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 .
Notice to Applicant
Receipt of Applicant’s Amendment filed November 19, 2025 is acknowledged.
Response to Amendment
Claims 1-10, 12-17, and 19 have been amended. Claims 11, 18, and 20 have not been modified. Claims 1-20 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments filed November 19, 2025 have been fully considered but they are not persuasive. A response is provided below.
Applicant argues Claim Objections, pg. 10 of Remarks:
Examiner acknowledges Applicant amendment and withdraws the claim objections.
Applicant argues 35 U.S.C. §112 Rejections, pg. 10 of Remarks:
Examiner acknowledges Applicant amendment and withdraws the §112 rejection.
Applicant argues 35 U.S.C. §101 Rejections, pg. 10 of Remarks:
Regarding Applicant argument that the claims cannot be performed in a human mind, the Examiner agrees and notes that the claims were not characterized under mental processes. Instead the claimed invention is directed towards an abstract idea of mathematical concepts as well as certain methods of organizing human activity; see pgs. 8-9 of the prior Non-Final Rejection. Thus, Applicant argument is moot.
Regarding Applicant assertion that the Examiner overlooks concrete improvements in computer performance, Examiner notes that such improvements are analyzed under Prong 2 of Step 2A, not Prong 1. However, even under Prong 2 analysis, the claims, as a whole, are directed towards the generation of an improved whole health index rather than improvements to the underlying technology ([0005] of Applicant specification, “there is a need for tools, systems and methods for calculating whole health index.”). Furthermore, efficiency is not enough to amount to a practical application via an improvement to computer or technology under Step 2A Prong 2 (see MPEP § 2106.05(a)(I) examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: ii. accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)) (also see MPEP § 2106.05(f)(2) stating “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not provide an inventive concept (Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367 (Fed. Cir. 2015)”), and, thus, the combination of the generic computer components do not provide a non-conventional and non-generic arrangement of known, conventional pieces; note this is applied to Step 2B as well as Step 2A Prong 2).
Regarding Applicant argument that the broad characterization of the claimed subject matter is improper and unfounded, Examiner respectfully disagrees. As an example, Applicant notes that “extracting, transforming, and loading health data from disparate sources” is a technically complex operation which is more than a trivial computation. However, Applicant specification notes that the ETL functions are performed using existing scripts, such as Glue job, which is an Amazon Web Services ETL product that is not the invention of the Applicant ([0092], “The data from the one or more tables is extracted, transformed and loaded (ETL) into tables 1212 and 1214 in the staging module 1204, using one or more ETL scripts (e.g., using ExecuteSql Glue job (ANTM-EDL- prod-Gj-ExecuteSql)).”). As another example, Applicant notes that “normalizing the data for a population” entails algorithmic processing and “weight generation based on a random sample” is a specific statistical technique. Examiner agrees with Applicant. However, these data normalization and weight generation on samples are statistical processes, which could otherwise be performed by a statistician using generic computing tools. No improvements are being made to statistical analysis or technological implementation of statistical analysis by inclusion of known statistical techniques.
Regarding Applicant argument that the calculation is applied in a concrete technological environment and provides a tangible output, Examiner respectfully disagrees. Each of the additional elements that comprise the technological environment, although concrete, are generic and do not represent a non-obvious arrangement that provides a specific, technical improvement (processors, memory, a data cloud, and database management system). Additionally, the output of the whole health index itself is considered an abstract output, as a whole health index is an health assessment of a patient or patient population. Health assessments of patients or populations is a human activity typically performed by biostatisticians and doctors (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). Thus, the improvements are directed towards “how health data is processed and used”, which is a business method and not a technical field.
Regarding Step 2B, Applicant argues that the Office Action provides no evidence to suggest that it is conventional to automatically gather metrics, normalize them, apply a weighted index computation, and dynamically select and weigh indicators. However, the consideration under Step 2B is if the additional elements, alone or in combination, (processors, memory, data cloud, database management system) are well-understood, routine and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount to instruction to implement the abstract idea of generating a whole health index using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Applicant argues 35 U.S.C. §103 Rejections, pg. 9 of Remarks:
Applicant argues that Derrick, Salsbury, and Bruckhaus do not teach or suggest all of the features of claims 1 and 17. The Examiner respectfully disagrees.
Regarding Applicant assertion that Derrick is diabetes specific and does not teach a whole health index, Examiner respectfully disagrees. [0005] of Applicant specification recites: “calculate a whole health index (WHI) as a composite score that measures an individual's health by incorporating geographic level health factors with individual health factors such as social needs, clinical quality measures and diagnoses.”
Similarly, [0270] of Derrick recites: “Additional adjustments to the coefficient or plurality of coefficients, where their use is as the primary indicator, may be made for age, gender, and comorbidities. Further adjustments may be made for State-level characteristics such as population level measures of income, and education, and the supply of healthcare resources. Adjustments also may be made for measures of socioeconomic status at the patient level, and for geographic variations.” [0209] of Derrick further recites: “a plurality of indices or scores associated with a plurality of Indicators Of Health may represent a quantitative contribution to an overall Indicator Of Health index or score (“Indicator Index”).” As an overall health index (whole health index) is calculated with factors such as socioeconomic status (suggestive of social needs), supply of healthcare resources (clinical quality measures), and comorbidities (diagnoses) ([0005] of Applicant specification, “Some embodiments calculate a whole health index (WHI) as a composite score that measures an individual's health by incorporating geographic level health factors with individual health factors such as social needs, clinical quality measures and diagnoses”) Examiner maintains that Derrick teaches a whole health index.
Regarding Salsbury and Bruckhaus, Applicant notes that both do not teach or suggest utilization of data at different levels and populations. However, Derrick, not Salsbury or Bruckhaus, was applied to teach that subject matter. Applicant further argues that Bruckhaus only teaches using fixed weights instead of random-sampling weight generation. However, Col. 55, lines 62-66, of Bruckhaus teach that weights/model parameters can be randomly generated using randomly selected subsets (random sampling) (“One simple technique is to generate models randomly, based on random selections among the algorithms of the appropriate type, random settings of their parameters, and randomly selected subsets of the available inputs as inputs to the algorithm.”). Examiner notes this is still a moot point, as Derrick, not Bruckhaus, was applied to teach the limitation argued.
Regarding reliability vs. timeliness, Examiner notes that the recited section of Salsbury recites wherein reliability is defined as data that is consistently collected. Under the broadest reasonable interpretation, consistently collecting data refers to an appropriate timeliness in the collection of said data. If data is not collected on a routine, timely manner, then it cannot be reliable ([0043], “Reliability--is based on consistently collected, compiled and calculated data”). Thus, Examiner maintains the rejection.
Regarding the feature of removing indicators, Examiner respectfully disagrees. Examiner maintains that the combination of Derrick in view of Salsbury teaches the claim limitation. As cited in the previous Office Action, Derrick, [0232], “Questionable variables may be removed from the model specification, and predictive power and robustness may be reassessed. In cases where the impact of removal may be marginal, the offending variables may be permanently removed.” Salsbury, [0041], “The Indicator selection was based on criteria of” [0042], “ Availability--is available, accessible and affordable”.
It would be obvious to one of ordinary skill in the art that if the data for an indicator is not fully available, and fails the availability criteria, it would be removed from consideration.
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 1-20 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 method and a machine (claims 1-20). Accordingly, claims 1-20 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).
The Examiner has identified system claim 1 as the claims that represent the claimed invention for analysis, and are similar to method claim 17 and product claim 20.
Claim 1:
A system for calculating a whole health index, the system comprising:
one or more processors and memory storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to:
interface with a plurality of disparate data sources, via a data cloud, wherein the plurality of disparate data sources includes (i) at least one data source for managing health plan enrollments and claims data, (ii) at least one data source for storing clinical data, and (iii) at least one data source storing public health data;
monitor the plurality of data sources for updates to health data and/or at predetermined time intervals to identify health receipt time periods;
receive the health data from the plurality of data sources, according to the health receipt time periods, via a database management system, including extracting, transforming and loading the health data into one or more tables, wherein the health data includes (i) health plan enrollments and claims data, (ii) clinical data, and (iii) public health data, for a population;
obtain the health data from the one or more tables;
normalize the health data from the plurality of data sources including reformatting and/or range filling comorbidity scores, age ranges median household income ranges, walkability data ranges, and affordability ranges;
select a plurality of domains and a plurality of indicators based on (i) significance to health, (ii) validity of the indicators, (iii) availability of the indicators at large scales, (iv) applicability of indicators to the population, and (v) timeliness of the indicators; and
select a subset of indicators for the plurality of domains including removing indicators that (i) amount to incomplete data capture using the plurality of disparate data sources or (ii) applicable only to a subset of the population;
generate weights for each of the plurality of domains and the subset of indicators based on a random sample of the health data; and
calculate a weighted sum of the health data based on the weights to obtain the whole health index for the population.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as mathematical processes. The claim elements are directed towards “generat[ing] weights for each of the plurality of domains and the subset of indicators based on a random sample of the health data” and “calculat[ing] a weighted sum of the health data to obtain a whole health index for the population”, which describe statistical analysis of the obtained health data. Further support is found in [0050] of Applicant specification, which recites: “In some embodiments, the three domains of the whole health index are weighted as follows: social drivers, 50%; global health, 30%; clinical quality, 20% as informed by the National Academy of Medicine's Vital Signs framework. In some embodiments, the whole health index score is calculated as the weighted sum of the three-domain score.”
These claims further recite: certain methods of organizing human activity as managing personal behaviors. The claim elements are directed towards “monitor[ing] the plurality of data sources for updates”, “normaliz[ing] the health data from the plurality of data sources including reformatting and/or range fitting”, “select[ing] a plurality of domains and plurality of indicators”, and “removing indicators”, which constitute a human activity of data entry, regularly performed by data entry specialists. It is important to note that the examples provided by the MPEP such as social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing and that 2106.04(a)(2)II states certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping.
Accordingly, the claim recites at least one abstract idea.
Claims 17 and 20 are abstract for similar reasons.
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 idea into a practical application. As noted at MPEP §2106.04 (ID)(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 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(I)(A).
In the present case, the additional elements beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional elements” while the underlined portions continue to represent the at least one “abstract idea”):
Additional elements cited in the Claims:
processors (1-10,13-17,20); memory (1,17,20); a data cloud (1,17,20); a database management system (1); a computer system (17,20); displays (17,20); a non-transitory computer-readable storage medium (20)
The independent claims teach insignificant extra-solution activities of receiving/obtaining data and selecting a type or source of data for manipulation. See MPEP 2106.05(g).
Any computing devices and their associated components (computer system, processors) that would be able to perform the method are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0024] of Applicant specification recites: “a computer system has one or more processors, memory, and a display. The one or more programs include instructions for performing any of the methods described herein.” No specific, technical improvements are being made to computing devices as any generic computing device is applied to perform the abstract idea.
The storage devices (memory, non-transitory computer readable medium) are also taught at a high level of generality. [0044] of Applicant specification recites: “In some embodiments, memory 202 stores a subset of the modules identified above.” No specific, technical improvements are being made to storage devices as they are simply used to perform the insignificant extra-solution activity of storing data.
The display is also taught at a high level of generality. [0042] of Applicant specification recites: “the information security risk manager 102 also includes a display 244 for displaying visualizations (e.g., risk scores, probabilities). In some embodiments, the whole health index calculation server 102 generates displays or visualizations, and transmits the visualization (e.g., as a visual specification) to a client device for display.” No specific, technical improvements are being made to display devices as they are simply used to perform the insignificant extra-solution activity of outputting data.
The data cloud and database management system are also taught at a high level of generality. [0092] of Applicant specification recites: “The system includes a cloud data warehouse 1202 (e.g., an elastically scalable cloud data warehouse, such as Snowflake) coupled to a staging module 1204. The cloud data warehouse 1202 loads data from one or more data warehouses (sometimes referred to as data sources) (e.g., optionally, via a relational database management system 1210, such as Teradata).” No specific, technical improvements are being made to data clouds or database management systems as commercially available products are applied to the claimed invention.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements 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 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 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).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 2 and 18: These claims recite wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors: calculate the whole health index under a plurality of weighting schemes to determine weighting schemes across the plurality of domains and subdomains; and select a final weighting scheme based on an option that yields validation results in accordance with a predetermined criterion; which teaches an abstract idea of mathematical processes, as calculating weights for a model. This claim further teaches an insignificant extra-solution activity of selecting a data type or source for manipulation.
Claims 3 and 19: These claims recite wherein one or more programs, when executed by the one or more processors, also cause the one or more processors to: select the final weighting scheme by examining the predetermined criterion validity of the whole health index by analyzing Spearman correlations between average whole health index at a county level and public health indicators including length of life and quality of life; which teaches an abstract idea of mathematical processes, such as performing Spearman correlations. This claim further teaches an insignificant extra-solution activity of selecting a data source or type of manipulation.
Claim 4: This claim recites wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors to select the final weighting scheme by (i) assessing if the whole health index reflects known differences in health across different populations, based on age groups, sex, race/ethnicities, rural/urban status, and/or insurance types, and (ii) selecting the final weighting scheme by determining if a scheme yields a predetermined level of performance in terms of criterion validity and discriminant validity; which teaches an abstract idea of mathematical processes, as statistical analysis via criterion and discriminant validity. This claim further teaches an insignificant extra-solution activity of selecting a data source or type of manipulation.
Claim 5: This claim recites wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors to: in accordance with a determination that individuals in the population have missing scores in global health or clinical quality domains, impute scores based on median domain score of other individuals in a same age band and sex living in a same state, respectively; which teaches an abstract idea of mathematical processes, as imputing scores based on median data. This claim further teaches an insignificant extra-solution activity of selecting a data source or type of manipulation.
Claim 6: This claim recites wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors to: in accordance with a determination that individuals in the population have missing social driver scores, impute social driver score based on median value among other individuals with same insurance types and living in a same state; which teaches an abstract idea of mathematical processes, as imputing scores based on median data. This claim further teaches an insignificant extra-solution activity of selecting a data source or type of manipulation.
Claim 7: This claim recites wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors to: validate the whole health index on a predetermined portion of the health data, including analyzing Spearman correlation between average whole health index at county level and predetermined health indicators at county-level, based on health indicators comprising length of life and quality of life; which teaches an abstract idea of mathematical processes, as performing Spearman correlation. This claim further teaches an insignificant extra-solution activity of selecting a data source or type of manipulation.
Claim 8: This claim recites wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors to: assess validity of whole health index, including estimating construct validity of composite of the whole health index, computing correlations between three domains, conditioning these correlations on number of conditions present; which teaches an abstract idea of mathematical processes, as statistical analysis by assessing validity.
Claim 9: This claim recites wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors to: assessing discriminant validity by determining if the whole health index reflects an expected impact of clinical conditions, including assessing if individuals with multiple conditions have lower whole health index on average compared to individuals without multiple conditions, and if individuals with more severe health conditions have lower whole health index compared to those with less severe health conditions; which teaches an abstract idea of mathematical processes, as statistical analysis via discriminant validity. This claim further teaches an insignificant extra-solution activity of selecting a data source or type of manipulation.
Claim 10: This claim recites wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors to: evaluate reliability of the whole health index at varying levels of geography by assessing stability of the whole health index; which teaches an abstract idea of mathematical processes, as statistical analysis by evaluating reliability and assessing stability of the score. Please see [0060] of Applicant specification, which describes the statistical process of assessing stability.
Claim 11: This claim recites wherein evaluating reliability comprises: computing split-half reliability of the whole health index at county and 5-digit ZIP code levels by: splitting individuals within a geographical level into two groups using random sampling; computing area-level whole health index scores in both samples; and computing Pearson, Spearman, and intra-class correlations for the whole health index two samples; which teaches an abstract idea of mathematical processes, as computing split-half reliabillity for statistical analysis.
Claim 12: This claim recites wherein evaluating reliability comprises: assessing precision of whole health index scores across various levels of geography, including computing within geographic unit variance to between geographic unit variance (WGVBGV) of the whole health index scores at census tract, 5-digit ZIP Code, and county level, wherein WGVBGV is a ratio of signal variance to a sum of signal and noise variances, wherein the WGVBGV statistic, ranging from 0 to 1, summarizes proportion of total variation in the whole health index scores at an area level due to differences between areas in relation to individual-level variation within each area, wherein if WGVBGV is equal to 1, variation in whole health index scores is due to differences in quality observed at a geographic level, and wherein if WGVBGV is equal to zero, whole health index scores are not driven by differences in health but rather due to random variation and will therefore not be useful to compare health across areas; which teaches an abstract idea of mathematical processes, as computing WGVBGV for statistical analysis.
Claim 13: This claim recites wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors to: subdivide the health data corresponding to social drivers domain into data for six subdomains including (1) financial strain, (2) healthcare affordability, (3) food insecurity, (4) transportation barriers, (5) housing insecurity, and (6) minority status and language; generate weights for each of the subdomains, including: calculating subdomain scores by combining individual and area- level data with equal weights; and in accordance with a determination that individuals did not have individual-level social driver data, using area-level data for the subdomain scores; and calculate the weighted sum for a social drivers domain by summing percentiles of each subdomain multiplied by a weighting factor; which teaches an abstract idea of mathematical processes, as calculating weights and scores to obtain a weighted sum.
Claim 14: This claim recites wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors to: subdivide the health data corresponding to clinical quality domain into data for six subdomains for (1) access to care, prevention, and screening, (2) acute care and care coordination, (3) overuse, appropriateness, and safety, (4) cardiovascular conditions, diabetes, oncology, and respiratory conditions, (5) behavioral health, and (6) women's health; generate weights for each of the subdomains, including: assigning higher weights to subdomains with more measures and more direct impact on wellbeing than other subdomains; identifying measures within each subdomain as either a process or an outcome measures, and using a 1:3 process-to-outcome ratio to weight outcome measures more heavily; calculating subdomain scores by combining individual and area- level data with equal weights; and scoring individuals only for measures they are qualified for; and calculate the weighted sum for a clinical quality domain by summing percentiles of each subdomain multiplied by a weighting factor; which teaches an abstract idea of mathematical processes, as using 1:3 ratios for weighting factors and calculating weighted sums.
Claim 15: This claim recites wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors to: provide each domain score to a plurality of computing resources corresponding to care teams to identify potential needs beyond their clinical program offering, and to provide additional care solutions, such as meal delivery services, transportation support, or hearing aid consultation, to improve whole health for the population; which teaches an abstract idea of certain methods of organizing human activity, as managing the personal behaviors of clinical program staff.
Claim 16: This claim recites wherein the one or more programs, when executed by the one or more processors, also cause the one or more processors to: use the whole health index to direct members of the population to appropriate solutions for their specific health and social needs; which teaches an abstract idea of certain methods of organizing human activity, as managing the personal behaviors of members of the population through recommendations.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims 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.
These 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 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 activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
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 additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-16 and 18-19, additional limitations which amount to elements that have been recognized as activities in particular fields, claims 2-16 and 18-19, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2-16 and 18-19, 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 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-3, 7-8, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Derrick (US 20210319887) in view of Salsbury (US 20100082362) further in view of Bruckhaus (US 8417715).
Regarding claim 1, Derrick teaches a system for calculating whole health index, the system comprising:
one or more processors and memory storing one or more programs, which, when executed by the one or more processors, cause the one or more processors ([0413], “The software 724 may also reside, completely or at least partially, within the main memory 704 and/or within the processor 702 during execution of the software by the computer system 700, the main memory 704 and the processor 702 also constituting computer-readable media.”) to:
interface with a plurality of disparate data sources, via a data cloud ([0235], “Structured data may include Social Determinants Of Health population information collected from government, commercial, and other studies, and other health-related data (such as administrative, health insurance claims, clinical, population health data), as well as consumer, socioeconomic, sociocultural, and similar data evidencing patients' and consumers' patterns of life. Data collected as unstructured information may include books, journals, documents, metadata, health records,…” [0411], “The machine may be a server computer, …, a cloud computer, a tablet PC,…”),
wherein the plurality of disparate data sources includes (i) at least one data source for managing health plan enrollments and claims data ([0235], “Structured data may include Social Determinants Of Health population information collected from government, commercial, and other studies, and other health-related data (such as administrative, health insurance claims, clinical, population health data),…”),
(ii) at least one data source for storing clinical data ([0235], “Structured data may include Social Determinants Of Health population information collected from government, commercial, and other studies, and other health-related data (such as administrative, health insurance claims, clinical, population health data), as well as consumer, socioeconomic, sociocultural, and similar data evidencing patients' and consumers' patterns of life. Data collected as unstructured information may include books, journals, documents, metadata, health records,…”), and
(iii) at least one data source storing public health data ([0235], “Structured data may include Social Determinants Of Health population information collected from government, commercial, and other studies, and other health-related data (such as administrative, health insurance claims, clinical, population health data), as well as consumer, socioeconomic, sociocultural, and similar data evidencing patients' and consumers' patterns of life. Data collected as unstructured information may include books, journals, documents, metadata, health records,…”);
monitor the plurality of data sources for updates to health data and/or at predetermined time intervals to identify health receipt time periods ([0285], “The adherence index or score may be continuously or periodically updated including the new information being gathered.”); and
receive the health data from the plurality of data sources, according to the health receipt time periods, via a database management system, including extracting, transforming and loading the health data, wherein the health data includes (i) health plan enrollments and claims data, (ii) clinical data, and (iii) public health data, for a population ([0234], “ An embodiment may be directed to accessing, cleaning, storing, extracting, retrieving, and converting a type or plurality of types of data including by way of example, but not limited to, the following: an Indicator Of Health, a Component Of Health, and/or a Social Determinant Of Health, or a plurality of Indicators Of Health, Components Of Health, and/or Social Determinants Of Health, and/or other pattern-of-life or every-day life data, familiar to one of ordinary experience in the art” [0237], “solutions are available from companies such as NetOwl, LogRhythm, ZL Technologies, Brainspace, SAS, Provalis Research, Inxight, ORKASH and IBM's SPSS or Watson… find and structure data in unstructured sources, then integrate and transform the data along with structured data for business intelligence and analytic purposes.”). Examiner interprets utilizing products from companies that are known for offering ETL software that may extract and transform data for use encompasses extracting, transforming, and loading the health data.
normalize the health data from the plurality of data sources including reformatting and/or range filling comorbidity scores, age ranges median household income ranges, walkability data ranges, and affordability ranges ([0281], “An aspect of the inventive subject matter may be directed to normalizing data by a rules engine that converts data to a predetermined format, processing disparate data from the databases into normalized data formatted by the engine's rules.” [0315], “Such embodiments may apply a quality of life HALex-utility index method in making health status, health risk, and other determinations associated with comorbidities of overweight and obesity, such as for example, hypertension, elevated cholesterol, and elevated blood pressure.” [0322], “Quantification may include categorization of the vulnerabilities and disparities. Categories may include, for example: education attainment; economic (such as, indicators of household well-being, food and non-food consumption or expenditure, and income, and non-monetary proxies of household well-being such as ownership of productive assets or durables); demographic (such as, gender and age”)” Table 1: “Percent of households paying more than 0 = 1 point 25% of their household income for rent 1-2 = 2 points 3-4 = 3 points” [0081], “Social and environmental factors are contributors to disparities in diabetes… neighborhoods with increased walkability have been associated with lower BMI.” Table 5: “Socioeconomic Status Indicator: Income… Can’t afford prescription medicine past 12 months… Can’t afford dental care past 12 months… Can’t afford eyeglasses past 12 months”);
select a plurality of domains (Claim 3, “patient outreach and engagement informed by mSDOH further comprising (a) designing and choosing campaigns that are appropriate informed by and based on the patient's pattern-of-life and changes or alteration to mSDOH;”);
generate weights for each of the plurality of domains and the subset of indicators based on a random sample of the health data ([0209], “utilize smart databases that apply rules engines to execute instructions to establish and/or to combine with weighted importance the quantitative value of an Indicator Of Health and/or the quantitative values of a plurality of Indicators Of Health. A rules engine may execute instructions to establish the weighted importance of one such quantitative value or a plurality of such quantitative values utilizing aggregative methods, stratification, inverse weighting, propensity scoring, principal components analysis, factor analysis, and/or other relative methods each of which methods is readily known to persons of ordinary skill in the art”); and
calculate a weighted sum of the health data based on the weights to obtain a whole health index for the population ([0073], “The safe-harbor applies specifically to services to identify and address a patient's social determinants of health used to directly advance specified goals, such as improving patient adherence to certain treatment regimens and improving evidence-based health outcomes for a target patient population” [0383], “Results of such analysis and synthesis may include indexing or scores correlated with Social Determinants Of Health, patient populations, and specific patients.” [0209], “A rules engine may execute instructions to perform indexing or scoring and to arrive at one or more indices or scores associating and/or correlating an index or score with an Indicator Of Health and/or a plurality of indices or scores with a plurality of Indicators Of Health. One index or score associated with an Indicator Of Health, and/or one index or score associated with a plurality of Indicators Of Health, and/or a plurality of indices or scores associated with a plurality of Indicators Of Health may represent a quantitative contribution to an overall Indicator Of Health index or score (“Indicator Index”).”).
Derrick does not teach extracting, transforming and loading the health data into one or more tables;
obtaining the health data from the one or more tables; and
an indicator selection module coupled to the data normalizing module, configured to:
select a plurality of indicators based on (i) significance to health, (ii) validity of the indicators, (iii) availability of the indicators at large scales, (iv) applicability of indicators to the population, and (v) timeliness of the indicators; and
select a subset of indicators for the plurality of domains including removing indicators that (i) amount to incomplete data capture using the plurality of disparate data sources or (ii) applicable only to a subset of the population.
However, the combination of Derrick in view of Salsbury does teach
obtaining the health data from the one or more tables (Salsbury, [0358], “The tables store the value and score for the data point, the geographic source for the data, the detail level at which that record has been recorded, the year for which the record contains data, and the data point name.” [0359], “Since the data tables are named by data point name, they are easy to access programmatically by first referring to the data matrix or outcomes matrix table to get the name.”);
an indicator selection module coupled to the data normalizing module (Salsbury, [0041], “ The Indicator selection was based on criteria” [0146], “it is preferred to normalize or standardize the index so as to equate the power and contribution of each”), configured to:
select a plurality of domains and a plurality of indicators based on (i) significance to health (Salsbury, [0050], “Relevance/action-oriented--measures a factor or condition concerning a social determinant of health over which community stakeholders can achieve positive change through public decision-making and social/political action.”),
(ii) validity of the indicators (Salsbury, [0044], “Validity--measures what it purports to measure”),
(iii) availability of the indicators at large scales (Salsbury, [0042], “Availability--is available, accessible and affordable” [0045], “Measurability--is easily quantifiable and lends itself to numeric scaling”),
(iv) applicability of indicators to the population (Salsbury, [0046], “Capacity to be Disaggregated--can be disaggregated into target groups of interest based on race/ethnicity, gender, age and place of residence”), and
(v) timeliness of the indicators (Salsbury, [0043], “Reliability--is based on consistently collected, compiled and calculated data”). Under the broadest reasonable interpretation, consistently collecting data refers to an appropriate timeliness in the collection of said data. If data is not collected on a routine, timely manner, then it cannot be reliable.
And select a subset of indicators for the plurality of domains including removing indicators that (i) amount to incomplete data capture using the plurality of disparate data sources or (ii) applicable only to a subset of the population (Derrick, [0232], “Questionable variables may be removed from the model specification, and predictive power and robustness may be reassessed. In cases where the impact of removal may be marginal, the offending variables may be permanently removed.” Salsbury, [0041], “The Indicator selection was based on criteria of” [0042], “ Availability--is available, accessible and affordable”). It would be obvious to one of ordinary skill in the art that if the data for an indicator is not fully available, and fails the availability criteria, it would be removed from consideration.
Derrick in view of Salsbury are considered analogous to the claimed invention because they are in the field of population health indexes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Derrick with Salsbury for the advantage of utilizing an “Indicator selection criteria” (Salsbury; [0021]).
Derrick in view of Salsbury does not explicitly teach extracting, transforming and loading the health data into one or more tables.
However, Bruckhaus does teach extracting, transforming and loading the health data into one or more tables (Col. 73, lines 63-66, “FIG. 26E shows the metadata the data management component uses to transport the source data into staging tables, as is the practice in the art for extract transform and load (ETL) processes.”).
Derrick in view of Salsbury further in view of Bruckhaus are considered analogous to the claimed invention because they are in the field of population health indexes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Derrick in view of Salsbury with Bruckhaus for the advantage of “transport[ing] the source data into staging tables” (Bruckhaus; Col. 73, lines 64-65).
Regarding claim 2, Derrick in view of Salsbury further in view of Bruckhaus teach the system of claim 1, as described above. Derrick further teaches wherein the one or more processors and memory storing one or more programs, which, when executed by the one or more processors, cause the one or more processors ([0413], “The software 724 may also reside, completely or at least partially, within the main memory 704 and/or within the processor 702 during execution of the software by the computer system 700, the main memory 704 and the processor 702 also constituting computer-readable media.”) to:
calculate the whole health index under a plurality of weighting schemes to determine weighting schemes across the plurality of domains and subdomains ([0301], “The variables may be scaled or weighted, with each variable or category of variables having a proportional weight... A rules engine may execute instructions to establish the weighted importance of one such variable or a plurality of such variables utilizing aggregative methods, stratification, inverse weighting, propensity scoring, principal components analysis, factor analysis, and/or other relative methods.” [0323], “The scope of quantification also may include weighting or scaling each indicator and category. Weighting decisions may include scaling where, for example, all factor loadings are considered relatively equal. Under this approach, gender, education, and income, for example, may be deemed to be equal in their impact on the patient's adherence or compliance with a therapy program.”[0324], “More complex and precise weighting or scaling decisions may include ranking by ordinal each indicator and/or each category. An ordinal ranking may be placed into an interval scale.” [0487], “2. Weight the predictor variables—Domains and Items—as sub-indices or scores”). It would be obvious to one of ordinay skill in the art that the plurality of weighting methods as taught by [0301] of Derrick, and the two examples further provided, encompasses calculating the whole health index under a plurality of weighting schemes.
and select a final weighting scheme based on an option that yields validation results in accordance with a predetermined criterion ([0487], “2. Weight the predictor variables—Domains and Items—as sub-indices or scores, whereby:” [0489], “ i. The Domain 9 sub-index or score is weighted 5% of the total Domain weight on the basis that environment is correlated with 5% (or the otherwise then-current percent) of the premature causes of death in the U.S. ” [0491], “i. The Domain 8-7-6 sub-index or score is weighted 10% of the total Domains weight on the basis that claims, clinical and health administrative data characterize health care, which is correlated with 10% (or the otherwise then-current percent) of the premature causes of death in the U.S.”). The Examiner interprets selecting domain weights based on a specific criteria of correlating with certain percentages to yield a specific result encompasses selecting a final weighting scheme.
Regarding claim 3, Derrick in view of Salsbury further in view of Bruckhaus teach the system of claims 1 and 2, as described above. Derrick further teaches wherein the one or more processors and memory storing one or more programs, which, when executed by the one or more processors, cause the one or more processors ([0413], “The software 724 may also reside, completely or at least partially, within the main memory 704 and/or within the processor 702 during execution of the software by the computer system 700, the main memory 704 and the processor 702 also constituting computer-readable media.”) to:
select the final weighting scheme by examining the predetermined criterion validity of the whole health index by analyzing Spearmen correlations between average whole health index at a county level and public health indicators including length of life and quality of life ([0230], “To focus on the most promising input variables, bivariate screening may be performed on the remaining input variable set. The method may compute Spearman's rank correlation coefficient and Hoeffding's dependence coefficient (D-Statistic) for each remaining input variable against percent compliant.” [0445], “Essentialities are many in number and are common in the study of human behaviors and their impact on health. Accordingly, each Essentiality, except for lifestyle, is expressed by a score for the Essentiality ascribed by the patient from one to three, with three being the highest in importance as perceived by the patient. Lifestyle is disproportionately weighted at no less than a ratio the numerator of which is the mSDOH-burden hazard ratio applicable to the patient (such as city, county, State or national) and the denominator is the score of the sum of the other Essentialities.” [0126], “Essentialities: Evidence of the patient's unique social and personal competencies, particularized to the dimensions, preferences and factors valuable, as well as harmful, that the patient utilizes in navigating the patient's pattern-of-life and situated within and expressed by the patient's pattern-of-life, comprised of perceived HRQoL components further comprising demographics and compliance Domains” [0128], “HRQoL: Health-related quality of life is an individual's satisfaction or happiness with Domains of life insofar as they affect or are affected by “health”. ” [0198], “The term “Social Determinants Of Health,” including the respective Components Of Health of such determinants and the respective Indicators Of Health of such components, as used with respect to the inventive subject matter shall mean the conditions in which people are born, grow up, live, work, and age, including the heath system, including without limitation: … (b) the circumstances, patterns, and/or conditions of daily life that influence a person's opportunity to be healthy, a person's risk of illness, and/or a person's life expectancy; ”).
Regarding claim 7, Derrick in view of Salsbury further in view of Bruckhaus teach the system of claim 1, as described above. Derrick further teaches wherein the one or more processors and memory storing one or more programs, which, when executed by the one or more processors, cause the one or more processors ([0413], “The software 724 may also reside, completely or at least partially, within the main memory 704 and/or within the processor 702 during execution of the software by the computer system 700, the main memory 704 and the processor 702 also constituting computer-readable media.”) to:
validate the whole health index on a predetermined portion of the health data ([0530], “The PRO Instrument's validity takes into account content validity and construct validity. With respect to content validity, the PRO Instrument evidences that it measures the Concepts of interest, including evidence from qualitative studies that the Items and Domains of the PRO Instrument are appropriate and comprehensive relative to the PRO's measurement Concept, population and use.”),
including analyzing Spearman correlation between average whole health index at county level and predetermined health indicators at county-level, based on health indicators comprising length of life and quality of life ([0230], “The method may compute Spearman's rank correlation coefficient and Hoeffding's dependence coefficient (D-Statistic) for each remaining input variable against percent compliant.” [0445], “Essentialities are many in number and are common in the study of human behaviors and their impact on health. Accordingly, each Essentiality, except for lifestyle, is expressed by a score for the Essentiality ascribed by the patient from one to three, with three being the highest in importance as perceived by the patient. Lifestyle is disproportionately weighted at no less than a ratio the numerator of which is the mSDOH-burden hazard ratio applicable to the patient (such as city, county, State or national) and the denominator is the score of the sum of the other Essentialities.”).
Regarding claim 8, Derrick in view of Salsbury further in view of Bruckhaus teach the system of claim 1, as described above. Derrick further teaches wherein the one or more processors and memory storing one or more programs, which, when executed by the one or more processors, cause the one or more processors ([0413], “The software 724 may also reside, completely or at least partially, within the main memory 704 and/or within the processor 702 during execution of the software by the computer system 700, the main memory 704 and the processor 702 also constituting computer-readable media.”) to:
assess validity of whole health index, including estimating construct validity of composite of the whole health index ([0530], “The PRO Instrument's validity takes into account content validity and construct validity.”),
computing correlations between three domains ([0533], “With respect to construct validity, the PRO Instrument evidences that relationships among Items, Domains and Concepts conform to logical relationships that exist with measures of related Domains, Concepts or scores produced in the mSDOH plan”),
conditioning these correlations on number of conditions present ([0558], “Domain 8 Data Card—Comorbidities: In a preferred embodiment, mSDOH are introduced as novel data to the Comorbidities Data Card, which commonly includes the patient's: (a) comorbid chronic disease dyads; (b) the comorbid chronic disease triads; and (c) the any additional of the most prevalent comorbid chronic diseases the patient has. Such three groups of chronic conditions collectively are referred to as the “coexisting most-prevalent chronic diseases”. The coexisting most-prevalent chronic diseases mix is based on the latest CMS report presenting an analysis of the most prevalent chronic condition comorbidities with diabetes”). Examiner notes that [0016] of Applicant specification specifies wherein conditions refers to clinical conditions, which is encompassed by comorbidities of a patient.
Regarding claim 15, Derrick in view of Salsbury further in view of Bruckhaus teach the system of claim 1, as described above. Derrick further teaches wherein the one or more processors and memory storing one or more programs, which, when executed by the one or more processors, cause the one or more processors ([0413], “The software 724 may also reside, completely or at least partially, within the main memory 704 and/or within the processor 702 during execution of the software by the computer system 700, the main memory 704 and the processor 702 also constituting computer-readable media.”) to:
provide each domain score to a plurality of computing resources corresponding to care teams to identify potential needs beyond their clinical program offering, and to provide additional care solutions, such as meal delivery services, transportation support, or hearing aid consultation, to improve whole health for the population ([0513], “The novel mSDOH plan (FIG. 31 (900.80)) of the lifestyle modification instrument assists the healthcare team by informing the team of the characteristics of the patient's mSDOH and their impacts on the patient's lifestyle, pattern-of-life and persona for the team's use in medical decision-making and in the design, updates and management of the comprehensive care plan. Such characteristics reveal or update lifestyle modification prescriptions and compliance, for example: the patient's need for transportation to the healthcare entity and whether the patient's healthcare plan has a transport benefit”).
Regarding claim 16, Derrick in view of Salsbury further in view of Bruckhaus teach the system of claim 1, as described above. Derrick further teaches wherein the one or more processors and memory storing one or more programs, which, when executed by the one or more processors, cause the one or more processors ([0413], “The software 724 may also reside, completely or at least partially, within the main memory 704 and/or within the processor 702 during execution of the software by the computer system 700, the main memory 704 and the processor 702 also constituting computer-readable media.”) to:
use the whole health index to direct members of the population to appropriate solutions for their specific health and social needs ([0226], “An embodiment may be directed to utilizing rules-based engines and mathematic and statistical methodologies and techniques for application of actionable insights derived from determinants, components, indicators, dimensions, and disparities data to improve adherence to, compliance with and/or cessation of treatment therapies through patient interventions and engagement domains.” [0346], “patient engagement and intervention domains may include performing prescribed or recommended activities at facilities of a healthcare provider, a retail healthcare site or at a web, mobile device, wearable technology or other electronic healthcare domain.”).
Regarding claims 17, 18, 19, and 20, these claims are rejected for the same reasons as claims 1, 2, 3, and 1, as described above. Derrick further teaches a non-transitory computer readable storage medium storing one or more programs configured for execution by a computer system having a display, memory and one or more processors, the one or more programs comprising instructions ([0412], “The example computer system 700 includes a processor 702 (e.g., a central processing unit (CPU) a graphics processing unit (GPU) or both), a main memory 704 and a static memory 706, which communicate with each other via a bus 708. The computer system 700 may further include a video display unit 710 (such as for example, a liquid crystal display (LCD) or a cathode ray tube (CRT)).” [0413], “The drive unit 716 includes a computer-readable medium 722 on which is stored one or more sets of instructions (that is, software 724) embodying any one or more of the methodologies or functions described herein. The software 724 may also reside, completely or at least partially, within the main memory 704 and/or within the processor 702 during execution of the software by the computer system 700, the main memory 704 and the processor 702 also constituting computer-readable media.”).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Derrick (US 20210319887) in view of Salsbury (US 20100082362) further in view of Bruckhaus (US 8417715) and Kaplan (Kaplan; Robert M., Health Status: Types of Validity and the Index of Well-being, Winter 1976, Health Serv Res., 11(4):478-507).
Regarding claim 4, Derrick in view of Salsbury further in view of Bruckhaus teach the system of claims 1-3, as described above. Derrick further teaches wherein the one or more processors and memory storing one or more programs, which, when executed by the one or more processors, cause the one or more processors ([0413], “The software 724 may also reside, completely or at least partially, within the main memory 704 and/or within the processor 702 during execution of the software by the computer system 700, the main memory 704 and the processor 702 also constituting computer-readable media.”) to select the final weighting scheme by
(i) assessing if the whole health index reflects known differences in health across different populations, based on age groups, sex, race/ethnicities, rural/urban status, and/or insurance types ([0533], “With respect to construct validity, the PRO Instrument evidences that relationships among Items, Domains and Concepts conform to logical relationships that exist with measures of related Domains, Concepts or scores produced in the mSDOH plan.” [0534], “The PRO Instrument's ability to detect change evidences that the instrument can identify differences in scores over time in individuals or groups who have changed with respect to the measurement Concept,...” [0535], “Characteristics of the PRO Instrument take into account the: … (d) Concepts being measured;” [0208], “standardized variables for health care utilization (such as, age, gender, urban location, and health need);”); and
(ii) selecting the final weighting scheme by determining if a scheme yields a predetermined level of performance in terms of criterion validity ([0487], “2. Weight the predictor variables—Domains and Items—as sub-indices or scores, whereby:” [0489], “ i. The Domain 9 sub-index or score is weighted 5% of the total Domain weight on the basis that environment is correlated with 5% (or the otherwise then-current percent) of the premature causes of death in the U.S. ” [0491], “i. The Domain 8-7-6 sub-index or score is weighted 10% of the total Domains weight on the basis that claims, clinical and health administrative data characterize health care, which is correlated with 10% (or the otherwise then-current percent) of the premature causes of death in the U.S.”). The Examiner interprets selecting domain weights based on a specific criteria of correlating with certain percentages to yield a specific result encompasses selecting a final weighting scheme.
Derrick in view of Salsbury further in view of Bruckhaus does not explicitly teach (ii) selecting the final weighting scheme by determining if a scheme yields a predetermined level of performance in terms of discriminant validity.
However, Kaplan does teach (ii) selecting the final weighting scheme by determining if a scheme yields a predetermined level of performance in terms of discriminant validity (Kaplan, Pg. 495, “We contend that our proposed Index of Well-being contains almost all the time-specific content of a comprehensive health status measure (and, we hope, little else). We must now see if the data yielded by the IWB relate as expected to data yielded by other measures. Such relationships provide the two major types of external evidence for construct validity, convergent evidence and discriminant evidence. Because of their importance, these two types of evidence are frequently referred to as convergent validity and discriminant validity.” Pg. 503, “Discriminant evidence indicates that the measure does not represent a construct other than the one it is devised to measure. That is, it correlates more strongly with measures that are more closely related to the construct than with other measures that bear a looser relation to the construct [23]… The first comparison correlates W* for each of the eight days preceding the 1975 interview with self-rated well-being for the same day and with self-ratings for each of the other seven days. The matrix of correlations is shown in Table 4. If the Index of Well-being is really a sensitive time-specific measure, W* should correlate most highly with self-ratings for the specific day on which W* is assessed, that is, the correlations on the diagonal of the matrix (shown in boldface) should be higher than the off-diagonal entries.” Pg. 504, “On an individual basis, in fact, the correlation of W* for one day with self-rated overall health status is so low that W* appears to give almost no information about expected future well-being as perceived by a single respondent. The marked divergence in the two measures dramatically underscores the fact that consumers recognize the difference between their current level of wellbeing and their prognostic outlook. This difference provides substantial discriminant evidence for the validity of separating prognoses from the time-specific dimension of well-being in the basic health status construct.”). The Examiner interprets using discriminant evidence to determine the correlatory effect that certain variables have on the output Index of Well-being to then validate the construct to encompass the claim limitation.
Derrick in view of Salsbury further in view of Bruckhaus and Kaplan are considered analogous to the claimed invention because they are in the field of population health indexes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Derrick in view of Salsbury further in view of Bruckhaus with Kaplan for the advantage of demonstrating “Discriminant evidence of construct validity” (Kaplan; pg. 478).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Derrick (US 20210319887) in view of Salsbury (US 20100082362) further in view of Bruckhaus (US 8417715) and Cebrecos (Cebrecos; Alba, Geographic and statistic stability of deprivation aggregated measures at different spatial units in health research, June 2018, Applied Geography, Volume 95, Pages 9-18 (Year: 2018)).
Regarding claim 10, Derrick in view of Salsbury further in view of Bruckhaus teach the system of claim 1, as described above. Derrick in view of Salsbury further in view of Bruckhaus does not teach wherein the one or more processors and memory storing one or more programs, which, when executed by the one or more processors, cause the one or more processors ([0413], “The software 724 may also reside, completely or at least partially, within the main memory 704 and/or within the processor 702 during execution of the software by the computer system 700, the main memory 704 and the processor 702 also constituting computer-readable media.”) to:
evaluate reliability of the whole health index at varying levels of geography by assessing stability of the whole health index.
However, Cebrecos does teach wherein the whole health index calculation module is further configured to:
evaluate reliability of the whole health index at varying levels of geography by assessing stability of the whole health index (Pg. 10, “The present study assesses the statistic and geographic (in)stability that arises when using deprivation data aggregated at different scales when understanding social determinants of health with a geographical perspective. The aims of the study are twofold: first, to build an Area Based Deprivation Index (ABDI) at three different spatial scales (census section, neighborhoods and districts) for the city of Madrid. Second, to study the statistical and geographical stability of the ABDI throughout the three scales and its relationship with cardiovascular disease (CVD) prevalence.” Pg. 12, “The analyses allow to locate areas or clusters where the indexes were stable or unstable throughout the three scales.” Pg. 13, “To unravel what relationships there are between the deprivation indices across the scales, how stable is the relationship and how the geographical distribution is, Fig. 2 was developed… These results add to the already published evidence warnings about the need to examine the effects of varying the geographic extent when calculating measures and associations between urban contextual environment and health outcomes.”).
Derrick in view of Salsbury further in view of Bruckhaus and Cebrecos are considered analogous to the claimed invention because they are in the field of population health indexes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Derrick in view of Salsbury further in view of Bruckhaus with Cebrecos for the advantage of “understand[ing] the associations between the urban context and health” (Cebrecos; pg. 15).
Regarding claims 5-6, 9, and 11-14, these claims have been searched and considered but do not result in a prior art rejection at this time.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684