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 .
Status of Claims
Claims 2, 7, 11, 16, and 20 were previously cancelled. Claims 1,10, 14, 17, and 19 have been amended.
Claims 1, 3-6, 8-10, 12-15, and 17-19 have been examined and are currently pending.
Priority
Application 17/978,699 was filed 11/01/2022.
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, 3-6, 8-10, 12-15, and 17-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 3-6, 8-10, 12-15, and 17-19 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
Claims 1, 10, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method, computing apparatus, and non-transitory computer readable storage medium for masking of target categories of information in resume documents. For Claims 1, 10 and 19 the limitations of (Claim 1 being representative):
receiving, […], a first resume document that is printable on paper;
retrieving, […], at least one category that relates to a type of information to be masked;
extracting, […] from the first resume document, information that belongs to the at least one category;
masking, […], the extracted information; and
outputting, […], a modified version of the first resume document that includes a result of the masking,
wherein the masking comprises generating a black box that covers the extracted information such that the extracted information is not readable by a person[…], and
wherein when the corresponding set of bounding box coordinates for a first portion is adjacent to the corresponding set of bounding box coordinates for a second portion, the method further comprises merging the first portion with the second portion,
generating, […], a plurality of bounding boxes over the extracted information, wherein the plurality of bounding boxes correspond to coordinates of the extracted information and the coordinate of the extracted information includes a starting point coordinate at an upper left hand corner and an end point coordinate at a lower right hand corner;
when the plurality of bounding boxes are determined to be adjacent to one another, merging the plurality of bounding boxes into a merged bounding box;
generating, […], a black box in a size corresponding to the merged bounding box;
applying, […], the black box generated over the merged bounding box to mask the extracted information from view;
subsequent to the applying of the black box, converting, […], the first resume document into an image for destroying the extracted information masked by the black box from an outputted document; and
outputting, […], the image of the first resume document as a final output document that includes the black box, wherein the extracted information under the black box has been destroyed, as drafted, are processes that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The Examiner notes that “certain method[s] of organizing human activity” includes a person's interaction with a computer (see MPEP 2106.04(a)(2)(II)). That is, other than reciting a system implemented by a processor and memory, the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the processor and memory, this claim encompasses a person to receive a document that is printable on paper, retrieve a category to a type of information to be masked, extract information from the document that belongs the category, generate a plurality of bounding boxes over the extracted information, wherein the plurality of bounding boxes correspond to coordinates of the extracted information and the coordinate of the extracted information includes a starting point coordinate at an upper left hand corner and an end point coordinate at a lower right hand corner, when the plurality of bounding boxes are determined to be adjacent to one another, merging the plurality of bounding boxes into a merged bounding box, generate a black box in a size corresponding to the merged bounding box, apply the black box generated over the merged bounding box to mask the extracted information from view, subsequent to the applying of the black box convert the first resume document into an image for destroying the extracted information masked by the black box from an outputted document, and output the image of the first resume document as a final output document that includes the black box, wherein the extracted information under the black box has been destroyed based on this data in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, Claims 1, 10 and 19 recite an abstract idea. (Step 2A- Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. Claims 1, 10, and 19 recites the additional elements of a processor (Claims 1, 10, and 19), a memory (Claims 1, 10, and 19), communication interface (Claim 10), a non-transitory computer-readable storage medium (Claim 19), and the first resume document is printed on paper (Claims 1, 10, and 19), that implements the identified abstract idea. These additional elements are not described by the applicant and are recited at a high-level of generality (i.e., one or more generic computers performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer components. Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Claims 1, 10, and 19 are directed to an abstract idea. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a processor (Claims 1, 10, and 19), a memory (Claims 1, 10, and 19), communication interface (Claim 10), a non-transitory computer-readable storage medium (Claim 19), and the first resume document is printed on paper (Claims 1, 10, and 19), to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such claims 1, 10, and 19 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims 3-6, 8-9, 12-15, and 17-18 are similarly rejected because they either further define/narrow the abstract idea of independent claims 1, 10 and 19 as discussed above. Claim(s) 3 & 12 merely describe(s) the at least one category. Claim(s) 4 & 13 merely describe(s) receiving from a user, at least one additional category that relates to an additional type of information to be masked. Claim(s) 6 & 15 merely describe(s) outputting for each respective one of the at least one portion a corresponding set of bounding box coordinates that relates to a physical position of the at least one portion within the first resume document. Claim(s) 8 & 17 merely describe(s) wherein the extracting of the information that belongs to the at least one category comprises extracting all text strings from the first resume document and comparing each one of the text strings to a first predetermined list of place names and universities. Claim(s) 9 & 18 merely describes wherein the extracting of the information that belongs to the at least one category further comprises using a regular expression string search with respect to a second predetermined list of email address suffixes and residential address style types. Therefore claims 3-4, 6, 8-9, 12-13, 15, and 17-18 are considered patent ineligible for the reasons given above.
Dependent Claim(s) 5 and 14 recite limitations that further define the abstract idea noted in independent claims 1, 10, and 19. In addition, it recites the additional element of an artificial intelligence algorithm that implements machine learning. The artificial intelligence algorithm that implements machine learning, is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computing component. Even in combination, this additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Therefore, dependent claims 3-6, 8-9, 12-15, and 17-18 are considered patent ineligible for the reasons given above.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1, 3-5, 10, 12-14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Affinda, in view of Skinner (US 20200042837 A1), and in further view of Levay (US 20200293685 A1).
In Regards to Claim 1, Claim 10, and Claim 19
Affinda discloses, (Currently Amended) A method for masking information in a resume document, the method comprising [claim 1], A computing apparatus for masking information in a resume document, the computing apparatus comprising [claim 10], A non-transitory computer readable storage medium storing instructions for masking information in a resume document, the storage medium comprising executable code which, when executed by a processor, causes the processor to [claim 19]:
receiving, You can upload 25 resumes in a single batch. This helps ensure that all our users enjoy fast processing speeds, and that everyone accessing the tool gets a fair share of bandwidth and machine learning intelligence. However, if you’re interested in redacting an unlimited volume of uploaded resumes, consider using our professional solution! Get in touch with one of our AI experts to find out how” (Affinda).
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Examiner note: A PDF and word documents are electronic files that are printable on paper
retrieving, Our resume redactor can remove more than 20 fields from resumes, including (but not limited to) name, age, nationality, gender, ethnicity, disability, sexual orientation and religion. Ultimately, though, the choice of which fields to redact is up to you. Our redaction technology is highly flexible, enabling you to redact only fields related to a candidate’s origin and education, or those related to gender and sexual orientation — or all these, or none of them. It’s entirely your choice which specific fields you’d like to be redacted, and those you’d prefer to leave visible” (Affinda).
extracting, Our resume redactor can remove more than 20 fields from resumes, including (but not limited to) name, age, nationality, gender, ethnicity, disability, sexual orientation and religion. Ultimately, though, the choice of which fields to redact is up to you. Our redaction technology is highly flexible, enabling you to redact only fields related to a candidate’s origin and education, or those related to gender and sexual orientation — or all these, or none of them. It’s entirely your choice which specific fields you’d like to be redacted, and those you’d prefer to leave visible". (Affinda). "Basically, resume redaction is the process of screening resumes and blocking out bias-susceptible data. Redactor technology enables hiring managers to focus on what really matters about a candidate — like job skills, experience and qualifications — rather than on information that could inadvertently bias them against a well-qualified candidate, such as gender, ethnicity, nationality, social background or religious beliefs. Redacted resumes look just like the originals, and remain in the same file formats. The only difference is that personally identifying information has been blocked out. You might also have seen terms like “blind CV,” “anonymous CV,” and “anonymous resume,” which refer to the results of this same process" (Affinda).
Affinda discloses masking information in a resume document by receiving a document, retrieving a category to be masked, extracting information that belongs to that category. Affinda fails to disclose the method being implemented by at least one processor, a memory, non-transitory computer readable storage medium, generating a plurality of bounding boxes over the extracted information, wherein the plurality of bounding boxes correspond to coordinates of the extracted information and the coordinate of the extracted information includes a starting point coordinate at an upper left hand corner and an end point coordinate at a lower right hand corner when the plurality of bounding boxes are determined to be adjacent to one another, merging the plurality of bounding boxes into a merged bounding box, generating a black box in a size corresponding to the merged bounding box, applying the black box generated over the merged bounding box to mask the extracted information from view, subsequent to the applying of the black box, converting the first resume document into an image for destroying the extracted information masked by the black box from an outputted document, and outputting the image of the first resume document as a final output document that includes the black box, wherein the extracted information under the black box has been destroyed. Skinner, however, does disclose the following:
generating, by the at least one processor, a plurality of bounding boxes over the extracted information, wherein the plurality of bounding boxes correspond to coordinates of the extracted information and the coordinate of the extracted information includes a starting point coordinate at an upper left hand corner and an end point coordinate at a lower right hand corner; "As noted above, bitmap images generally do not explicitly encode text as such, but rather represent content as a collection of pixels having pixel coordinates, like horizontal and vertical coordinates in an image (referred to as image-space herein)" (Skinner Par. 0049). "Accordingly, some embodiments may cause bitmap images to be sent to the OCR service 18, which may return an OCR record for the image indicating in a text format text appearing in the image and the location of that text in the image. In some embodiments, OCR records may indicate a bounding box of text specified with pixel coordinates of a bounding box in the image and a text encoded representation of the depicted text, like a string appearing in that bounding box, e.g., indicating the text “hello world” appears in bounding box “1, 1; 1, 50; 200; 1; 200, 50.” Or some embodiments may indicate the area corresponding to depicted text by specifying other shapes or specifying a box in other ways, like indicating a bottom left corner and width, and implicitly specifying a height by identifying the bottom left corner of a line above. In some embodiments, OCR records may be returned in a hierarchical data serialization format, like extensible markup language (XML) or JavaScript™ object notation (JSON)" (Skinner Par. 0050). "In some embodiments, the obfuscator 44 may determine which units of text are classified as confidential and then determine bounding boxes from the OCR record in pixel coordinates of those bodies of text. Thus, some embodiments of the obfuscator 44 may transform a set of tokens classified as confidential into a set of bounding boxes of regions of pixels depicting that text into the bitmap image. Some embodiments may merge adjacent bounding boxes into a single bounding box, for instance, by determining that two confidential units of text are separated by a delimiter and in response changing a rightmost bounding box coordinate set from one token to be equal to the rightmost bounding box coordinate set for the adjacent token" (Skinner Par. 0067, and Par. 0076)
when the plurality of bounding boxes are determined to be adjacent to one another, merging the plurality of bounding boxes into a merged bounding box; " Some embodiments may merge adjacent bounding boxes into a single bounding box, for instance, by determining that two confidential units of text are separated by a delimiter and in response changing a rightmost bounding box coordinate set from one token to be equal to the rightmost bounding box coordinate set for the adjacent token. In this manner, some embodiments may mitigate information leakage in the form of the number of characters in a redacted string. Or some embodiments may leave white space characters unredacted, which is not to imply other features may not be varied" (Skinner Par. 0067)
generating, by the at least one processor, a black box in a size corresponding to the merged bounding box; "Alternatively in block 74, upon determining that the patterns match, some embodiments may proceed to block 76 and determine a bounding box. In some embodiments, this may include accessing one or more bounding boxes specified in the OCR record, in some cases appending those bounding boxes to a set of bounding boxes in which text is to be redacted, and in some cases merging the bounding boxes, for instance, by determining a convex hull of the bounding boxes, determining a minimum bounding box that contains all the bounding boxes, merging those bounding boxes separated by white space characters, or the like" (Skinner Par. 0080). "Some embodiments may then modify pixel values in the regions of the bitmap image designated by these bounding boxes. To this end, some embodiments may iterate through the bounding box or other shape, for instance, rastering from a top left corner to a bottom right corner to select coordinates of individual pixels and then modifying those pixel values. Pixel values may be modified in a variety of ways to redact text. Some embodiments may set pixel values to display the color black. " (Skinner Par. 0068).
applying, by the at least one processor, the black box generated over the merged bounding box to mask the extracted information from view; "FIG. 4 shows a modified version 104 of the screenshot in which some of the regions 106 have been modified to redact portions of the text while other regions 102 remain unredacted" (Skinner Par. 0086, and Par. 0076)
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It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method and system of masking data as taught by Affinda with a method being implemented by at least one processor, a memory, non-transitory computer readable storage medium, generating a plurality of bounding boxes over the extracted information, wherein the plurality of bounding boxes correspond to coordinates of the extracted information and the coordinate of the extracted information includes a starting point coordinate at an upper left hand corner and an end point coordinate at a lower right hand corner when the plurality of bounding boxes are determined to be adjacent to one another, merging the plurality of bounding boxes into a merged bounding box, generating a black box in a size corresponding to the merged bounding box, applying the black box generated over the merged bounding box to mask the extracted information from the view as taught by Skinner since there is a need in some use cases to monitor risk related to use of these applications, as individual sharing instances may not reveal patterns of behavior that, in the aggregate, warrant concern. (Skinner Par. 0020).
The combination of Affinda and Skinner disclose the method being implemented by at least one processor, a memory, non-transitory computer readable storage medium, generating a plurality of bounding boxes over the extracted information, wherein the plurality of bounding boxes correspond to coordinates of the extracted information and the coordinate of the extracted information includes a starting point coordinate at an upper left hand corner and an end point coordinate at a lower right hand corner when the plurality of bounding boxes are determined to be adjacent to one another, merging the plurality of bounding boxes into a merged bounding box, generating a black box in a size corresponding to the merged bounding box, applying the black box generated over the merged bounding box to mask the extracted information from view. The combination of Affinda and Skinner fail to disclose subsequent to the applying of the black box, converting the first resume document into an image for destroying the extracted information masked by the black box from an outputted document, and outputting the image of the first resume document as a final output document that includes the black box, wherein the extracted information under the black box has been destroyed. Alternatively, Levay, does disclose,
subsequent to the applying of the black box, converting, by the at least one processor, the first resume document into an image for destroying the extracted information masked by the black box from an outputted document; and "The redaction methodology selector is preferably configured to select a desired redaction methodology for identifying information to be redacted. The identified information marker is preferably configured to mark the identified information for redaction…Preferably, the desired placeholder information includes at least one of … a set of one or more solid boxes, … , a set of one or more characters spelling a phrase of one or more terms, a randomized set of one or more characters, a set of one or more space characters, blurred text, and blurred image" (Levay Par. 0059). "In certain embodiments, the system then permanently deletes the information, and any trace of the information, from the document, and generates a redacted version of the document in which the information is replaced with placeholder information. In preferred embodiments, the redaction cannot be reversed, ‘cracked’ by hacking, or otherwise compromised once it is completed" (Levay Par. 0008). "Further preferably, the Redaction API removes the marked information, permanently deletes it from the document file, and makes a redacted version of the document available" (Levay Par. 0101). "The redacted version of the document is saved in the file type, and during identifying the information to be redacted, marking the identified information to be redacted, performing redaction on the marked information, and saving the redacted version of the document, the file type of the document is maintained unchanged from the file type. It should be understood that the invention also encompasses maintaining the file type unchanged in one, some, or all of these steps, in any permutation. … At least one of the file types is preferably Portable Document Format (PDF). It should be understood that the invention encompasses any and all file types, whether now known or hereafter developed" (Levay Par. 0089-0090, Par. 0054)
outputting, by the at least one processor, the image of the first resume document as a final output document that includes the black box, wherein the extracted information under the black box has been destroyed, "The redaction methodology selector is preferably configured to select a desired redaction methodology for identifying information to be redacted. The identified information marker is preferably configured to mark the identified information for redaction…Preferably, the desired placeholder information includes at least one of … a set of one or more solid boxes, … , a set of one or more characters spelling a phrase of one or more terms, a randomized set of one or more characters, a set of one or more space characters, blurred text, and blurred image" (Levay Par. 0059). "The redacted version of the document is saved in the file type, and during identifying the information to be redacted, marking the identified information to be redacted, performing redaction on the marked information, and saving the redacted version of the document, the file type of the document is maintained unchanged from the file type. It should be understood that the invention also encompasses maintaining the file type unchanged in one, some, or all of these steps, in any permutation. … At least one of the file types is preferably Portable Document Format (PDF). It should be understood that the invention encompasses any and all file types, whether now known or hereafter developed" (Levay Par. 0089-0090). "In certain embodiments, the system then permanently deletes the information, and any trace of the information, from the document, and generates a redacted version of the document in which the information is replaced with placeholder information. In preferred embodiments, the redaction cannot be reversed, ‘cracked’ by hacking, or otherwise compromised once it is completed" (Levay Par. 0008, Par. 0054).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method and system of masking data as taught by Affinda, and Skinner converting the first resume document into an image for destroying the extracted information masked by the black box from an outputted document and outputting the image of the first resume document as a final output document that includes the black box, wherein the extracted information under the black box has been destroyed as taught by Levay to disclose, or hide, a different amount of document content to different target audiences (Levay Par. 0028).
In Regards to Claim 3, and Claim 12,
The combination of Affinda, Skinner, and Levay disclose the method of claim 1, and the computing apparatus of claim 10, as shown above. Affinda further discloses, (Previously Presented) The method of claim 1, wherein the at least one category includes at least one from among a university, an email address, a residential address,"Our resume redactor can remove more than 20 fields from resumes, including (but not limited to) name, age, nationality, gender, ethnicity, disability, sexual orientation and religion. Ultimately, though, the choice of which fields to redact is up to you. Our redaction technology is highly flexible, enabling you to redact only fields related to a candidate’s origin and education, or those related to gender and sexual orientation — or all these, or none of them. It’s entirely your choice which specific fields you’d like to be redacted, and those you’d prefer to leave visible" (Affinda) "Whether your goal is simply to remove bias in a specific area, or to create a totally blind recruitment process, Affinda’s redactor will help you maintain recruitment best practices through redaction, while making it easier to ignore traits that won’t affect an individual’s job performance. You’re able to choose what is redacted, so you can focus on eliminating gender or age bias, along with biases related to sexuality, national origin or religion. Or you can choose to redact all this information, leaving you free to focus exclusively on each candidate’s skills and achievements, with no distractions" (Affinda). “Any personal information on a resume — from a candidate’s name, to their place of birth, to their marital status, to their age and gender — can impact your hiring decisions in small unconscious ways. Affinda’s AI-driven solution minimizes these unconscious biases from your hiring process, by redacting a wide range of potentially biasing information. You’re free to choose which fields to redact — and which specific fields you want to anonymize. Redactable fields include” (Affinda).
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In Regards to Claim 4, and Claim 13,
The combination of Affinda, Skinner, and Levay disclose the method of claim 1, and the computing apparatus of claim 10, as shown above. Affinda further discloses, (Original) The method of claim 1, and the computing apparatus of claim 10, further comprising receiving, from a user, at least one additional category that relates to an additional type of information to be masked. "Whether your goal is simply to remove bias in a specific area, or to create a totally blind recruitment process, Affinda’s redactor will help you maintain recruitment best practices through redaction, while making it easier to ignore traits that won’t affect an individual’s job performance. You’re able to choose what is redacted, so you can focus on eliminating gender or age bias, along with biases related to sexuality, national origin or religion. Or you can choose to redact all this information, leaving you free to focus exclusively on each candidate’s skills and achievements, with no distractions" (Affinda).
In Regards to Claim 5, and Claim 14,
The combination of Affinda, Skinner, and Levay disclose the method of claim 1, and the computing apparatus of claim 10, as shown above. Affinda further discloses, (Original) The method of claim 1, and the computing apparatus of claim 10, wherein the extracting comprises applying an artificial intelligence (AI) algorithm that implements a machine learning technique in order to determine at least one portion of the first resume document to be extracted. "Affinda’s leading-edge artificial intelligence (AI) system uses machine learning abilities such as natural language processing (NLP) and image recognition to pinpoint personally identifying fields within each candidate’s resume. Then our algorithm goes to work, blocking out all the fields you’ve specified, while leaving the overall appearance and format of each resume perfectly intact. That means your resume review process can proceed as it normally would — except now it’s blind and diversity-friendly" (Affinda).
Claim(s) 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Affinda, in view of Skinner (US 20200042837 A1), in view of Levay (US 20200293685 A1), and in further view of Hoehne (US 20200082218 A1).
In Regards to Claim 6, and Claim 15,
The combination of Affinda, Skinner, and Levay disclose the method of claim 1, and computing apparatus of claim 15, as shown above. The combination of Affinda, Skinner, and Levay fail to disclose the Al algorithm is configured to output a corresponding set of bounding box coordinates that relates to a physical position of the at least one portion within the first resume document. Hoehne discloses extracting information from documents. Hoehne further discloses, (Original) The method of claim 5, and the computing apparatus of claim 15, wherein the Al algorithm is configured to output, for each respective one of the at least one portion, a corresponding set of bounding box coordinates that relates to a physical position of the at least one portion within the first resume document. “In an embodiment, CNN 140 may determine bounding boxes in document 120 around words. A word bounding box may represent a pixel area (e.g., width×height) occupied by the word. For example, referring to FIG. 4, CNN 140 may identify string 410 in document 120. CNN 140 may identify three separate words in this string as “THE”, “MERCHANT”, and “COMPANY”. CNN 140 may recognize these words and associate each word with a word bounding box. CNN 140 may also identify the location of the word bounding boxes in document 120. For example, CNN 140 may utilize a coordinate system or a relative pixel location"(Hoehne Par. 0026). "In cases where bounding box mask 200C and/or 200D is used, based on the training of CNN 140, OCR system 110 may generate bounding box mask 200C and/or 200D using bounding box detector 150. OCR system 110 may identify specified groupings of characters, including individual characters, words, lines and/or sentences as specified by the training data. In an embodiment, if individual characters is specified, OCR system 110 may not generate a bounding box mask 200C and/or 200D and instead may rely on segmentation mask 200B to designate the character positions. In the other groupings, OCR system 110 may generate bounding boxes around the groups of characters according to the training data. Bounding box detector 150 may combine the bounding boxes with a coordinate system to map the bounding boxes to the corresponding locations of document 120 to generate bounding box mask 200C and/or 200D" (Hoehne Par. 0071). “OCR system 110 may identify groups of numbers 215 and/or associate the groups of numbers with metadata. For example, OCR system 110 may identify numbers of a zip code 215A, an invoice number 215B that may comprise a number of digits, and/or a price 215C-215D that may include a decimal point (or a symbol other than a number). OCR system 110 may convert these numbers to index values so that they may be identified in the segmentation mask along with letters. OCR system 110 may also identify groupings having a combination of numbers and letters such as, for example, dates 225A-225B. Even though this grouping may include both numbers and letters, OCR system 110 may be able to index each character of the grouping as well as identify the grouping with bounding boxes. This processing may yield a bounding box mask that may be utilized in another document processing system to, for example, determine semantic information related to document 200A” (Hoehne Par. 0038).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method and system of masking data as taught by Affinda, Skinner, and Levay with a corresponding set of bounding box coordinates that relates to a physical position of the at least one portion within the first resume document as taught by Hoehne to identify the areas of private information to mask in order to ensure privacy of the user, and to reduce bias in hiring.
Claim(s) 8-9 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Affinda, in view of Skinner (US 20200042837 A1), in view of Levay (US 20200293685 A1), and in further view of Obeid (US20160140504A1).
In Regards to Claim 8, and Claim 17,
The combination of Affinda, Skinner, and Levay disclose the method of claim 1, and the computing apparatus of claim 10, as shown above. The combination of Affinda, Skinner, and Levay disclose the method of claim 1, and computing apparatus of claim 15, as shown above. The combination of Affinda, Skinner, and Levay fail to disclose extracting all text strings from the first resume document and comparing each respective one of the text strings to a first predetermined list of place names and universities. Obeid discloses matching resumes that satisfy job descriptions. Obeid further discloses, (Original) The method of claim 1, and the computing apparatus of claim 10, wherein the extracting of the information that belongs to the at least one category comprises:
extracting all text strings from the first resume document; and "After receiving resume 115a, resume parser program 312 parses the resume to extract certain attributes of the candidate and all experience-related phrases and the maximum-total-duration of experience for each phrase. The attributes include the candidate's name, residence or business address, telephone numbers (e.g., home, work, facsimile, and cellular), e-mail addresses, education data (e.g., degree, major, year, and school name), past employer data (e.g., employer name, last title, and period of employment), and salary (e.g., current and expected)" (Obeid Par. 0061).
comparing each respective one of the text strings to a first predetermined list of place names and universities. “FIG. 1 is a block diagram that illustrates the integration of resume management and recruitment workflow system 140 into an operating environment for a traditional job-opening fulfillment or employment-seeking situation. In one embodiment, candidate 110a authors resume 115a in an electronic format and stores resume 115a in resume management and recruitment workflow system 140 as a public resume. The phrase “public resume” indicates that the system stores the resume and allows any buyer (e.g., recruiter 120a) to access the resume without restriction. The electronic format includes any standard digital document format comprising text file formats such as American Standard Code for Information Interchange (ASCII) or Extended Binary Coded Data Interchange Code (EBCDIC), word processor file formats such as Microsoft Word or WordPerfect, and markup language file formats such as Standard Generalized Markup Language (SGML), HyperText Markup Language (HTML), or eXtensible Markup Language (XML). Recruiter 120a determines a set of requirements and searches resume management and recruitment workflow system 140 for any resume that matches the set of requirements. Each requirement includes at least one required skill or experience-related phrase and a required minimum duration of experience associated with the skill or experience-related phrase. The phrase “matches the set of requirements” indicates that the resume includes each experience-related phrase in the set of requirements and that the maximum possible duration of experience for each experience-related phrase exceeds the required minimum duration of experience defined by hiring manager 130a. In another embodiment, the phrase “matches the set of requirements” indicates that the resume includes additional criteria (e.g., education, salary range, or geographic location) as defined by hiring manager 130a. If resume 115a matches the set of requirements, recruiter 120a retrieves a copy of resume 115a, optionally reviews the resume or interviews the candidate as an additional screening step, and sends it to hiring manager 130a for review. Even though this embodiment only illustrates the interaction of candidate 110a, recruiter 120a, and hiring manager 130a, it is to be understood that resume management and recruitment workflow system 140 can accommodate any number of candidates, recruiters, and hiring managers" (Obeid Par. 0044).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method and system of masking data as taught by Affinda, Skinner, and Levay with extracting text in the document and comparing to a list of place names and universities as taught by Obeid to reduce a resume database to a manageable number of resumes by identifying a set of candidates who can possibly satisfy the qualifications and requirements sought by a hiring manager (Obeid Par. 0011).
In Regards to Claim 9, and Claim 18,
The combination of Affinda, Skinner, and Levay, and Obeid disclose the method of claim 8, the computing apparatus of claim 17, as shown above. Levay further discloses, (Original) The method of claim 8, the computing apparatus of claim 17, wherein the extracting of the information that belongs to the at least one category further comprises a second predetermined list of email address suffixes “It should be understood that the invention encompasses using, to any degree, for the purposes of locating or otherwise being aware of sensitive content in the type of document, format, location, and any and all other aspects of content that can be known about the content. Associations with any and all such aspects can be established by hard programming, artificial intelligence, machine learning, computer vision, or any other methods or technologies. Further preferably, the sensitive content is information detected by, when the known information is information known to be in the known format in the type of document, the system searching in the document for any content in the known format, and the system finding in the document all content in the known format. Further preferably, the known format is one or more of email address format…. It should be understood that the invention encompasses any and all formats, whether now known or hereafter developed.” (Levay Par. 0084-0086).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method and system of masking data as taught by Affinda, Skinner, and Levay with a second predetermined list of email address suffixes as taught by Levay to find sensitive information in the document based on known formats or known locations in in which sensitive information commonly is found (Levay Par. 0088).
The combination of Affinda, Skinner, Levay disclose a second predetermined list of email address suffixes. Obeid, further discloses, using a Regular Expression string search with respect to residential address style types. “After receiving resume 115a, resume parser program 312 parses the resume to extract certain attributes of the candidate and all experience-related phrases and the maximum-total-duration of experience for each phrase. The attributes include the candidate's name, residence or business address, telephone numbers (e.g., home, work, facsimile, and cellular), e-mail addresses, education data (e.g., degree, major, year, and school name), past employer data (e.g., employer name, last title, and period of employment), and salary (e.g., current and expected)” (Obeid Par. 0061). “In another embodiment, resume management and recruitment workflow system 140 offers electronic mail capability from the site. A user can send individual e-mail messages using the e-mail address hyperlink from the candidate information screen. In addition, the user can send multiple e-mail messages within a very short time period. In another embodiment, resume management and recruitment workflow system 140 includes tool tips in the user interface. In another embodiment, resume management and recruitment workflow system 140 consolidates events into a calendar as well as a reporting form for the user to view different activities. In another embodiment, resume management and recruitment workflow system 140 provides the ability to find a candidate using the candidate's name, electronic mail address, postal address, phone number, and the like. In another embodiment, as the user views the candidate or the job, the system shades the list of candidates and jobs that result from a search and have been viewed. Thus, the screen display will indicate those candidates or jobs reviewed by the user. In another embodiment, resume management and recruitment workflow system 140 allows the user to open and print a resume from the candidate screen and re-format it or save it wherever the user wants. In another embodiment, resume management and recruitment workflow system 140 discerns that multiple resumes sharing the same electronic mail address belong to the same candidate attributes. Resume management and recruitment workflow system 140 resolves the latest most relevant data and presents that latest attributes to the user. In another embodiment, resume management and recruitment workflow system 140 allows the user to use the full resume as a search platform by selecting zero years or specifying no years for the phrases in the search criteria. In another embodiment, resume management and recruitment workflow system 140 utilizes a state, area code, education, and desired salary filtering for a search for candidates” (Obeid Par. 0136).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the method and system of masking data as taught by Affinda, Skinner, Levay, and Obeid with extracting information that belongs to the at least one category further comprises using string search with respect to residential address style types as taught by Obeid to allow the hiring manager or recruiting representative to reduce a large pool of resumes to a short list of resumes and to obtain a short list of candidates (Obeid Par. 0052).
Response to Arguments
Applicant's arguments filed 11/13/2025 with respect to 35 U.S.C. § 101, have been fully considered but they are not persuasive. Applicant argues that the present claims are not reasonably understood as being directed to “social activities” or “teaching” and that that the claims are directed to producing a data secure document at a visible level as well as at an underlying data level for providing data security over sensitive information. Further arguing that destroying underlying masked data does not reasonably correspond to traditional human activity that can be performed by humans alone. The Examiner respectfully disagrees. The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer (a computing device or a scanning device), would follow to mask and destroy data by a black box that covers extracted information so it cannot be readable by a person when the first resume document is printed on paper. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to mask data by generating a black box that covers extracted information in a particular location, and destroy information so it cannot be readable by a person when the first resume document is printed on paper (including when and how), the claimed invention is directed to an abstract idea.
Applicant further argues that the amended claims do not qualify as an abstract idea under a mental process grouping since humans cannot practically convert the first resume document into an image for destroying the underlying data masked by the black box from an outputted document. However, this argument is moot, as the rejection only asserts the claim for falling under certain methods of organizing human activity.
Applicant further argues that the claim provides features as an improvement in a relevant technological field to provide data security at a visible level as well as an underlying data level with ensuring data security even if documents are uploaded without requiring any preprocessing for more efficient processing and utilization of computing resources, and that bias stemming from humans and machine learning models may be prevented to ensure a more accurate analysis/output. The Examiner respectfully disagrees. There is no technical improvement to the field of security, or elimination of bias, only an abstract idea of an unbiased hiring process. The machine learning model is not made to run more efficiently, it is performing as expected. Thus, the machine learning model is not improved. This argument is not persuasive because ensuring data security and eliminating bias from humans and machine learning models, is not an improvement to a particular technological environment or field of use, it is merely a certain method of organizing human activity.
Applicant's arguments filed 11/13/2025 with respect to 35 U.S.C. § 112, have been fully considered, and are persuasive. The 35 U.S.C. § 112(a) and 35 U.S.C. § 112(b) rejections are withdrawn in light of the amendments.
Applicant's arguments filed 11/13/2025 with respect to 35 U.S.C. § 103, have been fully considered but they are not persuasive. Applicant argues that Affinda and prior art listed fail to teach “applying the black box generated over the merged bounding box to mask the extracted information from view” and “further destroying the underlying data beneath the black box”, and the Examiner agrees that Affinda and the listed prior art fail to teach the limitation. However, since the amendment necessitated a new grounds of rejection as shown above, the argument is moot. Therefore, the 103 Rejection is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 70