.”); and
encode the additional content as storing the token, structure, and semantic information in the model (Satyaki, Page 4487 “The Lexical analyzer preprocesses the data and tokenizes them The Syntactic analyzer takes the tokens and finds the structure in it. The parse tree diagrammatically represents the syntactic structure in the form of a tree. The Semantic analyzer studies the structure of the data to find their language-independent meaning.”) in the enhanced document producing an augmented enhanced document as JSON format (Satyaki, Page 4487 Section VI. Expected Outcome: “Once the user confirms the result of our parser the system generates a JSON resume and stores it in the NoSQL database.”.
With regard to claim 17 Satyaki teaches A method, comprising:
receiving content as reading the content (Satyaki, Page 4485-Page 4486 Section A. The Uploader: “First, the client uploads a file. The algorithm blocks any file having an extension other than '.pdf, '.doc', '.docx', '.odt', ' .ads ', '.txt'. After the client has successfully uploaded the file , the algorithm takes the file, reads the contents and writes the content into a text file before passing on the data to the parser.”) associated with creating an initial document as the text file created (Id) such as the original unstructured text resume submitted by a user (Satyaki, Page 4485 “student trying to beautify his/her unstructured text resume and convert into a beautiful pdf format. In either case, the algorithm remains same.”) with a document creation platform as a resume created using a websites particular formatting (Satyaki, Page 4484, Section III. History of Hiring: “These agencies required the applicants to upload their resumes on their websites in particular formats”), wherein the content is unstructured as the uploaded file and the generated text file are both ‘unstructured’ (Page 4485-Page 4486 Section A. The Uploader: “First, the client uploads a file. The algorithm blocks any file having an extension other than '.pdf, '.doc', '.docx', '.odt', ' .ads ', '.txt'. After the client has successfully uploaded the file , the algorithm takes the file, reads the contents and writes the content into a text file before passing on the data to the parser.”; Please note that the term “unstructured” has been read interpreted as detailed in the 112b rejection above);
generating an enhanced document as generating a JSON resume (Satyaki, Page 4487 Section VI. Expected Outcome: “Once the user confirms the result of our parser the system generates a JSON resume and stores it in the NoSQL database.”) from the content as the results of our parser (Id) of the initial document as the text file that is passed to the parser (Satyaki, Page 4485-Page 4486 Section A. The Uploader: “First, the client uploads a file. The algorithm blocks any file having an extension other than '.pdf, '.doc', '.docx', '.odt', ' .ads ', '.txt'. After the client has successfully uploaded the file , the algorithm takes the file, reads the contents and writes the content into a text file before passing on the data to the parser.”) that encodes the content in a structured form as JSON format (Satyaki, Page 4487 Section VI. Expected Outcome: “Once the user confirms the result of our parser the system generates a JSON resume and stores it in the NoSQL database.”; Please note this claim limitation has been interpreted as detailed in the 112b rejection above) according to a defined schema as the defined JSON schema (Id), based on semantic meaning of elements of the content (Satyaki, Page 4486 Section II. Semantic Analysis: “Semantic analysis can be defined as the study of semantics i.e the structure and meaning of speech.”);
determining one or more keywords as “Calcutta University” is used in place of “University of Calcutta” (Satyaki, Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”; In Information Retrieval research, text classification system is given the utmost focus which bounds the decisions to either relevant or non-relevant depending upon the information need of the user. It is not a hard task to get the user information need.”; Please note this claim limitation has been read in light of Paragraph [0041] of the original specification which provides the example of “money handling” or “customer relations” as characterizations of experiences extracted from the underlying resume) based on the semantic meaning of the elements (Satyaki, Page 4486 Section II. Semantic Analysis: “Semantic analysis can be defined as the study of semantics i.e the structure and meaning of speech.”);; and
generating an augmented enhanced document as JSON format which includes “Calcutta Univeristy” (Satyaki, Page 4487 Section VI. Expected Outcome: “Once the user confirms the result of our parser the system generates a JSON resume and stores it in the NoSQL database.”; Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”;) including the one or more keywords as storing the token, structure, and semantic information in the model (Satyaki, Page 4487 “The Lexical analyzer preprocesses the data and tokenizes them The Syntactic analyzer takes the tokens and finds the structure in it. The parse tree diagrammatically represents the syntactic structure in the form of a tree. The Semantic analyzer studies the structure of the data to find their language-independent meaning.”).
Claim Rejections - 35 USC § 103
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 4-7, 12-15, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Satyaki in view of Bostick[2017/0083599].
With regard to claims 4 and 12, Satyaki further teaches wherein determining the additional content comprises determining the additional content as the classification of the text, for example “Calcutta University” is used in place of “University of Calcutta” (Satyaki, Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”; In Information Retrieval research, text classification system is given the utmost focus which bounds the decisions to either relevant or non-relevant depending upon the information need of the user. It is not a hard task to get the user information need.”) from the semantic meaning (Satyaki, Page 4486 Section II. Semantic Analysis: “Semantic analysis can be defined as the study of semantics i.e the structure and meaning of speech.”) of a [[ as the terms or words in the document (Satyaki, Page 4486 “Tokens are usually referred to as terms or words, but sometimes fabricating a type/token distinction is essential”).
Satyaki does not explicitly teach determining the additional content from the semantic meaning of a graphical element.
Bostick teaches determining the additional content as multiple categories (Bostick, ¶12 “Embodiments of the present invention create multiple categories of sentiment based on the semantics of comments and determined objects from within an image.”) from the semantic meaning as the semantics (Id; ¶3 “Semantics is the study of meaning”) of a graphical element as objects within an image (Bostick, ¶12).
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented the semantic analysis taught by Satyaki (Page 4486 Section II. Semantic analysis) to be able to analyze images as taught by Bostick as it yields the predictable results of enabling the system to process and analyze not only the text of the website based resumes (Satyaki, Page 4484 “These agencies required the applicants to upload their resumes on their websites in particular formats.”) but also the images (Bostick, ¶1) of the website based content (Bostick, ¶1 and ¶2).
With regard to claims 5 and 13, Satyaki further teaches wherein determining the additional content as categories, e.g. the classification of the text, for example “Calcutta University” is used in place of “University of Calcutta” (Satyaki, Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”; In Information Retrieval research, text classification system is given the utmost focus which bounds the decisions to either relevant or non-relevant depending upon the information need of the user. It is not a hard task to get the user information need.”; Bostick, ¶12 “Embodiments of the present invention create multiple categories of sentiment based on the semantics of comments and determined objects from within an image.”) comprises determining a value as semantic attributes (Bostick, ¶26 “For example, when semantic sentiment program 200 utilizes image analysis to analyze image 116, more semantic attributes may be identified”) captured by the graphical element as objects within an image (Bostick, ¶12).
With regard to claims 6 and 14, Satyaki further teaches wherein determining the value as semantic attributes (Bostick, ¶26 “For example, when semantic sentiment program 200 utilizes image analysis to analyze image 116, more semantic attributes may be identified”) captured by the graphical element as objects within an image (Bostick, ¶12) comprises determining a skill level in a subject matter as skills (Satyaki, Page 4486 “It parses all the relevant data from the uploaded resume including name, emails, contact numbers, social profile links, personal websites, years of work experience, work experiences, years of education, degrees, volunteer experiences, publications, skills, cluster(s) and languages through natural language processing and without any human interaction.”).
With regard to claims 7 and 15, Satyaki further teaches wherein determining the additional content as the classification of the text, for example “Calcutta University” is used in place of “University of Calcutta” (Satyaki, Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”; In Information Retrieval research, text classification system is given the utmost focus which bounds the decisions to either relevant or non-relevant depending upon the information need of the user. It is not a hard task to get the user information need.”) comprises determining [[
Satyaki does not explicitly teach determining metadata associated with a graphical element.
Bostick teaches wherein determining the additional content as multiple categories (Bostick, ¶12 “Embodiments of the present invention create multiple categories of sentiment based on the semantics of comments and determined objects from within an image.”) comprises determining metadata as semantic attributes (Bostick, ¶26 “For example, when semantic sentiment program 200 utilizes image analysis to analyze image 116, more semantic attributes may be identified”) associated with a graphical element as the image (Id).
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented the semantic analysis taught by Satyaki (Page 4486 Section II. Semantic analysis) to be able to analyze images as taught by Bostick as it yields the predictable results of enabling the system to process and analyze not only the text of the website based resumes (Satyaki, Page 4484 “These agencies required the applicants to upload their resumes on their websites in particular formats.”) but also the images (Bostick, ¶1) of the website based content (Bostick, ¶1 and ¶2).
With regard to claim 19 Satyaki further teaches determining additional content as the classification of the text, for example “Calcutta University” is used in place of “University of Calcutta” (Satyaki, Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”; In Information Retrieval research, text classification system is given the utmost focus which bounds the decisions to either relevant or non-relevant depending upon the information need of the user. It is not a hard task to get the user information need.”) associated with a [[
generating the augmented enhanced document as JSON format which includes “Calcutta Univeristy” (Satyaki, Page 4487 Section VI. Expected Outcome: “Once the user confirms the result of our parser the system generates a JSON resume and stores it in the NoSQL database.”; Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”) with the additional content as storing the token, structure, and semantic information in the model (Satyaki, Page 4487 “The Lexical analyzer preprocesses the data and tokenizes them The Syntactic analyzer takes the tokens and finds the structure in it. The parse tree diagrammatically represents the syntactic structure in the form of a tree. The Semantic analyzer studies the structure of the data to find their language-independent meaning.”).
Satyaki does not explicitly teach determining additional content associated with a a graphical element.
Bostick teaches determining the additional content as multiple categories (Bostick, ¶12 “Embodiments of the present invention create multiple categories of sentiment based on the semantics of comments and determined objects from within an image.”) associated with a graphical element as objects within an image (Bostick, ¶12).
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented the semantic analysis taught by Satyaki (Page 4486 Section II. Semantic analysis) to be able to analyze images as taught by Bostick as it yields the predictable results of enabling the system to process and analyze not only the text of the website-based resumes (Satyaki, Page 4484 “These agencies required the applicants to upload their resumes on their websites in particular formats.”) but also the images (Bostick, ¶1) of the website-based content (Bostick, ¶1 and ¶2).
With regard to claim 20 the proposed combination further teaches wherein determining the additional content as categories, e.g. the classification of the text, for example “Calcutta University” is used in place of “University of Calcutta” (Satyaki, Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”; In Information Retrieval research, text classification system is given the utmost focus which bounds the decisions to either relevant or non-relevant depending upon the information need of the user. It is not a hard task to get the user information need.”; Bostick, ¶12 “Embodiments of the present invention create multiple categories of sentiment based on the semantics of comments and determined objects from within an image.”) includes at least one of a value as semantic attributes (Bostick, ¶26 “For example, when semantic sentiment program 200 utilizes image analysis to analyze image 116, more semantic attributes may be identified”) captured by the graphical element as objects within an image (Bostick, ¶12) or metadata data as semantic attributes (Bostick, ¶26 “For example, when semantic sentiment program 200 utilizes image analysis to analyze image 116, more semantic attributes may be identified”) associated with the graphical element as the image (Id).
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Satyaki in view of Nunes [2018/0082074].
With regard to claims 8 and 16, Satyaki further teaches encoding as generating a JSON resume (Satyaki, Page 4487 Section VI. Expected Outcome: “Once the user confirms the result of our parser the system generates a JSON resume and stores it in the NoSQL database.”) the additional content as the classification of the text, for example “Calcutta University” is used in place of “University of Calcutta” (Satyaki, Page 4486 “So what semantic analyzer does is convert "University of Calcutta" to "Calcutta University".”; In Information Retrieval research, text classification system is given the utmost focus which bounds the decisions to either relevant or non-relevant depending upon the information need of the user. It is not a hard task to get the user information need.”) as [[
Satyaki does not explicitly teach that the encoded additional content is non-visible content excluded from rendering.
Nunes teaches encoding (Nunes, ¶29 “For example, metadata 204, data blocks 206, and a content map 208 may be signed and stored as a hash. e.g., hash 210. In some examples, the hash may be signed with a user's private key, e.g., private key 212. The resulting signature may then be stored in an assembled PDF, e.g., PDF 240, or in a custom field of the assembled PDF. Various signature techniques or cryptographic hash functions may be used, such as the Secure Hash Algorithm ("SHA"), at varying hash values, e.g., SHA-256. The signature, in some examples, may be encoded. e.g., in BASE64 or another encoding scheme.”) the additional content as storing the encrypted content in the custom properties of the PDF (¶16 “In some examples, the PDF file may contain content and/or metadata, as well as custom fields or custom properties (hereinafter "custom fields") that may allow for custom values or data to be set or stored within the PDF, and with the PDF remaining readable across various platforms”) as non-visible content excluded from rendering as redacted or otherwise encrypted confidential content (Nune, ¶26 “As shown in the examples of PDF 122 and 124, a PDF may be output with an of the PDF content visible, as in PDF 122, or with certain content redacted or otherwise unviewable as in PDF 124.”; ¶17 “Unencrypted content may represent non-confidential data, while encrypted content may represent confidential content.”).
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have incorporated the encryption techniques taught by Nune within the enhanced resume generation device taught by Satyaki as it would yield the predictable results of retaining the capability of PDF while increasing security and the ability to store unencrypted content within the single PDF file (Nune, ¶14; ¶11) this would allow users to redact (Nune, ¶26) certain parts of their resume while allowing other parts to remain visible (Nune, ¶26).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11188707 in view of Bostick. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented patent 11188707 using the image analysis taught by Bostick as it is a known means of performing semantic analysis that can be used to analyze the types of documents Patent 11188707 may encounter.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/236080 in view of Bostick. This is a provisional nonstatutory double patenting rejection. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented co-pending application 18236080 using the image analysis taught by Bostick as it is a known means of performing semantic analysis that can be used to analyze the types of documents which may be encountered.
Conclusion
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/AMANDA L WILLIS/Primary Examiner, Art Unit 2156