DETAILED ACTION
Office Action Summary
Claims 1-20 are pending in the instant application.
Applicants amendments overcome the previous 35 USC § 112 for Claims 1-8, therefore the previous 35 USC § 112 has been withdrawn.
Claims 1-5, 7-12, 14-18 and 20 are rejected under 35 USC § 102.
Claims 1-20 are rejected under 35 USC § 103.
Applicant’s arguments/amendments filed 7/14/2026 have been considered but are not persuasive. (See “Applicant’s Arguments and Examiner’s Response” section below)
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Applicant’s Arguments and Examiner’s Response
Applicant’s arguments/amendments filed 7/14/2026 have been considered but are not persuasive. Applicant argues that the cited prior art does not teach “identifying at least one structured field in the document;” however examiner disagrees as Mustafa, [0023], teaches “KOs can represent and categorize different types of canonical structures (or information objects) through patterns provided by the KOs.” AND [0024], teaches “a system receives a first plurality of knowledge objects (KOs) from a KO discovery engine, each KO in the first plurality of KOs being representative of an underlying unit of structured, semi-structured, or unstructured data”, in other words Mustafa’s documents has different structured fields which are identified, also see figure 1 and corresponding paragraphs such as [0031] and [0032], which teach “The KO discovery engine can identify the corresponding KOs that are associated to the CC for the KO discovery process. Examples of KOs for medical records can be JSON files having a pattern for a name, social security number, health insurance policy number, date of birth, addresses, or phone number. Examples of KOs for payment card information can include credit card number, type of credit card (visa, American Express, discovery, etc.), expiry date, CVV2 code, etc. These pieces of information stored at enterprises data centers are compliance-relevant data and are required to comply with the requirements of one or more data compliance categories.”, so the structures are identified.
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 1-5, 7-12, 14-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tarique Mustafa (US Pre-Grant Publication No: 2024/0160785) hereinafter referred to as Mustafa.
As per claims 1, 9 and 15, Mustafa teaches … accessing a document; (Mustafa, [0023], teaches “These information objects can be the underlying data in a text file, a document, a PDF file, an email, an image file, a binary file, a database entry, or a field in a database.”)
identifying at least one structured field in the document; (Mustafa, [0023], teaches “KOs can represent and categorize different types of canonical structures (or information objects) through patterns provided by the KOs.” AND [0024], teaches “a system receives a first plurality of knowledge objects (KOs) from a KO discovery engine, each KO in the first plurality of KOs being representative of an underlying unit of structured, semi-structured, or unstructured data”)
using a first machine learning model to identify at least one attribute in the at least one structured field; (Mustafa, figure 11 and [0069], teaches “Referring to FIG. 11, at block 1101, processing logic receives a first plurality of knowledge objects (KOs) from a KO discovery engine, each KO in the first plurality of KOs being representative of an underlying unit of structured or unstructured data stored at one or more data repositories and containing no underlying structured or unstructured data.”)
identifying at least one unstructured field in the document; (Mustafa, [0023], teaches “KOs can represent and categorize different types of canonical structures (or information objects) through patterns provided by the KOs.” AND [0024], teaches “a system receives a first plurality of knowledge objects (KOs) from a KO discovery engine, each KO in the first plurality of KOs being representative of an underlying unit of structured, semi-structured, or unstructured data”)
using a second machine learning model and the identified at least one attribute from the at least one structured field to identify at least one attribute in the at least one unstructured field; (Mustafa, [0072], teaches “At block 1103, processing logic groups the first plurality of KOs based on the name, type, tag attributes, and storage paths of the underlying unit of structured and unstructured data corresponding to the KOs to generate a plurality of groups of KOs.”, claimed second machine learning module not shown how it differs from the first ML and could be the same)
redacting the identified at least one attribute from the structured and unstructured fields from the document to produce a deidentified document. (Mustafa, [0028], teaches “using KOs to store representations of the data for enterprises without a copy of the underlying data reduce the risks of data compromise. At the same time, having a mapping of the KOs to the repositories enables a compliance officer to perform compliance enforcement actions on the underlying data from the information of the KO-repository mapping, such as analyzing compliance-related information, updating, anonymizing, obfuscating, encrypting, and/or redacting user privacy related data, etc.”)
As per claims 2, 10 and 16, Mustafa teaches … wherein the field analyzer has knowledge of a location for the at least one structured field and the at least one unstructured field within the document. (Mustafa, [0062], teaches “Matching locations of a repo and storage location for a KO can indicate the KO has underlying structured and/or unstructured data stored at the data repository location.”)
As per claim 3, Mustafa teaches … wherein the field analyzer determines a location for the at least one structured field and the at least one unstructured field within the document. (Mustafa, [0062], teaches “In one embodiment, the mapping can be performed by matching the repository location attribute from the repository data structures corresponding to the data repositories to the storage location attribute corresponding to the KO data structures of the KOs. Matching locations of a repo and storage location for a KO can indicate the KO has underlying structured and/or unstructured data stored at the data repository location.”)
As per claims 4, 11 and 17, Mustafa teaches … further comprising a robustness analyzer comprising a third machine learning model for reviewing the deidentified document to decipher any attributes from the content of the deidentified document. (Mustafa, [0035], teaches “In another embodiment, if an enterprise client requests review of compliance related to HIPAA, an administrator can access the KOs related to HIPAA to retrieve information about the underlying data that are stored in associated repositories for reporting purposes”)
As per claims 5, 12 and 18, Mustafa teaches … wherein synthetic training data is used to train the first and second machine learning models. (Mustafa, [0087], teaches “injecting synthetic data into the data that are connected to the KO”, the first and second ML are the same as per analysis in claim 1)
As per claims 7, 14 and 20, Mustafa teaches … wherein the at least one attribute comprises personally identifiable information. (Mustafa, [0031], teaches “The KO discovery engine can identify the corresponding KOs that are associated to the CC for the KO discovery process. Examples of KOs for medical records can be JSON files having a pattern for a name, social security number, health insurance policy number, date of birth, addresses, or phone number. Examples of KOs for payment card information can include credit card number, type of credit card (visa, American Express, discovery, etc.), expiry date, CVV2 code, etc. These pieces of information stored at enterprises data centers are compliance-relevant data and are required to comply with the requirements of one or more data compliance categories”)
As per claim 8, Mustafa teaches …further comprising a feedback module, coupled to at least one of the unstructured field analyzer or the redactor, to review at least one of the identified attributes or the redacted document and update the fields identified by the field analyzer. (Mustafa, [0035], teaches “Once mapped, analysis, reporting, and enforcement can be targeted at the underlying data at any data repositories through their associated KOs. For example, if a corporate client's health records need to be redacted for compliance measures, an administrator can issue an enforcement action to the KOs associated with the users of the corporate client's health records to cause the underlying data at the repository to be redacted. In another embodiment”)
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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 6, 13 and 19 rejected under 35 U.S.C. 103 as being unpatentable over Mustafa in view of Arnab Dan (US Patent No: 11,755,848) hereinafter referred to as D2.
As per claims 6, 13 and 19, Mustafa teaches (Mustafa, [0087], teaches “Anonymization is the process of protecting private or sensitive data by data masking, pseudonymization, generalization, data perturbation, injecting synthetic data into the data that are connected to the KO”) But does not specifically teach … wherein the unstructured field analyzer determines a context of a sentence containing the at least one attribute to confirm the identified attribute is sensitive information.
D2 (column 6, lines 7-20, teaches “To identify instances of unstructured sensitive data 124, a natural language processing (NLP)-based unstructured data scrubbing module (“NLP”) 175 of computing system 160 applies NLP and, in some examples, artificial intelligence to identify, within the text included within unscrubbed transcript 119, each instance of unstructured sensitive data 124. In some examples, unstructured data scrubbing module 175 may represent or include a machine learning model trained to identify instances of unstructured sensitive data 124 within unscrubbed transcript 119 using the context of a line or sentence within unscrubbed transcript 119”)
It would have been obvious to one having ordinary skill in the art, before the effective filing of the claimed invention to modify the invention of Mustafa with the method of D2, to help secure un-structured data.
Other Art of Record
Garg et al. (US 20190114360 A1) teaches “The method involves receiving an input file to be analyzed, where the input file comprises structured and unstructured data. The input file is divided into multiple documents, and the input file is a Zip file. Pages of each document are classified as one of structured or unstructured data by a classifier (63). The pages of each document are parsed. Relevant data is extracted from the parsed pages. The extracted relevant data is mapped to standardized outputs. Canonical data sets are generated based on the standardized outputs. A raw file is extracted from the ZIP file. Other relevant data is extracted from each document.”
Krishnan et al. (US 20200242465 A1) teaches “Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving attribute data comprising a set of unstructured attribute data and a set of structured attribute data, analyzing the set of unstructured attribute data by processing through a first set of one or more Long Short Term Memory (LSTM) layers, to obtain an unstructured semantic signature, analyzing the set of the structured attribute data by processing through a first set of one or more Convolutional Neural Network (CNN) layers, to obtain a structured semantic signature, analyzing the unstructured semantic signature and the structured semantic signature, and classifying the item in one or more item categories. Other embodiments are disclosed herein.”
Middleton et al. (WO 2023205445 A1) teaches “Methods, systems, and apparatus, including computer programs encoded on computer storage media, for anonymizing unstructured data. In some implementations, a server can receive unstructured data. The server can automatically detect attributes in the unstructured data using a trained machine-learning model and can determine an amount of undetected attributes and detected attributes in the unstructured data. The server can simulate additional attributes for the unstructured data according to the amount of undetected attributes. The server can analyze a risk of disclosure in the unstructured data, using the detected attributes and the simulated additional attributes. The server can modify the detected attributes according to the analyzed risk of disclosure and replace the detected attributes with the modified detected attributes in the unstructured data.”
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIMON P KANAAN whose telephone number is (571)270-3906. The examiner can normally be reached on M-F (7AM-4PM).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Thiaw can be reached on (571) 270-1138. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SIMON P KANAAN/Primary Examiner, Art Unit 2407
