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 .
Claim Objections
Claim 4 objected to because of the following informalities: Regarding Claim 4 the following is stated “wherein the step of de-identifying t comprises…” The Examiner believes the letter “t” was mistakenly added to the limitation. Appropriate correction is required.
Allowable Subject Matter
Claims 13-15 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.
Regarding Claim 13, known prior art does not explicitly disclose: wherein the step of fictionalizing comprises: performing iterative fictionalization of the any given one or more of the enumerated objects for the fact vectors in the one or more enumerated categories; relaxing constraints used in the fictionalization from an initial iteration to a subsequent iteration; and balancing anonymity of the training dataset relative to the functional utility of the training dataset at each iteration.
Regarding Claim 14, the Claim is allowed due to its dependency to Claim 13.
Regarding Claim 15, known prior art does not explicitly disclose: training, with the one or more processors, a preparatory untrained Al model of the computerized system into a preparatory trained Al model; and fictionalizing the any given one or more of the enumerated objects for the fact vectors at least in the one or more enumerated categories by using the preparatory trained Al model.
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.
Claim(s) 1-6 and 16-21 are rejected under 35 U.S.C. 103 as being unpatentable over Carlson (US20210240853) in view of Gkoulalas-Divanis (US20190188416) herein after ‘Aris’.
Regarding Claims 1 and 21, Carlson discloses the one or more identifier categories categorizing direct identifiers of personally identifiable information, (Paragraph [0180] E.N. The plurality of data points may include a plurality of identifying (or at least potentially identifying) features that are usable to identify the one or more subjects. For example, each data set may include, for a respective subject, one or more identifiers (e.g., social security number, driver's license number, medical record number, etc.), one or more location data types (e.g., ZIP code, city, state, etc.), one or more dates/times (e.g., birthday, hospital admission, hospital encounter, etc.), and so forth.)
obtaining, with one or more processors of the computerized system, medical facts for a plurality of patients; (Paragraph [0017] E.N. The one or more subjects may include one or more patients, and the one or more raw data sets associated with the one or more subjects may include medical records associated with the one or more patients.)
vectorizing, with the one or more processors, the medical facts into fact vectors according to the fact categories; (Paragraph [0189] E.N. The classifier can learn weights in a training stage utilizing one or more machine learning algorithms as appropriate to the classification task in accordance with many embodiments including linear regression, logistic regression, linear discriminant analysis, principal component analysis, classification trees, regression trees, naïve Bayes, k-nearest neighbors, learning vector quantization, support vector machines, bagging forests, random forests, boosting, AdaBoost, neural network(s), etc.)
de-identifying, in a de-identification process with the one or more processors, any given one or more of the direct identifiers for the fact vectors in the one or more identifier categories; (Paragraph [0037] E.N. A structured de-id application programming interface (“API”) module may receive, e.g., from one or more client devices operated by medical personnel, researchers, patients, etc., a request that includes or identifies a payload of data to be de-identified. The request may also be made available through events as and/or when a new dataset arrives and/or is imported into the system. This may provide a continuous de-identification pipeline for the datasets.)
fictionalizing, in a fictionalization process with the one or more processors, any given one or more of the enumerated objects for the fact vectors in the one or more enumerated categories; (Paragraph [0132] E.N. The first field has a class of “patientID,” which may be PHI and therefore may be processed using a “patientID” handler to obfuscate the patient's identity. The second field has a type of “PERFORMED_DT_TM” and specifies that the datetime at which the event occurs should be handled using the “datetime” handler, which may, for instance, shift or otherwise obfuscate the date.)
storing, in the memory, the medical facts resulting from the de-identification process and the fictionalization process as a training dataset; (Paragraph [0038] E.N. The payload may specify, e.g., within external data sources (e.g., remote hospitals, deployed personal physiological sensors, etc.) or internal data sources (e.g., EMRs, hospital information systems, or “HIS”, etc.), input data or other data sources that provide data to be de-identified, as well as locations for storing the resulting de-identified data. Input data may come in various formats, such as coma separate values (“CSV”), relational databases, JavaScript Object Notation (“JSON”), Health Level Seven (“HL7”), DICOM, PACS, and so forth. The payload may specify a corresponding schema file that declares the data type and the kind of de-identification required for each data element, and/or the output location where the de-identified data should be stored.
training, with the one or more processors, an untrained artificial intelligence (Al) model of the computer system into a trained Al model using the training dataset; and testing, with the one or more processors, the trained Al model for functional utility (Paragraph [0189] E.N. The system may train a machine learning model/classifier. The classifier can learn weights in a training stage utilizing one or more machine learning algorithms as appropriate to the classification task in accordance with many embodiments including linear regression, logistic regression, linear discriminant analysis, principal component analysis, classification trees, regression trees, naïve Bayes, k-nearest neighbors, learning vector quantization, support vector machines, bagging forests, random forests, boosting, AdaBoost, neural network(s), etc. As noted previously, in some embodiments, a first “training” portion of the de-identified data may be used for training (e.g., 70% or some other fraction)
Carlson does not, but in related art, Aris discloses: A method used in a computerized system, the method comprising: defining, in memory of the computerized system, fact categories including one or more identifier categories and including one or more enumerated categories, (Figure 6 and 7 E.N. A flowchart of a manner of evaluating configuration options for data de-identification processes based on introduction of quasi-identifiers within de-identified data is disclosed)
the one or more enumerated categories categorizing enumerated objects being separate from the direct identifiers and being at least related to information that is personally identifiable; (Figure 6 and 7 E.N. A flowchart of a manner of evaluating configuration options for data de-identification processes based on introduction of quasi-identifiers within de-identified data is disclosed)
and resistance to triangulation attacks. (Paragraph [0009] E.N. This evaluation utilizes the de-identified data from a generated dataset against known entities in a publicly available dataset to determine whether or not identities of entities in the de-identified data can be determined through triangulation attacks, thereby providing significant confidence that a recommended data de-identification process with associated configuration options maintains privacy.)
Therefore, it would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to have modified Carlson to incorporate the teachings of Aris because Carlson does not explicitly disclose triangulation attacks and categorizing enumerated objects which is taught by Aris. Incorporating the teachings of Aris to Carlson allows for the compartmentalization of sensitive information and prevent effective triangulation attacks from occurring.
Regarding Claim 2, Carlson in view of Aris discloses the method of Claim 1. Carlson further discloses: wherein the one or more identifier categories are selected from the group consisting of patient name, geographical element, a street address, city, county, zip code, date related to health of an individual, date related to identity of an individual, birthdate, date of admission, date of discharge, date of death, exact age of a patient older than 89, telephone number, fax number, e-mail address, social security number, medical record number, health insurance beneficiary number, account number, certificate/license number, vehicle detail, device attribute or serial number, digital identifier, website URL, IP address, biometric element, fingerprint, retinal image, voiceprint, full face photographic image, identifying number, and identifying code. (Paragraph [0180] E.N. The plurality of data points may include a plurality of identifying (or at least potentially identifying) features that are usable to identify the one or more subjects. For example, each data set may include, for a respective subject, one or more identifiers (e.g., social security number, driver's license number, medical record number, etc.), one or more location data types (e.g., ZIP code, city, state, etc.), one or more dates/times (e.g., birthday, hospital admission, hospital encounter, etc.), and so forth.)
Regarding Claim 3, Carlson in view of Aris discloses the method of claim 1. Carlson further discloses wherein the step of de-identifying comprises redacting the any given one or more of the direct identifiers. (Paragraph [0053] E.N. If a given data point does not match any more specific classification in the policy then the data point may be redacted and/or replaced with a label such as “removed”.)
Regarding Claim 4, Carlson in view of Aris discloses the method of claim 1. Carlson further discloses wherein the step of de-identifying t comprises replacing the any given one or more of the direct identifiers as a given direct identifier with an element selected from the group consisting of a de-identifier of the given direct identifier, a code being non-descriptive of the given direct identifier, an abstraction of the given direct identifier, a substitution for the given direct identifier, a truncation of the given direct identifier, a generalization of the given direct identifier, a cryptographic hash generated from the given direct identifier, and an object encrypted from the given direct identifier.(Paragraph [0054] E.N. Data points classified as identifiers (“id”) will be dropped by default, however internal (“id:int”) identifiers will be mapped to handlers, e.g., by PHI transformer 104, using a lookup table. Data points classified as “id:sys:row-id” (e.g., database row ids) will be allowed through unmodified (“passthrough”). Data points classified as locations (e.g., ZIP codes, cities, states, etc.) will be dropped. See also [0152])
Regarding Claim 5, Carlson in view of Aris discloses the method of claim 1. Carlson further discloses wherein the one or more enumerated categories are selected from the group consisting of geographic information, temporal information, employment information, education information, socioeconomic information, identity of physician treating a patient, prescription information, and contextual information. (Paragraph [0045] E.N. Individual data points may be obtained from a variety of sources (e.g., from 111 and/or 112), such as structured data files (e.g., JSON, CSV, etc., which may contain recorded physiological measurements, lab results, treatments applied, prescriptions, diagnoses, etc.), detected in images from DICOM or PACS data (e.g., detected within the images such as CT scans or MRI data, or within associated metadata), extracted from EMRs (which could include free-form text that describes diagnoses, treatments, prescriptions, etc.))
Regarding Claim 6, Carlson in view of Aris discloses the method of claim 1. Carlson does not, but in related art, Aris discloses wherein the step of fictionalizing comprises one or more of: swapping the any given one or more of the enumerated objects as given enumerated objects in the fact vectors with one another in at least a subset of the medical facts; shuffling the given enumerated objects in the fact vectors in at least a subset of the medical facts; randomly reordering the given enumerated objects in the fact vectors in at least a subset of the medical facts; repeating at least one of the given enumerated objects in the fact vectors throughout at least a subset of the medical facts; and replacing at least one of the given enumerated objects in at least one of the fact vectors in at least one of the medical facts with at least one of an abstraction of the at least one given enumerated object, a substitution for the at least one given enumerated object, and a generalization of the at least one given enumerated object. (Figure 6, 7 and Paragraph [0040] E.N. A data masking process may enable the name attribute to be masked with a fictionalized name that preserves or maintains consistency with the gender attribute.)
Therefore, it would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to have modified Carlson to incorporate the teachings of Aris because Carlson does not explicitly disclose fictionization by masking which is taught by Aris. Incorporating the teachings of Aris to Carlson allows for the compartmentalization of sensitive information and prevent effective triangulation attacks from occurring.
Regarding Claim 16, Carlson in view of Aris discloses the method of claim 1. Carlson further discloses wherein the steps of vectorizing, de-identifying, and fictionalizing comprise using a natural language processing platform of the computer system. (Paragraph [0183] E.N. the identifying features had to first be extracted/detected in DICOM images, and/or free text had to be analyzed, e.g., using natural language processing, to flag potentially identifying data point(s) for obfuscation/removal.)
Regarding Claim 17, Carlson in view of Aris discloses the method of claim 1. Carlson further discloses wherein the step of training the untrained Al model into the trained Al model using the training dataset comprises setting and adjusting weights in repeated processing of the training dataset to converge toward desired outputs by using a neural network training framework of the computer system. (Paragraph [0189] E.N. The system may train a machine learning model/classifier. The classifier can learn weights in a training stage utilizing one or more machine learning algorithms as appropriate to the classification task in accordance with many embodiments including linear regression, logistic regression, linear discriminant analysis, principal component analysis, classification trees, regression trees, naïve Bayes, k-nearest neighbors, learning vector quantization, support vector machines, bagging forests, random forests, boosting, AdaBoost, neural network(s), etc.)
Regarding Claim 18, Carlson in view of Aris discloses the method of claim 1. Carlson further discloses wherein the step of testing the trained Al model for the functional utility (Paragraph [0016] E.N. The method may further include training the machine learning model using the training portion of the plurality of de-identified data sets. In various embodiments, the applying may include applying a remaining validation portion of the plurality of de-identified data sets as input across the trained machine learning model as validation of the training.)
testing the trained Al model to reveal any of the medical facts by probing the Al model using public facts associated with the medical facts. (Paragraph [0155] E.N. Some of the data, say, 70%, may be used training, and the remainder of the data may be used for validation data. If, after training, the classifier is able to correctly predict the configuration origin of at least a threshold amount of the validation data (e.g. area under the curve, or “AUC”=0.80), then the de-identified data 566 may be considered tainted and appropriate personnel may be notified.)
Carlson does not, but in related art, Aris discloses and the resistance to the triangulation attacks comprises (Paragraph [0044] E.N. The evaluation analyzes a generated dataset for linkages to publicly available or external datasets (e.g., voter registration lists, yellow pages, census data, etc.). When a linkage exists (e.g., when a triangulation attack with the external dataset is successful), this indicates that an identity of an individual of the generated (or masked) dataset may be determined, thereby identifying a privacy vulnerability with respect to the data masking process and corresponding set of configuration options used to generate the dataset. In addition, the generated dataset may be analyzed to determine the presence of quasi-identifiers introduced into the generated dataset based on the data masking process and corresponding set of configuration options. The presence of a quasi-identifier indicates a privacy vulnerability with respect to the data masking process and corresponding set of configuration options used to generate the dataset.)
Therefore, it would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to have modified Carlson to incorporate the teachings of Aris because Carlson does not explicitly disclose triangulation attacks which is taught by Aris. Incorporating the teachings of Aris to Carlson allows for the compartmentalization of sensitive information and prevent effective triangulation attacks from occurring.
Regarding Claim 19, Carlson in view of Aris discloses the method of claim 1. Carlson further discloses further comprising modifying, based on a result from the testing of the trained Al model, an aspect of at least one of: the one or more enumerated categories to be fictionalized; the fictionalization process used to fictionalize the enumerated objects; the training of the Al model; and the testing of the Al model. (Paragraph [0016] E.N. The method may further include training the machine learning model using the training portion of the plurality of de-identified data sets. The applying may include applying a remaining validation portion of the plurality of de-identified data sets as input across the trained machine learning model as validation of the training.)
Regarding Claim 20, Carlson in view of Aris disclose a programmable storage device having program instructions stored thereon for causing a programmable control device to perform a method of claim 1 used in a computerized system. Examiner Notation: Claim 20 is rejected under the same basis as Claim 1.
Claim(s) 7-12 are rejected under 35 U.S.C. 103 as being unpatentable over Carlson (US20210240853) in view of Gkoulalas-Divanis (US20190188416) herein after ‘Aris’ and in further view of Gkoulalas-Divanis (US20210286898) herein after ‘Paul’.
Regarding Claim 7, Carlson in view of Aris discloses the method of claim 1. Carlson and Aris do not, but in related art, Paul discloses wherein the step of fictionalizing comprises one or more of: perturbing the any given one or more of the enumerated objects as given enumerated objects in the fact vectors; truncating the given enumerated objects in the fact vectors; truncating a date of the given enumerated objects; adjusting the given enumerated objects by an increment; adjusting a numerical value of the given enumerated objects within a range of values; shifting a date of the given enumerated objects; and averaging the given enumerated objects in the fact vectors in at least a subset of the medical facts. (Paragraph [0060] E.N. The clustering algorithm may use a mean or median value of k-anonymous data, such as generalized or suppressed data. The clustering algorithm may use the entire combination of categories as the clustering value. The clustering algorithm may also use a weighted number based on the corresponding number of categories to determine Euclidean distances or densities for clustering.)
Therefore, it would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to have modified Carlson in view of Aris to incorporate the teachings of Paul because Carlson and Aris do not explicitly disclose averaging the given enumerated objects in the fact vectors in at least a subset of the medical facts which is disclosed by Paul. Incorporating the teachings of Paul to Carlson and Aris allows for the use of clustering to find similar data that may then be anonymized for better security of data.
Regarding Claim 8, Carlson in view of Aris discloses the method of claim 1. Carlson and Aris do not, but in related art, Paul discloses wherein the step of fictionalizing comprises: clustering the medical facts into clusters based on the fact vectors in two or more of the fact categories; and fictionalizing the any given one or more of the enumerated objects for the fact vectors in each of the clusters using a same form of the fictionalization process. (Figure 6 and Paragraph [0061] E.N. The clusters of datasets having anonymous records according to one or more implementations of the present invention is disclosed)
Therefore, it would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to have modified Carlson in view of Aris to incorporate the teachings of Paul because Carlson and Aris do not explicitly disclose clustering medical data based on fact vectors which is disclosed by Paul. Incorporating the teachings of Paul to Carlson and Aris allows for the use of clustering to find similar data that may then be anonymized for better security of data.
Regarding Claim 9, Carlson in view of Aris discloses the method of claim 1. Carlson and Aris do not, but in related art, Paul discloses wherein the step of fictionalizing comprises: identifying quasi-identifiers in the any given one or more of the enumerated objects; applying K-anonymity to the identified quasi-identifiers. (Paragraph [0044] E.N. It should be appreciated that anonymity of datasets may be a term of degree. That is, datasets may be more or less anonymous based on a k-anonymity threshold with respect to their quasi-identifying columns)
Therefore, it would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to have modified Carlson in view of Aris to incorporate the teachings of Paul because Carlson and Aris do not explicitly disclose identifying quasi-identifiers which is disclosed by Paul. Incorporating the teachings of Paul to Carlson and Aris allows for the use of clustering to find quasi-identifier data that may then be anonymized for better security of data.
Regarding Claim 10, Carlson in view of Aris discloses the method of claim 9. Carlson and Aris do not, but in related art, Paul discloses wherein the step of applying the K-anonymity to the identified quasi-identifiers comprises reducing distortion of the fact vectors for the any given one or more of the enumerated objects by minimizing a number of the quasi-identifiers identified. (Paragraph [0038] E.N. Data anonymization aims to create a counterpart of an original data that sufficiently protects the privacy and discernable inferences of the individuals who are represented in the data, while incurring minimal data distortion. Data distortion corresponds to changes made to the data values of the original dataset in order to accommodate for privacy protection. When data are anonymized without consideration of a particular workload that they will need to support, minimum data distortion corresponds to minimum overall information loss or, equivalently, maximum data utility. See [0044-0045])
Therefore, it would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to have modified Carlson in view of Aris to incorporate the teachings of Paul because Carlson and Aris do not explicitly disclose reducing distortion of fact vectors which is disclosed by Paul. Incorporating the teachings of Paul to Carlson and Aris allows for reducing distortion of data when it comes to anonymization of data.
Regarding Claim 11, Carlson in view of Aris discloses the method of claim 9. Carlson further discloses wherein the step of identifying the quasi-identifiers comprises preserving at least some of the fact vectors for the any given one or more of the enumerated objects by defining generalization hierarchies of the quasi- identifiers. (Paragraph [0045] E.N. As noted previously, in various implementations, techniques described herein may rely on a hierarchal taxonomy to classify individual data points of input data. These classifications may be used, e.g., by structured de-id modules, to select handlers, e.g., from a library of handlers provided by PHI transformer. The selected handlers may then be applied to (e.g., used to process) the input data to generate de-identified and/or de-duplicated data that is usable for various purposes, such as studies, research, etc.)
Regarding Claim 12, Carlson in view of Aris discloses the method of claim 1. Carlson and Aris do not, but in related art, Paul discloses wherein the step of fictionalizing comprises preserving a correlation between the fact vectors in the one or more enumerated categories by applying micro-aggregation to the fact vectors. (Paragraph [0043] E.N. The direct identifiers and the quasi-identifiers may be completely joinable, partially joinable, or unjoinable among datasets, to form a combined dataset. The filtration program may perform clustering on the combined dataset, whether joined, partially joined, or unjoined. In some practical applications datasets, may include the same or similar records, or the same quasi-identifiers.)
Therefore, it would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to have modified Carlson in view of Aris to incorporate the teachings of Paul because Carlson and Aris do not explicitly disclose preserving correlation and applying micro-aggregation which is disclosed by Paul. Incorporating the teachings of Paul to Carlson and Aris allows for the use of clustering to determine the similarity of data and obfuscate the sensitive information.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AAYUSH ARYAL whose telephone number is (571)272-2838. The examiner can normally be reached 8:00 a.m. - 5:30 p.m..
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/AAYUSH ARYAL/Examiner, Art Unit 2435
/AMIR MEHRMANESH/Supervisory Patent Examiner, Art Unit 2435
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