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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/7/2024 have been considered and entered by the examiner.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4-5, 11-12, 14-15 & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bhat et al, US Patent Pub. 20130173718 A1, in view of Gauthier, US Patent Pub. 20210182677 A1.
Re Claim 1, Bhat et al discloses a computer-implemented method in a data processing system, for executing a privacy preserving computing operation, the method comprising: retrieving a set of data, from a data storage (para 0033: EMAIL/(electronic communication) is typically stored on a cloud server database (para 0014: cloud storage service)), that comprises an electronic communication corresponding to the privacy preserving computing operation (para 0033: subject lines such as sender’s name(signature portion of email/personal information(para 0043)) of an email can be masked/obfuscated/redacted(privacy)); and executing the privacy preserving computing operation on the electronic communication based on the annotated set of data (para 0033: subject lines such as sender’s name(signature portion of email/personal information(para 0043)) of an email can be masked/obfuscated/redacted(privacy)); but fails to disclose extracting first features from one or more first lines of the electronic communication; processing, by one or more trained machine learning computer models, the extracted first features as input to the one or more trained machine learning computer models and which generate classification outputs for the one or more first lines, wherein the one or more trained machine learning computer models process the first features and generate a classification output for each first line in the one or more first lines, which specifies, for a corresponding first line in the one or more first lines, a corresponding classification of the corresponding first line as to whether it is a signature line or non-signature line, based on patterns in the first features; annotating each first line in the one or more first lines with metadata specifying the corresponding classification of the first line based on the classification output, to thereby generate an annotated set of data. However, Gauthier teaches the concept of being able to extract meaningful information from an email (Gauthier, para 0003), wherein neural network machine learning algorithms are trained to selectively isolate/extract/(point out) lines of an email such as headers and/or signature blocks (Gauthier, para 0023: analysis will be carried out by the neural network machine learning algorithm to be trained to analyze the email to identify the individual lines); whereby metadata is also further utilized to delineate between the lines/segments such as the headers, message bodies and/or signature blocks (Gauthier, para 0033: system is able to classify lines of the email including signature block/lines by attaching metadata to each line; whereby the non-signature lines will naturally be classified with metadata indicating they are not signature lines). It would have been obvious to modify the Bhat et al system such that it utilizes neural network machine learning algorithms to select lines in an email and assign metadata to the selected lines to indicate what kind of line was selected as taught in Gauthier for the purpose of being able to maximize the pattern recognition and classification capabilities of the neural network when identifying the lines within the email to mask/redact/obfuscate.
Re Claim 2, the combined teachings of Bhat et al and Gauthier disclose the computer-implemented method of claim 1, wherein executing the privacy preserving computing operation comprises: processing the one or more first lines of the electronic communication that are annotated as being signature lines to identify one or more instances of personal information present in the one or more first lines (Bhat et al, para 0033: subject lines such as sender’s name(signature portion of email/personal information(para 0043)) of an email can be masked/obfuscated/redacted(privacy)); and obfuscating the one or more instances of personal information to generate obfuscated electronic communication data (Bhat et al, para 0033: subject lines such as sender’s name(signature portion of email/personal information(para 0043)) of an email can be masked/obfuscated/redacted(privacy)).
Re Claim 4, the combined teachings of Bhat et al and Gauthier disclose the computer-implemented method of claim 1, wherein processing, by the one or more trained machine learning computer models, the extracted first features as input to the trained machine learning computer model which generates classification outputs for the one or more first lines (Gauthier, para 0023: neural network includes pattern recognition capabilities, para 0033), comprises processing, by the one or more machine learning computer models, first features from a plurality of the first lines of the electronic communication in combination to identify a signature block of the electronic communication based on a pattern of features from the plurality of first lines (Gauthier, para 0023: neural network includes pattern recognition capabilities, para 0033).
Re Claim 5, the combined teachings of Bhat et al and Gauthier disclose the computer-implemented method of claim 4, but fail to explicitly disclose wherein identifying the signature block of the electronic communication based on the pattern of features from the plurality of first lines comprises executing named entity recognition and natural language processing on a subset of the plurality of first lines to extract named entities and part of speech information for text in the subset of the plurality of first lines, wherein the subset comprises a portion of the plurality of first lines identified as being part of the signature block. Since neural network machine learning algorithms have the ability to perform pattern recognitions and classifications (Gauthier, para 0023: neural network includes pattern recognition capabilities, para 0033), it would have been obvious for one of ordinary skill in the art to modify Gauthier, as used to modify Bhat et al such that its neural network includes natural language processing along with its named entity recognition capabilities since they are natural subtasks used by machine learning models such as neural networks to classify data.
Claim 11 has been analyzed and rejected according to claim 1.
Claim 12 has been analyzed and rejected according to claim 2.
Claim 14 has been analyzed and rejected according to claim 4.
Claim 15 has been analyzed and rejected according to claim 5.
Claim 20 has been analyzed and rejected according to claim 1.
Claims 3 & 13 are rejected under 35 U.S.C. 103 as being unpatentable over Bhat et al, US Patent Pub. 20130173718 A1, and Gauthier, US Patent Pub. 20210182677 A1 as applied to claim 2 above, in view of Lingarkar et al, US Patent Pub. 20240314156 A1.
Re Claim 3, the combined teachings of Bhat et al and Gauthier disclose the computer-implemented method of claim 2, but fail to disclose wherein executing the privacy preserving computing operation further comprises executing one or more operations to service a data subject access request (DSAR) based on the obfuscated electronic communication data. However, Lingarkar et al taches the concept of employing a natural language processing machine learning model for interpreting DSAR submissions along with AI-driven redaction and anonymizations (Lingarkar et al, paras 0097, 0102). It would have been obvious to one or ordinary skill in the art to modify Bhat et al system to be able to retrieve DSAR information while redacting personal information to ensure privacy of the data provided in response to the DSAR.
Claim 13 has been analyzed and rejected according to claim 3.
Claims 6 & 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bhat et al, US Patent Pub. 20130173718 A1, and Gauthier, US Patent Pub. 20210182677 A1 as applied to claim 5 above, in view of Waibel et al, US Patent Pub. 20150254238 A1.
Re Claim 6, the combined teachings of Bhat et al and Gauthier disclose the computer-implemented method of claim 5, wherein identifying the pattern of features by executing the named entity recognition and natural language processing on the subset of the plurality of first lines (analyzed and rejected in claim 5 above); but fails to explicitly disclose comprises identifying, by the named entity recognition, a person name followed by one or more subsequent first lines that do not have verbs or auxiliary verbs present in the one or more subsequent first lines, as determined by the natural language processing. However, Waibel et al teaches the concept of a named entity recognition being able to identify name, place, nouns and verbs (Waibel et al, para 0055). It would have been obvious to modify the neural network of Gauthier, as used to modify Bhat et al to include named entity recognition that can identify name, place, verbs, nouns etc as taught in Waibel et al for the purpose of being able to identify the content of the different lines by also better understanding verbs and nouns within the lines to better understand context of the line.
Claim 16 has been analyzed and rejected according to claim 6.
Allowable Subject Matter
Claims 7-10, 17-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter for claims 7-8: The prior art does not teach or moderately suggest the following limitations:
Further comprising processing, by the one or more trained machine learning computer models, extracted second features extracted from one or more second lines of one or more other second electronic communications in the set of data, to generate the classification output for each first line in the one or more first lines of the first electronic communication, wherein the first features are local features corresponding to a first electronic communication thread in which the first electronic communication is a part, and wherein the second features are global features corresponding to the one or more other second electronic communications which are not part of the first electronic communication thread.
Limitations such as these may be useful in combination with other limitations of claim 1.
The following is a statement of reasons for the indication of allowable subject matter for claim 9: The prior art does not teach or moderately suggest the following limitations:
Further comprising generating, for each unique sender identifier of each sender of electronic communications in the set of data, and for each phrase or sentence present in lines of electronic communications sent by each sender of electronic communications, a frequency of email lines per sender identifier (FEL/SID) metric, wherein the FEL/SID metric is a ratio of a number of appearances of the corresponding phrase or sentence in an electronic communication line over a number of different electronic communications sent by the same unique sender identifier, and wherein the FEL/SID metric is processed by the one or more trained machine learning computer models along with the first features to classify the one or more first lines.
Limitations such as these may be useful in combination with other limitations of claim 1.
The following is a statement of reasons for the indication of allowable subject matter for claim 10: The prior art does not teach or moderately suggest the following limitations:
Further comprising training the one or more machine learning computer models through an iterative machine learning training process, until a convergence condition is reached, based on a training dataset that is input to the one or more machine learning computer models, wherein: the training dataset comprises electronic communication lines for multiple different electronic communication threads and corresponding ground truth labels for each electronic communication line in the training dataset specifying whether the corresponding electronic communication line is a signature line or not a signature line, and the one or more machine learning computer models are trained to classify the input electronic communication lines of the electronic communications in the training dataset as to whether they are signature lines or non-signature lines at least by processing features of the input electronic communication lines, generating a predicted classification output for each input electronic communication line, comparing the predicted classification output to a corresponding ground truth label for each input electronic communication line to determine an error, and executing a machine learning training algorithm on the error to adjust operational parameters of the one or more machine learning computer models to reduce the error.
Limitations such as these may be useful in combination with other limitations of claim 1.
The following is a statement of reasons for the indication of allowable subject matter for claims 17-18: The prior art does not teach or moderately suggest the following limitations:
Wherein the computer readable program further causes the computing device to process, by the one or more trained machine learning computer models, extracted second features extracted from one or more second lines of one or more other second electronic communications in the set of data, to generate the classification output for each first line in the one or more first lines of the first electronic communication, wherein the first features are local features corresponding to a first electronic communication thread in which the first electronic communication is a part, and wherein the second features are global features corresponding to the one or more other second electronic communications which are not part of the first electronic communication thread.
Limitations such as these may be useful in combination with other limitations of claim 11.
The following is a statement of reasons for the indication of allowable subject matter for claim 19: The prior art does not teach or moderately suggest the following limitations:
Wherein the computer readable program further causes the computing device to generate, for each unique sender identifier of each sender of electronic communications in the set of data, and for each phrase or sentence present in lines of electronic communications sent by each sender of electronic communications, a frequency of email lines per sender identifier (FEL/SID) metric, wherein the FEL/SID metric is a ratio of a number of appearances of the corresponding phrase or sentence in an electronic communication line over a number of different electronic communications sent by the same unique sender identifier, and wherein the FEL/SID metric is processed by the one or more trained machine learning computer models along with the first features to classify the one or more first lines.
Limitations such as these may be useful in combination with other limitations of claim 11.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE C MONIKANG whose telephone number is (571)270-1190. The examiner can normally be reached Mon. - Fri., 9AM-5PM, ALT. Fridays off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carolyn R Edwards can be reached at 571-270-7136. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GEORGE C MONIKANG/Primary Examiner, Art Unit 2692
/CAROLYN R EDWARDS/Supervisory Patent Examiner, Art Unit 2692
2/21/2026