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 .
Prosecution History Summary
Claim 17 is cancelled.
Claims 1, 6, 10-11, 14, and 20 are amended.
Claim 25 is new.
Claims 1-16 and 17-25 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 and 17-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process (claims 1-13, 21-24), machine (claims 14-16 and 18-19), and article of manufacture (claim 20). Accordingly, claims 1-16 and 18-25 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A computer-implemented method comprising:
-detecting, by one or more hardware processors associated with one or both of a healthcare cloud computing platform and an electronic database at the healthcare cloud computing platform, a triggering activity;
-in response to the detecting of the triggering activity via the one or more hardware processors: performing, by a record retrieval module and the one or more hardware processors, an operation selected from a group comprising at least (a) plugging into and loading, via a first application program interface (API), first medical data from a first remote database disparate from a healthcare cloud computing platform into the healthcare cloud computing platform and (b) plugging into and loading via a second API, second medical data from a second remote database disparate from the healthcare cloud computing platform into the healthcare cloud computing platform;
-reading the first medical data for detecting a presence of information associated with: (i) a medical concept, (ii) a patient associated with the triggering activity, the triggering activity corresponding to a registration event of the patient, a admission event of the patient, a transfer events of the patient, a discharge event of the patient, or a combination thereof, and (iii) a first set of multiple medical properties that relate to the medical concept and that include at least a first patient identifier;
-reading the second medical data for detecting a presence of information associated with: the medical concept, the patient, and a second set of multiple medical properties that relate to the medical concept and that include at least a second patient identifier;
-mapping by the one or more hardware processors, the first medical data and the second medical data to first normalized data based on the first set of multiple medical properties and to second normalized data based on the second set of multiple medical properties, respectively;
-matching the first patient identifier to the second patient identifier using the electronic database in the healthcare cloud computing platform;
-accessing ranking rules for the medical concept from the healthcare cloud computing platform; and
-in response to applying the ranking rules to the first normalized data and the second normalized data:
(a) determining, via the one or more hardware processors, that content, indicating the first medical data, is to be stored on the healthcare cloud computing platform; and
(b) storing, via the one or more hardware processors, the content, in a primary medical record for the patient, on the healthcare cloud computing platform.
Examiner states submits that the foregoing underlined limitations constitute: “certain methods of organizing human activity” because applying ranking rules to medical data that is normalized and storing in a patients record is same as following rules or instructions.
Furthermore, the foregoing underlined limitation constitute: a “mental process” because reading medical data from multiple sources and matching identifiers and applying ranking rules can all be performed in the human mind.
Accordingly, the claim recites at least one abstract idea.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether
the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP
§$2106.04(1D(A)(2), it must be determined whether any additional elements in the claim beyond
the abstract idea integrate the exception into a practical application in a manner that imposes a
meaningful limit on the judicial exception. The courts have indicated that additional elements
merely using a computer to implement an abstract idea, adding insignificant extra solution
activity, or generally linking use of a judicial exception to a particular technological environment
or field of use do not integrate a judicial exception into a “practical application.” MPEP
§2106.05(1(A).
In the present case, the additional limitations beyond the above-noted at least one abstract
idea recited in the claim are as follows (where the bolded portions are the “additional
limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A computer-implemented method comprising:
-detecting, by one or more hardware processors associated with one or both of a healthcare cloud computing platform (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 24, 26, 28) and an electronic database at the healthcare cloud computing platform (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 26-27), a triggering activity;
-in response to the detecting of the triggering activity via the one or more hardware processors: performing, by a record retrieval module and the one or more hardware processors (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 30-32), an operation selected from a group comprising at least (a) plugging into and loading, via a first application program interface (API) (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 34), first medical data from a first remote database disparate from a healthcare cloud computing platform into the healthcare cloud computing platform and (b) plugging into and loading via a second API (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 34), second medical data from a second remote database disparate from the healthcare cloud computing platform into the healthcare cloud computing platform (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 30-32);
-reading the first medical data for detecting a presence of information associated with: (i) a medical concept, (ii) a patient associated with the triggering activity, the triggering activity corresponding to a registration event of the patient, a admission event of the patient, a transfer events of the patient, a discharge event of the patient, or a combination thereof, and (iii) a first set of multiple medical properties that relate to the medical concept and that include at least a first patient identifier;
-reading the second medical data for detecting a presence of information associated with: the medical concept, the patient, and a second set of multiple medical properties that relate to the medical concept and that include at least a second patient identifier;
-mapping by the one or more hardware processors (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 30-32), the first medical data and the second medical data to first normalized data based on the first set of multiple medical properties and to second normalized data based on the second set of multiple medical properties, respectively;
-matching the first patient identifier to the second patient identifier using the electronic database in the healthcare cloud computing platform (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 26-27, 30);
-accessing ranking rules for the medical concept from the healthcare cloud computing platform (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 26-27, 30); and
-in response to applying the ranking rules to the first normalized data and the second normalized data:
(a) determining, via the one or more hardware processors (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 30-32), that content, indicating the first medical data, is to be stored on the healthcare cloud computing platform; and
(b) storing, via the one or more hardware processors (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 24, 26, 28), the content, in a primary medical record for the patient, on the healthcare cloud computing platform (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 26-27, 30).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID(A)(2).
For these reasons, representative independent claims 14 and 20 and analogous independent claim 1 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claims 14 and 20 and analogous independent claim 1 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the
abstract idea into a practical application as set forth below:
Claim 2, 15: The claim specifies the multiple medical properties, which further narrows the abstract idea.
Claim 3: The claim specifies matching the database identifier by the healthcare cloud computing platform, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 4, 16: The claim specifies determining if the database identifier is a trusted source (mental process) using a healthcare cloud computing platform, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 5, 16: The claim specifies determining if the first medical data should be stored in the primary medical record based on determining if it’s a trusted source, which further narrows the abstract idea.
Claim 6: The claim specifies performing parallel reads and transformation utilizing state transformation, which further narrows the abstract idea.
Claim 7, 18: The claim specifies transforming based on FHIR under HL7 standards, which further narrows the abstract idea.
Claim 8-9, 19: The claim specifies the remote database associations which does no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the use of at least one abstract idea is performed (see MPEP 2106.05(h)).
Claim 10: The claim specifies medical concepts, which further narrows the abstract idea.
Claim 11: The claim specifies medical properties, which further narrows the abstract idea.
Claim 12: The claim specifies access to data on the healthcare computing platform, which further narrows the abstract idea.
Claim 13: The claim specifies primary medical record from databases and data on healthcare computing platform, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 21: The claim specifies trigger that is non-periodic and performing loading of medical data, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 22: The claim specifies comparing medical properties to detect duplicates, which further narrows the abstract idea.
Claim 23: The claim specifies determining patient identity, which further narrows the abstract idea.
Claim 24: The claim specifies medical concept associations, which further narrows the abstract idea.
Claim 25: The claim specifies performing parallel reads of medical data from remote databases and performing transformations based on state data, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Thus, when the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims 1, 14, and 20 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as loading medical data from remote database disparate from healthcare cloud computing, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); reading first and second medical data from first and second databases, mapping medical data, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); storing the content, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 6, 12, 17, 21 and 25, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 12 (providing access to data), 21(loading medical data) e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 6, 25 (parallel reads and transformation of medical data), e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-16 and 18-25 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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 1-16 and 18-25 are rejected under 35 U.S.C. 103 as being unpatentable over Zolla et al. (U.S. Publication No. 2017/0091388) in view of Mynhier et al. (U.S. Publication No. 2016/0210427).
As per claim 1, Zolla teaches a computer implemented method comprising:
-detecting, by one or more hardware processors associated with one or both of a healthcare cloud computing platform and an electronic database at the healthcare cloud computing platform, a triggering activity (Zolla: para. 42; para. 55; Fetching data records based on a triggering condition.),
-in response to the detecting of the triggering activity via the one or more hardware processors: performing, by a record retrieval module and the one or more hardware processors, an operation selected from a group comprising at least (a) plugging into and loading, via a first application program interface (API) (Zolla: para. 9; an API configured to receive one or more inbound records from one or more data sources.), fist medical data from a first remote database disparate from a healthcare cloud computing platform into the healthcare cloud computing platform (Zolla: para. 21, 26, 28, 33; Healthcare cloud based system retrieves/pulls/receives healthcare data records from variety of data sources.) and (b) plugging into and loading, via a second API (Zolla: para. 9; an API configured to receive one or more inbound records from one or more data sources.), second medical data from a second remote database disparate from the healthcare cloud computing platform into the healthcare cloud computing platform (Zolla: para. 21, 26, 28, 33; Healthcare cloud based system retrieves/pulls/receives healthcare data records from variety of data sources. The data sources are disparate from the healthcare cloud based system.);
-reading the first medical data for detecting a presence of information associated with: (i) a medical concept (Zolla: para. 56; Identify data fields. Para. 26, 28-30; Inbound medical data is parsed/analyzed to determine type of data in each field. The data can include types such as medical offices, medical providers, demographics, patient identifiers, etc.), (ii) a patient associated with the triggering activity, the triggering activity corresponding to a registration event of the patient, an admission event of the patient, a transfer events of the patient, a discharge event of the patient, or a combination thereof (Zolla: para. 55; The server receives inbound records from a data source that is transmitting a batch nightly. The records could be any combination of records from the source, including registering, admission, transfer, or discharge. The records are broad to contain any of the activity listed.), and (iii) a first set of multiple medical properties that relate to the medical concept and that include at least a first patient identifier (Zolla: para. 26, 28-30; Inbound medical data is parsed/analyzed to determine type of data in each field. The data can include types such as medical offices, medical providers, demographics, patient identifiers, etc.);
-reading the second medical data for detecting a presence of information associated with: the medical concept, the patient, and a second set of multiple medical properties that relate to the medical concept and that include at least a second patient identifier (Zolla: para. 26, 28-30; Inbound medical data is parsed/analyzed to determine type of data in each field. The data can include types such as medical offices, medical providers, demographics, patient identifiers, etc.);
-mapping by the one or more hardware processors, the first medical data and the second medical data to first normalized data based on the first set of multiple medical properties and to second normalized data based on the second set of multiple medical properties, respectively (Zolla: para. 22, 26, 33; The inbound data from disparate data sources is converted into standard format.);
-matching the first patient identifier to the second patient identifier using the electronic database in the healthcare cloud computing platform (Zolla: para. 28, 34, 37; The medical data is compared and matched to determine if the data is related to the same patient via a patient identifier.).
Zolla does not explicitly teach the following, however, Mynhier teaches:
-accessing ranking rules for the medical concept from the healthcare cloud computing platform (Mynhier: para. 84; Assigning weights to different attributes and some attributes are given more weight than others.);
-in response to applying the ranking rules to the first normalized data and the second normalized data:
(a) determining, via the one or more hardware processors, that content, indicating the first medical data, is to be stored on the healthcare cloud computing platform (Mynhier: para. 85; Storing the cleansed structured standardized data into the database after the ranking rules are applied.);
-(b) storing, via the one or more hardware processors, the content in a primary medical record for the patient, on the healthcare cloud computing platform (Mynhier: para. 85; Storing the cleansed structured standardized data into the database after the ranking rules are applied.).
One of ordinary skill in the art would have recognized that applying the known technique of Mynhier would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mynhier to the teachings of Zolla would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features into similar systems. Further, applying ranking rules to medical concepts for matching patient records to Zolla teaching merging of patient records would have been recognized by those of ordinary skill in the art as resulting in an improved system that would facilitate reuse and avoid duplication or mis-representation of data (Mynhier: para 63).
As per claim 2, the computer-implemented method of claim 1 is as described. Zolla does not explicitly teach the following, however, Mynhier teaches wherein the first set of multiple medical properties comprises a database identifier of the first remote database (Mynhier: para. 71, 94, 117; Data source/third-party suppliers are assigned an identifier).
One of ordinary skill in the art would have recognized that applying the known technique of Mynhier would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mynhier to the teachings of Zolla would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features into similar systems. Further, applying identification and validation of third party sources to Zolla teaching merging of patient records would have been recognized by those of ordinary skill in the art as resulting in an improved system that would facilitate reuse and avoid duplication or mis-representation of data (Mynhier: para 63).
As per claim 3, the computer-implemented method of claim 2 is as described. Zolla does not explicitly teach the following, however, Mynhier teaches further comprising matching the database identifier to a known third party by the healthcare cloud computing platform (Mynhier: para. 71, 94, 117; Data source/third-party suppliers are assigned an identifier and are able to authenticate to the health data system for exchange of information.).
The motivation to combine the teachings is same as claim 2.
As per claim 4, the computer-implemented method of claim 3 is as described. Zolla does not explicitly teach the following, however, Mynhier teaches further comprising determining that the database identifier is a trusted source by the healthcare cloud computing platform (Mynhier: para. 71, 94, 117; Data source/third-party suppliers are assigned an identifier and are able to authenticate to the health data system for exchange of information.).
The motivation to combine the teachings is same as claim 2.
As per claim 5, the computer-implemented method of claim 4 is as described. Zolla does not explicitly teach the following, however, Mynhier teaches further comprising determining that the first medical data should be stored in the primary medical record on the healthcare cloud computing platform (Mynhier: para. 84; Applying the weights to the attributes and calculating a confidence score. Calculation of weight for attribute types determines the confidence score for the match. Once a matching is done, the record with higher weight is tagged.) based on the first remote database being determined as a trusted source of the healthcare cloud computing platform (Mynhier: para. 71, 94, 117; Data source/third-party suppliers are assigned an identifier and are able to authenticate to the health data system for exchange of information.).
The motivation to combine the teachings is same as claim 2.
As per claim 6, the computer-implemented method of claim 1 is as described. Zolla further teaches further comprising performing parallel reads and transformations based on a state machine of medical data from the first remote database and the second remote database utilizing state transformation (Zolla: para. 22, 26, 33; The medical data is parsed, analyzed, and converted.).
As per claim 7, the computer-implemented method of claim 6 is as described. Zolla does not explicitly teach the following, however, Mynhier teaches wherein the medical data is transformed based on Fast Healthcare Interoperability Resources (FHIR) under HL7 standards (Mynhier: para. 85, 117; The medical data is standardized using FHIR standards specifying HL7.).
One of ordinary skill in the art would have recognized that applying the known technique of Mynhier would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mynhier to the teachings of Zolla would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features into similar systems. Further, applying HL7 standards to transformed medical data to Zolla teaching merging of patient records would have been recognized by those of ordinary skill in the art as resulting in an improved system that would provide a healthcare management framework that delivers patient-centric information with mobility and fluidity (Mynhier: para. 10-11).
As per claim 8, the computer-implemented method of claim 1 is as described. Zolla further teaches wherein the first remote database is associated with one of a health information exchange, immunization registry, governmental healthcare registry, pharmaceutical registry, or third-party electronic medical record provider (Zolla: para. 35, 50-52).
As per claim 9, the computer-implemented method of claim 8 is as described. Zolla further teaches wherein the second remote database is associated with one of a health information exchange, immunization registry, governmental healthcare registry, pharmaceutical registry, and/or third-party electronic medical record provider and is different from the first remote database (Zolla: para. 35, 50-52).
As per claim 10, the computer-implemented method of claim 1 is as described. Zolla further teaches wherein the medical concept includes one or more of a healthcare organization, practitioner, encounters, problems, encounter diagnosis, allergies, medications, immunizations, procedures, general lab results, CCD, clinical notes, pathology documents, cardiology documents, radiology documents, and microbiology documents (Zolla: para. 29-30, 34-35, 52).
As per claim 11, the computer-implemented method of claim 10 is as described. Zolla further teaches wherein medical properties associated with the medical concept include name identifiers, medications, procedures, treatments, medical coding, text data, date of onset, date of documentation, condition type, and diagnosis coding (Zolla: para. 29, 35, 59-60).
As per claim 12, the computer-implemented method of claim 1 is as described. Zolla further teaches further comprising providing access to the first medical data to a user accessing the primary medical record on the healthcare cloud computing platform (Zolla: para. 60).
As per claim 13, the computer-implemented method of claim 1 is as described. Zolla further teaches wherein the primary medical record includes a longitudinal patient record having data from multiple third-party databases and medical record data on the healthcare cloud computing platform (Zolla: para. 60).
Claims 14-15 recite substantially similar limitations as those already addressed in claims 1-2, and, as such, are rejected for similar reasons as given above.
Claim 16 recite substantially similar limitations as those already addressed in claims 3-5, and, as such, are rejected for similar reasons as given above.
Claims 18 recite substantially similar limitations as those already addressed in claims 7, and, as such, are rejected for similar reasons as given above.
Claim 19 recite substantially similar limitations as those already addressed in claims 8-9, and, as such, are rejected for similar reasons as given above.
Claim 20 recite substantially similar limitations as those already addressed in claim 1, and, as such, are rejected for similar reasons as given above.
As per claim 21, the computer-implemented method of claim 1 is as described. Zolla further teaches further comprising: in response to a trigger that is non-periodic and associated with activity of the patient, performing at least one operation selected from a group comprising loading the first medical data from the first remote database and loading the second medical data from the second remote database (Zolla: para. 21, 26, 28, 33, 42; Healthcare cloud based system retrieves/pulls/receives healthcare data records from variety of data sources. Information can be fetched based on a triggering condition.).
As per claim 22, the computer-implemented method of claim 1 is as described. Zolla further teaches further comprising: prior to accessing the ranking rules and based on the medical concept, comparing the first set of multiple medical properties and the second set of multiple medical properties to detect duplicates among the first set of multiple medical properties and the second set of multiple medical properties (Zolla: para. 37).
As per claim 23, the computer-implemented method of claim 22 is as described. Zolla further teaches further comprising reading the first medical data and the second medical data to determine a patient identity prior to accessing the ranking rules (Zolla: para. 28, 34, 37; The medical data is compared and matched to determine if the data is related to the same patient via a patient identifier.).
As per claim 24, the computer-implemented method of claim 1 is as described. Zolla does not explicitly teach the following, however, Mynhier teaches wherein the medical concept is associated with a standards-based set of medical concepts, and wherein the first medical data and the second medical data are transformed to the standards-based set of medical concepts (Mynhier: para. 85, 117; The medical data is standardized using FHIR standards specifying HL7.).
One of ordinary skill in the art would have recognized that applying the known technique of Mynhier would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Mynhier to the teachings of Zolla would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features into similar systems. Further, applying HL7 standards to transformed medical data to Zolla teaching merging of patient records would have been recognized by those of ordinary skill in the art as resulting in an improved system that would provide a healthcare management framework that delivers patient-centric information with mobility and fluidity (Mynhier: para. 10-11).
Claim 25 recite substantially similar limitations as those already addressed in claim 6, and, as such, are rejected for similar reasons as given above.
Response to Arguments
Applicant’s amendments, filed 10/8/2025, with respect to 35 U.S.C. 112 have been fully considered and are persuasive. The 35 U.S.C. 112 rejection of claims 6, 10-11, and 17 has been withdrawn.
Applicant's arguments filed for 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that no human activity is recited or described in the claim as being organized. Examiner states that reading, mapping, and analyzing data can all be performed in the mind while using computer as a generic tool.
Applicant's arguments filed for claims 1-24 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant argues that Zolla does not teach “based on a triggering activity…”. Examiner disagrees and provides support and explanation in the rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rogers et al. – U.S. Patent No. 8,898,798 – Teaches a system for reintegrating medical information based on validation of sources.
Sundararaman et al. – U.S. Publication No. 2019/0384849 – Teaches a system for converting data files, standardizing data elements, and mapping data.
Friedlander et al. – U.S. Patent No. 8,495,069 – Teaches a system for determining a weighted score for attributes from multiple records and compare and merge the records.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEETAL R. PAULSON whose telephone number is (571)270-1368. The examiner can normally be reached M-F 8am-5pm.
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/SHEETAL R PAULSON/Primary Examiner, Art Unit 3686