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 Preliminary Amendment filed on December 6, 2024 has been entered. Claim 27 was cancelled in the preliminary amendment. Claims 1-26 are pending and are rejected for the reasons set forth below.
Information Disclosure Statement
3. The Non-Patent Literature article titled, “American College of Occupational and Environmental Medicine, Copyright 2022 ACOEM” does not appear to be provided in the file wrapper for this application and/or parent application number 16/124,960. Therefore, this reference has not been considered by the examiner.
Claim Objections
4. The claims are objected to because of the following informalities, and the following is suggested to overcome the informalities and to improve claim clarity:
Claim 10 recites the limitation, “wherein the administrative rule set database: receiving, at an administrative rule set database from a client computer…” It appears that the applicant may have intended to state, “wherein the administrative rule set database: receives, from a client computer…” a similar issue is present throughout claim 10, and the claims should be amended to more clearly describe which process steps are performed by the administrative rule set database.
Appropriate correction or clarification is requested.
Claim Rejections - 35 USC §112
5. 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.
6. Claims 21-26 are rejected under 35 U.S.C. §112(b) or 35 U.S.C. §112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 21 recites the limitation, “wherein the percentile score is created…” There is insufficient antecedent basis for this limitation in the claim. Specifically, claim 20, from which claim 21 depends does not introduce the term “the percentile score” before it is referred to in this limitation. For the purpose of examination, this limitation has been interpreted as stating, “wherein a percentile score is created…”
Since claims 22-26 have the substantially same issue as claim 21, claims 22-26 are rejected for the grounds and rationale used to reject claim 21. Appropriate correction or clarification of these claims is required. No new matter may be added.
Double Patenting
7. 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(e) 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.
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.
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 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: 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,
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.
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 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.
21. The method of Claim 20, wherein the 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 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.
23. The method of Claim 20, 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.
24. The method of Claim 20, 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.
25. The method of Claim 20, 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.
26. The method of Claim 20, 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 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) 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, 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) 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 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 is obvious over claims 1-26 of the issued patent.
Claim Rejections - 35 USC § 101
8. 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.
8. 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
9. 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
10. 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
11. 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 limitation:
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 ideal data sets. However, the claims do not provide significant technical detail regarding how the ideal data sets are stored and/or how the 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
12. 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 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
13. 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)).
Dependent Claims
14. 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 falls 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).
Citation of Pertinent Prior Art
15. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bingham (U.S. Pre-Grant Publication No. 20120232924): Bingham is the prior art reference that is most closely related to the claims of the instant application. Bingham describes a system/method for improving the accuracy and consistency of the impairment rating process. The system may be used to compare an assessed impairment rating based on a diagnosis (i.e. an observed data set) to a probable impairment rating (i.e., an ideal data set). However, Bingham does not disclose many of the specifics recited in the independent claims. For example, Bingham is silent regarding the use of statistical models and pattern recognition techniques to analyze the diagnoses. Similarly, Bingham is silent regarding the use of real-time alerts to notify the clinician of invalid data entries. Additionally, Bingham does not state that the analysis comprises a percentage of data missing from the observed data. A suitable combination of prior art references could not be identified by the examiner to reasonable cure the deficiencies of Bingham. Therefore, for at least these reasons, the claims have not been rejected under 35 U.S.C. 102/103.
Tyuluman (U.S. Patent No. 5924073): Describes a system for assessing physician performance using robust multivariate techniques of statistical analysis. The statistical analysis may identify outliers in the data and establish a dynamic standard of care.
Ephrat (U.S. Patent No. 9892475): Describes a method and system for assisting clinical staff in providing optimal care in real-time and assisting in compliance with clinical standards and guidelines.
Murata (U.S. Pre-Grant Publication No. 20140236630): Describes a system and method for collecting, sharing and analyzing data of Electronic Medical Records (EMRs) for improved health analytics. Quality of health care delivery is assessed and improved through use of data from EMRs.
Conclusion
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/WILLIAM D NEWLON/Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696