Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/25/2024 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Form PTO-1449 is signed and attached hereto.
Drawings
The drawings filed on 10/25/2024 are accepted by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims of Patent # 12,182,311 contains every element of claims of the instant application. Claims of the instant application therefore are not patently distinct from the earlier patent claims and as such are unpatentable over obvious-type double patenting. A later patent claim is not patentably distinct from an earlier claim if the later claim is anticipated by the earlier claim. See the claim comparison below.
“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 within 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 Comparison
Instant Application # 18/927829
US Patent # 12,182311
1. An apparatus for generating a dictionary data filter for data deidentification, 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, wherein the plurality of user data comprises a plurality of localized terms;
identify a plurality of patient identifiers within the plurality of user data;
generate a dictionary data filter as a function of the plurality of localized terms; determine one or more misidentified patient identifiers within the plurality of patient identifiers using the dictionary data filter; and
modify the plurality of patient identifiers as a function of the one or more misidentified patient identifiers.
2. The apparatus of claim 1, wherein receiving the plurality of user data comprises: training a dictionary classifier using dictionary training data, wherein dictionary training data comprises examples of contextual data correlated to examples of localized terms; and identifying the plurality of localized terms within the plurality of user data as a function of the contextual data using the trained dictionary classifier.
3. The apparatus of claim 2, wherein training the dictionary classifier comprises: receiving user feedback; and generating an accuracy score of an output of the dictionary classifier as a function of the user feedback.
4. The apparatus of claim 2, wherein training the dictionary classifier comprises refining the dictionary training data by modifying correlations of user data of the dictionary training data as a function of user feedback.
5. The apparatus of claim 1, wherein identifying the plurality of patient identifiers comprises: training an identification machine-learning model using identification training data, wherein the identification training data comprises exemplary user data correlated to exemplary patient identifiers; and identifying the plurality of patient identifiers using the trained identification machine-learning model.
6. The apparatus of claim 1, wherein modifying the plurality of patient identifiers comprises replacing the plurality of localized terms with their corresponding semantic definitions.
7. The apparatus of claim 1, wherein the memory contains instructions further configuring the at least a processor to: structure the plurality of user data into structured user data as a function of the one or more modified patient identifiers using an indexing system; and store the structured user data in an index structure, wherein the indexing system is configured to iteratively modify the index structure in response to receiving additional user data.
8. The apparatus of claim 7, wherein structuring the plurality of user data comprises structuring the plurality of user data as a function of classification of the plurality of localized terms to dictionary categories.
9. The apparatus of claim 7, wherein the memory contains instructions further configuring the at least a processor to generate a user interface to display the index structure on a display device.
10. The apparatus of claim 1, wherein the memory contains instructions further configuring the at least a processor to generate anonymized data as a function of the plurality of patient identifiers, wherein generating the anonymized data comprises replacing a portion of the plurality of patient identifiers with generalized categories.
11. A method for generating a dictionary data filter for data deidentification, wherein the method comprises: receiving, using at least a processor, a plurality of user data, wherein the plurality of user data comprises a plurality of localized terms; identifying, using the at least a processor, a plurality of patient identifiers within the plurality of user data; generating, using the at least a processor, a dictionary data filter as a function of the plurality of localized terms; determining, using the at least a processor, one or more misidentified patient identifiers within the plurality of patient identifiers using the dictionary data filter; and modifying, using the at least a processor, the plurality of patient identifiers as a function of the one or more misidentified patient identifiers.
12. The method of claim 11, wherein receiving the plurality of user data comprises: training a dictionary classifier using dictionary training data, wherein dictionary training data comprises examples of contextual data correlated to examples of localized terms; and identifying the plurality of localized terms within the plurality of user data as a function of the contextual data using the trained dictionary classifier.
13. The method of claim 12, wherein training the dictionary classifier comprises: receiving user feedback; and generating an accuracy score of an output of the dictionary classifier as a function of the user feedback.
14. The method of claim 12, wherein training the dictionary classifier comprises refining the dictionary training data by modifying correlations of user data of the dictionary training data as a function of user feedback.
15. The method of claim 11, wherein identifying the plurality of patient identifiers comprises: training an identification machine-learning model using identification training data, wherein the identification training data comprises exemplary user data correlated to exemplary patient identifiers; and identifying the plurality of patient identifiers using the trained identification machine-learning model.
16. The method of claim 11, wherein modifying the plurality of patient identifiers comprises replacing the plurality of localized terms with their corresponding semantic definitions.
17. The method of claim 11, further comprising: structuring, using the at least a processor, the plurality of user data into structured user data as a function of the one or more modified patient identifiers using an indexing system; and storing, using the at least a processor, the structured user data in an index structure, wherein the indexing system is configured to iteratively modify the index structure in response to receiving additional user data.
18. The method of claim 17, wherein structuring the plurality of user data comprises structuring the plurality of user data as a function of classification of the plurality of localized terms to dictionary categories.
19. The method of claim 17, further comprising: generating, using the at least a processor, a user interface to display the index structure on a display device.
20. The method of claim 11, further comprising: generating, using the at least a processor, anonymized data as a function of the plurality of patient identifiers, wherein generating the anonymized data comprises replacing a portion of the plurality of patient identifiers with generalized categories.
1. An apparatus for generating a dictionary data filter for data deidentification, 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; generate contextual data as a function of the plurality of user data;
identify a plurality of patient identifiers within the plurality of user data;
identify a plurality of localized terms within the plurality of user data as a function of the contextual data;
generate a dictionary data filter as a function of the plurality of localized terms; identify one or more misidentified patient identifiers within the plurality of patient identifiers using the dictionary data filter; and modify the plurality of patient identifiers as a function of the one or more misidentified patient identifiers.
2. The apparatus of claim 1, wherein identifying the plurality of localized terms further comprises: generating a plurality of tokens associated with the plurality of user data; classifying each token of the plurality of tokens into at least one semantic category using a dictionary classifier; and identifying the plurality of localized terms as a function of the classification.
3. The apparatus of claim 1, wherein generating the contextual data further comprises identifying a plurality of related terms as a function of the plurality of user data.
4. The apparatus of claim 3, wherein identifying the plurality of localized terms further comprises identifying the plurality of localized terms as a function their proximity to at least one related term of the plurality of related terms.
5. The apparatus of claim 1, wherein the memory further instructs the processor to: structure the plurality of user data as a function of the modified patient identifiers using an indexing system; and store the structured user data in an index structure, wherein the indexing system is configured to iteratively modify the index structure in response to receiving additional user data.
6. The apparatus of claim 1, wherein receiving the plurality of user data comprises receiving the plurality of user data from an electronic health record (EHR).
7. The apparatus of claim 1, wherein memory further instructs the processor to generate anonymized data as a function of the plurality of patient identifiers.
8. The apparatus of claim 1, wherein the dictionary data filter comprises a lookup table.
9. The apparatus of claim 1, wherein identifying the plurality of localized terms comprises: training a dictionary classifier using dictionary training data, wherein dictionary training data comprises examples of contextual data correlated to examples of localized terms; and identifying the plurality of localized terms within the plurality of user data as a function of the contextual data using the trained dictionary classifier.
10. The apparatus of claim 9, wherein the dictionary classifier comprises a large language model.
11. A method for generating a dictionary data filter for data deidentification, wherein the method comprises: receiving, using at least a processor, a plurality of user data; generating, using the at least a processor, contextual data as a function of the plurality of user data; identifying, using the at least a processor, a plurality of patient identifiers within the plurality of user data; identifying, using the at least a processor, a plurality of localized terms within the plurality of user data as a function of the contextual data; generating, using the at least a processor, a dictionary data filter as a function of the plurality of localized terms; identifying, using the at least a processor, one or more misidentified patient identifiers within the plurality of patient identifiers using the dictionary data filter; and modifying, using the at least a processor, the plurality of patient identifiers as a function of the one or more misidentified patient identifiers.
12. The method of claim 11, wherein identifying the plurality of localized terms further comprises: generating a plurality of tokens associated with the plurality of user data; classifying each token of the plurality of tokens into at least one semantic category using a dictionary classifier; and identifying the plurality of localized terms as a function of the classification.
13. The method of claim 11, wherein generating the contextual data further comprises identifying a plurality of related terms as a function of the plurality of user data.
14. The method of claim 13, wherein identifying the plurality of localized terms further comprises identifying the plurality of localized terms as a function their proximity to at least one related term of the plurality of related terms.
15. The method of claim 11, wherein the method further comprises: structuring, using the at least a processor, the plurality of user data as a function of the modified patient identifiers using an indexing system; and storing, using the at least a processor, the structured user data in an index structure, wherein the indexing system is configured to iteratively modify the index structure in response to receiving additional user data.
16. The method of claim 11, wherein receiving the plurality of user data comprises receiving the plurality of user data from an electronic health record (EHR).
17. The method of claim 11, wherein the method further comprises generating, using the at least a processor, anonymized data as a function of the plurality of patient identifiers.
18. The method of claim 11, wherein the dictionary data filter comprises a lookup table.
19. The method of claim 11, wherein identifying the plurality of localized terms comprises: training a dictionary classifier using dictionary training data, wherein dictionary training data comprises examples of contextual data correlated to examples of localized terms; and identifying the plurality of localized terms within the plurality of user data as a function of the contextual data using the trained dictionary classifier.
20. The method of claim 19, wherein the dictionary classifier comprises a large language model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent No. 11,113,418, “A computer system for de-identifying protected health information (PHI) associated with electronic medical records (EMRs) based on a common analysis structure (CAS) is provided. The computer system may include one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, whereby the computer system is capable of performing a method. The method may include detecting a system event associated with a system comprising the electronic medical records (EMRs). The method may further include in response to detecting the system event, detecting a first common analysis structure (CAS) associated with the EMRs. The method may further include extracting first CAS data associated with the first CAS from one or more log files, wherein the first CAS data comprises unstructured data associated with the EMRs and comprises 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 based on the extracted first CAS data”.
US Publication No. 2023/0379659, “a system for localized information provision using wireless communication, includes a computing device designed and configured to receive, from a wireless signal generator located in a navigable space, a location identifier, input at least a user-entered datum associated with the location identifier, instantiate a display data structure as a function of the at least a user-entered datum, wherein, the display data structure includes a plurality of data signals including the at least a user-entered datum, each display signal of the plurality of display signals includes a subset of a plurality of categories of data, and the display data structure includes a display order for the plurality of data signals, record at least an element of contextual data, and generate a localized data record, wherein the localized data record include display data structure, location identifier, and an association of the contextual data with the display order”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MORSHED MEHEDI whose telephone number is (571) 270-7640. The examiner can normally be reached on M - F, 8:00 am to 4:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Linglan Edwards can be reach on (571) 270-5440. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MORSHED MEHEDI/Primary Examiner, Art Unit 2408