Notice of Pre-AIA or AIA Status
This action is made in response to the Request for Continued Examination filed on June 8, 2026. This action is made NON-FINAL.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/08/2026 has been entered.
Response to Amendment
The amendment filed 06/08/2026 has been entered. Claims 1-4, 7-17, and 20 remain pending in the application. Claims 5-6 and 18-19 have been cancelled. The amendments have overcome the 112(b) rejection previously set forth in the Final Office Action mailed 03/11/2026.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 14, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 14, and 20 recite the limitation "selecting one or more entities based on the selected data…" in line 30, 31, and 29 respectively, and “selecting the one or more entities from the set of candidate entities based on the selected data” in lines 36-37, 37-38, and 35-36, respectively. It is unclear what “the selected data” is in the claims. Claims 1, 14, and 20 recite “selecting one or more entities based on the collected data” in lines 5, 8, and 6 respectively, therefore, it is unclear if the selecting one or more entities is based on the collected data or some selected data which has no antecedent basis. There is insufficient antecedent basis for this limitation in the claim.
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 1-4, 7-17, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Step 1 analysis:
Claim 1 is drawn to a method (i.e., process), Claim 14 is drawn to a system, and Claim 20 is drawn to a non-transitory computer readable medium (i.e., manufacture), which are all within the four statutory categories. (Step 1 – Yes, the claims fall into one of the statutory categories).
Step 2A analysis – Prong One:
Claim 1 recites:
A computer-implemented method for ranking selected entities in association with at least one procedure and/or at least one condition, the method comprising:
collecting, using one or more processors, a plurality of data from one or more sources;
selecting, one or more procedures and/or conditions based on the collected data;
selecting, one or more entities based on the collected data;
for each one of the selected one or more procedures and/or conditions:
determining, using the one or more processors and using one or more models, a performance score for each of the one or more selected entities based on one or more performance indicators, wherein the one or more performance indicators include one or more risk-adjusted outcomes; and
generating, using the one or more processors, a rank for each of the one or more selected entities based on the performance score determined for each of the one or more selected entities; and
causing, using the one or more processors, a display of the rank for the one or more selected entities in association with the one or more procedures and/or conditions in a user interface of a device, wherein
the one or more risk-adjusted outcomes have been risk-adjusted based on one or more risk-adjustment variables,
the one or more risk-adjustment variables include one or more:
age at admission; inbound transfer status; year of hospital admission; Elixhauser comorbidities; Medicare status code; socioeconomic status; condition cohort-specific covariates; surgical cohort-specific covariates; history of stroke; or Covid-19 diagnosis,
selecting one or more entities based on the selected data comprises:
excluding, from a plurality of entities associated with a database, each entity that is associated with one or more attributes indicative of non-inclusion, thereby forming a set of candidate entities wherein none of the entities included in the set of candidate entities is associated with an attribute indicative of non-inclusions; and
selecting the one or more entities from the set of candidate entities based on the selected data, and
the one or more attributes indicative of non-inclusion include one or more of:
federal government ownership; an absence of Medicare provider number; an absence of clinically-integrated facility; a primary service (SERV) code indicating a service type other than a specific set of conditions; or a volume insufficient to allow estimation for at least one outcome.
The series of steps as recited above, excluding the underlined portions, falls within the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Selecting procedures, conditions, and entities, determining a score based on risk-adjusted outcomes using a model, generating a rank, the one or more risk-adjusted outcomes being risk-adjusted based on one or more risk-adjustment variables, selecting entities comprising excluding each entity that is associated with one or more attributes indicative of non-inclusion and selecting based on the selected data can all be performed in the human mind, with or without the use of a physical aid. Therefore, the claim recites an abstract idea of a mental process.
Claims 14 and 20 recite/describe nearly identical steps as claim 1 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Step 2A analysis – Prong 2:
This judicial exception is not integrated into a practical application. Specifically, independent claims 1, 14, and 20 recite the following additional elements beyond the abstract idea: one or more processors, a display, a user interface, a device, a computing system, and a non-transitory computer readable medium. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Specifically, the term "processor" may refer to any device or portion of a device that processes electronic data (see specification [0097]). The computer system may operate in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The computer system can also be implemented as or incorporated into various devices (see specification [0098]). A display, such as a liquid crystal display (LCD), an organic light emitting diode (OLEO), a flat panel display, a solid-state display, a cathode ray tube (CRT), a projector, a printer or other now known or later developed display device for outputting determined information (see specification [00101]). The term "computer-readable medium" may also include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by a processor (see specification [00105]).
The limitations “collecting a plurality of data from one or more sources” and “causing, using the one or more processors, a display of the rank for the one or more selected entities in association with the one or more procedures and/or conditions in a user interface of a device” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, Claims 1, 14, and 20 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional elements are not integrated into a practical application).
Step 2B analysis:
As discussed above in “Step 2A analysis – Prong 2”, the identified additional elements in Independent Claims 1, 14, and 20 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The additional element of "collecting a plurality of data from one or more sources" and “causing, using the one or more processors, a display of the rank for the one or more selected entities in association with the one or more procedures and/or conditions in a user interface of a device” were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
Generic computer components recited as performing generic computer functions that are well understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Here, the claim limitations are similar to receiving and sending information over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OJP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); See MPEP 2106.05(d)(ll)(i)).
The applicant’s specification discloses: the term "processor" may refer to any device or portion of a device that processes electronic data (see specification [0097]). the computer system may operate in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The computer system can also be implemented as or incorporated into various devices (see specification [0098]). A display, such as a liquid crystal display (LCD), an organic light emitting diode (OLEO), a flat panel display, a solid-state display, a cathode ray tube (CRT), a projector, a printer or other now known or later developed display device for outputting determined information (see specification [00101]). The term "computer-readable medium" may also include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by a processor (see specification [00105]).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the steps for classification amount to no more than using computer related devices to implement the abstract idea.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible. The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: Independent claims - NO).
Dependent Claims
Dependent Claims 2-4, 7-13 and 15-17 are directed towards elements used to describe the one or more procedures and conditions, entities, models, process measures, structural measures, and sources. These elements include: (Claim 2 and 15) the one or more procedures and/or conditions are selected based on one or more of: a frequency of admission, an ability to make entity-to-entity comparisons, or a presence of a sufficient degree of risk or complexity such that a quality of an entity's performance is important; (Claim 3 and 16) procedures and/or conditions are selected based on an inclusion criteria and/or an exclusion criteria; (Claim 4 and 17) the inclusion criteria and/or the exclusion criteria are defined based on one or more of maximal homogeneity, maximal sample size, or minimal coding variation; (Claim 7) the one or more models are selected by: evaluating model statistics for combinations of performance indicators; and determining the one or more models associated with an optimal combination of one or more of: a number of performance indicators, a model fit, or consistency with models in related cohorts; (Claim 8) the one or more risk-adjusted outcomes have been risk-adjusted; (Claim 9) the one or more risk-adjustment variables include: age at admission; inbound transfer status; and/or year of hospital admission; (Claim 10) the one or more risk-adjusted outcomes include one or more of: mortality within a pre-determined time period; unplanned readmission within a pre-determined time period; surgical site infection, hip replacement, knee replacement,… or time spent at home within a pre-determined time period of discharge; (Claim 11) the one or more performance indicators further comprises one or more process measures; (Claim 12) the one or more performance indicators further comprises one or more structural measures; (Claim 13) the one or more sources include one or more of: publicly available indicators… or total volume data from American Hospital Directory (AHD). These elements fall within the “mental processes” grouping of abstract ideas as stated above in the independent claims, and describe concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Therefore, the dependent claims recite the same abstract idea of a mental process as the independent claims.
This judicial exception is not integrated into a practical application. Specifically, the dependent
claims recite the following additional elements beyond the abstract idea: a computer and using a multi-level logistic regression model. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The additional elements do not show an improvement to the functioning of a computer or to
any other technology, rather the additional elements perform general computing functions and do not
indicate how the particular combination improves any technology or provides a technical solution to a
technical problem. Accordingly, these additional elements, when considered separately and as an
ordered combination, do not integrate the abstract idea into a practical application because they do not
impose any meaningful limits on practicing the abstract idea. Therefore, the dependent claims are
directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional
elements are not integrated into a practical application).
As discussed above, the identified additional elements in Dependent Claims 2-4, 7-13 and 15-17,
are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the
judicial exception to a particular technological environment or field of use. Therefore, the claims as a
whole do not amount to significantly more than the judicial exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in
the context of this analysis, it must involve more than performance of “well-understood, routine, [and]
conventional activities previously known to the industry.” Further, “the mere recitation of a generic
computer cannot transform a patent ineligible abstract idea into a patent-eligible invention.”
The use of a computer or processor to merely automate or implement the abstract idea cannot
provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the
additional limitations alone or in combination improves the functioning of a computer or any other
technology, improves another technology or technical field, or effects a transformation or reduction of a
particular article to a different state or thing. Therefore, the claims are not patent eligible.
The Examiner has therefore determined that no additional element, or combination of
additional claims elements is/are sufficient to ensure the claims amount to significantly more than the
abstract idea identified above (Step 2B: Dependent claims - NO).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 7, 9, 11-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bachik et al. (US 2010/0169113) (hereinafter Bachik) in view of Dong et al., Rating Hospital Performance in China: Review of Publicly Available Measures and Development of a Ranking System (Hereinafter Dong), in further view of Ostrovsky et al. (US 2015/0220699) (hereinafter Ostrovsky), in further view of Brownlee et al. 2022 Methodology Lown Institute Hospitals Index for Social Responsibility (Hereinafter Brownlee).
Regarding Claim 1, Bachik discloses:
A computer-implemented method for ranking selected entities in association with at least one procedure and/or at least one condition ([0019] The step of collecting data and programmatic information relevant to the service line may involve, for example, making personal observations, gathering demographic data from the surrounding geographic area, inspecting hospital facilities, interviewing hospital staff, administrators, and referral sources, analyzing financial data (such as market share), analyzing patient care data, and evaluating competitors of the hospital.), the method comprising,
collecting, using one or more processors ([0057] the computing device includes controllers that comprise computer processors), a plurality of data from one or more sources (See Fig. 1, [0008], [0019], [0034]: Collecting data relevant to the service line of the hospital);
selecting, one or more procedures and/or conditions based on the collected data ([0019] The step of collecting data and programmatic information relevant to the service line may involve, for example, making personal observations, gathering demographic data from the surrounding geographic area, inspecting hospital facilities, interviewing hospital staff, administrators, and referral sources, analyzing financial data (such as market share), analyzing patient care data, and evaluating competitors of the hospital. According to an exemplary embodiment of the present invention, the data collected should reflect strengths, weaknesses, opportunities, and potential threats to the service line of the hospital);
for each one of the one or more procedures and/or conditions: determining, using the one or more processors ([0057] the computing device includes controllers that comprise computer processors), a performance score for each of the one or more selected entities based on one or more performance indicators ([0034], Fig. 2: the scoring step of block 104 (FIG. 1) may involve scoring each category 10, subcategory 12, and/or subset 14, on a scale to produce raw score. ), wherein the one or more performance indicators include one or more risk-adjusted outcomes (Tables 1-10 of Bachik disclose several parameters for producing the score of the entities being measured, including provider performance, volume of staff available, training and education of staff, patient outcomes, and more.); and
causing a display for the one or more selected entities in association with the one or more procedures and/or conditions in a user interface of a device ([0056] System 200 includes computing device 202, such as a general purpose computer or a portable computing device having display 203).
However, Bachik does not disclose the following that is met by Dong:
selecting one or more entities based on the collected data (Pg. 5, Col. 1, par. 4: focused on the tertiary hospital sector based on the availability of current data as well as to provide an option for comparison with other available measures);
one or more models (Pg. 5, Col. 2, Par. 1-2: developed PCA/CATPCA models for each of the three domains of ranking)
generating a rank for each of the one or more selected entities based on the performance score determined for each of the one or more selected entities (Pg. 6, 7: The results of our CHDI rankings by score for the top 10 hospitals are shown in Table 6.);
selecting one or more entities based on the selected data comprises: ii) selecting the one or more entities from the set of candidate entities based on the selected data (Pg. 5, Col. 1, par. 4: focused on the tertiary hospital sector based on the availability of current data as well as to provide an option for comparison with other available measures. After applying the inclusion criteria, a total of 310 hospital were deemed eligible for ranking under the full criteria. Thus, the examiner interprets this as selecting entities, i.e., hospitals, based on the inclusion criteria set forth.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system of Bachik with the method of Dong to include selecting the entities, incorporating a model, and generating a rank of the hospitals because it would allow for a more comprehensive ranking system that can be applied to low- and middle- income counties or regions as well as more developed countries (See Dong pg. 2, par. 3). Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system of Bachik with the method of Dong to include the inclusion criteria because it allows for the ranking system to be comparable with other respective performance measures (See Dong Pg. 5, par. 4).
However, Bachik and Dong do not teach the following that is met by Ostrovsky:
the one or more risk-adjusted outcomes have been risk-adjusted based on one or more risk-adjustment variables ([0057]-[0059] Risk score may be dependent on factors used to adjust the risk score), and
the one or more risk-adjustment variables include one or more: age at admission; inbound transfer status; year of hospital admission; Elixhauser comorbidities; Medicare status code; socioeconomic status; condition cohort-specific covariates; surgical cohort-specific covariates; history of stroke; or Covid-19 diagnosis ([0008], [0059] the risk score may be dependent on other factors than just the intervention type. In some embodiments, information such as prior hospitalizations, prior occurrences of the same intervention type, and/or vital statistics (such as age, weight, height, gender, blood pressure, and/or the like) may be used to adjust the risk score for an intervention of an intervention type. The hospital admission risk may be obtained by computing a log transformation of the risk scores).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of Bachik and Dong with the risk-adjustment functions, as taught by Ostrovsky, because it may simplify calculation of the hospital admission risk 340 and allow for more meaningful clinical comparison to new risk factors and/or improvement after summation of the risk scores (See Ostrovsky [0105]).
However, the combination of Bachik, Dong, and Ostrovsky does not teach the following which is met by Brownlee:
i) excluding, from a plurality of entities associated with a database, each entity that is associated with one or more attributes indicative of non-inclusion, thereby forming a set of candidate entities wherein none of the entities included in the set of candidate entities is associated with an attribute indicative of non-inclusions (Pg. 4, para. 3: Non-acute care and non-critical access hospitals, federal hospitals (e.g. Veterans Health Administration) and those outside of the 50 states and Washington, D.C. were excluded. We made further restrictions based on hospitalizations during the pre COVID period 2018 - 2019: we used inpatient fee-for-service (FFS) excluding Medicare Advantage claims. We excluded specialty hospitals with more than 45% admissions for orthopedic, more than 45% for cardiac, more than 80% surgical procedures, more than 80% elective surgeries (among hospitals with > 45% surgical procedures). We eliminated hospitals that were closed as of October 2021 by checking against Care Compare, a website run by the Centers for Medicare and Medicaid Services (CMS) and formerly known as Hospital Compare. Hospitals with patient volume below 50 annual patient stays were also eliminated as well as hospitals that did not perform any surgery. This left a list of 3,764 hospitals: 549 for-profits, 2,444 private nonprofits, and 771 public nonprofits.)
the one or more attributes indicative of non-inclusion include one or more of: federal government ownership; an absence of Medicare provider number; an absence of clinically-integrated facility; a primary service (SERV) code indicating a service type other than a specific set of conditions; or a volume insufficient to allow estimation for at least one outcome (Pg. 4, para. 3 and Pg. 17, para. 1: Non-acute care and non-critical access hospitals, federal hospitals (e.g. Veterans Health Administration) and those outside of the 50 states and Washington, D.C. were excluded. Hospitals without capacity to perform a service, as reflected in their claim history, were excluded from the rating for that particular service.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Bachik, Dong, and Ostrovsky, to include the selection of the entities incorporating an exclusion of entities associated with one or more attributes indicative of non-inclusion, as taught by Brownlee, because the claimed invention is only a combination of these well-known elements which would have performed the same function in combination as each did separately. Bachik, Dong, and Ostrovsky already disclose selecting entities based on certain criteria, including procedures and conditions, and including more selection criteria, such as excluding federal hospitals as taught by Brownlee, would still perform the same function of selecting entities for ranking. Therefore, the results would have been predictable to one of ordinary skill in the art (MPEP 2143).
Regarding Claim 2, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches The computer-implemented method of claim 1, and Bachik further teaches:
wherein the one or more procedures and/or conditions are selected based on one or more of: a frequency of admission, an ability to make entity-to-entity comparisons, or a presence of a sufficient degree of risk or complexity such that a quality of an entity’s performance is important ([0019] the data collected should reflect strengths, weaknesses, opportunities, and potential threats to the service line of the hospital).
Regarding Claim 3, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches The computer-implemented method of claim 1, and Dong further teaches:
The computer-implemented method of claim 1, wherein the one or more procedures and/or conditions are selected based on an inclusion criteria and/or an exclusion criteria (Dong pg. 5, par. 4: inclusion criteria, therefore, required hospitals to be a grade III, level A hospital, featuring on one of the lists of the four Chinese Hospital rankings in any previous year, and have at least 500 beds.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system of Bachik with the method of Dong to include the inclusion criteria because it allows for the ranking system to be comparable with other respective performance measures (See Dong Pg. 5, par. 4).
Regarding Claim 4, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches, The computer-implemented method of claim 3, and Dong further teaches:
wherein the inclusion criteria and/or the exclusion criteria are defined based on one or more of maximal homogeneity, maximal sample size, or minimal coding variation (Dong Pg. 5, par. 4 states that the inclusion of these hospitals over others was on the basis that these organizations continue to be the focus of attention in China given their prominence and popularity. These hospitals have also been the focus on other performance rankings in China; hence, the development of any new ranking system would be comparable with other respective performance measures. Therefore, Dong describes inclusion criteria that is based on maximal homogeneity).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system of Bachik with the method of Dong to include the inclusion criteria because it allows for the ranking system to be comparable with other respective performance measures (See Dong Pg. 5, par. 4).
Regarding Claim 7, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches, The computer-implemented method of claim 1, and Dong further teaches:
The computer-implemented method of claim 1, wherein the one or more models are selected by: evaluating model statistics for combinations of performance indicators (Dong Pg. 5, Col. 2, par. 2: We developed PCA/CATPCA models for each of the three domains of ranking, by evaluating model statistics for all possible combinations of indicators that included at least one indicator); and determining the one or more models associated with an optimal combination of one or more of: a number of performance indicators, a model fit, or consistency with models in related cohorts (Dong Pg. 5, Col. 2, par. 2: From the resulting list of candidate models showing acceptable fit statistics, we selected a final model for each domain, providing a combination of the number of indicators (models with more indicators produce more accurate component scores), number of outcomes, and model fit).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system of Bachik with the method of Dong to include the model selection process because it provides a way to reduce the number of observed variables into a smaller number of linear, uncorrelated summary variables called principal components (PCs) that account for variation in observed variables (See Dong Pg. 5, Par. 5).
Regarding Claim 9, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches, The computer-implemented method of claim 1, and Ostrovsky further discloses:
The computer-implemented method of claim 1, wherein the one or more risk-adjustment variables include: age at admission; inbound transfer status; and/or year of hospital admission ([0008], [0059] the risk score may be dependent on other factors than just the intervention type. In some embodiments, information such as prior hospitalizations, prior occurrences of the same intervention type, and/or vital statistics (such as age, weight, height, gender, blood pressure, and/or the like) may be used to adjust the risk score for an intervention of an intervention type. The hospital admission risk may be obtained by computing a log transformation of the risk scores).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of Bachik and Dong with the risk-adjustment functions, as taught by Ostrovsky, because it may simplify calculation of the hospital admission risk 340 and allow for more meaningful clinical comparison to new risk factors and/or improvement after summation of the risk scores (See Ostrovsky [0105]).
Regarding Claim 11, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches, The computer-implemented method of claim 1, and Bachik further teaches:
The computer-implemented method of claim 1, wherein the one or more performance indicators further comprises one or more process measures, and the one or more process measures include one or more of: worker flu immunization; noninvasive ventilation; patient experience; board certification; emergency room visits after chemotherapy; unplanned visits after colonoscopy; compliance with a septic shock bundle; or public transparency (Table 10 of Bachik describes categories involved in scoring the hospital entities, including Action plan to make consumer/patient's first experience a positive one; consumer inquiries tracked to evaluate success of activities/promotions. And the collection of data involves collecting patient care experience data (See Claim 2 of Bachik).
Regarding Claim 12, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches, The computer-implemented method of claim 1, and Bachik further teaches:
The computer-implemented method of claim 1, wherein the one or more performance indicators further comprises one or more structural measures, and the one or more structural measures include one or more of: volume of procedures; nurse staffing; or National Cancer Institute (NCI)-designated Cancer Center and/or American College of Surgeons (ACS) Commission on Cancer (Table 10 of Bachik describes categories involved in scoring the hospital entities, including Sufficient staffing mix throughout hours of operation and Patient visit volumes tracked (e.g., on a monthly basis) by site).
Regarding Claim 13, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches, The computer-implemented method of claim 1, and Bachik further teaches:
The computer-implemented method of claim 1, wherein the one or more sources include one or more of: publicly available indicators; Medicare Beneficiary Summary Files (MBSF); Medicare inpatient Limited Data Set Standard Analytical Files (LDS SAF); Medicare outpatient limited data set standard analytical files; Medicare Skilled Nursing Facility (SNF) limited data set standard analytical files; American Hospital Association (AHA) annual survey; Hospital Consumer Assessment of Healthcare Providers and Systems Survey (HCAHPS); Orthopedic Board Certification Data; or total volume data from American Hospital Directory (AHD) ([0019] The step of collecting data and programmatic information relevant to the service line may involve, for example, making personal observations, gathering demographic data from the surrounding geographic area, inspecting hospital facilities, interviewing hospital staff, administrators, and referral sources, analyzing financial data ( such as market share), analyzing patient care data, and evaluating competitors of the hospital.).
Regarding Claim 14, Bachik discloses:
A system comprising: one or more processors of a computing system ([0057] the computing device includes controllers that comprise computer processors); and
at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising ([0057] Memory 204 is a computer readable medium and may be a single storage device or may include multiple storage devices, located either locally with system 200 or accessible across a network):
collecting a plurality of data from one or more sources (See Fig. 1, [0008], [0019], [0034]: Collecting data relevant to the service line of the hospital);
selecting, one or more procedures and/or conditions based on the collected data ([0019] The step of collecting data and programmatic information relevant to the service line may involve, for example, making personal observations, gathering demographic data from the surrounding geographic area, inspecting hospital facilities, interviewing hospital staff, administrators, and referral sources, analyzing financial data (such as market share), analyzing patient care data, and evaluating competitors of the hospital. According to an exemplary embodiment of the present invention, the data collected should reflect strengths, weaknesses, opportunities, and potential threats to the service line of the hospital);
for each one of the one or more procedures and/or conditions: determining, a performance score for each of the one or more selected entities based on one or more performance indicators ([0034], Fig. 2: the scoring step of block 104 (FIG. 1) may involve scoring each category 10, subcategory 12, and/or subset 14, on a scale to produce raw score.), wherein the one or more performance indicators include one or more risk-adjusted outcomes (Tables 1-10 of Bachik disclose several parameters for producing the score of the entities being measured, including provider performance, volume of staff available, training and education of staff, patient outcomes, and more.); and
causing a display of the rank for the one or more selected entities in association with the one or more procedures and/or conditions in a user interface of a device ([0056] System 200 includes computing device 202, such as a general purpose computer or a portable computing device having display 203).
However, Bachik does not disclose the following that is met by Dong:
selecting one or more entities based on the collected data (Pg. 4, Col. 1, par. 4: focused on the tertiary hospital sector based on the availability of current data as well as to provide an option for comparison with other available measures);
one or more models (Pg. 5, Col. 2, Par. 1-2: developed PCA/CATPCA models for each of the three domains of ranking)
generating a rank for each of the one or more selected entities based on the performance score determined for each of the one or more selected entities (Pg. 6, 7: The results of our CHDI rankings by score for the top 10 hospitals are shown in Table 6.);
selecting one or more entities based on the selected data comprises: ii) selecting the one or more entities from the set of candidate entities based on the selected data (Pg. 5, Col. 1, par. 4: focused on the tertiary hospital sector based on the availability of current data as well as to provide an option for comparison with other available measures. After applying the inclusion criteria, a total of 310 hospital were deemed eligible for ranking under the full criteria. Thus, the examiner interprets this as selecting entities, i.e., hospitals, based on the inclusion criteria set forth.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system of Bachik with the method of Dong to include selecting the entities, incorporating a model, and generating a rank of the hospitals because it would allow for a more comprehensive ranking system that can be applied to low- and middle- income counties or regions as well as more developed countries (See Dong pg. 2, par. 3). Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system of Bachik with the method of Dong to include the inclusion criteria because it allows for the ranking system to be comparable with other respective performance measures (See Dong Pg. 5, par. 4).
However, Bachik and Dong do not teach the following that is met by Ostrovsky:
the one or more risk-adjusted outcomes have been risk-adjusted based on one or more risk-adjustment variables ([0057]-[0059] Risk score may be dependent on factors used to adjust the risk score), and
the one or more risk-adjustment variables include one or more: age at admission; inbound transfer status; year of hospital admission; Elixhauser comorbidities; Medicare status code; socioeconomic status; condition cohort-specific covariates; surgical cohort-specific covariates; history of stroke; or Covid-19 diagnosis ([0008], [0059] the risk score may be dependent on other factors than just the intervention type. In some embodiments, information such as prior hospitalizations, prior occurrences of the same intervention type, and/or vital statistics (such as age, weight, height, gender, blood pressure, and/or the like) may be used to adjust the risk score for an intervention of an intervention type. The hospital admission risk may be obtained by computing a log transformation of the risk scores).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of Bachik and Dong with the risk-adjustment functions, as taught by Ostrovsky, because it may simplify calculation of the hospital admission risk 340 and allow for more meaningful clinical comparison to new risk factors and/or improvement after summation of the risk scores (See Ostrovsky [0105]).
However, the combination of Bachik, Dong, and Ostrovsky does not teach the following which is met by Brownlee:
i) excluding, from a plurality of entities associated with a database, each entity that is associated with one or more attributes indicative of non-inclusion, thereby forming a set of candidate entities wherein none of the entities included in the set of candidate entities is associated with an attribute indicative of non-inclusions (Pg. 4, para. 3: Non-acute care and non-critical access hospitals, federal hospitals (e.g. Veterans Health Administration) and those outside of the 50 states and Washington, D.C. were excluded. We made further restrictions based on hospitalizations during the pre COVID period 2018 - 2019: we used inpatient fee-for-service (FFS) excluding Medicare Advantage claims. We excluded specialty hospitals with more than 45% admissions for orthopedic, more than 45% for cardiac, more than 80% surgical procedures, more than 80% elective surgeries (among hospitals with > 45% surgical procedures). We eliminated hospitals that were closed as of October 2021 by checking against Care Compare, a website run by the Centers for Medicare and Medicaid Services (CMS) and formerly known as Hospital Compare. Hospitals with patient volume below 50 annual patient stays were also eliminated as well as hospitals that did not perform any surgery. This left a list of 3,764 hospitals: 549 for-profits, 2,444 private nonprofits, and 771 public nonprofits.)
the one or more attributes indicative of non-inclusion include one or more of: federal government ownership; an absence of Medicare provider number; an absence of clinically-integrated facility; a primary service (SERV) code indicating a service type other than a specific set of conditions; or a volume insufficient to allow estimation for at least one outcome (Pg. 4, para. 3 and Pg. 17, para. 1: Non-acute care and non-critical access hospitals, federal hospitals (e.g. Veterans Health Administration) and those outside of the 50 states and Washington, D.C. were excluded. Hospitals without capacity to perform a service, as reflected in their claim history, were excluded from the rating for that particular service.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Bachik, Dong, and Ostrovsky, to include the selection of the entities incorporating an exclusion of entities associated with one or more attributes indicative of non-inclusion, as taught by Brownlee, because the claimed invention is only a combination of these well-known elements which would have performed the same function in combination as each did separately. Bachik, Dong, and Ostrovsky already disclose selecting entities based on certain criteria, including procedures and conditions, and including more selection criteria, such as excluding federal hospitals as taught by Brownlee, would still perform the same function of selecting entities for ranking. Therefore, the results would have been predictable to one of ordinary skill in the art (MPEP 2143).
Regarding Claim 15, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches The system of claim 14, and Bachik further teaches:
wherein the one or more procedures and/or conditions are selected based on one or more of: a frequency of admission, an ability to make entity-to-entity comparisons, or a presence of a sufficient degree of risk or complexity such that a quality of an entity’s performance is important ([0019] the data collected should reflect strengths, weaknesses, opportunities, and potential threats to the service line of the hospital).
Regarding Claim 16, the combination of Bachik, Dong, and Ostrovsky teaches The system of claim 14, and Dong further teaches:
The system of claim 14, wherein the one or more procedures and/or conditions are selected based on an inclusion criteria and/or an exclusion criteria (Dong pg. 5, par. 4: inclusion criteria, therefore, required hospitals to be a grade III, level A hospital, featuring on one of the lists of the four Chinese Hospital rankings in any previous year, and have at least 500 beds.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system of Bachik with the method of Dong to include the inclusion criteria because it allows for the ranking system to be comparable with other respective performance measures (See Dong Pg. 5, par. 4).
Regarding Claim 17, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches, The system of claim 16, and Dong further teaches
wherein the inclusion criteria and/or the exclusion criteria are defined based on one or more of maximal homogeneity, maximal sample size, or minimal coding variation (Dong Pg. 5, par. 4 states that the inclusion of these hospitals over others was on the basis that these organizations continue to be the focus of attention in China given their prominence and popularity. These hospitals have also been the focus on other performance rankings in China; hence, the development of any new ranking system would be comparable with other respective performance measures. Therefore, Dong describes inclusion criteria that is based on maximal homogeneity).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system of Bachik with the method of Dong to include the inclusion criteria because it allows for the ranking system to be comparable with other respective performance measures (See Dong Pg. 5, par. 4).
Regarding Claim 20, Bachik discloses:
A non-transitory computer readable medium ([0057] Memory 204 is a computer readable medium and may be a single storage device or may include multiple storage devices, located either locally with system 200 or accessible across a network), the non-transitory computer readable medium storing instructions which, when executed by one or more processors ([0057] the computing device includes controllers that comprise computer processors) of a computing system, cause the one or more processors to perform operations comprising:
collecting a plurality of data from one or more sources (See Fig. 1, [0008], [0019], [0034]: Collecting data relevant to the service line of the hospital);
selecting, one or more procedures and/or conditions based on the collected data ([0019] The step of collecting data and programmatic information relevant to the service line may involve, for example, making personal observations, gathering demographic data from the surrounding geographic area, inspecting hospital facilities, interviewing hospital staff, administrators, and referral sources, analyzing financial data (such as market share), analyzing patient care data, and evaluating competitors of the hospital. According to an exemplary embodiment of the present invention, the data collected should reflect strengths, weaknesses, opportunities, and potential threats to the service line of the hospital);
for each one of the one or more procedures and/or conditions: determining, a performance score for each of the one or more selected entities based on one or more performance indicators ([0034], Fig. 2: the scoring step of block 104 (FIG. 1) may involve scoring each category 10, subcategory 12, and/or subset 14, on a scale to produce raw score.), wherein the one or more performance indicators include one or more risk-adjusted outcomes (Tables 1-10 of Bachik disclose several parameters for producing the score of the entities being measured, including provider performance, volume of staff available, training and education of staff, patient outcomes, and more.); and
causing a display of the rank for the one or more selected entities in association with the one or more procedures and/or conditions in a user interface of a device ([0056] System 200 includes computing device 202, such as a general purpose computer or a portable computing device having display 203).
However, Bachik does not disclose the following that is met by Dong:
selecting one or more entities based on the collected data (Pg. 4, Col. 1, par. 4: focused on the tertiary hospital sector based on the availability of current data as well as to provide an option for comparison with other available measures);
one or more models (Pg. 5, Col. 2, Par. 1-2: developed PCA/CATPCA models for each of the three domains of ranking)
generating a rank for each of the one or more selected entities based on the performance score determined for each of the one or more selected entities (Pg. 6, 7: The results of our CHDI rankings by score for the top 10 hospitals are shown in Table 6.);
selecting one or more entities based on the selected data comprises: ii) selecting the one or more entities from the set of candidate entities based on the selected data (Pg. 5, Col. 1, par. 4: focused on the tertiary hospital sector based on the availability of current data as well as to provide an option for comparison with other available measures. After applying the inclusion criteria, a total of 310 hospital were deemed eligible for ranking under the full criteria. Thus, the examiner interprets this as selecting entities, i.e., hospitals, based on the inclusion criteria set forth.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system of Bachik with the method of Dong to include selecting the entities, incorporating a model, and generating a rank of the hospitals because it would allow for a more comprehensive ranking system that can be applied to low- and middle- income counties or regions as well as more developed countries (See Dong pg. 2, par. 3). Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system of Bachik with the method of Dong to include the inclusion criteria because it allows for the ranking system to be comparable with other respective performance measures (See Dong Pg. 5, par. 4).
However, Bachik and Dong do not teach the following that is met by Ostrovsky:
the one or more risk-adjusted outcomes have been risk-adjusted based on one or more risk-adjustment variables ([0057]-[0059] Risk score may be dependent on factors used to adjust the risk score), and
the one or more risk-adjustment variables include one or more: age at admission; inbound transfer status; year of hospital admission; Elixhauser comorbidities; Medicare status code; socioeconomic status; condition cohort-specific covariates; surgical cohort-specific covariates; history of stroke; or Covid-19 diagnosis ([0008], [0059] the risk score may be dependent on other factors than just the intervention type. In some embodiments, information such as prior hospitalizations, prior occurrences of the same intervention type, and/or vital statistics (such as age, weight, height, gender, blood pressure, and/or the like) may be used to adjust the risk score for an intervention of an intervention type. The hospital admission risk may be obtained by computing a log transformation of the risk scores).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of Bachik and Dong with the risk-adjustment functions, as taught by Ostrovsky, because it may simplify calculation of the hospital admission risk 340 and allow for more meaningful clinical comparison to new risk factors and/or improvement after summation of the risk scores (See Ostrovsky [0105]).
However, the combination of Bachik, Dong, and Ostrovsky does not teach the following which is met by Brownlee:
excluding, from a plurality of entities associated with a database, each entity that is associated with one or more attributes indicative of non-inclusion, thereby forming a set of candidate entities wherein none of the entities included in the set of candidate entities is associated with an attribute indicative of non-inclusions (Pg. 4, para. 3: Non-acute care and non-critical access hospitals, federal hospitals (e.g. Veterans Health Administration) and those outside of the 50 states and Washington, D.C. were excluded. We made further restrictions based on hospitalizations during the pre COVID period 2018 - 2019: we used inpatient fee-for-service (FFS) excluding Medicare Advantage claims. We excluded specialty hospitals with more than 45% admissions for orthopedic, more than 45% for cardiac, more than 80% surgical procedures, more than 80% elective surgeries (among hospitals with > 45% surgical procedures). We eliminated hospitals that were closed as of October 2021 by checking against Care Compare, a website run by the Centers for Medicare and Medicaid Services (CMS) and formerly known as Hospital Compare. Hospitals with patient volume below 50 annual patient stays were also eliminated as well as hospitals that did not perform any surgery. This left a list of 3,764 hospitals: 549 for-profits, 2,444 private nonprofits, and 771 public nonprofits.)
the one or more attributes indicative of non-inclusion include one or more of: federal government ownership; an absence of Medicare provider number; an absence of clinically-integrated facility; a primary service (SERV) code indicating a service type other than a specific set of conditions; or a volume insufficient to allow estimation for at least one outcome (Pg. 4, para. 3 and Pg. 17, para. 1: Non-acute care and non-critical access hospitals, federal hospitals (e.g. Veterans Health Administration) and those outside of the 50 states and Washington, D.C. were excluded. Hospitals without capacity to perform a service, as reflected in their claim history, were excluded from the rating for that particular service.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Bachik, Dong, and Ostrovsky, to include the selection of the entities incorporating an exclusion of entities associated with one or more attributes indicative of non-inclusion, as taught by Brownlee, because the claimed invention is only a combination of these well-known elements which would have performed the same function in combination as each did separately. Bachik, Dong, and Ostrovsky already disclose selecting entities based on certain criteria, including procedures and conditions, and including more selection criteria, such as excluding federal hospitals as taught by Brownlee, would still perform the same function of selecting entities for ranking. Therefore, the results would have been predictable to one of ordinary skill in the art (MPEP 2143).
Claims 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Bachik et al. (US 2010/0169113) (hereinafter Bachik) in view of Dong et al., Rating Hospital Performance in China: Review of Publicly Available Measures and Development of a Ranking System (Hereinafter Dong), further in view of Ostrovsky et al. (US2015/0220699) (Hereinafter Ostrovsky), further in view of Brownlee et al. 2022 Methodology Lown Institute Hospitals Index for Social Responsibility (Hereinafter Brownlee), further in view of Forthman (US 2014/0207477).
Regarding Claim 8, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches, The computer-implemented method of claim 1, and Forthman further teaches:
The computer-implemented method of claim 1, wherein the one or more risk-adjusted outcomes have been risk-adjusted using a multi-level logistic regression model ([0027-31] Based on output derived from four (4) separate binary logistic regression models, the risk-adjusted quality indicators may be calculated).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Bachik, Dong, Ostrovsky, and Brownlee with the risk adjustment capabilities of Forthman because it allows the hospitals the ability to assimilate the various components of medical quality into a single, coherent measure of performance (Forthman [0022]).
Regarding Claim 10, the combination of Bachik, Dong, Ostrovsky, and Brownlee teaches, The computer-implemented method of claim 1, and Forthman further teaches:
The computer-implemented method of claim 1, wherein the one or more risk-adjusted outcomes include one or more of: mortality within a pre-determined time period; unplanned readmission within a pre-determined time period; surgical site infection, hip replacement, knee replacement, Abdominal Aortic Aneurysm Repair (AAA), Heart Bypass Surgery (CABG), and Aortic Valve Surgery (AVR) cohorts; revision within a pre-determined time period, hip replacement, and knee replacement cohorts; prolonged hospitalization, leukemia, lymphoma and myeloma and procedure cohorts; discharge to a location other than a patient’s home; stroke on procedure date, CABG, AVR, and Transcatheter Aortic Valve Replacement (TAVR) cohorts; or time spent at home within a pre-determined time period of discharge ([0028]-[0031] Risk-Adjusted Mortality Index, Risk-Adjusted Complications Index, Risk-Adjusted Inpatient Quality Index, Risk-Adjusted Patient Safety Index).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Bachik, Dong, Ostrovsky, and Brownlee with the risk adjustment capabilities of Forthman because it allows the hospitals the ability to assimilate the various components of medical quality into a single, coherent measure of performance (Forthman [0022]).
Response to Arguments
Applicant's arguments filed 05/07/2026 have been fully considered but they are not persuasive. With respect to the previous 101 rejection, Applicant argues the claims do not recite an abstract idea and that the claims cannot be practically performed in the human mind, however, the examiner respectfully disagrees. Applicant references SRI Int’l, Inc. v. Cisco Sys., Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) “the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims”, and states that the limitation in applicant’s claims “causing, using the one or more processors, a display of the rank for the one or more selected entities in association with the one or more procedures and/or conditions in a user interface of a device” similarly does not recite a mental process. However, the examiner determined that the step of causing a display of the rank is an additional element and is insignificant extra-solution activity, and therefore, the claim still recites a mental process. The steps of selecting entities and procedures and/or conditions, determining a performance score for each entity, generating a rank for the entities, risk-adjusting based on age, year of hospital admission, etc., and excluding certain entities based on attributes indicative of non-inclusion, are all functions that can be practically performed in the human mind.
Regarding Step 2A – Prong Two, applicant argues the claimed invention provides a technological improvement in the way in which the entities are selected. However, excluding entities from the set of candidate entities and ranking entities were found to be abstract ideas, specifically mental processes, which could practically be performed in the human mind. A person could filter out certain hospitals in their mind by determining if they meet the certain exclusion criteria listed, and could rank the filtered entities in their mind through observation, evaluation, judgement, and opinion. Therefore, the claims do not provide a technological improvement.
Applicant argues the office has not provided evidence proving the features recited in claim 1 are well-understood, routine, or conventional, however, examiner respectfully disagrees. In view of pages 6-7 of the Final Rejection mailed 03/11/2026, the examiner pointed out that the additional element of “collecting a plurality of data from one or more sources” is well-understood, routine, and conventional, and is similar to receiving and sending information over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OJP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); See MPEP 2106.05(d)(II)(i)). Additionally, the examiner has provided evidence in this Office Action proving the features recited in amended claim 1 are well-understood, routine, and conventional. Therefore, the rejection under 35 U.S.C. 101 is maintained.
Applicant’s arguments, see page 12 of Applicant’s Remarks, filed 05/07/2026, with respect to the rejection of claims 1-4, 7-17, and 20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art reference Brownlee et al. Brownlee discloses an index for ranking hospitals based on their performance across health outcomes, value, and equity, where creating the dataset of hospitals which will be selected involves excluding hospitals for various reasons. In particular, non-acute care and non-critical access hospitals, federal hospitals (e.g. Veterans Health Administration) and those outside of the 50 states and Washington, D.C. were excluded (See Brownlee Pg. 4, section “Creating the Hospital Set”).
Conclusion
Relevant Prior Art of Record Not Currently Being Applied
The relevant art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Ma et al. (CN111048194A) describes a regional hospital performance assessment method which compares hospital in a certain region and utilizes standardization and homogenization.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXIS K VAN DUZER whose telephone number is (571)270-5832. The examiner can normally be reached Monday thru Thursday 8-5 CT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.K.V./Examiner, Art Unit 3682
/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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