DETAILED ACTION
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.
This non-final action is in response to the communication received on 12/22/2025.
Claims 5, 7-9, 11, and 12 have been amended. Claims 21-26 newly added. Claims 3, 4, 10, and 13-20 have been canceled. Claims 1-2, 5-9, 11-12 and 21-26 are pending.
Continuation Acknowledgement
This application is a continuation application of U.S. application no. 15/087,438 filed on 03/31/2016, now abandoned (“Parent Application”). See MPEP §201.07. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicant(s) desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2.
Information Disclosure Statement
Information Disclosure Statement received on 8/1/2024 is being considered by the examiner.
Claim Objection
Claim 22 depends on claim 21 which is directed to a central data hub while claim 22 being directed to a computing system that comprises the central data hub of claim 21 along with a data analyzer. The applicant is advised to amend claim 22 as independent claim form to avoid any issues that may arise from claim interpretation(s).
Claims 23-26 recite “The computer system …” The claims are dependent on claim 21, wherein claim 21 recites “A central data hub” without recitation of “computer system”. The applicant is advised to amend the claims to recite “The central data hub …”
Restriction/Election Acknowledgement
The Applicant’s election on claims 1, 2, 5-9, 11, 12, and 21-26 without traverse in the reply on 12/22/2025 is acknowledged.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
central data hub configured to store …, store …, store …, process …, and generate … in claim 21;
central data hub further configured to receive …, generate …, and transmit … in claim 11;
the computer system is configured to receive, at the data analyzer computing device, one or more user input …, generate, at the central data hub, an extract based on the one or more user inputs …, transmit the extract from the central data hub to the data analyzer computing device …, and perform, at the data analyzer computing device, an analysis on the subset of longitudinal data profiles included in the extract in claim 22;
the data analyzer computing device is configured to perform the analysis … in claim 23;
the data analyzer computing device is further configured to compare … in claim 24;
the data analyzer computing device is further configured to analyze … in claim 25;
the data analyzer computing device is further configured to analyze … in claim 26.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 23-26 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.
Per claims 23-26, the claims attempt to recites particular functions of “the data analyzer computing device”. Claim 21, on which claims 23-26, however is void of any recitation regarding “data analyzer computing device” resulting in unclear scope as it is unclear whether the data analyzer computing device is part of the central data hub or if not, how the data analyzer computing device further limits the scope of the central data hub recited in claim 21.
In further reference to claim 23, the claim recites that the data analyzer computing device is configured to perform the analysis …” The scope of the claim is unclear as claim 21 which claim 23 depends does not particularly recite “analysis” but suggests some analysis, i.e., “process the first, second, and third de-identified data …” and “to generate a comprehensive profile that includes data from multiple disparate data sources”. It is unclear what is “the analysis” is referring to.
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, 2, 5-9, 11, 12, and 21-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does not fall within at least one of the four categories of patent eligible subject matter 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.
MPEP 2106 provides step(s) in determining eligibility under 35 U.S.C. § 101. Specifically, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any additional elements in the claim must integrate the judicial exception into a practical application. If not, the inquiry continues to see whether any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include mathematical concepts, mental processes, and certain methods of organizing human activities.
Under Step 1, claims 1, 2, and 5-9 are directed to a computer-implemented method (i.e. process) while claims 12 and 21-26 are directed to a central data hub (see above claim interpretation). Thus, the claimed inventions are directed towards one of the four statutory categories under 35 USC § 101. Nevertheless, the claims also fall within the judicial exception of an abstract idea without significantly more.
Step 2A, 1st prong:
Claim 1. A computer-implemented method for generating a longitudinal data profile from multiple disparate data sources, the method comprising:
storing, at a central data hub, first de-identified data received from a first data source, the first de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the anonymous ID is assigned based on a master list that includes a list of identifiers and corresponding anonymous IDs for each identifier;
storing, at the central data hub, second de-identified data received from a second data source, the second de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the anonymous ID is assigned based on the master list;
storing, at the central data hub, third de-identified data received from a third data source, the third de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the anonymous ID is assigned based on the master list;
processing, at the central data hub, the first, second, and third de-identified data to link the first, second, and third de-identified data using the anonymous ID; and
generating, at the central data hub, the longitudinal data profile from the linked first, second, and third de-identified data by organizing the first, second, and third de- identified data to generate a comprehensive profile that includes data from multiple disparate data sources.
(Bold emphasis added for additional element(s))
The claim recites a process that generates a longitudinal data profile from three data sources, wherein data are linked using an anonymous ID (arbitrary ID) to generate a comprehensive profile that includes data from the disparate data sources. The process achieves this by storing three de-identified data, each received from different data sources and each including plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, the anonymous ID assigned based on a master list. The three de-identified data is processed to link the three de-identified data using the anonymous ID. As such, the claim recites an abstract idea, i.e., mental process and/or mathematical relationships/calculations.
Even when considering the specification that discloses collecting and aggregation of the de-identification record from multiple sources such as patient information, i.e., see background and [0040] and [0041], the claim further recites a certain method of organizing human activities, i.e., following rules or instructions in managing personal behavior or managing relationship or interaction between people.
Claim 21 is significantly similar to claim 1. As such, claims 1 and 21 recite abstract idea.
Under the Step 2A (prong 2), this judicial exception is not integrated into a practical application. Specifically, the additional elements in the claim(s), i.e., central data hub and computer-implemented, are recited at a high-level generality such that it amounts to no more than mere instructions to implement the abstract idea on a computer or merely uses a computer. These limitations do not improve upon the computer, i.e., central data hub.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). Here, the claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the claims as a whole, taken individually and in combination, do not provide an inventive concept. As explained above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed judicial exception amount to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to implement the abstract idea on a computer, or merely using the computer as a tool to perform an abstract idea to apply the exception using a generic computer component cannot provide an inventive concept. Looking at the limitations as a combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of the elements improves the functioning of the recited central data hub individually or in combination.
For these reasons, the independent claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Dependent claims 2, 5-9, 11, 12, 21, and 22-26 further expands on the abstract idea of abstract idea, i.e., mental process/mathematical concepts/certain method of organizing human activity.
The additional element(s) of data analyzer computing device in claims 2, 7, 11, and 23-26 and computer system comprising the central data hub and the data analyzer computing device in claim 22 amount to no more than mere instructions to implement the abstract idea on a computer or merely uses a computer. Furthermore, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the claims as a whole, taken individually and in combination, do not provide an inventive concept.
For the reasons outlined above, the claims are directed to abstract idea without significantly more.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 9, 12, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 6,397,224 (“Zubeldia”) in view of US Patent Publication No. 20150149208 A1 (“Lynch”).
Per claims 1 and 21, Zubeldia discloses a computer-implemented method for generating a longitudinal data profile from multiple disparate data sources, the method comprising:
storing, at a central data hub, first de-identified data, the first de-identified data including a plurality of data records having identifying data and an anonymous ID assigned to each record, wherein the anonymous ID is assigned based on a master list that includes a list of identifiers and corresponding anonymous IDs for each identifier (see Fig. 2; col. 1, lines 20-30, to associate multiple records (from one or more data sources) related to a single individual. For example, the linking of related data records is crucial in performing certain research studies, such as horizontal and longitudinal studies … conducted on services provided over time to the same patient or population; col. 1, lines 39-43, records from different sources; col. 3, lines 1-22, assignment of anonymization code based on the stored information within an anonymization code database; col. 4, lines 9-15; col. 6, line 54 – col. 7, line 7, anonymization code database stores the anonymization code 66 assignments for the encoded identify reference 60 … conceptualized as “anonymous index” which may link each encoded identity reference 60 to an assigned anonymization code 66; col. 7, lines 39-49, anonymization code assignment module 72 is depicted as including an anonymization code generation module 74 and a database update module 76; col. 7, line 64 - col. 8, line 6, insertion module 80 inserts the assigned anonymization code 66 into anonymized data record 82; col. 8, line 7 – col. 10, 45, process of assigning anonymous code in the database reference; col. 11, lines 4-10, second record);
storing, at the central data hub, second de-identified data, the second de-identified data including a plurality of data records having identifying data and an anonymous ID assigned to each record, wherein the anonymous ID is assigned based on the master list (see Fig. 2; col. 1, lines 20-30, to associate multiple records (from one or more data sources) related to a single individual. For example, the linking of related data records is crucial in performing certain research studies, such as horizontal and longitudinal studies … conducted on services provided over time to the same patient or population; col. 1, lines 39-43, records from different sources; col. 3, lines 1-22, assignment of anonymization code based on the stored information within an anonymization code database; col. 4, lines 9-15; col. 6, line 54 – col. 7, line 7, anonymization code database stores the anonymization code 66 assignments for the encoded identify reference 60 … conceptualized as “anonymous index” which may link each encoded identity reference 60 to an assigned anonymization code 66; col. 7, lines 39-49, anonymization code assignment module 72 is depicted as including an anonymization code generation module 74 and a database update module 76; col. 7, line 64 - col. 8, line 6, insertion module 80 inserts the assigned anonymization code 66 into anonymized data record 82; col. 8, line 7 – col. 10, 45, process of assigning anonymous code in the database reference; col. 11, lines 4-10, second record);
storing, at the central data hub, third de-identified data, the third de-identified data including a plurality of data records having identifying data and an anonymous ID assigned to each record, wherein the anonymous ID is assigned based on the master list (see Fig. 2; col. 1, lines 20-30, to associate multiple records (from one or more data sources) related to a single individual. For example, the linking of related data records is crucial in performing certain research studies, such as horizontal and longitudinal studies … conducted on services provided over time to the same patient or population; col. 1, lines 39-43, records from different sources; col. 3, lines 1-22, assignment of anonymization code based on the stored information within an anonymization code database; col. 4, lines 9-15; col. 6, line 54 – col. 7, line 7, anonymization code database stores the anonymization code 66 assignments for the encoded identify reference 60 … conceptualized as “anonymous index” which may link each encoded identity reference 60 to an assigned anonymization code 66; col. 7, lines 39-49, anonymization code assignment module 72 is depicted as including an anonymization code generation module 74 and a database update module 76; col. 7, line 64 - col. 8, line 6, insertion module 80 inserts the assigned anonymization code 66 into anonymized data record 82; col. 8, lines 1-5, same individual includes the same assigned anonymization code 66, the anonymized records 84 are effectively linked; col. 8, line 7 – col. 10, 45, process of assigning anonymous code in the database reference; col. 11, lines 4-10, second record);
processing, at the central data hub, the first, second, and third de-identified data to link the first, second, and third de-identified data using the anonymous ID (see Fig. 2; col. 1, lines 20-30, to associate multiple records (from one or more data sources) related to a single individual. For example, the linking of related data records is crucial in performing certain research studies, such as horizontal and longitudinal studies … conducted on services provided over time to the same patient or population; col. 1, lines 39-43, records from different sources; col. 3, lines 1-22, assignment of anonymization code based on the stored information within an anonymization code database; col. 4, lines 9-15; col. 6, line 54 – col. 7, line 7, anonymization code database stores the anonymization code 66 assignments for the encoded identify reference 60 … conceptualized as “anonymous index” which may link each encoded identity reference 60 to an assigned anonymization code 66; col. 7, lines 39-49, anonymization code assignment module 72 is depicted as including an anonymization code generation module 74 and a database update module 76; col. 7, line 64 - col. 8, line 6, insertion module 80 inserts the assigned anonymization code 66 into anonymized data record 82; col. 8, lines 1-5, same individual includes the same assigned anonymization code 66, the anonymized records 84 are effectively linked; col. 8, line 7 – col. 10, 45, process of assigning anonymous code in the database reference; col. 11, lines 4-10, second record); and
generating, at the central data hub, the longitudinal data profile from the linked first, second, and third de-identified data by organizing the first, second, and third de- identified data to generate a comprehensive profile that includes data from multiple disparate data sources (see Fig. 2; col. 1, lines 20-30, to associate multiple records (from one or more data sources) related to a single individual. For example, the linking of related data records is crucial in performing certain research studies, such as horizontal and longitudinal studies … conducted on services provided over time to the same patient or population; col. 1, lines 39-43, records from different sources; col. 3, lines 1-22, assignment of anonymization code based on the stored information within an anonymization code database; col. 4, lines 9-15; col. 6, line 54 – col. 7, line 7, anonymization code database stores the anonymization code 66 assignments for the encoded identify reference 60 … conceptualized as “anonymous index” which may link each encoded identity reference 60 to an assigned anonymization code 66; col. 7, lines 39-49, anonymization code assignment module 72 is depicted as including an anonymization code generation module 74 and a database update module 76; col. 7, line 64 - col. 8, line 6, insertion module 80 inserts the assigned anonymization code 66 into anonymized data record 82; col. 8, lines 1-5, same individual includes the same assigned anonymization code 66, the anonymized records 84 are effectively linked; col. 8, line 7 – col. 10, 45, process of assigning anonymous code in the database reference; col. 11, lines 4-10, second record).
Zubeldia does not particularly teach that the identifying data in the plurality of data records of the first, second, and third de-identified data are encrypted. However, as Zubeldia teaches a technique of encoding sensitive data by encrypting (see col. 5, line 65-67, encoding technique such as encrypting the first subset 60A), it would have been obvious to one of ordinary skill in the art prior to the effective filing of the claim(s) to apply the technique of encrypting the identifying data in the plurality of data records of the first, second, and third de-identified data prior to storing the records in the output database 84 as the combination provides protection of the identifying data in the records stored in the output database.
While Zubeldia teaches linking of the records from multiple sources in col. 1, lines 20-30, Zubeldia does not particularly teach that the de-identified data is received from the multiple data sources. Lynch, an analogous art of patient information processing, discloses multiple sources, i.e., source system, providing the de-identified data to the data warehouse system (see Fig. 2; ¶0023-¶0026, multiple source system may be associated with various providers, such as hospitals, medical offices, pharmacies, etc., each performing encrypting and anonymized patient records, for the data warehouse system to store).
It would have been obvious to one of ordinary skill in the art before the effective filing of instant claim to combine the technique of each sources performing the task of anonymization as taught by Lynch as the offloading the anonymization that would otherwise be performed on the server to each clients offer improved scalability.
As per claims 9 and 12, Zubeldia/Lynch further teaches wherein at least one of the first, second, or third de-identified data is encrypting using a respective encryption algorithm before being assigned the anonymous ID (Lynch: ¶0037, data are encrypted and transmitted to the ETL supervisor; ¶0041).
Furthermore, regarding the timing aspect of encrypting of the de-identified data, there are finite timing aspect, i.e., before, concurrent, and after the assignment of the anonymous ID. Hence, as prior art teaches encrypting of the de-identified data, it would have been obvious to one of ordinary skill in the art before the effective filing of the claim(s) to try any one of these finite timing aspects, each of which had a reasonable expectation of success.
Claim(s) 2, 5-8, 11, and 22-26 are rejected under 35 U.S.C. 103 as being unpatentable over “Zubeldia” and “Lynch” as applied in claims 1 and 21, in further view of US Patent No. 9,378,271 (“Rassen”).
Per claims 2 and 11, while Zubeldia teaches using the aggregated records, i.e., patients’ records, in performing certain research studies (see col. 1, lines 20-25), Zubeldia/Lynch does not particularly teach receiving, at a data analyzer computing device communicatively coupled to the central data hub, one or more user inputs, wherein the one or more user inputs define a subset of longitudinal data profiles stored at the central data hub, and wherein the one or more user inputs identify an organizational scheme for the subset of longitudinal data profiles; generating, at the central data hub, an extract based on the one or more user inputs, wherein the extract includes the subset of longitudinal data profiles with each longitudinal data profile in the subset organized according to the organizational scheme; transmitting the extract from the central data hub to the data analyzer computing device; and performing, at the data analyzer computing device, an analysis on the subset of longitudinal data profiles included in the extract.
Rassen, an analogous art of medical records, teaches receiving, at a data analyzer computing device communicatively coupled to the central data hub, one or more user inputs, wherein the one or more user inputs define a subset of longitudinal data profiles stored at the central data hub, and wherein the one or more user inputs identify an organizational scheme for the subset of longitudinal data profiles; generating, at the central data hub, an extract based on the one or more user inputs, wherein the extract includes the subset of longitudinal data profiles with each longitudinal data profile in the subset organized according to the organizational scheme; transmitting the extract from the central data hub to the data analyzer computing device; and performing, at the data analyzer computing device, an analysis on the subset of longitudinal data profiles included in the extract (see col. 3, lines 11-67, query builder and analytic engine which uses one or more query definitions to perform various analysis on data in the database. The kinds of analyses that can be performed by an analytical engine 116 can be computation of measures, examples of which are defined below, from the data 114 for a cohort, and/or any well-known statistical methods applied to the longitudinal data retrieved from the database and/or measures derived from such longitudinal data. Such statistical methods can include those for assessing safety, effectiveness and/or value related to treatments, medical conditions, medicines, medical devices and the like. Such statistical methods can include statistical adjustment techniques that address the problem of confounders in the longitudinal data sets. Such statistical adjustment techniques utilize data obtained for different subsets of a cohort as specified by the query definitions; col. 4, lines 4-55, results 118 of analyses performed by an analytical engine 116 can be provided to a report generator 120 to generate one or more reports 112 using such results).
It would have been obvious to one of ordinary skill in the art before the effective filing of instant claim combine Rassen methods and system of querying, analysis, and building report to Zubeldia/Lynch for the purpose of addressing challenges in assembling data for inquiry, in maintaining and preserving data to provide for reproducibility of results over time, in providing a clear audit trail of how the inquiry was carried out and with what specific data and methodologies (see Rassen: col. 1, lines 40-50).
As per claims 5-6 and 23-24, Zubeldia/Lynch/Rassen further teaches wherein performing an analysis comprises comparing a first plurality of longitudinal data profiles associated with patients who are enrolled in a patient support program against a second plurality of longitudinal data profiles associated with patients who are not enrolled in the patient support program to determine an efficacy of the patient support program, wherein comparing the first plurality of longitudinal data profiles against the second plurality of longitudinal data profiles comprises comparing adherence data, i.e., whether the patient took particular drug, for the first and second plurality of longitudinal data profiles (Rassen: col. 1, lines 15-28, assessments compare a treatment to no treatment and comparative assessment compare a treatment to no treatment in order to assess the safety, effectiveness and/or value of medical treatments or interventions; col. 3, lines 44-67; col. 6, lines 45-56).
As per claims 7 and 25, Zubeldia/Lynch/Rassen further teaches analyzing, using a data analyzer computing device communicatively coupled to the central data hub, the generated longitudinal data profile for at least one of health economics outcomes research (Rassan: col. 4, lines 9-25, research).
Zubeldia/Lynch/Rassen does not particularly teach that the analysis is for marketing analytics business intelligence research. However, the intended use of the analysis does not move to distinguish over prior art.
Furthermore, the examiner takes Official Notice that using analysis of effectiveness of treatment such as drug for marketing analytics business intelligence research is old and well known in the art. As Zubeldia/Lynch/Rassen teaches analysis of the generated longitudinal data profile for determining effectiveness, it would have been obvious to one of ordinary skill in the art prior to the effective date of the claim(s) to use the analysis in any known application including for marketing analytics business intelligence research in order to effectively utilize the information in marketing a particular drug for the purpose of economic benefits.
As per claims 8 and 26, Zubeldia/Lynch/Rassen further teaches analyzing the generated longitudinal data profile comprises generating an output that identifies a cost differential between a patient who participates in a patient support program and a patient who does not participate in the patient support program (Rassen: col. 1, lines 15-28, assessments compare a treatment to no treatment and comparative assessment compare a treatment to no treatment in order to assess the safety, effectiveness and/or value of medical treatments or interventions; col. 3, lines 44-67; col. 6, lines 45-56; col. 9, lines 1-67, events may also have numeric attributes, such as the cost, i.e., cost for drugs … sum of cost).
As per claim 22, Zubeldia/Lynch teaches a computer system comprising: the central data hub of claim 21 as described above.
Zubeldia/Lynch does not teach that the computer system includes a data analyzer computing device communicatively coupled to the central data hub, wherein the computer system is configured to: receive, at the data analyzer computing device, one or more user inputs, wherein the one or more user inputs define a subset of longitudinal data profiles stored at the central data hub, and wherein the one or more user inputs identify an organizational scheme for the subset of longitudinal data profiles; generate, at the central data hub, an extract based on the one or more user inputs, wherein the extract includes the subset of longitudinal data profiles with each longitudinal data profile in the subset organized according to the organizational scheme; transmit the extract from the central data hub to the data analyzer computing device; and perform, at the data analyzer computing device, an analysis on the subset of longitudinal data profiles included in the extract.
Rassen, an analogous art of medical records, teaches a data analyzer computing device communicatively coupled to the central data hub (database) wherein the computing system is configured to: receive, at the data analyzer computing device, one or more user inputs, wherein the one or more user inputs define a subset of longitudinal data profiles stored at the central data hub, and wherein the one or more user inputs identify an organizational scheme for the subset of longitudinal data profiles; generate, at the central data hub, an extract based on the one or more user inputs, wherein the extract includes the subset of longitudinal data profiles with each longitudinal data profile in the subset organized according to the organizational scheme; transmit the extract from the central data hub to the data analyzer computing device; and perform, at the data analyzer computing device, an analysis on the subset of longitudinal data profiles included in the extract (see col. 3, lines 11-67, query builder and analytic engine which uses one or more query definitions to perform various analysis on data in the database. The kinds of analyses that can be performed by an analytical engine 116 can be computation of measures, examples of which are defined below, from the data 114 for a cohort, and/or any well-known statistical methods applied to the longitudinal data retrieved from the database and/or measures derived from such longitudinal data. Such statistical methods can include those for assessing safety, effectiveness and/or value related to treatments, medical conditions, medicines, medical devices and the like. Such statistical methods can include statistical adjustment techniques that address the problem of confounders in the longitudinal data sets. Such statistical adjustment techniques utilize data obtained for different subsets of a cohort as specified by the query definitions; col. 4, lines 4-55, results 118 of analyses performed by an analytical engine 116 can be provided to a report generator 120 to generate one or more reports 112 using such results).
It would have been obvious to one of ordinary skill in the art before the effective filing of instant claim combine Rassen methods and system of querying, analysis, and building report to Zubeldia/Lynch for the purpose of addressing challenges in assembling data for inquiry, in maintaining and preserving data to provide for reproducibility of results over time, in providing a clear audit trail of how the inquiry was carried out and with what specific data and methodologies (see Rassen: col. 1, lines 40-50).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20140358578 discloses pricing and utilization analysis of pharmaceutical items including metric utilization that include purchases, doses, days of therapy or treatment courses for pharmaceutical products, classes of pharmaceutical products or similarly grouped pharmaceutical products by analyzing actual purchase data for pharmaceutical products;
US 7921020 discloses a method for compiling, storing and organizing data, and gathering and reporting medical intelligence derived from patient-specific data. A patient's Minimum Data Set ("MDS") data generated by health care facilities are merged with that patient's pharmacy data to create a comprehensive clinical/pharmacological data set for each patient. The data may first be encrypted to ensure patient privacy before being transmitted by the facility to a data repository via an electronic communication network. Upon receipt at the data repository, the data first must pass through a security screen. If the data is determined to be valid and virus-free, it is decrypted as necessary before being added to a data warehouse for use in a wide variety of therapeutic, statistical, and economic analyses. The data may be partially or completely "de-identified" to remove patient-identifying information so as to protect patient privacy;
US 5557514 discloses a system and method for creating of a model of cost of a specific medical episode based on historical treatment patterns. Various treatment patterns for a particular diagnosis can be compared by treatment cost and patient outcome to determine the most cost-effective treatment approach.
US 10340037 discloses a system and method of aggregating a patient’s disparate medical data from multiple sources. The process aggregates medical data from a plurality of source providers. In the method, medical data is received from a source provider. This medical data may be associated with patient-identification information. In order to aggregate this data with other data about the same patient, this data is then reconciled with existing patient medical data records in a medical records database to identify a master patient to whom the received medical data relates. The medical records database includes a plurality of different types of patient medical data records, and each patient medical data record is associated with a corresponding time and a corresponding source.
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/STEVEN S KIM/Primary Examiner, Art Unit 3698