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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 29, 2026 has been entered.
Claim Objections
Claims 1-6, 8-11, 13-19, and 21-23 objected to because of the following informalities: “a number of the subset of the plurality of predictive classifications is dynamically set…” in claims 1, 13, and 16 should read “a number of predictive classifications of the plurality of predictive classifications in the subset is dynamically set…”. Appropriate correction is required.
Claims 2-6, 8-11, 14-15, 17-19, and 21-23 are further objected to for dependence on claims 1, 13, and 16.
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 1-6, 8-11, 13-19, and 21-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a computer-implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“responsive to determining that the one or more predictive classification deviate from the assigned classification by a deviation threshold, providing …an indication of an outlier associated with associated with the entity”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)), mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“wherein (i) the machine learning model comprises one or more layers trained to output the predictive classification data object for the entity based on the distribution data object, (ii) the predictive classification data object comprises (a) one or more predictive classifications and (b) one or more contextual attributes for a predictive classification of the one or more predictive classification, and (iii) the one or more predictive classifications comprise a subset of a plurality of predictive classifications and a number of the subset of the plurality of predictive classifications is dynamically set based on a granularity associated with the predictive classification data object”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“A computer-implemented method”
“by one or more processors/by the one or more processors”
“providing, by the one or more processors, a distribution data object comprising the plurality of identifier counts to a machine learning to produce a predictive classification data object for the entity”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
receiving, by one or more processors, a predictive identifier count data object for an entity based on a historical interaction dataset associated with a plurality of entities, wherein (i) the predictive identifier count data object is indicative of a plurality of identifier counts corresponding to a plurality of predictive identifiers associated with the entity and (ii) the entity is associated with an assigned classification”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way or are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, the receiving limitation recites the well-understood, routine, and convention activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Additional details, mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. As an ordered whole, the claim is directed to a mentally performable process of outputting predictive classification results. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a computer-implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the plurality of identifier counts for the plurality of predictive identifiers comprise at least a (a) a first identifier count for a first predictive identifier associated with the entity and (b) a second identifier count for a second predictive identifier associated with the entity”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a computer-implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …a distribution data object for the entity based on the first identifier count and the second identifier count, wherein the distribution data object is indicative of a proportional relevance of a predictive category associated with the entity, wherein generating the one or more predictive classifications is based on the distribution data object”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by the one or more processors”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements are “mere instructions to apply. Mere instructions to apply cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a computer-implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …a peer entity data object for the entity based on a distance between the distribution data object and the peer distribution data object”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“by the one or more processors”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“receiving, by the one or more processors, a peer distribution data object for a peer entity”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, the receiving limitations recite the well-understood, routine, and convention activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a computer-implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 4.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein: the distribution data object comprises a first predictive category vector that comprises one or more first proportional values corresponding to one or more first predictive categories associated with the entity, the peer distribution data object comprises a second predictive category vector that comprises one or more second proportional values corresponding to one or more second predictive categories associated with the peer entity, and the distance between the distribution data object and the peer distribution data object comprises a particular distance between the first predictive category vector and the second predictive category vector.”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a computer-implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …an assigned classification distribution data object for the assigned classification based on the plurality of distribution data objects”
“generating …an investigative output for the entity based on a comparison between the distribution data object for the entity and the assigned classification distribution data object”
“generating …an indication of the investigative output for the entity”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“by the one or more processors”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“receiving, by the one or more processors, a plurality of distribution data objects for the plurality of entities associated with the assigned classification”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, the receiving limitations recite the well-understood, routine, and convention activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a computer-implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein each of the one or more predictive classifications is indicative of a particular predictive category associated with the entity that satisfies the deviation threshold”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a computer-implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …a predictive category data object for the entity based on the predictive identifier count data object, wherein the predictive category data object is indicative of: (i) one or more predictive categories corresponding to a category type, wherein a particular predictive category of the one or more predictive categories corresponds to a subset of a plurality of predictive identifiers associated with the entity, (ii) a category count corresponding to the particular predictive category, and (iii) an aggregate category count corresponding to each of the one or more predictive categories”
“determining …a particular proportional relevance of the particular predictive category based on a comparison between the category count and the aggregate category count”
“generating …the distribution data object for the entity based on the particular proportional relevance”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“by the one or more processors”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements are “mere instructions to apply. Mere instructions to apply cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a computer-implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 9.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the category type is one of a plurality of category types, and wherein the distribution data object comprises a plurality of type-specific distributions corresponding to the plurality of category types”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a computer-implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …verification data for the entity based on a comparison between the assigned classification and the predictive classification”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“by the one or more processors”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“receiving, by the one or more processors, the assigned classification for the entity”
“providing, by the one or more processors, an indication of the verification data for the entity”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, the receiving and providing limitations recite the well-understood, routine, and convention activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 13,
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“responsive to determining that the one or more predictive classification deviate from the assigned classification by a deviation threshold, providing an indication of an outlier associated with associated with the entity”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)), mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“wherein (i) the machine learning model comprises one or more layers trained to output the predictive classification data object for the entity based on the distribution data object, (ii) the predictive classification data object comprises (a) one or more predictive classification and (b) one or more contextual attributes for a predictive classification of the one or more predictive classifications, and (iii) the one or more predictive classifications comprise a subset of a plurality of predictive classifications and a number of the subset of the plurality of predictive classifications is dynamically set based on a granularity associated with the predictive classification data object”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“A system comprising: one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations”
“providing a distribution data object comprising the plurality of identifier counts to a machine learning model to produce a predictive classification data object for the entity”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“receive a predictive identifier count data object for an entity based on a historical interaction dataset associated with a plurality of entities, wherein (i) the predictive identifier count data object is indicative of a plurality of identifier counts corresponding to a plurality of predictive identifiers associated with the entity and (ii) the entity is associated with an assigned classification”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements are additional details, “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, the receiving limitation recites the well-understood, routine, and convention activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Additional details, mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. As an ordered whole, the claim is directed to a mentally performable process of generating predictive classification results. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Regarding Claim 14,
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 13.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the plurality of identifier counts for the plurality of predictive identifiers comprise at least (a) a first identifier count for a first predictive identifier associated with the entity and (b) a second identifier count for a second predictive identifier associated with the entity”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 15,
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate the distribution data object for the entity based on the first identifier count and the second identifier count, wherein the distribution data object is indicative of a proportional relevance of a predictive category associated with the entity, wherein generating the one or more predictive classifications are based on the distribution data object”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 14.
Step 2B Analysis: See corresponding analysis of claim 14.
Regarding Claim 16,
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“responsive to determining that the one or more predictive classifications deviate from the assigned classification by a deviation threshold, providing an indication of an outlier associated with associated with the entity”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)), mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“wherein (i) the machine learning model comprises one or more layers trained to output the predictive classification data object for the entity based on the distribution data object, (ii) the predictive classification data object comprises (a) one or more predictive classification and (b) one or more contextual attributes for a predictive classification of the one or more predictive classifications, and (iii) the one or more predictive classifications comprise a subset of a plurality of predictive classifications and a number of the subset of the plurality of predictive classifications is dynamically set based on a granularity associated with the predictive classification data object”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations”
“providing a distribution data object comprising the plurality of identifier counts to a machine learning to produce a predictive classification data object for the entity”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“receiving a predictive identifier count data object for an entity based on a historical interaction dataset associated with a plurality of entities, wherein (i) the predictive identifier count data object is indicative of a plurality of identifier counts corresponding to a plurality of predictive identifiers associated with the entity and (ii) the entity is associated with an assigned classification”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements are additional details, “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, the receiving limitation recites the well-understood, routine, and convention activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Additional details, mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. As an ordered whole, the claim is directed to a mentally performable process of generating predictive classification results. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Regarding Claim 17,
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 16.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the plurality of identifier counts for the plurality of predictive identifiers comprise at least (a) a first identifier count for a first predictive identifier associated with the entity and (b) a second identifier count for a second predictive identifier associated with the entity”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 18,
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate the distribution data object for the entity based on the first identifier count and the second identifier count, wherein the distribution data object is indicative of a proportional relevance of a predictive category associated with the entity, wherein generating the one or more predictive classifications are based on the distribution data object”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 17.
Step 2B Analysis: See corresponding analysis of claim 17.
Regarding Claim 19,
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate an assigned classification distribution data object for the assigned classification based on the plurality of distribution data objects”
“generate an investigative output for the entity based on a comparison between the distribution data object for the entity and the assigned classification distribution data object”
“generate an indication of the investigative output for the entity”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“receive a plurality of distribution data objects for the plurality of entities associated with the assigned classification”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements are “insignificant extra-solution activity”. Specifically, the receiving limitation recites the well-understood, routine, and convention activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 21,
Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 21 is directed to a computer-implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating …an updated list of predictive classifications by augmenting the defined list of predictive classifications to include the assigned classification”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)) and mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the assigned classification is not included within a defined list of predictive classifications”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“by the one or more processors”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements are additional details that do not apply the exception in a meaningful way and “mere instructions to apply. Additional details that do not apply the exception in a meaningful way and mere instructions to apply cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 22,
Claim 22 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 22 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate an updated list of predictive classifications by augmenting the defined list of predictive classifications to include the assigned classification”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)) and mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the assigned classification is not included within a defined list of predictive classifications”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“wherein the one or more processors are further caused to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements are additional details that do not apply the exception in a meaningful way and “mere instructions to apply. Additional details that do not apply the exception in a meaningful way and mere instructions to apply cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 23,
Claim 23 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 23 is directed to a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate an updated list of predictive classifications by augmenting the defined list of predictive classifications to include the assigned classification”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)) and mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the assigned classification is not included within a defined list of predictive classifications”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“wherein the one or more processors are further caused to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does 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 ideas into a practical application, all of the additional elements are additional details that do not apply the exception in a meaningful way and “mere instructions to apply. Additional details that do not apply the exception in a meaningful way and mere instructions to apply cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 6, 8-11, and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Hannon et al. (U.S. Patent Publication No. 2023/0162846) (“Hannon”) in view of Koren et al. (U.S. Patent Publication No. 2022/0210079) (“Koren”) in further view of Adhikari et al. (U.S. Patent Publication No. 2023/0008936) (“Adhikari”).
Regarding claim 1, Hannon teaches a computer-implemented method comprising: receiving, by one or more processors, a predictive identifier count data object for an entity based on a historical interaction dataset associated with a plurality of entities, wherein (i) the predictive identifier count data object is indicative of a plurality of identifier counts corresponding to a plurality of predictive identifiers associated with the entity (Hannon [0003] “The subject entity can be, example, a provider, a healthcare claim, or a patient.”; [0076] “At 210, the processor 112 receives historical healthcare claim data. The historical healthcare claim data can include a plurality of historical healthcare claims. Each historical healthcare claim can include a claim code related to services performed, a healthcare provider who rendered the services, a disclosed specialty of the healthcare provider who rendered the services, and a patient who received the services.”; [0077] “Reference is now made to FIG. 3A, which illustrates example historical healthcare claim data 300, in accordance with an example embodiment. As shown in FIG. 3A, the historical healthcare claim data 300 can include a healthcare provider identifier 302, a specialty disclosed by the healthcare provider 304, healthcare claim codes 306, and line counts 308. Line counts 308 can be a total number of healthcare claims that utilize a healthcare claim code 306.” Hannon provides receiving, by a processor, historical healthcare claim data including a number of healthcare claims that utilize a healthcare claim code, wherein the healthcare claim code count corresponds to the predictive identifier count data object, the healthcare providers correspond to the entities, and the historical healthcare claim data corresponds to the historical interaction dataset associated with a plurality of entities.) and (ii) the entity is associated with an assigned classification (Hannon [0107] “Reference is now made to FIG. 4, which illustrates an example healthcare provider specialty prediction generated by the predictive model, in accordance with an example embodiment. As shown in FIG. 4, the predictive model can receive a query code utilization profile 400 for a healthcare provider 402, including the utilization percentages 404a, 404b, 404c, 404d, 404e . . . (herein collectively referred to as utilization percentages 404) for various healthcare claim codes. The predictive model can return a predicted specialty 406 for the healthcare provider.” Hannon provides generating predictions for healthcare provides, wherein the provider corresponds to the entity, corresponding to the entity is associated with an assigned classification.); providing, by the one or more processors, a distribution data object comprising the plurality of identifier counts to a machine learning model, to produce a predictive classification data object for the entity (Hannon [0107] “Reference is now made to FIG. 4, which illustrates an example healthcare provider specialty prediction generated by the predictive model, in accordance with an example embodiment. As shown in FIG. 4, the predictive model can receive a query code utilization profile 400 for a healthcare provider 402, including the utilization percentages 404a, 404b, 404c, 404d, 404e . . . (herein collectively referred to as utilization percentages 404) for various healthcare claim codes. The predictive model can return a predicted specialty 406 for the healthcare provider.” Hannon provides inputting healthcare claim code utilizations into a predictive machine learning model to generate predicted specialties for a healthcare provider, corresponding to inputting, by the one or more processors, a distribution data object comprising the plurality of identifier counts to a machine learning model, to receive a predictive classification data object for the entity.), … (ii) the predictive classification data object comprises (a) one or more predictive classifications, and (b) one or more contextual attributes for a predictive classification of the one or more predictive classifications (Hannon [0106] “The processor 112 can compare the healthcare provider specialty predicted by the predictive model to one or more pre-determined business rules and enforce the pre-determined business rules on the specialty prediction. The pre-determined business rules can be manually developed based on knowledge of subject matter experts. The pre-determined business rules can relate to, but is not limited to, time behavior, network coverage, geographic coverage. For example, a business rule can relate to the place of service (POS) code location. In particular, a business rule can require that certain healthcare provider specialties are only practiced at a select POS code locations. Accordingly, the processor 112 can identify healthcare claims associated with a particular healthcare provider specialty and POS code location that is not one of the select POS code locations for that healthcare provider specialty.” Hannon provides the predictive model generates predictive classifications including one or more pre-determined business rules and enforces the pre-determined business rules on the specialty prediction, corresponding to (a) a predictive classification and (b) one or more contextual attributes for the predictive classification.);
Hannon fails to explicitly teach …wherein: (i) the machine learning model comprises one or more layers trained to output the predictive classification data object for the entity based on the distribution data object …and (iii) the one or more predictive classifications comprise a subset of a plurality of predictive classifications and a number of the subset of the plurality of predictive classifications is dynamically set based on a granularity associated with the predictive classification data object; and responsive to determining that the one or more predictive classifications deviate from the assigned classification by a deviation threshold, providing, by the one or more processors, an indication of an outlier associated with associated with the entity.
However, Koren teaches …wherein: (i) the machine learning model comprises one or more layers trained to output the predictive classification data object for the entity based on the distribution data object (Koren [0077] “FIG. 3 depicts a diagram of aspects of classification using multiple machine learning models (e.g., multiple levels, stages, layers, tiers, hierarchies, etc., of machine learning models) in accordance with one implementation of the present disclosure. FIG. 3 depicts an example tree 300 with various models (e.g., machine learning models) at various levels, stages, layers, granularities, etc. The different machine learning models at each level allows for different granularities of classification and higher confidence classifications because the machine learning models are trained to perform classification at a particular granularity.” Koren teaches a machine learning model comprising layers and trained to output predictions based on model input.) …and (iii) the one or more predictive classifications comprise a subset of a plurality of predictive classifications (Koren [0079] “The classifications 341 through 347 may be classifications of entity at a second granularity. The classifications 341 through 347 may further define, refine, narrow, etc., the type, category, group, division, of the devices or entities. For example, the classification 341 may indicate a subtype, subcategory, subdivision, etc., of entities that are part of classification 321.” Koren teaches classifications comprising subsets.) and a number of the subset of the plurality of predictive classifications is dynamically set based on a granularity associated with the predictive classification data object (Koren [0094] “The use of models at different levels of granularity further allows flexibility in the classification granularity. The granularity can be controlled by a user through configuration of one or more thresholds (e.g., confidence thresholds) associated with one or more models... For example, if an entity cannot be classified with a confidence above a threshold using machine learning model 311 (e.g., a first level machine learning model), then machine learning models 331 through 337 (e.g., second level machine learning models) may not be used to attempt to classify the entity.” Koren teaches a configurable confidence threshold (i.e., controlled by a user) for whether or not to classify an entity (i.e., dynamically add the classification to a specific subset of classifications, thus increasing/setting a number of the subset) for controlling classification granularity, wherein a user setting a lower confidence threshold increases the number of the subset (more classifications), and a user setting a higher confidence threshold decreases the number in the subset (less classifications) since the entities are only classified when the confidence threshold is met.).
Hannon and Koren are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to healthcare classification. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon with the above teachings of Koren. Doing so enables avoiding use of resources (e.g., processing and memory) to attempt to classify an entity that cannot be classified at a higher level of the hierarchy (Koren [0094] “This control enables avoiding use of resources (e.g., processing and memory) to attempt to classify an entity that cannot be classified at a higher level of the hierarchy.”).
Hannon in view of Koren fails to explicitly teach and responsive to determining that the one or more predictive classifications deviate from the assigned classification by a deviation threshold, providing, by the one or more processors, an indication of an outlier associated with associated with the entity.
However, Adhikari teaches and responsive to determining that the one or more predictive classifications deviate from the assigned classification by a deviation threshold, providing, by the one or more processors, an indication of an outlier associated with associated with the entity (Adhikari [0067] “Following step 425 of the method, the processor of the patient flow system detects a deviation between the predicted patient flow and at least one actual data point at step 430 of the method. In embodiments, the deviation must meet or exceed a predetermined threshold value. The detected deviation is performed with any suitable anomaly detection on the arrival and occupancy predictions to derive meaningful insights to the hospital management. The term anomaly as used herein means an outlier data point which does not follow a common expected trend or seasonal or cyclic pattern of the entire data and is significantly distinct from the rest of the data… If there is a difference between the expected patient arrivals per hour and the one or more actual data points that were observed recently or last year, and the difference is equal to or greater than a predetermined threshold, then the patient flow system can be configured to display a visual indicator or alert on the user interface to notify the user of the detected deviation” Adhikari teaches detecting an outlier/anomaly based on a deviation threshold by comparing a predicted value to an expected value and providing a display to a visual indication or alert on a user device if the difference is greater than a threshold.).
Hannon, Koren and Adhikari are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to healthcare classification. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren with the above teachings of Adhikari. Doing so may improve accuracy of simulated patient trajectories (Adhikari [0055] “In embodiments, unit transitions are forecasted by patient-type in order to improve the accuracy of simulated patient trajectories.”)
Regarding claim 2, Hannon in view of Koren in further view of Adhikari teaches wherein the plurality of identifier counts for the plurality of predictive identifiers comprise at least (a) a first identifier count for a first predictive identifier associated with the entity and (b) a second identifier count for a second predictive identifier associated with the entity (Hannon [0077] “As shown in FIG. 3A, the historical healthcare claim data 300 can include a healthcare provider identifier 302, a specialty disclosed by the healthcare provider 304, healthcare claim codes 306, and line counts 308. Line counts 308 can be a total number of healthcare claims that utilize a healthcare claim code 306. That is, the healthcare claim data 300 shown in FIG. 3A is aggregated data. For example, provider P2 reported code C in 2000 lines of the historical healthcare claims and code D in 2500 line of the historical healthcare claims 300.”; [0107] “Reference is now made to FIG. 4, which illustrates an example healthcare provider specialty prediction generated by the predictive model, in accordance with an example embodiment. As shown in FIG. 4, the predictive model can receive a query code utilization profile 400 for a healthcare provider 402, including the utilization percentages 404a, 404b, 404c, 404d, 404e . . . (herein collectively referred to as utilization percentages 404) for various healthcare claim codes” Hannon provides a plurality of claim count information for a plurality of entities, as shown in Fig 3A, corresponding to the one or more identifier counts for the one or more predictive identifiers comprise (a) a first identifier count for a first predictive identifier associated with the entity and (b) a second identifier count for a second predictive identifier associated with the entity.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 3, Hannon in view of Koren in further view of Adhikari teaches further comprising: generating, by the one or more processors, a distribution data object for the entity based on the first identifier count and the second identifier count, wherein the distribution data object is indicative of a proportional relevance of a predictive category associated with the entity (Hannon [0080] “Returning now to FIG. 2, at 220, the processor 112 generates a code utilization profile for each healthcare provider based on the historical healthcare claim data. To generate a code utilization profile for a healthcare provider, the processor 112 can identify healthcare claims corresponding to the healthcare provider and determine a total number of healthcare claims corresponding to the healthcare provider. For each healthcare claim code, the processor 112 can determine a number of healthcare claims corresponding to the healthcare provider. The processor 112 can, for each healthcare claim code, determine a utilization percentage based on the number of healthcare claims corresponding to the healthcare provider for the healthcare claim code to the total number of healthcare claims corresponding to the healthcare provider.” Hannon provides generating claim code utilization profiles for a plurality of healthcare providers (entities) including determining utilization percentages based on the number of healthcare claims corresponding to the healthcare provider for the healthcare claim code to the total number of healthcare claims corresponding to the healthcare provider corresponding to generating a distribution data object for the entity based on the first identifier count and the second identifier count, wherein the distribution data object is indicative of a proportional relevance of a predictive category associated with the entity.), wherein generating the one or more predictive classifications is based on the distribution data object (Hannon [0107] “Reference is now made to FIG. 4, which illustrates an example healthcare provider specialty prediction generated by the predictive model, in accordance with an example embodiment. As shown in FIG. 4, the predictive model can receive a query code utilization profile 400 for a healthcare provider 402, including the utilization percentages 404a, 404b, 404c, 404d, 404e . . . (herein collectively referred to as utilization percentages 404) for various healthcare claim codes. The predictive model can return a predicted specialty 406 for the healthcare provider.” Hannon provides a predictive model which receives health care claim count information as input and returns a predicted specialty for a healthcare provider corresponding to generating the predictive classification is based on the distribution data object.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 2.
Regarding claim 4, Hannon in view of Koren in further view of Adhikari teaches further comprising: receiving, by the one or more processors, a peer distribution data object for a peer entity (Hannon [0058] “Healthcare fraud, waste, and abuse detection is typically based on a comparison of the behavior of a subject entity to the behavior of the subject entity's peers. Accordingly, comparison to appropriate peers is critical.”; [0086] “In some embodiments, the processor 112 can, for each healthcare provider, identify a registry specialty 324 within the registry data 320 received at 230 for the healthcare provider. The registry specialty 324 of the registry data 320 can be used to validate the disclosed specialty 304 of the historical healthcare claim data 300 for a healthcare provider.” Hannon provides receiving registry data for each healthcare provider corresponding to receiving a peer distribution data object for a peer entity.); and generating, by the one or more processors, a peer entity data object for the entity based on a distance between the distribution data object and the peer distribution data object (Hannon [0086] “The processor 112 can generate a specialty correspondence indicator representative of a correspondence between the registry specialty 324 of the registry data 320 and the disclosed specialty 304 of the historical healthcare claim data 300 for the healthcare provider. For example, a greater value of the specialty correspondence indicator can represent an accurate match between the registry data 320 and the disclosed specialty 304 and a lower value can represent an inaccurate match.”; [0087] “For example, the processor 112 can compare the specialty correspondence indicator with a pre-determined threshold value. If the specialty correspondence indicator is greater than or equal to the pre-determined threshold value, the registry data 320 and the disclosed specialty 304 can be considered an accurate match and the code utilization profile 310 and corresponding registry specialty 324 for the healthcare provider can be included in the training dataset 330.” Hannon provided generating specialty correspondence indicators representative of a correspondence between the registry specialty 324 of the registry data 320 and the disclosed specialty 304 of the historical healthcare claim data 300 for the healthcare provider and comparing to the code utilization profiles based on threshold values corresponding to generating a peer entity data object for the entity based on a distance between the distribution data object and the peer distribution data object.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 3.
Regarding claim 6, Hannon in view of Koren in further view of Adhikari teaches further comprising: receiving, by the one or more processors, a plurality of distribution data objects for the plurality of entities associated with the assigned classification (Hannon [0114] “The processor 112 can receive query healthcare claim data for a healthcare provider. The healthcare claim data can include at least the query healthcare claim 602. Each healthcare claim of the healthcare claim data can include a claim code and a disclosed specialty. The processor 112 can generate a query code utilization profile 400 for the healthcare provider of the query healthcare claim 602.” Hannon provides receiving claim data and a disclosed specialty for each healthcare provider corresponding to receiving a plurality of distribution data objects for the plurality of entities associated with an assigned classification.); generating, by the one or more processors, an assigned classification distribution data object for the assigned classification based on the plurality of distribution data objects (Hannon [0114] “The processor 112 can determine a predicted healthcare provider specialty for the query healthcare claim 602 by applying the query code utilization profile 400 to a predictive model 510 generated for predicting a healthcare provider specialty.” Hannon provides generating a predictive model for predicting a healthcare provider specialty based on the received claim data and a disclosed specialty corresponding to generating an assigned classification distribution data object for the assigned classification based on the plurality of distribution data objects.); generating, by the one or more processors, an investigative output for the entity based on a comparison between the distribution data object for the entity and the assigned classification distribution data object (Hannon [0114] “The processor 112 can determine whether a behavior of the healthcare provider of the query healthcare claim data is anomalous based on the predicted healthcare provider specialty. The processor 112 can assess the query healthcare claim for fraud, waste, or abuse based on the behavior of the healthcare provider.” Hannon provides the processor to determine whether a behavior of the healthcare provider of the query healthcare claim data is anomalous based on the predicted healthcare provider specialty corresponding to generating an investigative output for the entity based on a comparison between the distribution data object for the entity and the assigned classification distribution data object.); and generating, by the one or more processors, an indication of the investigative output for the entity (Hannon [0114] “The processor 112 can determine whether a behavior of the healthcare provider of the query healthcare claim data is anomalous based on the predicted healthcare provider specialty. The processor 112 can assess the query healthcare claim for fraud, waste, or abuse based on the behavior of the healthcare provider.”; [0121] “The output information is applied to one or more output devices, in known fashion.” Hannon provides outputting information including anomalous output corresponding generating an indication of the investigative output for the entity).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 3.
Regarding claim 8, Hannon in view of Koren in further view of Adhikari teaches wherein each of the one or more predictive classifications is indicative of a particular predictive category associated with the entity that satisfies the deviation threshold (Hannon [0102] “Returning now to FIG. 2, at 250, the processor 112 trains the predictive model with the training dataset selected at 240 to predict a healthcare provider specialty for a healthcare claim. The processor 112 can train the predictive model using artificial intelligence and/or machine learning methods. The healthcare provider specialty predicted by the predictive model can be based on a taxonomy that is different from the taxonomy of the registry specialty 324 and/or the taxonomy of the disclosed specialty 304.”; [0108] “The processor 112 can also monitor the performance of the predictive model and automatically retrain the predictive model when a model drift, or a degradation in performance below a pre-determined threshold, is observed.” Hannon provides predictive taxonomies corresponding to particular predictive categories, wherein a threshold is provided for a degradation in performance below a pre-determined threshold, corresponding to the predictive classification is indicative of a particular predictive category associated with the entity that satisfies the deviation threshold.) .
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 9, Hannon in view of Koren in further view of Adhikari teaches wherein generating the distribution data object comprises: generating, by the one or more processors, a predictive category data object for the entity based on the predictive identifier count data object (Hannon [0107] “Reference is now made to FIG. 4, which illustrates an example healthcare provider specialty prediction generated by the predictive model, in accordance with an example embodiment. As shown in FIG. 4, the predictive model can receive a query code utilization profile 400 for a healthcare provider 402, including the utilization percentages 404a, 404b, 404c, 404d, 404e . . . (herein collectively referred to as utilization percentages 404) for various healthcare claim codes. The predictive model can return a predicted specialty 406 for the healthcare provider.” Hannon provides a predictive model which receives health care claim count information as input and returns a predicted specialty for a healthcare provider corresponding to generating a predictive category data object for the entity based on the predictive identifier count data object), wherein the predictive category data object is indicative of: (i) one or more predictive categories corresponding to a category type, wherein a particular predictive category of the one or more predictive categories corresponds to a subset of a plurality of predictive identifiers associated with the entity (Hannon [0103] “Using a more robust taxonomy for the predictive model can minimize the misclassification rate. A taxonomy with larger groups can be more robust. For example, the taxonomy of the predictive model can include “general medicine” as a specialty. The “general medicine” specialty of the predictive model can encompass specialties such as “internal medicine”, “family medicine”, and “nurse practitioner” of the disclosed specialty 304 or the registry specialty 324.” Hannon provides a taxonomy of specialties for provider corresponding to the predictive category data object is indicative of one or more predictive categories corresponding to a category type, wherein a particular predictive category of the one or more predictive categories corresponds to a subset of a plurality of predictive identifiers associated with the entity.), (ii) a category count corresponding to the particular predictive category (Hannon [0077] “As shown in FIG. 3A, the historical healthcare claim data 300 can include a healthcare provider identifier 302, a specialty disclosed by the healthcare provider 304, healthcare claim codes 306, and line counts 308. Line counts 308 can be a total number of healthcare claims that utilize a healthcare claim code 306.” Hannon provides line counts for particular specialty and provider categories including claim code use count corresponding to a category count corresponding to the particular predictive category.), and (iii) an aggregate category count corresponding to each of the one or more predictive categories (Hannon [0077] “That is, the healthcare claim data 300 shown in FIG. 3A is aggregated data. For example, provider P2 reported code C in 2000 lines of the historical healthcare claims and code D in 2500 line of the historical healthcare claims 300.”; [0078] “The historical healthcare data 300 can be subject to various privacy and security restrictions and/or contractual obligations. Use of aggregated data allows for compliance with such restrictions and obligations.” Hannon provides aggregating claim data corresponding to an aggregate category count corresponding to each of the one or more predictive categories); determining, by the one or more processors, a particular proportional relevance of the particular predictive category based on a comparison between the category count and the aggregate category count (Hannon [0077] “That is, the healthcare claim data 300 shown in FIG. 3A is aggregated data.”; [0089] “In at least one embodiment, a preliminary specialty correspondence indicator can be a full ratio score between the disclosed specialty 304 and the registry specialty 324. A full ratio score can be determined based on a comparison of string text corresponding to the disclosed specialty 304 and string text corresponding to the registry specialty 324.”; [0107] “As shown in FIG. 4, the predictive model can receive a query code utilization profile 400 for a healthcare provider 402, including the utilization percentages 404a, 404b, 404c, 404d, 404e . . . (herein collectively referred to as utilization percentages 404) for various healthcare claim codes.” Hannon provides utilization profiles for a plurality of providers and determining a ratio of disclosed and registered specialties for providers corresponding to determining a particular proportional relevance of the particular predictive category based on a comparison between the category count and the aggregate category count); and generating, by the one or more processors, the distribution data object for the entity based on the particular proportional relevance (Hannon [0107] “Reference is now made to FIG. 4, which illustrates an example healthcare provider specialty prediction generated by the predictive model, in accordance with an example embodiment. As shown in FIG. 4, the predictive model can receive a query code utilization profile 400 for a healthcare provider 402, including the utilization percentages 404a, 404b, 404c, 404d, 404e . . . (herein collectively referred to as utilization percentages 404) for various healthcare claim codes. The predictive model can return a predicted specialty 406 for the healthcare provider.”; [0110] “The processor 112 receives historical healthcare claim data 300 at 210 and generates code utilization profiles 310 for each healthcare provider at 220. The processor 112 also receives registry data 320 at 230 and compares the code utilization profiles 310 with the registry data 320.” Hannon provides generating utilization profiles corresponding to the distribution data object for the entity based on the particular proportional relevance.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 3.
Regarding claim 10, Hannon in view of Koren in further view of Adhikari teaches wherein the category type is one of a plurality of category types (Hannon [0103] “Using a more robust taxonomy for the predictive model can minimize the misclassification rate. A taxonomy with larger groups can be more robust. For example, the taxonomy of the predictive model can include “general medicine” as a specialty. The “general medicine” specialty of the predictive model can encompass specialties such as “internal medicine”, “family medicine”, and “nurse practitioner” of the disclosed specialty 304 or the registry specialty 324.” Hannon provides a classification taxonomy for the predicted specialties corresponding to the category type is one of a plurality of category types.), and wherein the distribution data object comprises a plurality of type-specific distributions corresponding to the plurality of category types (Hannon [0110] “The processor 112 receives historical healthcare claim data 300 at 210 and generates code utilization profiles 310 for each healthcare provider at 220. The processor 112 also receives registry data 320 at 230 and compares the code utilization profiles 310 with the registry data 320.” Hannon provides code utilization profiles for each healthcare provider corresponding to the distribution data object comprises a plurality of type-specific distributions corresponding to the plurality of category types.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 9.
Regarding claim 11, Hannon in view of Koren in further view of Adhikari teaches further comprising: receiving, by the one or more processors, the assigned classification for the entity (Hannon [0107] “As shown in FIG. 4, the predictive model can receive a query code utilization profile 400 for a healthcare provider 402, including the utilization percentages 404a, 404b, 404c, 404d, 404e . . . (herein collectively referred to as utilization percentages 404) for various healthcare claim codes. The predictive model can return a predicted specialty 406 for the healthcare provider.” Hannon provides returning a predicted specialty using a predictive model corresponding to receiving an assigned classification for the entity.); generating, by the one or more processors, verification data for the entity based on a comparison between the assigned classification and the predictive classification (Hannon [0108] “The processor 112 can also monitor the performance of the predictive model and automatically retrain the predictive model when a model drift, or a degradation in performance below a pre-determined threshold, is observed.” Hannon provides determining when to retrain the model based on a model drift, or a degradation in performance below a pre-determined threshold corresponding to generating verification data for the entity based on a comparison between the assigned classification and the predictive classification.); and providing, by the one or more processors, an indication of the verification data for the entity (Hannon [0109] “The predictive model can be trained using multiple algorithms and the best performing model can be identified during validation using metrics such as a precision/recall curve and a ROC-AUC curve.” Hannon provides validation using metric corresponding to providing an indication of the verification data for the entity.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 13, it is the system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found above in the rejection of claim 1. Further, Hannon teaches a system comprising: one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations (Hannon [0123] “Furthermore, the system, processes and methods of the described embodiments are capable of being distributed in a computer program product comprising a computer readable medium that bears computer usable instructions for one or more processors.”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 14, the rejection of claim 13 is incorporated herein. Further, the limitations in this claim are taught by Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 2.
Regarding claim 15, the rejection of claim 14 is incorporated herein. Further, the limitations in this claim are taught by Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 3.
Regarding claim 16, it is the non-transitory computer-readable storage media embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found above in the rejection of claim 1. Further, Hannon teaches one or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations (Hannon [0122] “Embodiments of the system may also be considered to be implemented as a non-transitory computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein.”; [0123] “Furthermore, the system, processes and methods of the described embodiments are capable of being distributed in a computer program product comprising a computer readable medium that bears computer usable instructions for one or more processors.”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 17, the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 2.
Regarding claim 18, the rejection of claim 17 is incorporated herein. Further, the limitations in this claim are taught by Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 3.
Regarding claim 19, the rejection of claim 18 is incorporated herein. Further, the limitations in this claim are taught by Hannon in view of Koren in further view of Adhikari for the same reasons disclosed above in the rejection of claim 6.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Hannon et al. (U.S. Patent Publication No. US 2023/0162846) (“Hannon”) in view of Koren et al. (U.S. Patent Publication No. 2022/0210079) (“Koren”) in further view of Adhikari et al. (U.S. Patent Publication No. 2023/0008936 ) (“Adhikari”) in further view of Rodkey (U.S. Patent Publication No. US 2017/0178245) (“Rodkey”).
Regarding claim 5, Hannon in view of Koren in further view of Adhikari teaches the computer-implemented method of claim 4 as discussed above in the rejection of claim 4, but fails to teach wherein: the distribution data object comprises a first predictive category vector that comprises one or more first proportional values corresponding to one or more first predictive categories associated with the entity, the peer distribution data object comprises a second predictive category vector that comprises one or more second proportional values corresponding to one or more second predictive categories associated with the peer entity, and the distance between the distribution data object and the peer distribution data object comprises a particular distance between the first predictive category vector and the second predictive category vector.
However, Rodkey teaches wherein: the distribution data object comprises a first predictive category vector that comprises one or more first proportional values corresponding to one or more first predictive categories associated with the entity (Rodkey [0639] “The server may include a machine learning engine executed by the server configured to train the healthcare expense prediction model with the financial data and the health data of the plurality of participants. The serve can input the multi-dimensional feature vector into the healthcare expense prediction model to output the predicted lifetime healthcare expenses of the participant, the predicted lifetime healthcare expenses of the participant based on data associated with similar participants used to generate the healthcare expense prediction model.”; [0669] “For example, the RCRS 1708 can generate a first vector based on the healthcare transaction event and one or more healthcare transaction events of the participants. The vector can include features of the event and historical events such as geographic area, type of event, or time of day.” Rodkey provides generating predictions using vectors comprising healthcare transaction events and one or more healthcare transaction events of the participants corresponding to a first predictive category vector that comprises one or more first proportional values corresponding to one or more first predictive categories associated with the entity.), the peer distribution data object comprises a second predictive category vector that comprises one or more second proportional values corresponding to one or more second predictive categories associated with the peer entity (Rodkey [0047] “The system can be configured with an administrator matching technique to identify peer or similar administrators. Peer administrator may refer to administrators having characteristics, features, or parameters that satisfy a matching or similarity criterion or criteria using a matching technique.”; [0669] “The RCRS 1708 can identify a second vector for each of a plurality of healthcare transaction trend models. The second vectors can each be based on feature values corresponding to the healthcare transaction trend models.” Rodkey provides a second vector comprising healthcare transaction events and one or more healthcare transaction events of the participants for peer comparison corresponding to a second predictive category vector that comprises one or more second proportional values corresponding to one or more second predictive categories associated with the peer entity.), and the distance between the distribution data object and the peer distribution data object comprises a particular distance between the first predictive category vector and the second predictive category vector (Rodkey [0047] “The system can be configured with an administrator matching technique to identify peer or similar administrators. Peer administrator may refer to administrators having characteristics, features, or parameters that satisfy a matching or similarity criterion or criteria using a matching technique.”; [0669] “The RCRS 1708 can determine distance between the first vector and each of the second vectors. The distance can refer to a vector or a magnitude of the distance. For example, the RCRS 1708 can determine a distance vector between an endpoint of the first vector and an end point of the second vector, and then determine the distance as a magnitude of the distance vector. The RCRS 1708 can identify a minimum distance vector from the determined distance vector for each of the plurality of healthcare transaction events, and select the healthcare trend model corresponding to the minimum distance vector.” Rodkey providing calculating the distance between the two vectors corresponding to the distance between the distribution data object and the peer distribution data object comprises a particular distance between the first predictive category vector and the second predictive category vector.).
Hannon, Koren, Adhikari and Rodkey are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically related to healthcare operations. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari with the above teachings of Rodkey. Doing so would allow for clustering of similar entities (Rodkey [0605] “The clustering technique can include generating vectors using multi-dimensional features associated with each profile, and determining a distance between vectors to identify a set of vectors within a threshold distance from one another. The identified set of vectors within the threshold distance from one another can form a cluster.”).
Claims 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Hannon et al. (U.S. Patent Publication No. US 2023/0162846) (“Hannon”) in view of Koren et al. (U.S. Patent Publication No. 2022/0210079) (“Koren”) in further view of Adhikari et al. (U.S. Patent Publication No. 2023/0008936 ) (“Adhikari”) in further view of Neumann (U.S. Patent Publication No. 2020/0380411) (“Neumann”).
Regarding claim 21, Hannon in view of Koren in further view of Adhikari teaches the computer-implemented method of claim 1, as discussed above in the rejection of claim 1, but fails to teach wherein the assigned classification is not included within a defined list of predictive classifications, and the computer-implemented method further comprises generating, by the one or more processors, an updated list of predictive classifications by augmenting the defined list of predictive classifications to include the assigned classification.
However, Neumann teaches wherein the assigned classification is not included within a defined list of predictive classifications (Neumann [0042] “Still referring to FIG. 1, classification device 104 may detect further significant categories of physiological data, relationships of such categories to prognostic labels, and/or categories of prognostic labels using machine-learning processes, including without limitation unsupervised machine-learning processes as described in further detail below; such newly identified categories, as well as categories entered by experts in free-form fields as described above, may be added to pre-populated lists of categories, lists used to identify language elements for language learning module, and/or lists used to identify and/or score categories detected in documents, as described above.” Neumann teaches pre-populated lists of categories, wherein newly identified categories, as well as categories entered by experts in free-form fields may be added to the list, wherein the newly identified categories correspond to the assigned classifications, which are subsequently added to the pre-populated lists, and are therefore not included in the list before being added.), and the computer-implemented method further comprises generating, by the one or more processors, an updated list of predictive classifications by augmenting the defined list of predictive classifications to include the assigned classification (Neumann [0042] “Still referring to FIG. 1, classification device 104 may detect further significant categories of physiological data, relationships of such categories to prognostic labels, and/or categories of prognostic labels using machine-learning processes, including without limitation unsupervised machine-learning processes as described in further detail below; such newly identified categories, as well as categories entered by experts in free-form fields as described above, may be added to pre-populated lists of categories, lists used to identify language elements for language learning module, and/or lists used to identify and/or score categories detected in documents, as described above.” Neumann teaches adding newly identified categories to a pre-populated list of categories, corresponding to updating a defined list to include a new classification (i.e., the newly identified category).).
Hannon, Koren, Adhikari and Neumann are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically related to predictive classifications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hannon in view of Koren in further view of Adhikari with the above teachings of Neumann. Doing so would enhance the accuracy and detail with which system may detect prognostic labels and/or ameliorative labels (Neumann [0074] “Use of unsupervised learning may greatly enhance the accuracy and detail with which system may detect prognostic labels and/or ameliorative labels.”).
Regarding claim 22, the rejection of claim 13 is incorporated herein. Further, the limitations in this claim are taught by Hannon in view of Koren in further view of Adhikari and Neumann for the same reasons disclosed above in the rejection of claim 21.
Regarding claim 23, the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Hannon in view of Koren in further view of Adhikari and Neumann for the same reasons disclosed above in the rejection of claim 21.
Response to Arguments
Regarding the rejection of the claims under 35 U.S.C. 112b, Applicant’s amendments overcome the rejection. Specifically, support for the amended negative limitation of claims 21-23 can be found in paragraph [0146] of the specification, which recites “For instance, a target entity may be identified that is associated with an entity classification that is not defined by the list of predictive classifications.”. Therefore, support for the amended limitation “wherein the assigned classification is not included within a defined list of predictive classifications” of claims 21-23 can be found in paragraph [0146].
Regarding the rejection of the claims under 35 U.S.C. 101, Applicant firstly asserts the amended claims recite an improved machine learning classification model that outperforms traditional machine learning classification models with respect to at least prediction granularity (“Remarks, Pg. 10). Specifically, Applicant asserts that the limitation reciting “a number of the subset of the plurality of predictive classifications is dynamically set based on a granularity associated with the predictive classification data object” provided an improvement in technology, like that in Desjardines.
Applicant further asserts that claims improve the granularity of machine learning model predictions, and the modification of the machine learning model to overcome a lack of granularity integrates the claims into a practical application (“Remarks”, Pg. 11).
However, as discussed above in the 35 U.S.C. 101 rejection of claim 1 above, the limitation reciting “responsive to determining that the one or more predictive classifications deviate from the assigned classification by a deviation threshold, providing… an indication of an outlier associated with associated with the entity.”, is an abstract idea because one can mentally determine a deviation of a classification and subsequently provide an indication of an outlier with the assistance of pen and paper (i.e., making a comparison and providing any indication of the result of said comparison). Accordingly, assuming the claims did recite an improvement, it would be in the abstract idea identified above. As recited in the MPEP, an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a).
Further, the present claims are not like the claims of Desjardines, because the claims in Desjardines included an improvement related to addressing catastrophic forgetting while the current claims only use machine learning models at a high level to perform an abstract idea. Further, Desjardines provided a specific training strategy that allows the model to preserve performance on earlier tasks even as it learns new ones, while the current claims merely employ machine learning at a high level. Therefore, the claims remain rejected under 35 U.S.C. 101.
Regarding the rejection of the claims under 35 U.S.C. 103, Applicant’s arguments with respect to claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Specifically, the new references Koren, Adhikari and Neumann have been cited to teach the matters specifically challenged in the argument.
Conclusion
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/KURT NICHOLAS PRESSLY/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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