DETAILED ACTION
This action is in response to amendments filled 5/18/2026. Claims 1, 2, 4-6, 8-9, 11-13, 15-16, 18-19 and 21-26 are pending with claims 1, 8 and 15 having been amended and claims 24-26 newly added.
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 .
Response to Arguments
Applicant's arguments filed 5/18/2026 have been fully considered.
A) Applicant’s arguments, with respect to the 103 rejection(s) of claim(s) 1, 8 and 15 under 103 that Ahmed in view of Hachey does not teach the newly add limitation of “inputting the first data into a first language model to obtain first embeddings as output and second data corresponding to context surrounding first data within the one or more sub-documents into a second language model to obtain second embeddings as output” have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of different interpretation of the previously applied reference.
Ahmed teaches the amended limitation in paragraph 0060-0061 i.e. In the embodiment of FIG. 5, models are training using a number of different data sources. In the embodiment, a main source of domain-specific data is a set of unstructured documents including PII labels 502 is received by text-based feature extraction component 508. In addition, a historical set of unstructured documents with PII labels by a particular author is received by a user-specific model component 510 to develop patterns of word and/or phrase usage for a particular user. In the embodiment, sets of unlabeled unstructured text data 506 is also received by text-based feature extraction component 508 to provide the models with an understanding of a large number of different word and/or phrase characteristics including, but not limited to, word contexts, parts of speech, and grammar. Text-based feature extraction component 508 extracts natural language processing (NLP) features from unstructured documents with PII labels 502 using an array of text-based feature extractors based upon the unlabeled unstructured text data 506. In the embodiment illustrated in FIG. 5, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.) and paragraph 0066 i.e. In the embodiment of FIG. 6, a vector space model (VSM) is generated as the last fully connected feature layer of a DNN and is trained to classify a word in a center of a window of words as either PII data or non-PII data. The text-based and user-specific features extracted on a per-word are used to represent each word as input for classification. In the embodiment of FIG. 6, a DNN classifier is illustrated depicting a plurality of DNN layers h1 to hN in which layer h1 to receives a plurality of feature vector inputs w0-w4 and w6-w10. In the embodiment, layers h1 to hN-2)) and paragraph 0078 i.e. In block 702, application 105 receives an unstructured document for a known author. In block 704, application 105 extracts text-based features as a first set of features using natural language processing).
This clearly teaches the sub-documents comprises first data that match one or more patterns and that the second data correspond to context surrounding the first data as well as the claimed multi models since Ahmed’s Text-based feature extraction component includes an array of text-based feature extractors that include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.).
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 1-2, 4, 8-9, 11, 15-16, 18 and 21-26 are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed et al (US 2020/0081978) in view of Hachey (US 2021/0256160).
With respect to claim 1 Ahmed teaches a method comprising:
identifying one or more sub-documents of one or more documents, the one or more sub-documents comprise first data that match one or more patterns for sensitive data (see figure 3 and paragraph 0052-0055 i.e. Applicant 302 receives one or more unstructured documents 304. Application 302 includes a text-based feature extraction component 306, a user-specific model component 308, a classifier component 310, and a PII labelling component 312…. Classifier component 310 is configured to receive the text-based features from text-based feature extraction component 306 and the user-specific features from user-specific model component 308 to classify the extracted features as either PII data (PII) or non-PII data. In particular embodiments, classifier component 310 includes one or more of a DNN or a maximum entropy framework to classify the extracted features as either PII data or non-PII data…PII labelling component 312 is configured to label the PII elements within unstructured document(s) 304 based upon the classified extracted features. Application 302 is configured to output a labelled unstructured document 314 in which PII information in unstructured document(s) 304 that is detected and classified by application 302 is labelled as PII. Application 302 is further configured to output classification result 316 indicative of the results of classification of the PII data by classifier component 310. In one or more embodiments, application 302 is configured to further train classifier component 310 based upon classification result 316);
inputting first data into a first language model to obtain first embeddings as output second data corresponding to context surrounding the first data in the one or more sub-documents into a second language model to obtain second embeddings as output (see Ahmed figure 5, paragraph 0060-0061 i.e. In the embodiment of FIG. 5, models are training using a number of different data sources. In the embodiment, a main source of domain-specific data is a set of unstructured documents including PII labels 502 is received by text-based feature extraction component 508. In addition, a historical set of unstructured documents with PII labels by a particular author is received by a user-specific model component 510 to develop patterns of word and/or phrase usage for a particular user. In the embodiment, sets of unlabeled unstructured text data 506 is also received by text-based feature extraction component 508 to provide the models with an understanding of a large number of different word and/or phrase characteristics including, but not limited to, word contexts, parts of speech, and grammar. Text-based feature extraction component 508 extracts natural language processing (NLP) features from unstructured documents with PII labels 502 using an array of text-based feature extractors based upon the unlabeled unstructured text data 506. In the embodiment illustrated in FIG. 5, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.), paragraph 0066 i.e. In the embodiment of FIG. 6, a vector space model (VSM) is generated as the last fully connected feature layer of a DNN and is trained to classify a word in a center of a window of words as either PII data or non-PII data. The text-based and user-specific features extracted on a per-word are used to represent each word as input for classification. In the embodiment of FIG. 6, a DNN classifier is illustrated depicting a plurality of DNN layers h1 to hN in which layer h1 to receives a plurality of feature vector inputs w0-w4 and w6-w10. In the embodiment, layers h1 to hN-2)) and paragraph 0078 i.e. In block 702, application 105 receives an unstructured document for a known author. In block 704, application 105 extracts text-based features as a first set of feature using natural language processing) and
concatenating the first embeddings and the second embeddings; providing the concatenated embeddings as input to a classification model to obtain one or more verdicts for each of the one or more sub-documents as output, wherein each verdict of the one or more verdicts indicates whether a corresponding sub- document in the one or more sub-documents comprises sensitive data (see Ahmed paragraph 0061 i.e. In an embodiment, text-based feature extraction component 508 represents the extracted features as vectors, joins the feature vectors from each text-based feature extractor into a single feature vector, and provides the single feature vector as an input to a classifier component 508); and
filtering, from the one or more documents, those documents that do not comprise a sub-document in the one or more sub-documents having a verdict in the one or more verdicts that indicates sensitive data (see Ahmed paragraph 0063 i.e. Classifier component 512 detects and classifies the extracted features to produce a classification output 514, paragraph 0065-0066 i.e. With reference to FIG. 6, this figure depicts a block diagram of an example deep neural network (DNN) classifier 600 for machine-learning based detection and classification of personally identifiable information in accordance with an illustrative embodiment. In the embodiment of FIG. 6, the DNN classifier may be used as a classifier in one or more of the embodiments as previously described with respect to FIGS. 3-5. In the embodiment of FIG. 6, a DNN is used to receive the text-based feature vectors and user-specific feature vectors and determine a classification of either PII or non-PII for each element, such as a word and/or phrase, of an unstructured document).
Ahmed does not disclose wherein the classification model comprises a gradient boosting model.
Hachey teaches wherein the classification model comprises a gradient boosting model (see Hachey paragraph 0035 i.e. model may be an AI machine learning model based on classifiers including an extreme gradient boost classifier, a light gradient boost machine, a gradient boosting classifier, naïve bayes, an ada boost classifier, a K-neighbors classifier, a decision tree classifier, a ridge classifier, natural language processing logic, recurrent neural networks (RNN), convolutional neural networks (CNN), multi-level perceptrons, feedforward neural networks, or a combination thereof. The AI model training component 128 may also store and execute the AI models (i.e. the zoning model) on the servers 120. The AI model training component 128 may train, store, and execute the machine learning tools).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ahmed in view of Hachey to have used a gradient boosting model as one of the many different AI models that could be used to identify personally identifiable information in a document (see Hachey paragraph 0035). Therefore one would have been motivated to have included driver’s license data as personally identifiable information as one of many different types of personally identifiable information that can be detected.
With respect to claim 2 Ahmed in view of Hachey teaches the method of claim 1. Hachey further teaches wherein the sensitive data comprises fields in driver's license data (see Hachey paragraph 0016 i.e. It is one advantage of at least one embodiment of the present invention to reasonably accurately detect and mask the following personal health information data types: person's name, date of birth, consultation date, other dates, patient's age/carer's age, any personal identification number (social security number, driver's license, passport number, etc), any residential or postal address, any phone or fax number, any e-mail address, any website/URL address, or any text corresponding to a profession).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ahmed in view of Hachey to have included driver’s license data as personally identifiable information as one of many different types of personally identifiable information that can be detected in a document (see Hachey paragraph 0016). Therefore one would have been motivated to have included driver’s license data as personally identifiable information as one of many different types of personally identifiable information that can be detected.
With respect to claim 3 Ahmed in view of Hachey teaches the method of claim 1, wherein identifying the one or more sub-documents comprises, extracting text data from the one or more documents; and applying the one or more patterns to the text data to identify the one or more sub- documents as subsets of the text data (see Ahmed Paragraph 0053 i.e. Applicant 302 receives one or more unstructured documents 304. Application 302 includes a text-based feature extraction component 306, a user-specific model component 308, a classifier component 310, and a PII labelling component 312. Text-based feature extraction component 306 is configured to extract text-based features from unstructured document(s) 304 using natural language processing (NLP) and paragraph 0057 i.e. In an embodiment, text-based feature extraction component 404 receives an unstructured document 402 potentially containing PII in which the author of unstructured document 402 is presumed to be known. Text-based feature extraction component 404 extracts natural language processing (NLP) features from unstructured document 402 using an array of text-based feature extractors. In the embodiment illustrated in FIG. 4, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.) and paragraph 0065).
With respect to claim 4 Ahmed in view of Hachey teaches the method of claim 1, wherein the one or more sub-documents comprise fields of text data in the one or more documents encoded in a schema, wherein inputting the first data into the first language model and the second data into the second language model comprises: identifying fields in the one or more sub-documents to input into each of the first language model and second language model, wherein identifying fields in the one or more sub-documents is according to the schema where the fields are encoded; and for each sub-document in the one or more sub-documents, inputting identified fields for the sub-document into corresponding ones of the first language model and second language model (see Ahmed paragraph 0058 i.e. In the embodiment, text-based feature extraction component 404 provides the current document features to classifier component 408 and user-specific model component 406 provides the user-specific features to classifier component 408. Classifier component 408 detects, classifies, and labels the PII elements in the current text of unstructured document 402 using one or more classifiers to produce labeled unstructured document 410. In particular embodiments, the one or more classifiers include one or more of a deep neural network classifier or a maximum entropy classifier as further described herein and paragraph 0065).
With respect to claim 8 Ahmed teaches one or more non-transitory machine-readable media having program code stored thereon, the program code comprising instructions to:
classify a document as comprising sensitive data, wherein the program code to classify the document as comprising sensitive data comprises instructions to identify one or more sub-documents of the document that comprise sensitive data, wherein each of the one or more sub-documents comprises first data that matches one or more patterns of sensitive data (see Ahmed paragraph 0052-0055 i.e. Applicant 302 receives one or more unstructured documents 304. Application 302 includes a text-based feature extraction component 306, a user-specific model component 308, a classifier component 310, and a PII labelling component 312. Text-based feature extraction component 306 is configured to extract text-based features from unstructured document(s) 304 using natural language processing (NLP) and paragraph 0060-0061 i.e. In the embodiment of FIG. 5, models are training using a number of different data sources. In the embodiment, a main source of domain-specific data is a set of unstructured documents including PII labels 502 is received by text-based feature extraction component 508. In addition, a historical set of unstructured documents with PII labels by a particular author is received by a user-specific model component 510 to develop patterns of word and/or phrase usage for a particular user. In the embodiment, sets of unlabeled unstructured text data 506 is also received by text-based feature extraction component 508 to provide the models with an understanding of a large number of different word and/or phrase characteristics including, but not limited to, word contexts, parts of speech, and grammar. Text-based feature extraction component 508 extracts natural language processing (NLP) features from unstructured documents with PII labels 502 using an array of text-based feature extractors based upon the unlabeled unstructured text data 506. In the embodiment illustrated in FIG. 5, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.));
input at least a subset of each of the one or more sub-documents into an ensemble of a first language models, a second language model and a classification model to obtain one or more verdicts for each of the one or more sub-documents as output, wherein the first language model takes the first data as input to output first embeddings and wherein the second language model takes second data corresponding to context surrounding the first data within the one or more sub-documents as input to output second embeddings (see Ahmed figure 5, paragraph 0060-0061 i.e. In the embodiment of FIG. 5, models are training using a number of different data sources. In the embodiment, a main source of domain-specific data is a set of unstructured documents including PII labels 502 is received by text-based feature extraction component 508. In addition, a historical set of unstructured documents with PII labels by a particular author is received by a user-specific model component 510 to develop patterns of word and/or phrase usage for a particular user. In the embodiment, sets of unlabeled unstructured text data 506 is also received by text-based feature extraction component 508 to provide the models with an understanding of a large number of different word and/or phrase characteristics including, but not limited to, word contexts, parts of speech, and grammar. Text-based feature extraction component 508 extracts natural language processing (NLP) features from unstructured documents with PII labels 502 using an array of text-based feature extractors based upon the unlabeled unstructured text data 506. In the embodiment illustrated in FIG. 5, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.), paragraph 0066 i.e. In the embodiment of FIG. 6, a vector space model (VSM) is generated as the last fully connected feature layer of a DNN and is trained to classify a word in a center of a window of words as either PII data or non-PII data. The text-based and user-specific features extracted on a per-word are used to represent each word as input for classification. In the embodiment of FIG. 6, a DNN classifier is illustrated depicting a plurality of DNN layers h1 to hN in which layer h1 to receives a plurality of feature vector inputs w0-w4 and w6-w10. In the embodiment, layers h1 to hN-2)) and paragraph 0078 i.e. In block 702, application 105 receives an unstructured document for a known author. In block 704, application 105 extracts text-based features as a first set of feature using natural language processing);
wherein the classification model takes concatenations of the first embeddings and second embeddings as input to output one or more verdicts (see Ahmed paragraph 0061 i.e. In an embodiment, text-based feature extraction component 508 represents the extracted features as vectors, joins the feature vectors from each text-based feature extractor into a single feature vector, and provides the single feature vector as an input to a classifier component 508); and
based on a determination that each of the one or more verdicts indicates that a corresponding sub-document of the one or more sub-documents does not comprise sensitive data, indicating the document as having a false positive sensitive data classification (see Ahmed paragraph 0063 i.e. Classifier component 512 detects and classifies the extracted features to produce a classification output 514, paragraph 0065-0066 i.e. With reference to FIG. 6, this figure depicts a block diagram of an example deep neural network (DNN) classifier 600 for machine-learning based detection and classification of personally identifiable information in accordance with an illustrative embodiment. In the embodiment of FIG. 6, the DNN classifier may be used as a classifier in one or more of the embodiments as previously described with respect to FIGS. 3-5. In the embodiment of FIG. 6, a DNN is used to receive the text-based feature vectors and user-specific feature vectors and determine a classification of either PII or non-PII for each element, such as a word and/or phrase, of an unstructured document).
Ahmed does not disclose wherein the classification model comprises a gradient boosting model.
Hachey teaches wherein the classification model comprises a gradient boosting model (see Hachey paragraph 0035 i.e. model may be an AI machine learning model based on classifiers including an extreme gradient boost classifier, a light gradient boost machine, a gradient boosting classifier, naïve bayes, an ada boost classifier, a K-neighbors classifier, a decision tree classifier, a ridge classifier, natural language processing logic, recurrent neural networks (RNN), convolutional neural networks (CNN), multi-level perceptrons, feedforward neural networks, or a combination thereof. The AI model training component 128 may also store and execute the AI models (i.e. the zoning model) on the servers 120. The AI model training component 128 may train, store, and execute the machine learning tools).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ahmed in view of Hachey to have used a gradient boosting model as one of the many different AI models that could be used to identify personally identifiable information in a document (see Hachey paragraph 0035). Therefore one would have been motivated to have included driver’s license data as personally identifiable information as one of many different types of personally identifiable information that can be detected.
With respect to claim 9 Ahmed in view of Hachey teaches the non-transitory machine-readable media of claim 8. Hachey further teaches wherein the sensitive data comprises fields in driver's license data (see Hachey paragraph 0016 i.e. It is one advantage of at least one embodiment of the present invention to reasonably accurately detect and mask the following personal health information data types: person's name, date of birth, consultation date, other dates, patient's age/carer's age, any personal identification number (social security number, driver's license, passport number, etc), any residential or postal address, any phone or fax number, any e-mail address, any website/URL address, or any text corresponding to a profession).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ahmed in view of Hachey to have included driver’s license data as personally identifiable information as one of many different types of personally identifiable information that can be detected in a document (see Hachey paragraph 0016). Therefore one would have been motivated to have included driver’s license data as personally identifiable information as one of many different types of personally identifiable information that can be detected.
With respect to claim 10 Ahmed in view of Hachey teaches the non-transitory machine-readable media of claim 8, wherein instructions to identify the one or more sub-documents comprise instructions to, extract text data from the document; and apply the one or more patterns to the text data to identify the one or more sub- documents as subsets of the text data (see Ahmed Paragraph 0053 i.e. Applicant 302 receives one or more unstructured documents 304. Application 302 includes a text-based feature extraction component 306, a user-specific model component 308, a classifier component 310, and a PII labelling component 312. Text-based feature extraction component 306 is configured to extract text-based features from unstructured document(s) 304 using natural language processing (NLP) and paragraph 0057 i.e. In an embodiment, text-based feature extraction component 404 receives an unstructured document 402 potentially containing PII in which the author of unstructured document 402 is presumed to be known. Text-based feature extraction component 404 extracts natural language processing (NLP) features from unstructured document 402 using an array of text-based feature extractors. In the embodiment illustrated in FIG. 4, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.)).
With respect to claim 11 Ahmed in view of Hachey teaches the non-transitory machine-readable media of claim 8, wherein the one or more sub-documents comprise fields of text data in the documents encoded in a schema, wherein the program code to input the at least subset of each of the document into the ensemble comprises instructions to, identify fields in the one or more sub-documents to input into each of the first language model and the second language model, wherein identifying fields in the one or more sub-documents is according to the schema where the fields are encoded; and for each sub-document in the one or more sub-documents, input identified fields for the sub-document into corresponding ones of first language model and the second language model (see Ahmed paragraph 0058 i.e. In the embodiment, text-based feature extraction component 404 provides the current document features to classifier component 408 and user-specific model component 406 provides the user-specific features to classifier component 408. Classifier component 408 detects, classifies, and labels the PII elements in the current text of unstructured document 402 using one or more classifiers to produce labeled unstructured document 410. In particular embodiments, the one or more classifiers include one or more of a deep neural network classifier or a maximum entropy classifier as further described herein).
With respect to claim 15 Ahmed teaches an apparatus comprising:
a processor; and a machine-readable medium having instructions stored thereon that are executable by the processor to cause the apparatus to,
preprocess documents to identify one or more documents comprising sensitive data for data loss prevention, wherein the instructions to identify the one or more documents comprise instructions executable by the processor to cause the apparatus to identify one or more patterns of sensitive data in text data of one or more sub- documents of the one or more documents (see Ahmed paragraph 0052-0055 i.e. Applicant 302 receives one or more unstructured documents 304. Application 302 includes a text-based feature extraction component 306, a user-specific model component 308, a classifier component 310, and a PII labelling component 312. Text-based feature extraction component 306 is configured to extract text-based features from unstructured document(s) 304 using natural language processing (NLP) and paragraph 0060-0061 i.e. In the embodiment of FIG. 5, models are training using a number of different data sources. In the embodiment, a main source of domain-specific data is a set of unstructured documents including PII labels 502 is received by text-based feature extraction component 508. In addition, a historical set of unstructured documents with PII labels by a particular author is received by a user-specific model component 510 to develop patterns of word and/or phrase usage for a particular user. In the embodiment, sets of unlabeled unstructured text data 506 is also received by text-based feature extraction component 508 to provide the models with an understanding of a large number of different word and/or phrase characteristics including, but not limited to, word contexts, parts of speech, and grammar. Text-based feature extraction component 508 extracts natural language processing (NLP) features from unstructured documents with PII labels 502 using an array of text-based feature extractors based upon the unlabeled unstructured text data 506. In the embodiment illustrated in FIG. 5, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.);
input at least a subset of each of the one or more sub-documents into an ensemble of a first language models, a second language model and a classification model to obtain one or more verdicts for each of the one or more sub-documents as output (see Ahmed paragraph 0058 i.e. In the embodiment, text-based feature extraction component 404 provides the current document features to classifier component 408 and user-specific model component 406 provides the user-specific features to classifier component 408. Classifier component 408 detects, classifies, and labels the PII elements in the current text of unstructured document 402 using one or more classifiers to produce labeled unstructured document 410. In particular embodiments, the one or more classifiers include one or more of a deep neural network classifier or a maximum entropy classifier as further described herein);
wherein the first language model takes first data as input to output first embeddings and wherein the second language model takes second data corresponding to context surrounding the first data within the one or more sub-documents as input to output second embeddings (see Ahmed figure 5, paragraph 0060-0061 i.e. In the embodiment of FIG. 5, models are training using a number of different data sources. In the embodiment, a main source of domain-specific data is a set of unstructured documents including PII labels 502 is received by text-based feature extraction component 508. In addition, a historical set of unstructured documents with PII labels by a particular author is received by a user-specific model component 510 to develop patterns of word and/or phrase usage for a particular user. In the embodiment, sets of unlabeled unstructured text data 506 is also received by text-based feature extraction component 508 to provide the models with an understanding of a large number of different word and/or phrase characteristics including, but not limited to, word contexts, parts of speech, and grammar. Text-based feature extraction component 508 extracts natural language processing (NLP) features from unstructured documents with PII labels 502 using an array of text-based feature extractors based upon the unlabeled unstructured text data 506. In the embodiment illustrated in FIG. 5, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.), paragraph 0066 i.e. In the embodiment of FIG. 6, a vector space model (VSM) is generated as the last fully connected feature layer of a DNN and is trained to classify a word in a center of a window of words as either PII data or non-PII data. The text-based and user-specific features extracted on a per-word are used to represent each word as input for classification. In the embodiment of FIG. 6, a DNN classifier is illustrated depicting a plurality of DNN layers h1 to hN in which layer h1 to receives a plurality of feature vector inputs w0-w4 and w6-w10. In the embodiment, layers h1 to hN-2)) and paragraph 0078 i.e. In block 702, application 105 receives an unstructured document for a known author. In block 704, application 105 extracts text-based features as a first set of feature using natural language processing);
wherein the classification model takes concatenations of the first embeddings and second embeddings as input to output one or more verdicts (see Ahmed paragraph 0061 i.e. In an embodiment, text-based feature extraction component 508 represents the extracted features as vectors, joins the feature vectors from each text-based feature extractor into a single feature vector, and provides the single feature vector as an input to a classifier component 508); and
filter, from the one or more documents, those documents that do not comprise a sub-document in the one or more sub-documents having a verdict in the one or more verdicts indicating sensitive data (see Ahmed paragraph 0063 i.e. Classifier component 512 detects and classifies the extracted features to produce a classification output 514, paragraph 0065-0066 i.e. With reference to FIG. 6, this figure depicts a block diagram of an example deep neural network (DNN) classifier 600 for machine-learning based detection and classification of personally identifiable information in accordance with an illustrative embodiment. In the embodiment of FIG. 6, the DNN classifier may be used as a classifier in one or more of the embodiments as previously described with respect to FIGS. 3-5. In the embodiment of FIG. 6, a DNN is used to receive the text-based feature vectors and user-specific feature vectors and determine a classification of either PII or non-PII for each element, such as a word and/or phrase, of an unstructured document).
Ahmed does not disclose wherein the classification model comprises a gradient boosting model.
Hachey teaches wherein the classification model comprises a gradient boosting model (see Hachey paragraph 0035 i.e. model may be an AI machine learning model based on classifiers including an extreme gradient boost classifier, a light gradient boost machine, a gradient boosting classifier, naïve bayes, an ada boost classifier, a K-neighbors classifier, a decision tree classifier, a ridge classifier, natural language processing logic, recurrent neural networks (RNN), convolutional neural networks (CNN), multi-level perceptrons, feedforward neural networks, or a combination thereof. The AI model training component 128 may also store and execute the AI models (i.e. the zoning model) on the servers 120. The AI model training component 128 may train, store, and execute the machine learning tools).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ahmed in view of Hachey to have used a gradient boosting model as one of the many different AI models that could be used to identify personally identifiable information in a document (see Hachey paragraph 0035). Therefore one would have been motivated to have included driver’s license data as personally identifiable information as one of many different types of personally identifiable information that can be detected.
With respect to claim 16 Ahmed in view of Hachey teaches the apparatus of claim 15. Hachey further teaches wherein the sensitive data comprises fields in driver's license data (see Hachey paragraph 0016 i.e. It is one advantage of at least one embodiment of the present invention to reasonably accurately detect and mask the following personal health information data types: person's name, date of birth, consultation date, other dates, patient's age/carer's age, any personal identification number (social security number, driver's license, passport number, etc), any residential or postal address, any phone or fax number, any e-mail address, any website/URL address, or any text corresponding to a profession).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ahmed in view of Hachey to have included driver’s license data as personally identifiable information as one of many different types of personally identifiable information that can be detected in a document (see Hachey paragraph 0016). Therefore one would have been motivated to have included driver’s license data as personally identifiable information as one of many different types of personally identifiable information that can be detected.
With respect to claim 17 Ahmed in view of Hachey teaches the apparatus of claim 15, wherein instructions to preprocess documents to identify the one or more documents comprise instructions executable by the processor to cause the apparatus to, extract text data from the one or more documents; and apply the one or more patterns to the text data to identify the one or more sub- documents as subsets of the text data (see Ahmed Paragraph 0053 i.e. Applicant 302 receives one or more unstructured documents 304. Application 302 includes a text-based feature extraction component 306, a user-specific model component 308, a classifier component 310, and a PII labelling component 312. Text-based feature extraction component 306 is configured to extract text-based features from unstructured document(s) 304 using natural language processing (NLP) and paragraph 0057 i.e. In an embodiment, text-based feature extraction component 404 receives an unstructured document 402 potentially containing PII in which the author of unstructured document 402 is presumed to be known. Text-based feature extraction component 404 extracts natural language processing (NLP) features from unstructured document 402 using an array of text-based feature extractors. In the embodiment illustrated in FIG. 4, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.)).
With respect to claim 18 Ahmed in view of Hachey teaches the apparatus of claim 15, wherein the one or more sub-documents comprise fields of text data in the documents encoded in a schema, wherein the instructions to input the at least subset of each of the one or more sub-document into the ensemble comprises instructions to, identify fields in the one or more sub-documents to input into each of the first language model and the second language model, wherein identifying fields in the one or more sub-documents is according to the schema where the fields are encoded; and for each sub-document in the one or more sub-documents, input identified fields for the sub-document into corresponding ones of first language model and the second language model (see Ahmed paragraph 0058 i.e. In the embodiment, text-based feature extraction component 404 provides the current document features to classifier component 408 and user-specific model component 406 provides the user-specific features to classifier component 408. Classifier component 408 detects, classifies, and labels the PII elements in the current text of unstructured document 402 using one or more classifiers to produce labeled unstructured document 410. In particular embodiments, the one or more classifiers include one or more of a deep neural network classifier or a maximum entropy classifier as further described herein).
With respect to claim 21 Ahmed in view of Hachey teaches the method of claim 1, wherein identifying the one or more sub-documents of the one or more documents that match the one or more patterns for sensitive data comprises: identifying sub-documents of the one or more documents comprise third data that match patterns including the one or more patterns and corresponding confidence values of each matched patterns; and filtering those of the sub-documents having low confidence of matched patterns according to the confidence values to obtain the one or more sub-documents (see figure 3 and paragraph 0052-0055 i.e. Applicant 302 receives one or more unstructured documents 304. Application 302 includes a text-based feature extraction component 306, a user-specific model component 308, a classifier component 310, and a PII labelling component 312…. Classifier component 310 is configured to receive the text-based features from text-based feature extraction component 306 and the user-specific features from user-specific model component 308 to classify the extracted features as either PII data (PII) or non-PII data. In particular embodiments, classifier component 310 includes one or more of a DNN or a maximum entropy framework to classify the extracted features as either PII data or non-PII data…PII labelling component 312 is configured to label the PII elements within unstructured document(s) 304 based upon the classified extracted features. Application 302 is configured to output a labelled unstructured document 314 in which PII information in unstructured document(s) 304 that is detected and classified by application 302 is labelled as PII. Application 302 is further configured to output classification result 316 indicative of the results of classification of the PII data by classifier component 310. In one or more embodiments, application 302 is configured to further train classifier component 310 based upon classification result 316).
With respect to claim 22 Ahmed in view of Hachey teaches the non-transitory machine-readable media of claim 8, wherein the instructions to identify the one or more sub-documents of the document that comprise sensitive data comprise instructions to: identify sub-documents of the document comprise third data that match patterns including the one or more patterns and corresponding confidence values of each matched pattern; and filter those of the sub-documents having low confidence of matched patterns according to the confidence values to obtain the one or more sub-documents (see figure 3 and paragraph 0052-0055 i.e. Applicant 302 receives one or more unstructured documents 304. Application 302 includes a text-based feature extraction component 306, a user-specific model component 308, a classifier component 310, and a PII labelling component 312…. Classifier component 310 is configured to receive the text-based features from text-based feature extraction component 306 and the user-specific features from user-specific model component 308 to classify the extracted features as either PII data (PII) or non-PII data. In particular embodiments, classifier component 310 includes one or more of a DNN or a maximum entropy framework to classify the extracted features as either PII data or non-PII data…PII labelling component 312 is configured to label the PII elements within unstructured document(s) 304 based upon the classified extracted features. Application 302 is configured to output a labelled unstructured document 314 in which PII information in unstructured document(s) 304 that is detected and classified by application 302 is labelled as PII. Application 302 is further configured to output classification result 316 indicative of the results of classification of the PII data by classifier component 310. In one or more embodiments, application 302 is configured to further train classifier component 310 based upon classification result 316).
With respect to claim 23 Ahmed in view of Hachey teaches the apparatus of claim 15, wherein the instructions to identify the one or more documents that comprise sensitive data comprise instructions executable by the processor to cause the apparatus to: identify sub-documents of the one or more documents comprise third data that match patterns including the one or more patterns and corresponding confidence values of each matched pattern; and filter those of the sub-documents having low confidence of matched patterns according to the confidence values to obtain the one or more sub-documents (see figure 3 and paragraph 0052-0055 i.e. Applicant 302 receives one or more unstructured documents 304. Application 302 includes a text-based feature extraction component 306, a user-specific model component 308, a classifier component 310, and a PII labelling component 312…. Classifier component 310 is configured to receive the text-based features from text-based feature extraction component 306 and the user-specific features from user-specific model component 308 to classify the extracted features as either PII data (PII) or non-PII data. In particular embodiments, classifier component 310 includes one or more of a DNN or a maximum entropy framework to classify the extracted features as either PII data or non-PII data…PII labelling component 312 is configured to label the PII elements within unstructured document(s) 304 based upon the classified extracted features. Application 302 is configured to output a labelled unstructured document 314 in which PII information in unstructured document(s) 304 that is detected and classified by application 302 is labelled as PII. Application 302 is further configured to output classification result 316 indicative of the results of classification of the PII data by classifier component 310. In one or more embodiments, application 302 is configured to further train classifier component 310 based upon classification result 316).
With respect to claim 24 Ahmed in view of Hachey teaches the method of claim 1, wherein the second data comprise sections of text surrounding the first data within the one or more sub-documents (see Ahmed figure 5, paragraph 0060-0061 i.e. In the embodiment of FIG. 5, models are training using a number of different data sources. In the embodiment, a main source of domain-specific data is a set of unstructured documents including PII labels 502 is received by text-based feature extraction component 508. In addition, a historical set of unstructured documents with PII labels by a particular author is received by a user-specific model component 510 to develop patterns of word and/or phrase usage for a particular user. In the embodiment, sets of unlabeled unstructured text data 506 is also received by text-based feature extraction component 508 to provide the models with an understanding of a large number of different word and/or phrase characteristics including, but not limited to, word contexts, parts of speech, and grammar. Text-based feature extraction component 508 extracts natural language processing (NLP) features from unstructured documents with PII labels 502 using an array of text-based feature extractors based upon the unlabeled unstructured text data 506. In the embodiment illustrated in FIG. 5, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.) and paragraph 0066 i.e. In the embodiment of FIG. 6, a vector space model (VSM) is generated as the last fully connected feature layer of a DNN and is trained to classify a word in a center of a window of words as either PII data or non-PII data. The text-based and user-specific features extracted on a per-word are used to represent each word as input for classification. In the embodiment of FIG. 6, a DNN classifier is illustrated depicting a plurality of DNN layers h1 to hN in which layer h1 to receives a plurality of feature vector inputs w0-w4 and w6-w10. In the embodiment, layers h1 to hN-2)).
With respect to claim 25 Ahmed in view of Hachey teaches the non-transitory machine-readable media of claim 8, wherein the second data comprise sections of text surrounding the first data within the one or more sub-documents (see Ahmed figure 5, paragraph 0060-0061 i.e. In the embodiment of FIG. 5, models are training using a number of different data sources. In the embodiment, a main source of domain-specific data is a set of unstructured documents including PII labels 502 is received by text-based feature extraction component 508. In addition, a historical set of unstructured documents with PII labels by a particular author is received by a user-specific model component 510 to develop patterns of word and/or phrase usage for a particular user. In the embodiment, sets of unlabeled unstructured text data 506 is also received by text-based feature extraction component 508 to provide the models with an understanding of a large number of different word and/or phrase characteristics including, but not limited to, word contexts, parts of speech, and grammar. Text-based feature extraction component 508 extracts natural language processing (NLP) features from unstructured documents with PII labels 502 using an array of text-based feature extractors based upon the unlabeled unstructured text data 506. In the embodiment illustrated in FIG. 5, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.) and paragraph 0066 i.e. In the embodiment of FIG. 6, a vector space model (VSM) is generated as the last fully connected feature layer of a DNN and is trained to classify a word in a center of a window of words as either PII data or non-PII data. The text-based and user-specific features extracted on a per-word are used to represent each word as input for classification. In the embodiment of FIG. 6, a DNN classifier is illustrated depicting a plurality of DNN layers h1 to hN in which layer h1 to receives a plurality of feature vector inputs w0-w4 and w6-w10. In the embodiment, layers h1 to hN-2)).
With respect to claim 26 Ahmed in view of Hachey teaches the apparatus of claim 15, wherein the second data comprise sections of text surrounding the first data within the one or more sub-documents (see Ahmed figure 5, paragraph 0060-0061 i.e. In the embodiment of FIG. 5, models are training using a number of different data sources. In the embodiment, a main source of domain-specific data is a set of unstructured documents including PII labels 502 is received by text-based feature extraction component 508. In addition, a historical set of unstructured documents with PII labels by a particular author is received by a user-specific model component 510 to develop patterns of word and/or phrase usage for a particular user. In the embodiment, sets of unlabeled unstructured text data 506 is also received by text-based feature extraction component 508 to provide the models with an understanding of a large number of different word and/or phrase characteristics including, but not limited to, word contexts, parts of speech, and grammar. Text-based feature extraction component 508 extracts natural language processing (NLP) features from unstructured documents with PII labels 502 using an array of text-based feature extractors based upon the unlabeled unstructured text data 506. In the embodiment illustrated in FIG. 5, the text-based feature extractors include an n-gram feature extractor, a dictionary-based feature extractor, a word embedding feature extractor, and a part of speech (e.g., verb, noun, adjective, etc.) and paragraph 0066 i.e. In the embodiment of FIG. 6, a vector space model (VSM) is generated as the last fully connected feature layer of a DNN and is trained to classify a word in a center of a window of words as either PII data or non-PII data. The text-based and user-specific features extracted on a per-word are used to represent each word as input for classification. In the embodiment of FIG. 6, a DNN classifier is illustrated depicting a plurality of DNN layers h1 to hN in which layer h1 to receives a plurality of feature vector inputs w0-w4 and w6-w10. In the embodiment, layers h1 to hN-2)).
Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed et al (US 2020/0081978) in view of Hachey (US 2021/0256160) in view of Sahu et al (US 2023/0128136).
With respect to claim 5 Ahmed in view of Hachey teaches the method of claim 1, but does not disclose wherein the first language models comprise a one-dimensional convolutional neural network.
S Nayar teaches wherein the first language models comprise a one-dimensional convolutional neural network (See S Nayar paragraph 0051 i.e. FIG. 6 illustrates a pictorial representation 600 of classification of the input dataset 222 using the convolutional neural network modeler 226 of the identification classifier 140, according to an example embodiment of present disclosure. As mentioned above, the data manipulator 130 may obtain the input dataset 222 defined in a one-dimensional data structure and convert the input dataset 222 into a formatted dataset of a two-dimensional data structure, wherein the format of the formatted dataset may be defined in accordance to a type of a deep neural network component. Also, the neural network component selector 150 may select the deep neural network based on the identification of characteristics associated with the input dataset 222 using a predefined parameter 256. The predefined parameter 256 may be a parameter that may be used to determine a characteristic associated with data of the input dataset 222. In an example, the predefined parameter 256 may be defined by a user. In an example, the predefined parameter 256 may include a size of the input dataset 222 and/or a length of individual elements in the dataset. As mentioned above, the neural network component selector 150 may select a convolutional neural network component to be used for processing the input dataset 222 when the input dataset 222 is identified to be associated with the first characteristic data 258. The pictorial representation 600 may illustrate the processing of the input dataset 222 based on the selection of the convolutional neural network component. The pictorial representation 600 illustrates the processing of the input dataset 222 by the CNN modeler 226).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ahmed in view of S Nayar to have used a one-dimensional convolutional neural network modeler as the first language models to process the input data. Therefore one would have been motivated to have used a one-dimensional convolutional neural network.
With respect to claim 12 Ahmed in view of Hachey teaches the non-transitory machine-readable media of claim 8, but does not disclose wherein the first language model comprise a one-dimensional convolutional neural network.
S Nayar teaches wherein the first language models comprise a one-dimensional convolutional neural network (See S Nayar paragraph 0051 i.e. FIG. 6 illustrates a pictorial representation 600 of classification of the input dataset 222 using the convolutional neural network modeler 226 of the identification classifier 140, according to an example embodiment of present disclosure. As mentioned above, the data manipulator 130 may obtain the input dataset 222 defined in a one-dimensional data structure and convert the input dataset 222 into a formatted dataset of a two-dimensional data structure, wherein the format of the formatted dataset may be defined in accordance to a type of a deep neural network component. Also, the neural network component selector 150 may select the deep neural network based on the identification of characteristics associated with the input dataset 222 using a predefined parameter 256. The predefined parameter 256 may be a parameter that may be used to determine a characteristic associated with data of the input dataset 222. In an example, the predefined parameter 256 may be defined by a user. In an example, the predefined parameter 256 may include a size of the input dataset 222 and/or a length of individual elements in the dataset. As mentioned above, the neural network component selector 150 may select a convolutional neural network component to be used for processing the input dataset 222 when the input dataset 222 is identified to be associated with the first characteristic data 258. The pictorial representation 600 may illustrate the processing of the input dataset 222 based on the selection of the convolutional neural network component. The pictorial representation 600 illustrates the processing of the input dataset 222 by the CNN modeler 226).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ahmed in view of S Nayar to have used a one-dimensional convolutional neural network modeler as the first language models to process the input data. Therefore one would have been motivated to have used a one-dimensional convolutional neural network.
With respect to claim 19 Ahmed in view of Hachey teaches the apparatus of claim 15, but does not disclose wherein the first language model comprise a one-dimensional convolutional neural network.
S Nayar teaches wherein the first language models comprise a one-dimensional convolutional neural network (See S Nayar paragraph 0051 i.e. FIG. 6 illustrates a pictorial representation 600 of classification of the input dataset 222 using the convolutional neural network modeler 226 of the identification classifier 140, according to an example embodiment of present disclosure. As mentioned above, the data manipulator 130 may obtain the input dataset 222 defined in a one-dimensional data structure and convert the input dataset 222 into a formatted dataset of a two-dimensional data structure, wherein the format of the formatted dataset may be defined in accordance to a type of a deep neural network component. Also, the neural network component selector 150 may select the deep neural network based on the identification of characteristics associated with the input dataset 222 using a predefined parameter 256. The predefined parameter 256 may be a parameter that may be used to determine a characteristic associated with data of the input dataset 222. In an example, the predefined parameter 256 may be defined by a user. In an example, the predefined parameter 256 may include a size of the input dataset 222 and/or a length of individual elements in the dataset. As mentioned above, the neural network component selector 150 may select a convolutional neural network component to be used for processing the input dataset 222 when the input dataset 222 is identified to be associated with the first characteristic data 258. The pictorial representation 600 may illustrate the processing of the input dataset 222 based on the selection of the convolutional neural network component. The pictorial representation 600 illustrates the processing of the input dataset 222 by the CNN modeler 226).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ahmed in view of S Nayar to have used a one-dimensional convolutional neural network modeler as the first language models to process the input data. Therefore one would have been motivated to have used a one-dimensional convolutional neural network.
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed et al (US 2020/0081978) in view of Hachey (US 2021/0256160) in view of Sahu et al (US 2023/0128136).
With respect to claim 6 Ahmed in view of Hachey teaches the method of claim 5, but does not disclose wherein the second language model comprises Sentence-Bidirectional Encoder Representations from Transformers.
Sahu teaches wherein the second language model comprises Sentence-Bidirectional Encoder Representations from Transformers (see Sahu paragraph 0076 i.e. In some implementations, the CCE 101 includes a bi-directional deep neural network. The CCE 101 includes a lexical parser to parse data from different sources. NLP techniques may also be utilized for some aspects of lexical parsing and validation, such as a Bi-directional Encoder Representations from Transformers (BERT)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ahmed in view of Sahu to have used a Sentence-Bidirectional Encoder Representations from Transformers as one of the many different AI models that could be used to identify personally identifiable information in data (see Sahu paragraph 0076). Therefore one would have been motivated to have included driver’s license data as personally identifiable information as one of many different types of personally identifiable information that can be detected.
With respect to claim 13 Ahmed in view of Hachey teaches the non-transitory machine-readable media of claim 12, but does not disclose wherein the second language model comprises Sentence-Bidirectional Encoder Representations from Transformers.
Sahu teaches wherein the second language model comprises Sentence-Bidirectional Encoder Representations from Transformers (see Sahu paragraph 0076 i.e. In some implementations, the CCE 101 includes a bi-directional deep neural network. The CCE 101 includes a lexical parser to parse data from different sources. NLP techniques may also be utilized for some aspects of lexical parsing and validation, such as a Bi-directional Encoder Representations from Transformers (BERT)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ahmed in view of Sahu to have used a Sentence-Bidirectional Encoder Representations from Transformers as one of the many different AI models that could be used to identify personally identifiable information in data (see Sahu paragraph 0076). Therefore one would have been motivated to have included driver’s license data as personally identifiable information as one of many different types of personally identifiable information that can be detected.
Prior Art
Kim et al (US 2025/0094600) titled “MACHINE LEARNING-BASED FILTERING OF FALSE POSITIVE PATTERN MATCHES FOR PERSONALLY IDENTIFIABLE INFORMATION”.
Gupta et al (US 2024/0289492) titled “MACHINE LEARNING MODELING TO IDENTIFY SENSITIVE DATA”.
Sirhani et al (US 2023/0385407) titled “SYSTEM AND METHOD FOR INTEGRATING MACHINE LEARNING IN DATA LEAKAGE DETECTION SOLUTION THROUGH KEYWORD POLICY PREDICTION”.
Dan (US 11,755,848) titled “Processing Structured And Unstructured Text To Identify Sensitive Information”.
Dancewicz et al (US 2023/0259709) titled “ITERATIVE TRAINING FOR TEXT-IMAGE-LAYOUT DATA IN NATURAL LANGUAGE PROCESSING”.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 DEVIN E ALMEIDA whose telephone number is (571)270-1018. The examiner can normally be reached on Monday-Thursday from 7:30 A.M. to 5:00 P.M. The examiner can also be reached on alternate Fridays from 7:30 A.M. to 4:00 P.M.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Rupal Dharia, can be reached on 571-272-3880. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEVIN E ALMEIDA/ Examiner, Art Unit 2492
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