Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. The Amendment filed July 24, 2026 has been entered. Claims 1-26 are pending and are rejected for the reasons set forth below.
Claim Objections
3. The claims are objected to because of the following informalities, and the following is suggested to overcome the informalities and to improve claim clarity:
Claims 21-26 were amended to recite the limitation, “wherein a percentile score is created…” However, claim 20 was amended to recite the limitation, “uploading the risk score to a historical evaluation database for creation of a percentile score based on one or more additional risk scores for similar body systems.” It is clear based on the applicant’s remarks and the structure of dependent claims 3-9 that the percentile scores referred to in these limitations refers to the same percentile score. Therefore, claims 21-26 should be amended to clarify this relationship between terms. For example, claims 21-26 should be amended to state, “wherein the percentile score is created…”
Appropriate correction or clarification is requested.
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07€ and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
5. Claims 1-26 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-26 of U.S. Patent No. 12165209. Although the claims at issue are not identical, they are not patentably distinct from each other. A mapping between the limitations of these claims is provided below. Additional context is provided where necessary to explain the similarities between the claims.
Instant Application
Issued Patent
1. (currently amended) A computer-implemented method of providing a deficiency analysis to improve quality and consistency of a clinician and quality and consistency across a medical provider network, the method comprising:
receiving, at an administrative rule set database from a client computer, an observed data set for an injury,
wherein the administrative rule set database stores a plurality of administrative rule sets,
wherein the observed data set is obtained from one or more tests performed by a clinician on an injured worker and transmitted to the administrative rule set database,
wherein the observed data set is used to generate an impairment rating determination of the injured worker,
wherein accuracy and integrity of the impairment rating determination for the injured worker are verified using statistical model and pattern recognition,
wherein the statistical model evaluates input data for anomalies and outliers, and
when the pattern recognition detects data that falls within a specified range, an anomaly response is triggered;
selecting at least one administrative rule set from the plurality of administrative rule sets based on a particular data collection sequence; and
based on the at least one administrative rule set, performing real-time validation calculations of the observed data set as the observed data set is being entered and alerting the clinician when the real-time validation calculations indicate that entered input data is outside expected data ranges for the injury,
wherein the administrative rule set database includes ideal data sets for injuries as determined according to the at least one administrative rule set;
comparing, at the administrative rule set database, the observed data set to an ideal data set, of the ideal data sets, for the injury to determine deficiencies in the observed data set, including determining that the observed data set is authentic and not synthesized; and
based on a comparison of the observed data set to the ideal data set, determining, at the administrative rule set database, a risk score for the observed data set,
wherein the risk score comprises a percentage of data missing from the observed data set as determined by the ideal data set; and
generating, by the administrative rule set database, a digital impairment deficiency report that includes the risk score.
2. The method of Claim 1, wherein the impairment rating comprises a maximum medical improvement.
3. The method of Claim 1, wherein the risk score is uploaded to a historical evaluation database for the creation of a percentile score based on one or more additional risk scores for similar body systems.
4. The method of Claim 3, wherein the percentile score created is based on a comparison to risk scores across all providers that have performed a similar exam.
5. The method of Claim 3, wherein the percentile score is created based on a comparison to risk scores of medical providers for the same specialty that have performed a similar exam.
6. The method of Claim 3, wherein the percentile score is created based on a comparison to risk scores of one or more doctors for a specific employer that have performed a similar exam.
7. The method of Claim 3, wherein the percentile score is created based on a comparison to risk scores of medical providers used by a specific insurance company that have performed a similar exam.
8. The method of Claim 3, wherein the percentile score is created based on a comparison to risk scores of medical providers within a specific area that have performed a similar exam.
9. The method of Claim 3, wherein the percentile score is created based on a comparison to risk scores of medical providers within a specific zip code that have performed a similar exam.
10. A system for providing a deficiency analysis to improve quality and consistency of a clinician and quality and consistency across a medical provider network, the system comprising:
an administrative rule set database including ideal data sets for injuries as determined according to an administrative rule set for the injuries,
wherein the administrative rule set database is configured to: receive from a client computer, an observed data set for an injury,
wherein the administrative rule set database stores a plurality of administrative rule sets,
wherein the observed data set is obtained from one or more tests performed by a clinician on an injured worker and transmitted to the administrative rule set database,
wherein the observed data set is used to generate an impairment rating determination of the injured worker,
wherein accuracy and integrity of the impairment rating determination for the injured worker are verified using statistical model and pattern recognition,
wherein the statistical model evaluates input data for anomalies and outliers, and
when the pattern recognition detects data that falls within a specified range, an anomaly response is triggered;
select at least one administrative rule set from the plurality of administrative rule sets based on a particular data collection sequence; and
based on the at least one administrative rule set, perform real-time validation calculations of the observed data set as the observed data set is being entered and alert the clinician when the real-time validation calculations indicate that entered input data is outside expected data ranges for the injury,
wherein the administrative rule set database includes ideal data sets for injuries as determined according to the at least one administrative rule set;
compare, at the administrative rule set database, the observed data set to an ideal data set, of the ideal data sets, for the injury to determine deficiencies in the observed data set, including determining that the observed data set is authentic and not synthesized; and
based on a comparison of the observed data set to the ideal data set, determine, at the administrative rule set database, a risk score for the observed data set,
wherein the risk score comprises a percentage of data missing from the observed data set as determined by the ideal data set; and
generate, by the administrative rule set database, a digital impairment deficiency report that includes the risk score.
11. The system of Claim 10, wherein the impairment rating comprises a maximum medical improvement.
12. The system of Claim 10, wherein the administrative rule set database comprises a HIPAA compliant database.
13. The system of Claim 10 comprising a historical evaluation database configured to compare the risk score to one or more additional risk scores for similar body systems.
14. The system of Claim 13, wherein based on the comparison of the risk score to the one or more additional risk scores for similar body systems, a percentile score for the impairment rating is created.
15. The system of Claim 14, wherein the percentile score is created based on a comparison to risk scores of medical providers for the same specialty that have performed a similar exam.
16. The system of Claim 14, wherein the percentile score is created based on a comparison to risk scores of one or more doctors for a specific employer that have performed a similar exam.
17. The system of Claim 14, wherein the percentile score is created based on a comparison to risk scores of medical providers used by a specific insurance company that have performed a similar exam.
18. The system of Claim 14, wherein the percentile score is created based on a comparison to risk scores of risk scores of medical providers within a specific area that have performed a similar exam.
19. The system of Claim 14, wherein the percentile score is created based on a comparison to risk scores of medical providers within a specific zip code that have performed a similar exam.
20. A computer-implemented method of providing a deficiency analysis to improve quality and consistency of a clinician and quality and consistency across a medical provider network, the method comprising:
receiving, at an administrative rule set database from a client computer, an observed data set for an injury,
wherein the administrative rule set database stores a plurality of administrative rule sets,
wherein the observed data set is obtained from one or more tests performed by a clinician on an injured worker and transmitted to the administrative rule set database,
wherein the observed data set is used to generate an impairment rating determination of the injured worker,
wherein accuracy and integrity of the impairment rating determination for the injured worker are verified using statistical model and pattern recognition,
wherein the statistical model evaluates input data for anomalies and outliers, and
when the pattern recognition detects data that falls within a specified range, an anomaly response is triggered;
selecting at least one administrative rule set from the plurality of administrative rule sets based on a particular data collection sequence; and
based on the at least one administrative rule set, performing real-time validation calculations of the observed data set as the observed data set is being entered and alerting the clinician when the real-time validation calculations indicate that entered input data is outside expected data ranges for the injury,
wherein the administrative rule set database includes ideal data sets for injuries as determined according to the at least one administrative rule set;
comparing, at the administrative rule set database, the observed data set to an ideal data set, of the ideal data sets, for the injury to determine deficiencies in the observed data set; and
based on a comparison of the observed data set to the ideal data set, determining, at the administrative rule set database, a risk score for the observed data set,
wherein the risk score comprises a percentage of data missing from the observed data set as determined by the ideal data set; and
generating, by the administrative rule set database, a digital impairment deficiency report that includes the risk score; and
uploading the risk score to a historical evaluation database for creation of a percentile score based on one or more additional risk scores for similar body systems.
21. The method of Claim 20, wherein a percentile score is created based on a comparison to risk scores across all providers that have performed a similar exam.
22. The method of Claim 20, wherein a percentile score is created based on a comparison to risk scores of medical providers for the same specialty that have performed a similar exam.
23. The method of Claim 20, wherein a percentile score is created based on a comparison to risk scores of one or more doctors for a specific employer that have performed a similar exam.
24. The method of Claim 20, wherein a percentile score is created based on a comparison to risk scores of medical providers used by a specific insurance company that have performed a similar exam.
25. The method of Claim 20, wherein a percentile score is created based on a comparison to risk scores of risk scores of medical providers within a specific area that have performed a similar exam.
26. The method of Claim 20, wherein a percentile score is created based on a comparison to risk scores of medical providers within a specific zip code that have performed a similar exam.
(claim 1) A computer-implemented method of providing a deficiency analysis to improve quality and consistency of a clinician and quality and consistency across a medical provider network, the method comprising:
(claim 1) receiving, at an administrative rule set database from a client computer, encrypted data including an observed data set for an injury,
(claim 1) selecting at least one administrative rule set from the plurality of administrative rule sets. Examiner’s Note: While these limitations are not identical, they are not patentably distinct. While the issued patent does not explicitly state that a plurality of administrative rule sets are stored at the administrative rule set database, the issued patent clearly indicates that a plurality of administrative rule sets may be selected from the administrative rule set database. Therefore, it would have been obvious to one of ordinary skill in the art that the administrative rule set database described in the issued patent stores a plurality of administrative rule sets.
(claim 1) wherein the observed data set is obtained from one or more tests performed by a clinician on an injured worker and… transmitted to the administrative rule set database
(claim 1) expanding the observed data set such that only necessary data is collected for an impairment rating determination of the injured worker. Examiner’s Note: While these limitations are not identical, they are not patentably distinct. Both limitations recite a process for utilizing an observed dataset to generate an impairment rating determination for the injured worker
(claim 1) wherein accuracy and integrity of the impairment rating determination for the injured worker are verified using statistical model and pattern recognition
(claim 1) wherein the statistical model evaluates input data for anomalies and outliers, and
(claim 1) when the pattern recognition detects data that falls within a specified range, an anomaly response is triggered,
(claim 1) selecting at least one administrative rule set from the plurality of administrative rule sets based on the particular data collection sequence; and
(claim 1) based on the at least one administrative rule set, performing real-time validation calculations of the observed data set as the observed data set is being entered and alerting the clinician when the real-time validation calculations indicate that entered input data is outside expected data ranges for the injury
(claim 1) wherein the administrative rule set database includes ideal data sets for injuries as determined according to the at least one administrative rule set;
(claim 1) comparing, at the administrative rule set database, the observed data set to an ideal data set, of the ideal data sets, for the injury to determine deficiencies in the observed data set, including determining that the observed data set is authentic and not synthesized;
(claim 1) based on the comparison of the observed data set to the ideal data set, determining, at the administrative rule set database, a risk score for the observed data set,
(claim 1) wherein the risk score comprises a percentage of data missing from the observed data set as determined by the ideal data set;
(claim 1) generating, by the administrative rule set database, a digital impairment deficiency report that includes the risk score
(claim 2) wherein an impairment rating associated with the observed data set comprises a maximum medical improvement
(claim 3) wherein the risk score is uploaded to a historical evaluation database for the creation of a percentile score based on one or more additional risk scores for similar body systems
(claim 4) wherein the percentile score created is based on a comparison to risk scores across all providers that have performed a similar exam
(claim 5) wherein the percentile score is created based on a comparison to risk scores of medical providers for the same specialty that have performed a similar exam
(claim 6) wherein the percentile score is created based on a comparison to risk scores of one or more doctors for a specific employer that have performed a similar exam
(claim 7) wherein the percentile score is created based on a comparison to risk scores of medical providers used by a specific insurance company that have performed a similar exam
(claim 8) wherein the percentile score is created based on a comparison to risk scores of medical providers within a specific area that have performed a similar exam.
(claim 9) wherein the percentile score is created based on a comparison to risk scores of medical providers within a specific zip code that have performed a similar exam.
(claim 10) A system for providing a deficiency analysis to improve quality and consistency of a clinician and quality and consistency across a medical provider network, the system comprising:
(claim 10) an administrative rule set database including ideal data sets for injuries as determined according to an administrative rule set for the injuries,
(claim 10) wherein the administrative rule set database: receives, from a client computer, encrypted data including an observed data set for an injury,
(claim 10) selecting at least one administrative rule set from the plurality of administrative rule sets. Examiner’s Note: While these limitations are not identical, they are not patentably distinct. While the issued patent does not explicitly state that a plurality of administrative rule sets are stored at the administrative rule set database, the issued patent clearly indicates that a plurality of administrative rule sets may be selected from the administrative rule set database. Therefore, it would have been obvious to one of ordinary skill in the art that the administrative rule set database described in the issued patent stores a plurality of administrative rule sets.
(claim 10) wherein the observed data set is obtained from one or more tests performed by a clinician on an injured worker and encrypted by a shell program executing on the client computer and transmitted to the administrative rule set database
(claim 10) thereby expanding the observed data set such that only necessary data is collected for an impairment rating determination of the injured worker, Examiner’s Note: While these limitations are not identical, they are not patentably distinct. Both limitations recite a process for utilizing an observed dataset to generate an impairment rating determination for the injured worker
(claim 10) wherein accuracy and integrity of the impairment rating determination for the injured worker are verified using statistical model and pattern recognition,
(claim 10) wherein the statistical model evaluates input data for anomalies and outliers, and
(claim 10) when the pattern recognition detects data that falls within a specified range, an anomaly response is triggered,
(claim 10) selecting at least one administrative rule set from the plurality of administrative rule sets based on the particular data collection sequence; and
(claim 10) based on the at least one administrative rule set, performing real-time validation calculations of the observed data set as the observed data set is being entered and alerting the clinician when the real-time validation calculations indicate that entered input data is outside expected data ranges for the injury,
(claim 10) wherein the administrative rule set database includes ideal data sets for injuries as determined according to the at least one administrative rule set;
(claim 10) compares the observed data set to an ideal data set, of the ideal data sets, for the injury to determine deficiencies in the observed data set, including determining that the observed data set is authentic and not synthesized;
(claim 10) based on the comparison of the observed data set and the ideal data set, determines a risk score for the observed data set,
(claim 10) wherein the risk score comprises a percentage of data missing from the observed data set as determined by the ideal data set;
(claim 10) generates a digital impairment deficiency report that includes the risk score
(claim 11) wherein an impairment rating associated with the observed data set comprises a maximum medical improvement
(claim 12) wherein the administrative rule set database comprises a HIPAA compliant database
(claim 13) a historical evaluation database configured to compare the risk score to one or more additional risk scores for similar body systems.
(claim 14) wherein based on the comparison of the risk score to the one or more additional risk scores for similar body systems, a percentile score for the impairment rating is created
(claim 15) wherein the percentile score is created based on a comparison to risk scores of medical providers for the same specialty that have performed a similar exam
(claim 16) wherein the percentile score is created based on a comparison to risk scores of one or more doctors for a specific employer that have performed a similar exam
(claim 17) wherein the percentile score is created based on a comparison to risk scores of medical providers used by a specific insurance company that have performed a similar exam
(claim 18) wherein the percentile score is created based on a comparison to risk scores of risk scores of medical providers within a specific area that have performed a similar exam
(claim 19) wherein the percentile score is created based on a comparison to risk scores of medical providers within a specific zip code that have performed a similar exam
(claim 20) A computer-implemented method of providing a deficiency analysis to track quality and consistency of a clinician and quality and consistency across a medical provider network, the method comprising:
(claim 20) receiving, at an administrative rule set database from a client computer, encrypted data including an observed data set for an injury,
(claim 20) selecting at least one administrative rule set from the plurality of administrative rule sets. Examiner’s Note: While these limitations are not identical, they are not patentably distinct. While the issued patent does not explicitly state that a plurality of administrative rule sets are stored at the administrative rule set database, the issued patent clearly indicates that a plurality of administrative rule sets may be selected from the administrative rule set database. Therefore, it would have been obvious to one of ordinary skill in the art that the administrative rule set database described in the issued patent stores a plurality of administrative rule sets.
(claim 20) wherein the observed data set is obtained from one or more tests performed by a clinician on an injured worker… and transmitted to the administrative rule set database
(claim 20) thereby expanding the observed data set such that only necessary data is collected for an impairment rating determination of the injured worker, Examiner’s Note: While these limitations are not identical, they are not patentably distinct. Both limitations recite a process for utilizing an observed dataset to generate an impairment rating determination for the injured worker
(claim 20) wherein accuracy and integrity of the impairment rating determination for the injured worker are verified using statistical model and pattern recognition,
(claim 20) wherein the statistical model evaluates input data for anomalies and outliers, and
(claim 20) when the pattern recognition detects data that falls within a specified range, an anomaly response is triggered,
(claim 20) selecting at least one administrative rule set from the plurality of administrative rule sets based on the particular data collection sequence; and
(claim 20) based on the at least one administrative rule set, performing real-time validation calculations of the observed data set as the observed data set is being entered and alerting the clinician when the real-time validation calculations indicate that entered input data is outside expected data ranges for the injury,
(claim 20) wherein the administrative rule set database includes ideal data sets for injuries as determined according to the at least one administrative rule set;
(claim 20) comparing, at the administrative rule set database, the observed data set to an ideal data set, of the ideal data sets, for the injury to determine deficiencies in the observed data set,
(claim 20) based on the comparison of the observed data set to the ideal data set, determining, at the administrative rule set database, a risk score for the observed data set,
(claim 20) wherein the risk score comprises a percentage of data missing from the observed data set as determined by the ideal data set;
(claim 20) generating, by the administrative rule set database, a digital impairment deficiency report that includes the risk score
(claim 3) wherein the risk score is uploaded to a historical evaluation database for the creation of a percentile score based on one or more additional risk scores for similar body systems
(claim 21) wherein the percentile score is created based on a comparison to risk scores across all providers that have performed a similar exam
(claim 22) wherein the percentile score is created based on a comparison to risk scores of medical providers for the same specialty that have performed a similar exam
(claim 23) wherein the percentile score is created based on a comparison to risk scores of one or more doctors for a specific employer that have performed a similar exam
(claim 24) wherein the percentile score is created based on a comparison to risk scores of medical providers used by a specific insurance company that have performed a similar exam
(claim 25) wherein the percentile score is created based on a comparison to risk scores of risk scores of medical providers within a specific area that have performed a similar exam
(claim 26) wherein the percentile score is created based on a comparison to risk scores of medical providers within a specific zip code that have performed a similar exam
Therefore, because claims 1-26 the issued patent teach each limitation of claims 1-26 of the instant application, claims 1-26 of the instant application are anticipated by claims 1-26 of the issued patent.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1-26 are rejected under 35 U.S.C. §101 because the claimed invention recites and is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and does not include an inventive concept that is “significantly more” than the judicial exception under the January 2019 and October 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Step 1
8. Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process (claims 1-9 and 20-26) and a machine (claims 10-19). Therefore, we proceed to step 2A, Prong 1.
Step 2A, Prong 1
9. Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Claim 1 recites the abstract idea of:
A computer-implemented method of providing a deficiency analysis to improve quality and consistency of a clinician and quality and consistency across a medical provider network, the method comprising:
receiving, [[at an administrative rule set database from a client computer]], an observed data set for an injury,
wherein the observed data set is obtained from one or more tests performed by a clinician on an injured worker and transmitted to [[the administrative rule set database]],
wherein the observed data set is used to generate an impairment rating determination of the injured worker,
selecting at least one administrative rule set from the plurality of administrative rule sets based on a particular data collection sequence; and
comparing, [[at the administrative rule set database]], the observed data set to an ideal data set, of the ideal data sets, for the injury to determine deficiencies in the observed data set, including determining that the observed data set is authentic and not synthesized; and
based on a comparison of the observed data set to the ideal data set, determining, [[at the administrative rule set database]], a risk score for the observed data set,
wherein the risk score comprises a percentage of data missing from the observed data set as determined by the ideal data set; and
generating, [[by the administrative rule set database]], a digital impairment deficiency report that includes the risk score.
Here, the recited abstract idea falls within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, to wit: certain methods of organizing human activity, which includes managing personal behavior (e.g., following a set of instructions to generate a report). In other words, the claim recites a set of instructions for receiving and evaluating a physician’s ability to produce an authentic and accurate medical report.
Step 2A, Prong 2
10. Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which claim 1 is directed does not include limitations or additional elements that integrate the abstract idea into a practical application.
Besides reciting the abstract idea, the limitations of claim 1 also recite generic computer components (e.g., an administrative rule set database, a client computer, and a statistical model). In particular, the recited features of the abstract idea are merely being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See e.g., MPEP §2106.05(f)). Therefore, these additional elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. In other words, the additional elements are simply used as tools to perform the abstract idea.
Additionally, claim 1 recites the limitation, “wherein accuracy and integrity of the impairment rating determination for the injured worker are verified using statistical model and pattern recognition, wherein the statistical model evaluates input data for anomalies and outliers, and when the pattern recognition detects data that falls within a specified range, an anomaly response is triggered.” These limitations simply state that the accuracy and integrity of the impairment rating determination is verified using “statistical model and pattern recognition.” However, the claims do not provide any technical detail regarding how the statistical model and/or pattern recognition are implemented. Rather, the claim simply states that the statistical model detects anomalies and outliers, and the pattern recognition detects data that falls within a specified range. Therefore, such limitations amount to no more than merely applying a generic computer-based model and techniques to implement the abstract idea on a computer.
Claim 1 also recites the following limitation:
based on the at least one administrative rule set, performing real-time validation calculations of the observed data set as the observed data set is being entered and alerting the clinician when the real-time validation calculations indicate that entered input data is outside expected data ranges for the injury.
This limitation merely states that the system outputs an alert to the clinician when the real-time validation calculations indicate that entered input data is outside expected data ranges for the injury. However, the claim does not provide significant technical detail regarding how the alert is displayed to the clinician and/or how the clinician interacts with the alert. Therefore, this limitation amounts to no more than merely outputting/displaying data, which is a form of insignificant extra-solution activity (See MPEP 2016.05(g): OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
Claim 1 also recites the following limitations:
wherein the administrative rule set database stores a plurality of administrative rule sets; and
wherein the administrative rule set database includes ideal data sets for injuries as determined according to the at least one administrative rule set.
These limitations merely state that the administrative rule set database includes/stores a plurality of administrative rule sets and ideal data sets. However, the claims do not provide significant technical detail regarding how the administrative rule sets and ideal data sets are stored and/or how the administrative rule sets and ideal data sets are retrieved from the database. Therefore, such limitations amount to no more than merely storing data, which is a form of insignificant extra-solution activity (See MPEP 2016.05(d): Versata Dev. Group, Inc. v. SAP Am., Inc., 793F.3d 1306, 1334 (Fed. Cir. 2015); and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d at 1363).
Thus, claim 1 does not include any limitations or additional elements that integrate the abstract idea into a practical application. As a result, claim 1 is directed to an abstract idea.
Step 2B
11. Under the 2019 PEG step 2B analysis, the additional elements of claim 1 are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the recited additional elements (e.g., an administrative rule set database, a client computer, and a statistical model), do not amount to an innovative concept since, as stated above in the Step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming (See e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality such that they are being used in the claims to simply implement the abstract idea and are not themselves being technologically improved (See e.g., MPEP 2106.05(I)(A)).
Additionally, the following limitation identified above as insignificant extra-solution activity (merely outputting/displaying data) has been reevaluated under Step 2B:
based on the at least one administrative rule set, performing real-time validation calculations of the observed data set as the observed data set is being entered and alerting the clinician when the real-time validation calculations indicate that entered input data is outside expected data ranges for the injury.
As stated in MPEP 2106.05(d), a factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018)). In view of this requirement set forth by Berkheimer, this limitation does not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of merely outputting/displaying data to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
Additionally, the following limitation identified above as insignificant extra-solution activity (merely storing data) has been reevaluated under Step 2B:
wherein the administrative rule set database stores a plurality of administrative rule sets; and
wherein the administrative rule set database includes ideal data sets for injuries as determined according to the at least one administrative rule set.
In view of the requirement set forth by Berkheimer, this limitation does not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of merely storing data to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): Versata Dev. Group, Inc. v. SAP Am., Inc., 793F.3d 1306, 1334 (Fed. Cir. 2015); and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d at 1363).
Thus, claim 1 does not recite any additional elements that amount to “significantly more” than the abstract idea.
Additional Independent Claims
12. Independent claims 10 and 20 are similarly rejected under 35 U.S.C. 101 for the reasons described below:
Claim 10 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 10 and 1 is that claim 10 is drafted as a system rather than as a method. Similarly, as described above regarding claim 1, claim 10 recites generic computer components (e.g., an administrative rule set database, a client computer, and a statistical model) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 1 and 10, claim 10 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Claim 20 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 20 and 1 is that claim 20 omits certain limitations recited in claim 1. Therefore, claim 20 is simply a more broadly recited method. Similarly, as described above regarding claim 1, claim 20 recites generic computer components (e.g., an administrative rule set database, a client computer, and a statistical model) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 1 and 20, claim 20 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Additionally, claim 20 recites the limitation, “uploading the risk score to a historical evaluation database for creation of a percentile score based on one or more additional risk scores for similar body systems.” This limitation simply states that the historical evaluation database receives the risk score in order to determine a percentile score. However, this limitation simply refines the abstract idea because it recite process steps (e.g., creating a percentile score based on the risk score) that falls under the category of organizing human activity, as described above regarding claim 1.
Dependent Claims
13. Dependent claims 2-9, 11-19, and 21-26 are also rejected under 35 U.S.C. 101 for the reasons described below:
Claims 2 and 11 simply provide further definition to the “impairment rating” recited in claims 1 and 10. Simply stating that the impairment rating comprises a maximum medical improvement does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of information included in the impairment rating.
Claims 3, 13, and 14 simply refine the abstract idea because they recite process steps (e.g., comparing the risk scores to similar body systems to generate a percentile score) that fall under the category of organizing human activity, as described above regarding claim 1. Additionally, merely stating that this process is performed by “a historical evaluation database” amounts to no more than merely applying generic computer components to implement the abstract idea on a computer.
Claims 4-9, 15-19, and 21-26 simply provide further definition to the “percentile score” recited in claims 3 and 14. Simply stating that the percentile score is based on various parameters (e.g., a comparison to risk scores across all providers that have performed a similar exam) does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of information used to determine the percentile score.
Claim 12 simply provides further definition to the “administrative rule set database” recited in claim 10. Simply stating that the administrative rule set database is HIPPA compliant does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of database used by the system.
Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Response to Arguments
14. Applicant’s arguments filed July 24, 2026 have been fully considered.
Arguments Regarding 35 U.S.C. 112(b)
15. The examiner notes that the amendments and remarks provided by the applicant address the antecedent basis issue identified by the examiner in the Non-Final Rejection. However, the applicant should amend claims 21-26 as noted in the claim objections above in order to further clarify that the percentile scores recited in claims 21-26 refer to the percentile score calculated in amended claim 20.
Arguments Regarding Double Patenting
16. As noted in the double patenting rejection above, the claims of the instant application have not been sufficiently differentiated from the claims of the issued patent. Therefore, the rejection has been maintained. However, the rejection has been updated to reflect the newly added claim amendments.
Arguments Regarding 35 U.S.C. 101
17. Applicant’s arguments (Amendment, Pgs. 9-17) concerning the prior rejection of the claims under 35 USC §101, including supposed deficiencies in the rejection, are not persuasive for the following reasons. Under the prior and current 101 analysis under 2019 PEG, the amended claims recite and are directed to a patent ineligible abstract idea, without something significantly more, for the reasons given above after consideration of the claimed features and elements. The abstract idea has been restated herein in line with the 2019 PEG guidance and the amended claims. Applicant is directed to the above full Alice/Mayo analysis in the 101 rejection.
Additionally, on pages 9 and 10 of their remarks, the applicant argues, “The independent claims recite multiple steps that cannot practically be performed in the human mind.” The examiner notes that the claims have not been categorized as a mental process. Rather, the claims have been categorized as a certain method of organizing human activity. Therefore, whether certain claim limitations can practically be performed in the human mind is not necessarily a consideration for the examiner’s analysis. Thus, the applicant’s argument is moot.
Additionally, on page 10 of their remarks, the applicant argues, “The Office Action further alleges that the claims fall within the "certain methods of organizing human activity" grouping under the sub-grouping of managing personal behavior (e.g., following a set of instructions to generate a report)… This characterization is an oversimplification of the claims. The claims do not merely instruct a clinician to follow rules; they recite a specific, computer-implemented data-validation workflow in which…” The examiner respectfully disagrees. Specifically, the examiner notes that the claims recite instructions for receiving and evaluating a dataset received from a clinician while performing one or more tests on an injured worker, and instructions for generating risk scores/reports corresponding to the observed dataset. The examiner notes that such limitations are similar to the examples provided in MPEP 2106.04(a)(2)(II)(C), such as a series of instructions of how to hedge risk. The limitations recited in the claims of the instant application correspond to a series of instructions for observing and reporting risk associated with a clinician’s reporting.
Additionally, on page 11 of their remarks, the applicant argues, “Here, likewise, the claims do not merely recite a desired result of identifying deficient impairment data; they recite the specific manner of achieving it… Moreover, the specificity of the claimed steps avoids the preemption concern that animates the judicial exception to § 101. The concern underlying the exclusion of abstract ideas is "one of pre-emption "-that a patent not foreclose use of the underlying idea itself. Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 216 (2014). The present claims do not preempt the general concept of reviewing medical data or generating a report.” The examiner notes that preemption is not a standalone test for patent eligibility. Furthermore, preemption concerns have already been addressed by the Examiner through the application of the two-step framework. A specific abstract idea is still an abstract idea and is not eligible for patent protection without significantly more recited in the claim (See Ariosa Diagnostics, In.c v. Seqenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015); see also OIP Tech., Inc. v. Amazon.com, Inc. 788 F.3d 1359, 1362-63 (Fed Cir. 2015); Return Mail, Inc. v. USPS, 123 USPQ2d 1813, 1827 (Fed. Cir. 2017)). While preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility (Ariosa, 788 F.3d 1379).
Additionally, on pages 11 and 12 of their remarks, the applicant argues, “The independent claims recite a specific technological improvement to electronic impairment-rating data integrity… The specification's worked example confirms that this is a concrete technological improvement and not an abstract result. As shown in FIGS. 3A-3B and the accompanying description, an impairment rating report may indicate a facially complete result-there, a 0% Whole Person Impairment rating-while the corresponding deficiency report generated by the claimed process reveals that 31.7% of the total ratable data set is missing, including 82.6% of the objective-findings data.” The examiner respectfully disagrees. Specifically, the examiner notes that the mere fact that the claimed invention is able to accurately identify missing data in a report does not necessarily indicate the claims recite a technical improvement to any technology or technological field. Rather, the improvement to technology that produces the desired result must be evident in the claims. As noted in the 101 rejection above, the claims do not provide any indication of an improvement to any technology or technological field that produces the result identified by the applicant. Rather, they merely recite limitations that amount to an improvement to the abstract idea itself.
Additionally, on page 12 of their remarks, the applicant argues, “The present claims are eligible for reasons similar to those in Claim 3 of Example 47 of the July 2024 PEG Examples (anomaly detection).” Similarly, on page 13 of their remarks, the applicant argues, “The independent claims are further eligible for reasons similar to those underlying Claim 2 of Example 46 of the Appendix 1 to the October 2019 Update: Subject Matter Eligibility (the "October 2019 PEG Examples").” The examiner respectfully disagrees. Specifically, the examiner notes that the claims of the instant application are not analogous to the claims of Examples 46 and 47. For example, claim 2 of Example 46 was found to be patent eligible because, “Limitation (d) in combination with the feed dispenser enables the control of appropriate farm equipment based on the automatic detection of grass tetany, which goes beyond merely automating the abstract idea.” In other words, Example 46 recites a specific process for physically controlling farm equipment. The claims of the instant application do not recite limitations similar to the limitations of Example 46. Merely stating that an alert is sent to the clinician when an anomaly is detected is not equivalent to physically controlling machinery, as discussed in Example 46. Similar arguments can be made regarding the relevance of Example 47 to the claims of the instant application.
Additionally, on pages 13 and 14 of their remarks, the applicant argues, “The Office Action's contrary characterization of the real-time validation limitation as insignificant extra-solution activity that merely outputs or displays data, citing OIP Technologies, Inc. v. Amazon.com, Inc. (Office Action, pages 16-17), does not withstand scrutiny… The validation is performed contemporaneously with data entry against rule-set-defined expected ranges, and the alert is conditioned on the outcome of that computation. This is a substantive computational operation that changes the data-collection process itself by intercepting deficient data at the point of entry, not a post-solution display of an already-computed result. The presence of a downstream output, does not convert the antecedent real-time computation into extra-solution activity.” The examiner respectfully disagrees. Specifically, the examiner notes that the claims do not provide significant technical detail regarding how the alert is displayed to the clinician and/or how the clinician interacts with the alert. Rather, the claim simply states that the alert is displayed in “real-time” when the values input by the clinician are outside of expected ranges. Such limitations do not provide any indication of a technical improvement to the functioning of the user interface itself or any other technology or technological field. Rather, such limitations amount to no more than merely outputting an alert based on an analysis of the input data.
Additionally, on page 14 of their remarks, the applicant argues, “Likewise, the Office Action characterizes the limitation reciting that the administrative rule set database includes ideal data sets as nothing more than storing data, citing Versata Development Group, Inc. v. SAP America, Inc. (Office Action, pages 18-19). But the limitation should not be evaluated in isolation. The ideal data sets are not stored as inert records; they are the reference standard against which the observed data set is compared to determine deficiencies and to compute the percentage-missing-data risk score, and they are themselves defined according to the selected administrative rule set.” The examiner respectfully disagrees. The mere fact that the data stored within the administrative rule set database is accessed to determine deficiencies of the clinician’s report does not integrate the abstract idea into a practical application. The claims do not provide any technical detail regarding how the data is stored and/or how the data is accessed. Therefore, such limitations amount to no more that soring data within a database.
Additionally, on pages 14 and 15 of their remarks, the applicant argues, “The Office Action's own prior-art discussion confirms that the record does not support a finding that the claimed ordered combination was well-understood, routine, and conventional… Where the Examiner cannot find the claimed combination in any combination of prior art, there is no basis for a finding that the combination was well-understood, routine, and conventional at the time of filing.” Similarly, on pages 15 and 16 of their remarks, the applicant argues, “Whether a claim element or combination is well-understood, routine, and conventional is a question of fact that must be supported by evidence in the record… Notably, the Office Action invokes this evidentiary standard only as to the limitations it characterizes as outputting/displaying data and storing data; it makes no factual showing whatsoever as to the claimed ordered combination as a whole.” The examiner respectfully disagrees. Specifically, it is not required for the examiner to provide Berkheimer evidence for each limitation recited in the claim. Rather, this evidence is only required when the examiner identifies a limitation as being well-understood, routine, and conventional (e.g., when identifying a limitation as reciting insignificant extra-solution activity). The examiner has not claimed that each limitation recited in the claim is well-understood, routine, and conventional. As stated in MPEP2106.07(a)(III), “At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B.” Such evidence has been provided by the examiner where appropriate under Steb 2B. Additionally, the examiner notes that the “well-understood, routine, and conventional” analysis is separate from the analysis of the claims under 35 U.S.C. 102/103. As stated in MPEP 2106.05(I): Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101.” Therefore, the lack of a prior art rejection does not necessarily indicate that a particular additional element does not recite well-understood, routine, and conventional activity.
Additionally, on pages 16 and 17 of their remarks, the applicant argues, “Even setting aside the patent eligibility of the independent claims, the dependent claims independently recite patent-eligible subject matter and should not have been rejected without separate consideration.” The examiner respectfully disagrees. Specifically, the examiner has considered the dependent claims for eligibility under 35 U.S.C. 101. (See the Non-Final Rejection, pages 19 and 20). For example, Claims 3, 13, and 14 simply refine the abstract idea because they recite process steps (e.g., comparing the risk scores to similar body systems to generate a percentile score) that fall under the category of organizing human activity. Simply stating that these process steps are performed by a “structurally distinct database” and “a specific computation operation” does not integrate the abstract idea into a practical application. The claims do not provide any technical detail regarding the structure of the historical evaluation database and/or the operations it performs. Therefore, such limitations amount to no more than merely applying a generic, known database type to perform the abstract idea. Similarly, claim 12 simply states that the administrative rule state database is a HIPAA-compliant database. While a HIPAA-compliant database may comprise a specific technical configuration, simply stating that the claimed processes are performed using a HIPAA-compliant database does not integrate the abstract idea into a practical application. Rather, this amounts to no more than merely applying an existing type of database to perform the abstract idea. The claims and specification provide no technical detail regarding how the HIPAA-compliant database is structured, how it has been improved, and/or how it is utilized to perform the claimed processes. Rather, the only detail provided in the claims and specification is that the administrative rule set database may comprise a HIPAA-compliant database. Similarly, claims 4-9, 15-19, and 21-26 simply define the type of data used to determine the percentile score. The claims do not, as argued by the applicant, add “structural and operational specificity to the claimed system.” The claims do not provide any technical detail regarding how the historical evaluation database utilizes this data to determine the percentile scores. Lastly, claims 2 and 12 simply state that the impairment rating comprises a maximum medical improvement. The mere fact that determining the maximum medical improvement requires specific data, timing of validation, and type of report generation does not integrate the abstract idea into a practical application. The claims and specification provide no technical detail regarding these operations. Rather, the claims simply state that the impairment rating may comprise a maximum medical improvement. Therefore, for at least these reasons, the dependent claims do not integrate the abstract idea into a practical application.
Therefore, for these reasons and the reasons given above, the rejection of these claims under 35 U.S.C. §101 is maintained.
Citation of Pertinent Prior Art
18. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Reiner (U.S. Pre-Grant Publication No. 20110276346): Describes an automated method for quality assurance (QA) which creates quality-centric data contained within a medical report, and uses these data elements to determine report accuracy and correlation with clinical outcomes.
Lamkin (U.S. Pre-Grant Publication No. 20160232472): Describes a method for improving physician performance in measured areas. A quantitative measure of a type of physician is established, the quantitative measure including compiling statistical data of such quantitative measures for the type of physician and calculating at least one threshold statistical value.
Conclusion
THIS ACTION IS MADE FINAL. 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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/WILLIAM D NEWLON/Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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