DETAILED ACTION
Status of Claims
This communication is in response to the amendment filed 03/13/2026.
Claims 1, 20 and 24 have been amended.
Claims 12 and 14-15 have been cancelled.
Claims 1-11, 13 and 16-24 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11, 13 and 16-24 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-11, 13 and 16-23 are directed to a method (i.e., a process) and claim 24 is directed to a system (i.e., a machine). Accordingly, claims 1-11, 13 and 16-24 are all within at least one of the four statutory categories.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 24 includes limitations that recite an abstract idea. Note that independent claim24 is the system claim, while claims 1 and 20 cover method claims.
Specifically, independent claim 24 recites:
A diagnostic test system, comprising:
a processor and memory, the memory including instructions executable on the processor to:
store in the memory a plurality of diagnostic test orders received from a plurality of medical providers and characteristics of the plurality of medical providers describing one or more characteristics of each of the plurality of medical providers;
identify a peer group of medical providers having one or more like characteristics determined from the stored characteristics of the plurality of medical providers, the processor employing k-means clustering to define a first cluster for a first characteristic of one or more of the plurality of medical providers stored in the memory, the k-means clustering assigning a data point to the first cluster for each of the plurality of medical providers having the first characteristic, the peer group including medical providers each represented by a respective data point assigned to the first cluster wherein the processor executes the k-means clustering on the stored plurality of diagnostic test orders to generate a peer group data structure comprising cluster assigned data points corresponding to the plurality of medical providers;
receive a diagnostic test order from a member of the peer group;
determine whether the diagnostic test order received from the member of the peer group includes an outlier test and whether the diagnostic test order received from the member of the peer group omits an outlier test by the processor comparing the diagnostic test order to the peer group data structure; and
send a notification to the peer group of medical providers in response to determining that the diagnostic test order received from the member of the peer group includes an outlier test, omits an outlier test, or both, the notification identifying the one or more outlier tests and prompting modification of the received diagnostic test order to include an omitted outlier test, disregard an included outlier test, or both.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain
methods of organizing human activity” because identifying a peer group of medical providers having like characteristics from a group of medical providers and prompting modification of a received diagnostic test order are ways of managing human behavior/interactions between people. The foregoing underlined limitations constitute (b) “a mental process” because describing characteristics of medical providers and analyzing diagnostic test orders from a group of medical providers and determining whether a diagnostic test order includes an outlier test are observations/evaluations/analyses that can be performed in the human mind or with a pen and paper. Furthermore, the underlined limitations constitute (c) “mathematical concepts” because employing k-means clustering to define a first cluster for a first characteristic of medical providers, generating a peer group data structure comprising cluster assigned data points corresponding to the medical providers and a respective data point assigned to the first cluster are ways of applying math. The foregoing underlined limitations also relate to claim 1 (similarly to claim 24).
Specifically, independent claim 20 recites:
A method of operating a diagnostic test system, comprising:
receiving at a diagnostic test system one or more diagnostic test orders from a first medical provider;
establishing, via a processor of the diagnostic test system, a peer group based on one or more like characteristics of the first medical provider and one or more second medical providers, the processor employing k-means clustering to define a first cluster for a first characteristic of the first medical provider and the one or more second medical providers, the k-means clustering assigning a data point to the first cluster for each of the first medical provider and the one or more second medical providers having the first characteristic, the peer group including medical providers each represented by a respective data point assigned to the first cluster, wherein the processor executes the k-means clustering on stored diagnostic test order data to generate a peer group data structure comprising cluster-assigned data points corresponding to medical providers;
determining, via the processor, historical diagnostic test orders for the peer group based on historical diagnostic test orders received from a plurality of medical providers that includes the first medical provider and the one or more second medical providers;
identifying, via the processor, one or more differences between the one or more diagnostic test orders from the first medical provider and the historical diagnostic test orders for the peer group;
determining, via the processor, whether the one or more diagnostic test orders include an outlier test and whether the one or more diagnostic test orders omit an outlier test in response to identifying the one or more differences, wherein the determining whether the one or more diagnostic test orders includes or omits an outlier test is performed by comparing the one or more diagnostic test orders to the peer group data structure; and
receiving at the diagnostic test system an adjusted one or more diagnostic test orders from the first medical provider adding an outlier test, removing an outlier test, or both.
In relation to claim 20, these claims constitute: (a) “certain
methods of organizing human activity” because determining historical diagnostic test orders for the peer group and identifying a peer group of medical providers having like characteristics from a group of medical providers relate to managing human behavior/interactions between people. Furthermore, the foregoing underlined limitations constitute (b) “a mental process” because analyzing diagnostic test orders from a group of medical providers and determining whether diagnostic test orders include or omits an outlier test is performed by comparing the one or more diagnostic test orders to the peer group data structure are observations/evaluations/analyses that can be performed in the human mind or with a pen and paper. Furthermore, the underlined limitations constitute (c) “mathematical concepts” because employing k-means clustering to define a first cluster for a first characteristic of medical providers, a respective data point assigned to the first cluster and executing the k-means clustering on stored diagnostic test order data to generate a peer group data structure comprising cluster-assigned data points corresponding to medical providers are ways of applying math.
Accordingly, the claim describes at least one abstract idea.
In relation to claims 2-7, these claims merely recite having specific kinds of characteristics such as medical specialties, demographics of the medical providers, cultural norms of the medical providers, demographics of patients of the medical providers, cultural norms of the medical providers, cultural norms of the patients of the medical providers and symptoms of patients. Claims 8-11 and 21-22 merely recite having specific kinds of outlier tests such as a test ordered by a first medical provider in the peer group, but not by one or more second medical providers in the peer group, by one or more second medical providers in the peer group, but not by a first medical provider in the peer group, by a first group of medical providers in the peer group, but not by a second group of medical providers in the peer group and a panel of diagnostic tests ordered by a first group of medical providers in the peer group, but not by a second group of medical providers in the peer group. Claims 13, 16-19 and 23 recites determining steps such as identifying the one or characteristics of the plurality of medical providers comprises using artificial intelligence, artificial intelligence, k-means clustering, using a support vector machine, artificial neural networks and notifying medical providers of the outlier test.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 20 and 24, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a diagnostic test system including a processor and memory including instructions executable on the processor, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment in the mind but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” and “Mental Process” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the diagnostic test system including a processor and memory including instructions executable on the processor are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components. Claims 1-11, 13 and 16-23 are directed to an abstract idea.
Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
Claims 2-11, 13, 16-19 and 21-23 are ultimately depend from claims 1 and 20 and include all the limitations of claims 1 and 20. Therefore, claims 2-11, 13, 16-19 and 21-23 recite the same abstract idea. Claims 2-7 merely recite having specific kinds of characteristics such as medical specialties, demographics of the medical providers, cultural norms of the medical providers, demographics of patients of the medical providers, cultural norms of the medical providers, cultural norms of the patients of the medical providers and symptoms of patients. Claims 8-11 and 21-22 merely recite having specific kinds of outlier tests such as a test ordered by a first medical provider in the peer group, but not by one or more second medical providers in the peer group, by one or more second medical providers in the peer group, but not by a first medical provider in the peer group, by a first group of medical providers in the peer group, but not by a second group of medical providers in the peer group and a panel of diagnostic tests ordered by a first group of medical providers in the peer group, but not by a second group of medical providers in the peer group. Claims 13, 16-19 and 23 recites determining steps such as identifying the one or characteristics of the plurality of medical providers comprises using artificial intelligence, artificial intelligence, k-means clustering, using a support vector machine, artificial neural networks and notifying medical providers of the outlier test. These are all just further describing the abstract idea recited in claims 1, 20 and 24, without adding significantly more.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 24, regarding the additional limitations of the diagnostic test system including a processor and memory including instructions executable on the processor, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 24 and analogous independent claims 1 and 20 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-11, 13 and 16-23 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 11, 13, 16-17, 19-20 and 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Hodgson (US 2020/0387810 A1) in view of Bostic (US 2021/0202100 A1).
Claim 1:
Hodgson discloses A method of analyzing diagnostic test orders (See clustering evaluated input and outcome data such as diagnosis, radiology findings, laboratory tests and results in P0006, P0102, P0127 and P0140. Also, see Fig. 1 items 112, 144 & 146.), comprising:
storing in a memory of or in communication with a laboratory interface system a plurality of diagnostic test orders received from a plurality of medical providers and characteristics of the plurality of medical providers (See Fig. 1 Healthcare Provider Interface 102 as a portal include orders 144 mentioned in P0195-P0196, Fig. 5, Fig. 7, P0220 CPUs, a memory, peer-to-peer network, a communication interface. With characteristics as things such as medical specialties, demographics, patient population, patient symptoms and diagnostic test orders, see results of certain treatment in P0129 where groups of patients sharing the characteristic of having the same tumor marker change in radiographic data as forms diagnostic test orders in P0137.), describing one or more characteristics of each of the plurality of medical providers (See exemplary Fig. 5 Overview Performance of an provider “Compared to Other Lung Specialist” in the “45th Percentile” mentioned in P0200.);
identifying, via a processor of or in communication with the laboratory interface system, a peer group of medical providers having one or more like characteristics determined from the stored characteristics of the plurality of medical providers (See at least [P0131] cluster (or group) individuals (or groups of individuals) in order to identify factors (e.g., environmental or personal) and/or characteristics (e.g., personal) that are associated with or cause a particular result or outcome. Also, see P0018, P0024.);
receiving a diagnostic test order at the laboratory interface system from a member of the peer group (See the portal in P0196, P0198 where the provider enters orders to be carried out.);
determining, via the processor and the stored plurality of diagnostic test orders, whether the diagnostic test order received from the member of the peer group includes an outlier test or whether the diagnostic test order received from the member of the peer group omits an outlier test, wherein the determining whether the diagnostic test order includes or omits an outlier test is performed by comparing the diagnostic test order to the peer group data structure (See P0195-P0196, P0198 where the analysis of orders placed by healthcare providers. Also, see provider performance with respect to patient satisfaction, cost effectiveness and patient outcomes in [P0200] FIG. 5 it is determined that the performance of the healthcare provider who is using the Healthcare Provider Interface 108 places him or her within the 45.sup.nd percentile with respect to the other healthcare providers.); and
sending, via the processor, a notification to the peer group of medical providers in response to determining that the diagnostic test order received from the member of the peer group includes an outlier test, omits an outlier test, or both, the notification identifying the one or more outlier tests and prompting modification of the received diagnostic test order to include an omitted outlier test or disregard an included outlier test (See P0196-P0198 where determining that an outlier test is needed or not can be done when the provider enters orders relevant to treatment and previous orders already carried out via voice and text.).
Although Hodgson discloses a method of analyzing diagnostic test orders to determine whether diagnostic test order includes an outlier test as mentioned above and employing k-means clustering machine learning to define a cluster as mentioned in P0006, Hodgson does not explicitly teach employing k-means clustering to define a cluster for characteristics of the medical providers, assigning a data point to the cluster for each of the medical providers, including medical providers each represented by a respective data point assigned to the cluster. Bostic teaches:
the processor employing k-means clustering to define a first cluster for a first characteristic of one or more of the plurality of medical providers stored in the memory, the k-means clustering assigning a data point to the first cluster for each of the plurality of medical providers having the first characteristic (See [P0209] the analytics module 302 performs statistical analytics techniques on a set of physician profiles to determine whether a physician is over-ordering or under-ordering lab tests. In some of these embodiments, the physician records may be clustered using K-nearest neighbors or K-means clustering to identify physician records that appear in the same cluster(s) as physician records that have been deemed to be anomalous (i.e., under-ordering or over-ordering).), the peer group including medical providers each represented by a respective data point assigned to the first cluster,
wherein the processor executes the k-means clustering on stored diagnostic test order data to generate a peer group data structure comprising cluster assigned data points corresponding to medical providers (See P0209 exemplary clusters under-ordering, over-ordering, the amount of patients seen, the amount of tests ordered during a time period, the types of tests ordered, the amount charged to insurance companies, the patient to run the tests and/or the diagnosis of the patients with ability to represent assigned data points.).
Therefore, it would have been obvious to one of ordinary skill in the art of identifying medical coding inconsistencies before the effective filing date of the claimed invention to modify the method of Hodgson to include employing k-means clustering to define a cluster for characteristics of the medical providers, assigning a data point to the cluster for each of the medical providers, including medical providers each represented by a respective data point assigned to the cluster as taught by Bostic to help improve pharmaceutical, medical and diagnostic states of a patient as mentioned in Bostic’s P0003, P0005.
Regarding claim 2, although Hodgson and Bostic teach the method of claim 1 mentioned above, Bostic further teaches wherein the one or more like characteristics include one or more medical specialties (See [P0209] As most physicians having the same or similar specialties, they will have consistent ordering histories, physicians deviating from the normal patterns may be identified based on the clusters to which they belong.).
Therefore, it would have been obvious to one of ordinary skill in the art of identifying medical coding inconsistencies before the effective filing date of the claimed invention to modify the method of Hodgson when like characteristics include medical specialties as taught by Bostic to help improve pharmaceutical, medical and diagnostic states of a patient as mentioned in Bostic’s P0003, P0005.
Regarding claim 3, although Hodgson and Bostic teach the method of claim 1 mentioned above, Bostic further teaches wherein the one or more like characteristics include one or more demographics of the medical providers (See P0046] from a plurality of healthcare providers, wherein the machine learning device is configured to train an artificial intelligence module based on the demographic records, the diagnosis records, the prescription records, and the testing records.).
Therefore, it would have been obvious to one of ordinary skill in the art of identifying medical coding inconsistencies before the effective filing date of the claimed invention to modify the method of Hodgson when like characteristics include demographics of the medical providers as taught by Bostic to help improve pharmaceutical, medical and diagnostic states of a patient as mentioned in Bostic’s P0003, P0005.
Regarding claim 4, Hodgson discloses wherein the one or more like characteristics include one or more demographics of patients of the medical providers (See [P0179] a Patient Overview data sub-grouping 114 comprises data that provides a snapshot of a patient including, for example, their demographic data, their diagnosis, a photo or other representation of the patient of the healthcare provider.).
Regarding claim 5, Hodgson discloses wherein the one or more like characteristics include one or more cultural norms of the medical providers (See P0191, where treatment goals, suggestions from experts in the field using the National Comprehensive Cancer Network (NCCN) guidelines for treatment of cancer allow peers to be compared according to culture norm.).
Regarding claim 6, Hodgson discloses wherein the one or more like characteristics include one or more cultural norms of patients of the medical providers (See P0191, where treatment goals, suggestions from experts in the field using the National Comprehensive Cancer Network (NCCN) guidelines for treatment of cancer allow peers to be compared according to culture norm.).
Regarding claim 7, Hodgson discloses wherein the one or more like characteristics include one or more symptoms of patients (See patient symptoms in P0153.).
Regarding claim 11, although Hodgson and Bostic teach the method of claim 1 mentioned above, Bostic further teaches wherein the outlier test is a diagnostic test included in a panel of diagnostic tests ordered by a first group of medical providers in the peer group, but not by a second group of medical providers in the peer group (Taught in P0283 as identified inconsistencies test panels.).
Therefore, it would have been obvious to one of ordinary skill in the art of identifying medical coding inconsistencies before the effective filing date of the claimed invention to modify the method of Hodgson to include a panel of diagnostic tests ordered by a group of medical providers in the peer group but not by a second group as taught by Bostic to help improve pharmaceutical, medical and diagnostic states of a patient as mentioned in Bostic’s P0003, P0005.
Regarding claim 13, although Hodgson and Bostic teach the method of claim 1 mentioned above, Bostic further teaches wherein identifying the one or more like characteristics of the plurality of medical providers further includes using the k- means clustering to define a respective cluster for each characteristic of one or more of the plurality of medical providers stored in the memory, the k-means clustering assigning a data point to one of the respective clusters for each of the plurality of medical providers having a characteristic of that respective cluster (Taught in P0283 as identified inconsistencies test panels.).
Therefore, it would have been obvious to one of ordinary skill in the art of identifying medical coding inconsistencies before the effective filing date of the claimed invention to modify the method of Hodgson to include employing k-means clustering to define a cluster for characteristics of the medical providers, assigning a data point to the cluster for each of the medical providers, including medical providers each represented by a respective data point assigned to the cluster as taught by Bostic to help improve pharmaceutical, medical and diagnostic states of a patient as mentioned in Bostic’s P0003, P0005.
Regarding claim 16, Hodgson teaches wherein determining whether the at least one diagnostic test order includes an outlier test comprises using artificial intelligence (See AI modules in P0108, P0122.).
Regarding claim 17, Hodgson and Bostic teach the method of claim 1 as mentioned above, Bostic further teaches wherein determining whether the at least one diagnostic test order includes an outlier test includes using k-means clustering (See [P0209] the analytics module 302 performs statistical analytics techniques on a set of physician profiles to determine whether a physician is over-ordering or under-ordering lab tests. In some of these embodiments, the physician records may be clustered using K-nearest neighbors or K-means clustering to identify physician records that appear in the same cluster(s) as physician records that have been deemed to be anomalous (i.e., under-ordering or over-ordering).).
Therefore, it would have been obvious to one of ordinary skill in the art of identifying medical coding inconsistencies before the effective filing date of the claimed invention to modify the method of Hodgson to include employing k-means clustering to define a cluster for characteristics of the medical providers as taught by Bostic to help improve pharmaceutical, medical and diagnostic states of a patient as mentioned in Bostic’s P0003, P0005.
Regarding claim 19, although Hodgson and Bostic teach the method of claim 1 as mentioned above, Bostic further teaches wherein determining whether the at least one diagnostic test order includes an outlier test comprises using one or more artificial neural networks (See P0108, P0111, P0115 where artificial intelligence software or a machine learning types include recurrent neural network.).
Therefore, it would have been obvious to one of ordinary skill in the art of identifying medical coding inconsistencies before the effective filing date of the claimed invention to modify the method of Hodgson to include an outlier test comprising artificial neural networks as taught by Bostic to help improve pharmaceutical, medical and diagnostic states of a patient as mentioned in Bostic’s P0003, P0005.
Claim 20:
Hodgson discloses A method of operating a diagnostic test system, comprising:
receiving at a diagnostic test system one or more diagnostic test orders from a first medical provider (See the portal in P0196, P0198 where the provider enters orders to be carried out.);
establishing, via a processor of the diagnostic test system, a peer group based on one or more like characteristics of the first medical provider and one or more second medical providers (With characteristics as things such as medical specialties, demographics, patient population, patient symptoms and diagnostic test orders, see results of certain treatment in P0129 where groups of patients sharing the characteristic of having the same tumor marker change in radiographic data as forms diagnostic test orders in P0137.),
determining, via the processor, historical diagnostic test orders for the peer group based on historical diagnostic test orders received from a plurality of medical providers that includes the first medical provider and the one or more second medical providers (See P0018, [P0024] a comparison of a performance of the healthcare provider to a performance of other healthcare providers.);
identifying, via the processor, one or more differences between the one or more diagnostic test orders from the first medical provider and the historical diagnostic test orders for the peer group (See the portal in P0196, P0198 where the provider enters orders to be carried out.);
determining, via the processor, whether the one or more diagnostic test orders include an outlier test and whether the one or more diagnostic test orders omit an outlier test in response to identifying the one or more differences, wherein the determining whether the one or more diagnostic test orders includes or omits an outlier test is performed by comparing the one or more diagnostic test orders to the peer group data structure,
(See P0195-P0196, P0198 where the analysis of orders placed by healthcare providers. Also, see provider performance with respect to patient satisfaction, cost effectiveness and patient outcomes in [P0200] FIG. 5 it is determined that the performance of the healthcare provider who is using the Healthcare Provider Interface 108 places him or her within the 45.sup.nd percentile with respect to the other healthcare providers.); and
receiving at the diagnostic test system an adjusted one or more diagnostic test orders from the first medical provider adding an outlier test, removing an outlier test, or both (See P0196-P0198 where determining that an outlier test is needed or not can be done when the provider enters orders relevant to treatment and previous orders already carried out via voice and text.).
Although Hodgson discloses a method of analyzing diagnostic test orders to determine whether diagnostic test order includes an outlier test as mentioned above and employing k-means clustering machine learning to define a cluster as mentioned in P0006, Hodgson does not explicitly teach employing k-means clustering to define a cluster for characteristics of the medical providers, assigning a data point to the cluster for each of the medical providers, including medical providers each represented by a respective data point assigned to the cluster. Bostic teaches:
the processor employing k-means clustering to define a first cluster for a first characteristic of one or more of the first medical provider and the one or more second medical providers, the k-means clustering assigning a data point to the first cluster for each of the first medical provider and the one or more second medical providers having the first characteristic (See [P0209] the analytics module 302 performs statistical analytics techniques on a set of physician profiles to determine whether a physician is over-ordering or under-ordering lab tests. In some of these embodiments, the physician records may be clustered using K-nearest neighbors or K-means clustering to identify physician records that appear in the same cluster(s) as physician records that have been deemed to be anomalous (i.e., under-ordering or over-ordering).), the peer group including medical providers each represented by a respective data point assigned to the first cluster, wherein the processor executes the k-means clustering on stored diagnostic test order data to generate a peer group data structure comprising cluster-assigned data points corresponding to medical providers (See P0209 exemplary clusters under-ordering, over-ordering, the amount of patients seen, the amount of tests ordered during a time period, the types of tests ordered, the amount charged to insurance companies, the patient to run the tests and/or the diagnosis of the patients with ability to represent assigned data points.).
Therefore, it would have been obvious to one of ordinary skill in the art of identifying medical coding inconsistencies before the effective filing date of the claimed invention to modify the method of Hodgson to include employing k-means clustering to define a cluster for characteristics of the medical providers, assigning a data point to the cluster for each of the medical providers, including medical providers each represented by a respective data point assigned to the cluster as taught by Bostic to help improve pharmaceutical, medical and diagnostic states of a patient as mentioned in Bostic’s P0003, P0005.
Regarding claim 22, although Hodgson and Bostic teach the method of claim 20 as mentioned above, Bostic further teaches wherein the outlier test includes a test ordered by the first medical provider, but not by the one or more second medical providers (See P0208, where identifying unnecessary or fraudulent test ordering serves as second medical provider not being able to order a test.).
Therefore, it would have been obvious to one of ordinary skill in the art of identifying medical coding inconsistencies before the effective filing date of the claimed invention to modify the method of Hodgson to include outlier analysis preventing a second medical provider from ordering a test as taught by Bostic to help improve pharmaceutical, medical and diagnostic states of a patient as mentioned in Bostic’s P0003, P0005.
Regarding claim 23, Hodgson discloses further comprising notifying one or more medical providers of the outlier test (See P0196-P0198 where determining that an outlier test is needed or not can be done when the provider enters orders relevant to treatment and previous orders already carried out via voice and text.).
Claim 24:
Hodgson discloses A diagnostic test system (See clustering evaluated input and outcome data such as diagnosis, radiology findings, laboratory tests and results in P0006, P0102, P0127 and P0140. Also, see Fig. 1 items 112, 144 & 146.), comprising:
a processor and memory, the memory including instructions executable on the processor to: store in the memory a plurality of diagnostic test orders received from a plurality of medical providers and characteristics of the plurality of medical providers (See Fig. 1 Healthcare Provider Interface 102 as a portal include orders 144 mentioned in P0195-P0196, Fig. 5, Fig. 7, P0220 CPUs, a memory, peer-to-peer network, a communication interface. With characteristics as things such as medical specialties, demographics, patient population, patient symptoms and diagnostic test orders, see results of certain treatment in P0129 where groups of patients sharing the characteristic of having the same tumor marker change in radiographic data as forms diagnostic test orders in P0137.), describing one or more characteristics of each of the plurality of medical providers (See exemplary Fig. 5 Overview Performance of an provider “Compared to Other Lung Specialist” in the “45th Percentile” mentioned in P0200.);
identify a peer group of medical providers having one or more like characteristics determined from the stored characteristics of the plurality of medical providers (See P0018, [P0024] a comparison of a performance of the healthcare provider to a performance of other healthcare providers.);
receive a diagnostic test order from a member of the peer group (See the portal in P0196, P0198 where the provider enters orders to be carried out.);
determine whether the diagnostic test order received from the member of the peer group includes an outlier test and whether the diagnostic test order received from the member of the peer group omits an outlier test by the processor comparing the diagnostic test order to the peer group data structure (See P0195-P0196, P0198 where the analysis of orders placed by healthcare providers. Also, see provider performance with respect to patient satisfaction, cost effectiveness and patient outcomes in [P0200] FIG. 5 it is determined that the performance of the healthcare provider who is using the Healthcare Provider Interface 108 places him or her within the 45.sup.nd percentile with respect to the other healthcare providers.); and
send a notification to the peer group of medical providers in response to determining that the diagnostic test order received from the member of the peer group includes an outlier test, omits an outlier test, or both, the notification identifying the one or more outlier tests and prompting modification of the received diagnostic test order to include an omitted outlier test, disregard an included outlier test, or both (See P0196-P0198 where determining that an outlier test is needed or not can be done when the provider enters orders relevant to treatment and previous orders already carried out via voice and text.).
Although Hodgson discloses a method of analyzing diagnostic test orders to determine whether diagnostic test order includes an outlier test as mentioned above and employing k-means clustering machine learning to define a cluster as mentioned in P0006, Hodgson does not explicitly teach employing k-means clustering to define a cluster for characteristics of the medical providers, assigning a data point to the cluster for each of the medical providers, including medical providers each represented by a respective data point assigned to the cluster. Bostic teaches:
wherein the processor executes the k-means clustering on the stored plurality of diagnostic test orders to generate a peer group data structure comprising cluster assigned data points corresponding to the plurality of medical providers, the processor employing k-means clustering to define a first cluster for a first characteristic of one or more of the plurality of medical providers stored in the memory, the k-means clustering assigning a data point to the first cluster for each of the plurality of medical providers having the first characteristic (See [P0209] the analytics module 302 performs statistical analytics techniques on a set of physician profiles to determine whether a physician is over-ordering or under-ordering lab tests. In some of these embodiments, the physician records may be clustered using K-nearest neighbors or K-means clustering to identify physician records that appear in the same cluster(s) as physician records that have been deemed to be anomalous (i.e., under-ordering or over-ordering).), the peer group including medical providers each represented by a respective data point assigned to the first cluster (See P0209 exemplary clusters under-ordering, over-ordering, the amount of patients seen, the amount of tests ordered during a time period, the types of tests ordered, the amount charged to insurance companies, the patient to run the tests and/or the diagnosis of the patients with ability to represent assigned data points.).
Therefore, it would have been obvious to one of ordinary skill in the art of identifying medical coding inconsistencies before the effective filing date of the claimed invention to modify the method of Hodgson to include employing k-means clustering to define a cluster for characteristics of the medical providers, assigning a data point to the cluster for each of the medical providers, including medical providers each represented by a respective data point assigned to the cluster as taught by Bostic to help improve pharmaceutical, medical and diagnostic states of a patient as mentioned in Bostic’s P0003, P0005.
Claims 8-10 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Hodgson (US 2020/0387810 A1) in view of Bostic (US 2021/0202100 A1) further in view of Mohlenbrock (US 2016/0034648 A1).
Regarding claim 8, although Hodgson and Bostic teach the method of claim 1 mentioned above, Hodgson and Bostic do not explicitly teach a test ordered by a first medical provider in the peer group, but not by one or more second medical providers in the peer group. Mohlenbrock teaches:
wherein the outlier test includes a test ordered by a first medical provider in the peer group, but not by one or more second medical providers in the peer group (See Fig. 8, Peer Group of Surgeons in P0078-0079. Also, see P0083-P0084, where exemplary tests should be ordered in the ambulatory (office) setting, not the hospital.).
Therefore, it would have been obvious to one of ordinary skill in the art of reducing clinical variation before the effective filing date of the claimed invention to modify the method of Hodgson and Bostic to include a test ordered by a first medical provider in the peer group, but not by one or more second medical providers in the peer group as taught by Mohlenbrock to objectively identify and replicate physicians and hospital's best clinical and operational practices as mentioned in Mohlenbrock’s P0001.
Regarding claim 9, although Hodgson and Bostic teach the method of claim 1 mentioned above, Hodgson and Bostic do not explicitly teach a test ordered by a second medical providers in the peer group, but not by a first medical provider in the peer group. Mohlenbrock teaches:
wherein the outlier test includes a test ordered by one or more second medical providers in the peer group, but not by a first medical provider in the peer group (See exemplary outlier that warrants investigation in [P0157-P0162] such outliers often serve as ax very useful indicator of a specific inefficiency in the doctor's ordering pattern.).
Therefore, it would have been obvious to one of ordinary skill in the art of reducing clinical variation before the effective filing date of the claimed invention to modify the method of Hodgson and Bostic to include a test ordered by a second medical providers in the peer group, but not by a first medical provider in the peer group as taught by Mohlenbrock to objectively identify and replicate physicians and hospital's best clinical and operational practices as mentioned in Mohlenbrock’s P0001.
Regarding claim 10, although Hodgson and Bostic teach the method of claim 1 mentioned above, Hodgson and Bostic do not explicitly teach the outlier test includes a test ordered by a group of medical providers in the peer group, but not by a second group of medical providers in the peer group. Mohlenbrock teaches:
wherein the outlier test includes a test ordered by a first group of medical providers in the peer group, but not by a second group of medical providers in the peer group (See exemplary outlier that warrants investigation in [P0157-P0162] such outliers often serve as ax very useful indicator of a specific inefficiency in the doctor's ordering pattern.).
Therefore, it would have been obvious to one of ordinary skill in the art of reducing clinical variation before the effective filing date of the claimed invention to modify the method of Hodgson and Bostic to include the outlier test includes a test ordered by a group of medical providers in the peer group, but not by a second group of medical providers in the peer group as taught by Mohlenbrock to objectively identify and replicate physicians and hospital's best clinical and operational practices as mentioned in Mohlenbrock’s P0001.
Regarding claim 21, although Hodgson and Bostic teach the method of claim 20 mentioned above, Hodgson and Bostic do not explicitly teach a test ordered by a first medical provider, but not by one or more second medical providers. Mohlenbrock teaches:
wherein the outlier test includes a test ordered by the one or more second medical providers, but not by the first medical provider (See Fig. 8, Peer Group of Surgeons in P0078-0079. Also, see P0083-P0084, where exemplary tests should be ordered in the ambulatory (office) setting, not the hospital.).
Therefore, it would have been obvious to one of ordinary skill in the art of reducing clinical variation before the effective filing date of the claimed invention to modify the method of Hodgson and Bostic to include a test ordered by a first medical provider, but not by one or more second medical providers as taught by Mohlenbrock to objectively identify and replicate physicians and hospital's best clinical and operational practices as mentioned in Mohlenbrock’s P0001.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Hodgson (US 2020/0387810 A1) in view of Bostic (US 2021/0202100 A1) further in view of Coli (US 6,018,713 A) and Goldman (US 2018/0239870 A1).
Regarding claim 18, although Hodgson and Bostic teach the method of claim 1 as mentioned above, Hodgson and Bostic do not explicitly teach determining whether the at least one diagnostic test order includes an outlier test comprises using a support vector machine. Goldman further teaches wherein determining whether the at least one diagnostic test order includes an outlier test comprises using a support vector machine (See P0100, support vector machines used to determine healthcare-based fraudulent activities including tests (P0005) mentioned in Abstract, P0142-P0143.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare fraud detection before the effective filing date of the claimed invention to modify the method of Hodgson and Bostic to include determining whether the at least one diagnostic test order includes an outlier test using a support vector machine as taught by Goldman to reduce, minimize, or eliminate fraudulent activity and to protect their customers' interests mentioned in Goldman’s P0006.
Response to Arguments
Applicant argues that amended independent claims 1, 20, and 24 recite significantly more than an alleged judicial exception, rather recite a specific internal data structure improving the operation of a system. see pgs. 10-11 of Remarks – Examiner disagrees.
With k-means clustering as merely being an algorithm that groups data points into a cluster without labels, in the instant case, employing k-means clustering to define a first cluster for a first characteristic of medical providers and the k-means clustering assigning a data point to the first cluster is more liken to low level data processing of a generic computer. For example, paragraphs 29 and 41 of Applicant’s specification talks about artificial intelligence and using k-means clustering to analyze diagnostic tests historically ordered, but knowing the number of diagnostic tests that medical providers ordered for analysis is merely counting or tallying an amount, which is a non-technical problem that has already been solved. Furthermore, prompting modification of a received diagnostic test order, generating a structured peer group using k-means clustering of historical diagnostic test order data, and by using that peer group data structure as an operational reference for determining whether a diagnostic test order includes or omits an outlier test are ways of merely using the computer as a tool to implement the abstract idea (saying “apply it”) and is merely using the computer in the manner in which it was designed to be used, i.e., performing generic computer functions.
Applicant argues that how diagnostic test orders are processed is not a mental step. see pgs. 10-11 of Remarks – Examiner disagrees.
Counting, tallying and/or notifying when a number of diagnostic tests that medical providers ordered or did not order can definitely be performed in the human mind. Identifying a peer group of medical providers having like, describable characteristics from a group of medical providers, analyzing diagnostic test orders from a group of medical providers and determining whether a diagnostic test order includes an outlier test are human activities that can also be performed in the human mind. Employing k-means clustering to define a first cluster for a first characteristic of medical providers and a respective data point assigned to the first cluster is a mathematical task, which all make up an abstract idea, because it is using categories to organize, identify, determine and send information, which happened to be performed on generic computer, but can be performed in the human mind using a pen and paper, as mere data gathering.
Applicant’s arguments have been fully considered, but are now moot in view of the new grounds of rejection. The Examiner has entered a new rejection under 35 USC § 103(a) and applied new art and art already of record.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/T.S.W./Examiner, Art Unit 3687 06/04/2026
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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