DETAILED ACTION
Claims 1-20 are pending and have been examined.
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 .
Requirement for Information
Applicant and the assignee of this application are required under 37 CFR 1.105 to provide the following information that the examiner has determined is reasonably necessary to the examination of this application. Articles published by inventors as it is unclear as to why the cited article was not provided to the Office given the relevance to the claimed subject matter. Additional articles published by inventors drawn to similar claimed subject matter.
Information Disclosure Statement
Applicants are reminded of the Duty to Disclose, from section 2001 of the MPEP (emphasis added). MPEP 2001 Duty of Disclosure, Candor, and Good Faith [R-08.2012] 37 C.F.R. 1.56 Duty to disclose information material to patentability.
(a) A patent by its very nature is affected with a public interest. The public interest is best served, and the most effective patent examination occurs when, at the time an application is being examined, the Office is aware of and evaluates the teachings of all information material to patentability. Each individual associated with the filing and prosecution of a patent application has a duty of candor and good faith in dealing with the Office, which includes a duty to disclose to the Office all information known to that individual to be material to patentability as defined in this section.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 11 recite the limitation "a plurality of patent identifiers" in the last limitation. There is insufficient antecedent basis for this limitation in the claim. Perhaps “patient” was intended.
If it is intended for claims 1 and 11 to recite “"a plurality of patient identifiers" that would introduce a lack of antecedent basis, indefiniteness, as there is already introduced "a plurality of patient identifiers" as part of the “receive…” step. There is insufficient antecedent basis for this limitation in the claim.
Claims 1 and 11 recite the limitation "a plurality of user data", to then refer to “the user data” in claims 2 and 12, and also “the plurality of user data” in claims 10 and 20, making it indefinite if intended to be the same or different “user data”. There is insufficient antecedent basis for this limitation in the claim.
This is not intended to be a complete list of such indefiniteness issues.
The dependent claims included in the statement of rejection but not specifically addressed in the body of the rejection have inherited the deficiencies of their parent claim and have not resolved the deficiencies. Therefore, they are rejected based on the same rationale as applied to their parent claims above.
Double Patenting
Claims 1-20 are provisionally rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims of Patent No. 12,530,498. Although the conflicting claims are not identical, they are not patentably distinct from each other because
“An apparatus, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a plurality of user data and a plurality of patient identifiers associated with the plurality of user data; generate contextual data associated with each patient identifier of the plurality of patient identifiers, wherein generating the contextual data associated with each patient identifier comprises identifying a set of composite terms within the plurality of user data; and identify, using a language processing model having a transformer architecture, one or more false positive patient identifiers within a plurality of patent identifiers as a function of the contextual data” (claim 1, instant application) is analogous to
“An apparatus for anonymizing user data, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a plurality of user data; identify a plurality of patient identifiers within the plurality of user data; generate contextual data associated with each patient identifier of the plurality of patient identifiers, wherein generating the contextual data associated with each patient identifier comprises identifying a set of composite terms within the plurality of user data, wherein at least one composite term of the set of composite terms is crafted by integrating one or more patient identifiers with one or more related terms, wherein the integration creates at least a phrase that provides additional context to the at least one composite term; generate anonymized data corresponding to the plurality of patient identifiers; and identify one or more false positive patient identifiers within the plurality of patient identifiers as a function of the contextual data” (claim 1, patent 12,530,498).
This is a provisional obviousness-type double patenting rejection because the conflicting claims of the instant application have not in fact been patented.
The claims of the conflicting patents and/or applications contain every element of claims 1-30 of the instant application and thus anticipate the claims of the instant application. Claims 1-30 of the instant application therefore are not patently distinct from the copending application claims and as such are unpatentable for obvious-type double patenting. A later patent/application claim is not patentably distinct from an earlier claim if the later claim is anticipated by the earlier claim.
“A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species with that genus). “ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
“Claim 12 and Claim 13 are generic to the species of invention covered by claim 3 of the patent. Thus, the generic invention is “anticipated” by the species of the patented invention. Cf., Titanium Metals Corp. v. Banner, 778 F.2d 775, 227 USPQ 773 (Fed. Cir. 1985) (holding that an earlier species disclosure in the prior art defeats any generic claim) 4. This court’s predecessor has held that, without a terminal disclaimer, the species claims preclude issuance of the generic claim. In re Van Ornum, 686 F.2d 937, 944, 214 USPQ 761, 767 (CCPA 1982); Schneller, 397 F.2d at 354. Accordingly, absent a terminal disclaimer, claims 12 and 13 were properly rejected under the doctrine of obviousness-type double patenting.” (In re Goodman (CA FC) 29 USPQ2d 2010 (12/3/1993).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Murugadoss (NPL “Building a Best-in-Class De-identification Tool for Electronic Medical Records Through Ensemble Learning”).
Regarding claims 1 and 11, Murugadoss teaches An apparatus, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: / A method, wherein the method comprises (abstract):
receive a plurality of user data and a plurality of patient identifiers associated with the plurality of user data (pages 7-9, data set for patients including PHI);
generate contextual data associated with each patient identifier of the plurality of patient identifiers, wherein generating the contextual data associated with each patient identifier comprises identifying a set of composite terms within the plurality of user data (pp.9-12, annotating data and determine PHI data, tokens, etc); and
identify, using a language processing model having a transformer architecture, one or more false positive patient identifiers within a plurality of patent identifiers as a function of the contextual data (pp.16-17, determine incorrectly identified PHI, false positives).
Regarding claims 2 and 12, Murugadoss teaches wherein the user data comprises patient data (pp.9-12, 16-17).
Regarding claims 3 and 13, Murugadoss teaches wherein at least one composite term of the set of composite terms is crafted by aggregating one or more patient identifiers with one or more related terms, wherein the aggregation creates at least a phrase that provides additional context to the at least one composite term (pp.3-7).
Regarding claims 4 and 14, Murugadoss teaches wherein the at least a processor is further configured to identify, using the language processing model, the one or more false positive patient identifiers by executing one or more attention mechanisms, wherein the one or more attention mechanisms is configured to: identify a plurality of relationships between words within the contextual data; and detect, as a function of the identified relationships, the one or more false positive patient identifiers within the plurality of patient identifiers (pp.3-7, 16-17).
Regarding claims 5 and 15, Murugadoss teaches wherein the one or more attention mechanisms comprise a plurality of layers of self-attention neural networks and feed-forward neural networks (pp.4-6).
Regarding claims 6 and 16, Murugadoss teaches wherein the at least a processor is further configured to identify, using the language processing model, the one or more false positive patient identifiers by: determining one or more indications within the contextual data, wherein the one or more indications are determined as a function of a comparison between extracted significant terms and a semantic relationship; and classify as a function of the one or more indications, each patient identifier as a true positive or a false positive (pp.9-12, 16-17).
Regarding claims 7 and 17, Murugadoss teaches wherein the one or more indications comprise one or more of a positive indication associated with the true positive and a negative indication associated with the false positive (pp.9-12, 16-17).
Regarding claims 8 and 18, Murugadoss teaches wherein the at least a processor is further configured to identify, using a deep learning model, the one or more false positive patient identifiers by: weighing an importance of sub-words within the contextual data; and detecting, as a function of a weighted importance of the sub-words, one or more associations between extracted significant terms; and identify the one or more false positive patient identifiers based on the one or more associations (pp.3-6).
Regarding claims 9 and 19, Murugadoss teaches wherein the memory further contains instructions configuring the at least a processor to generate anonymized data corresponding to the plurality of patient identifiers (pp.5-8).
Regarding claims 10 and 20, Murugadoss teaches wherein receiving the plurality of user data comprises receiving the plurality of user data from one or more electronic health records (EHRs) (pp.9-12, 16-17).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: the remaining references put forth on the PTO-892 form are directed to improving anonymization techniques and avoiding false positives.
Allen (20200175203) teaches A method for de-identifying protected health information (PHI) associated with electronic medical records (EMRs) based on a common analysis structure (CAS) is provided. The method may include detecting a system event associated with a system comprising the EMRs. The method may further include in response to detecting the system event, detecting a first CAS associated with the EMRs. The method may further include extracting first CAS data associated with the first CAS, wherein the first CAS data comprises unstructured data associated with the EMRs and normalized annotations based on CAS objects that are associated with the unstructured data. The method may further include obfuscating the unstructured data associated with the first CAS. The method may also include generating a second CAS comprising the obfuscated unstructured data and a copied version of the normalized annotations, wherein the copied version of normalized annotations are correlated with the obfuscated unstructured data.
Lo (12056258) teaches For anonymizing or other keyword identification medical patient data, a conditional random field sequence classifier is used for the NER model for NLP, providing a technical solution to help the computer perform better at identifying PHI from context and reduce manual anonym ization efforts of medical reports. One tool or executable integrates report format conversion, annotation, training, and application. These operations may be selected, or the tool configured for anonymization or keyword identification. Different files from each stage may be exported or used by others operating on other computers, allowing collaboration or sequential burden sharing for anonymization
Wright (20230351009) teaches A system and method for training an artificial intelligence engine for real-time monitoring to eliminate false positives is disclosed. The system includes at least one processor, a communication interface coupled to the processor, and a memory device storing executable code. Executing the executable code causes the processor to receive data from an AI security model, receive data from a false positive database, and correlate both sets of data. The correlated data is used to generate a training dataset and a test dataset used to train a false positive identification model. After evaluating the false positive identification model, an AI engine is applied to user registration. The AI engine includes an AI security model and the false positive identification model. Additionally, a system for evaluating the security of user registration utilizing the false positive identification model is disclosed
Any inquiry concerning this communication or earlier communications from the examiner should be directed to David García Cervetti whose telephone number is (571)272-5861. The examiner can normally be reached Monday-Friday 8AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HADI S ARMOUCHE can be reached at (571)270-3618. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/David Garcia Cervetti/Primary Examiner, Art Unit 2409
