Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 21-23 recite the negative limitation "responsive to determining that the assigned classification is not included within a defined list of predictive classifications". There is insufficient support from the specification for this negative limitation. See MPEP 2173.05(i).
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:
“output the predictive classification data object for the entity based on the distribution data object, the predictive classification data object comprising (a) a predictive classification and (b) one or more contextual attributes for the predictive classification”
“responsive to determining that the predictive classification deviates 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 mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A computer-implemented method”
“by one or more processors/by the one or more processors”
“inputting, by the one or more processors, a distribution data object comprising the plurality of identifier counts to a machine learning”
“wherein the machine learning model comprises one or more layers trained 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).
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”
“receive a predictive classification data object 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 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. 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, and wherein generating the predictive classification 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 the predictive classification 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:
“output the predictive classification data object for the entity based on the distribution data object, the predictive classification data object comprising (a) a predictive classification and (b) one or more contextual attributes for the predictive classification”
“responsive to determining that the predictive classification deviates from the assigned classification by a deviation threshold, provide 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 mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A system comprising: one or more processors; and at least one memory storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to”
“input a distribution data object comprising the plurality of identifier counts to a machine learning model”
“wherein the machine learning model comprises one or more layers trained 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).
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”
“receive a predictive classification data object 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 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. 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 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, and wherein generating the predictive classification 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: 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:
“output the predictive classification data object for the entity based on the distribution data object, the predictive classification data object comprising (a) a predictive classification and (b) one or more contextual attributes for the predictive classification”
“responsive to determining that the predictive classification deviates from the assigned classification by a deviation threshold, provide 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 mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to”
“input a distribution data object comprising the plurality of identifier counts to a machine learning”
“wherein the machine learning model comprises one or more layers trained 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).
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”
“receive a predictive classification data object 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 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. 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 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, and wherein generating the predictive classification 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: 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 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). 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:
“responsive to determining that the assigned classification is not included within a defined list of predictive classifications, 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 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 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:
“responsive to determining that the assigned classification is not included within a defined list of predictive classifications, 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 mere instructions to apply an exception (See MPEP 2106.05(f)).
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 “mere instructions to apply. 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:
“responsive to determining that the assigned classification is not included within a defined list of predictive classifications, 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 mere instructions to apply an exception (See MPEP 2106.05(f)).
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 “mere instructions to apply. 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, 13-19, and 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 Singh et al. (U.S. Patent Publication No. 2022/0188664) (“Singh”).
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.); 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 (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.), …the predictive classification data object comprising (a) a predictive classification and (b) one or more contextual attributes for the predictive classification (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.); and responsive to determining that the predictive classification deviates 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 (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 monitoring model performance including a pre-determined threshold for degradation in performance, model drift, and automatic retraining, corresponding to responsive to determining that the predictive classification deviates 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.).
Hannon fails to explicitly teach …wherein 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.
However, Singh teaches …wherein 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 (Singh [0066] “Some of the described techniques utilize a particular configuration of machine learning models and/or layers. The output of a machine learning model and/or layers therein may be supplied as an input for subsequent steps/operations by another machine learning model and/or layer. However, a person of ordinary skill in the art will recognize that predictive data analysis steps/operations discussed herein may be performed using different combinations of machine learning models/techniques than the particular combinations described herein.”; [0075] “FIG. 7 provides an operational example illustrating training data 402B that can be used to train a lifecycle inference machine learning model.”; [0086] “At step/operation 404-2, each pharmaceutical claim code is processed using a code embedding machine learning model to generate a pharmaceutical claim code embedding for the pharmaceutical claim code.” Singh provides a trained machine learning model comprising one or more layers which generates predictive outputs based on claim code inputs, corresponding to a machine learning model which comprises one or more layers trained to output the predictive classification data object for the entity based on the distribution data object.).
Hannon and Singh are both 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 with the above teachings of Singh. Doing so would improve predictive analysis data steps/operations by cross-correlating different data record types efficiently and quickly (Singh [0019] “Thus, various embodiments of the present invention improve predictive analysis data steps/operations by cross-correlating different data record types efficiently and quickly.”).
Regarding claim 2, Hannon in view of Singh teaches the computer-implemented method of claim 1, as discussed above in the rejection of claim 1, 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 Singh for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 3, Hannon in view of Singh teaches the computer-implemented method of claim 2, as discussed above in the rejection of claim 2, 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.), and wherein generating the predictive classification 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 Singh for the same reasons disclosed above in the rejection of claim 2.
Regarding claim 4, Hannon in view of Singh teaches the computer-implemented method of claim 3, as discussed above in the rejection of claim 3, 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 Singh for the same reasons disclosed above in the rejection of claim 3.
Regarding claim 6, Hannon in view of Singh teaches the computer-implemented method of claim 3, as discussed above in the rejection of claim 3, 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 Singh for the same reasons disclosed above in the rejection of claim 3.
Regarding claim 8, Hannon in view of Singh teaches the computer-implemented method of claim 1, as discussed above in the rejection of claim 1, wherein the predictive classification 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 Singh for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 9, Hannon in view of Singh teaches the computer-implemented method of claim 3, as discussed above in the rejection of claim 3, 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 Singh for the same reasons disclosed above in the rejection of claim 3.
Regarding claim 10, Hannon in view of Singh teaches the computer-implemented method of claim 9, as discussed above in the rejection of claim 9, 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 Singh for the same reasons disclosed above in the rejection of claim 9.
Regarding claim 11, Hannon in view of Singh teaches the computer-implemented method of claim 1, as discussed above in the rejection of claim 1 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 Singh 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 at least one memory storing processor-executable instructions (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 Singh 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 Singh 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 Singh 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 storage media including instructions (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 Singh 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 Singh 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 Singh 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 Singh for the same reasons disclosed above in the rejection of claim 6.
Regarding claim 21, Hannon in view of Singh teaches the computer-implemented method of claim 1, as discussed above in the rejection of claim 1, further comprising: responsive to determining that the assigned classification is not included within a defined list of predictive classifications (Hannon [0087] “The processor 112 can determine whether to include, in the training dataset 330, the code utilization profile 310 and corresponding registry specialty 324 for the healthcare provider based on the specialty correspondence indicator.” Hannon provides a processor which determines which data to include in the training dataset, corresponding to determining that the assigned classification is not included within a defined list of predictive classifications.), 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 (Hannon [0087] “The processor 112 can determine whether to include, in the training dataset 330, the code utilization profile 310 and corresponding registry specialty 324 for the healthcare provider based on the specialty correspondence indicator. 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 provides including the code utilization profile and corresponding registry specialties for the healthcare providers, which include the classifications, which are then added to the training data set if the specialty correspondence indicator is greater than or equal to the pre-determined threshold value, corresponding to updating a list of predictive classifications by augmenting the defined list of predictive classifications to include the assigned classification responsive to determining that the assigned classification is not included within a defined list of predictive classifications.).
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 Singh for the same reasons disclosed above in the rejection of claim 1.
Regarding claim 22, the rejection of claim 13 is incorporated herein. Further, the limitations in this claim are taught by Hannon in view of Singh 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 Singh for the same reasons disclosed above in the rejection of claim 21.
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 Singh et al. (U.S. Patent Publication No. 2022/0188664) (“Singh”) in view of Rodkey (U.S. Patent Publication No. US 2017/0178245) (“Rodkey”).
Regarding claim 5, Hannon in view of Singh 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, Singh 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 Singh 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.”).
Response to Arguments
Regarding the rejection applied under 35 U.S.C. 101, Applicant firstly asserts that Hannon fails to teach a predictive classification data object including one or more contextual attributes for the predictive classification (“Remarks”, Page 11).
However, Hannon does teach contextual attributes for the predictive classifications. As discussed in Hannon [0106], the predictive model results can be compared to one or more pre-determined business rules. 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. Therefore, Hannon teaches “the predictive classification data object comprising …one or more contextual attributes for the predictive classification”.
Regarding the rejection applied under 35 U.S.C. 101, Applicant firstly asserts that the present claims recite an improved training technique that enables a machine learning model to output a predictive classification and contextual attributes, which is similar to the claims recited in Desjardines, therefore integrating any abstract ideas into a practical application (“Remarks”, Page 12). Applicant further asserts that the claims do not recite any abstract ideas (“Remarks”, Page 14). Applicant further asserts that any abstract ideas are integrated into a practical application (“Remarks”, Page 15). Applicant further asserts that the claimed approach improves accuracy and efficiency of machine learning models by outputting one or more contextual attributes along with a predictive classification, which improves training data integrity by detecting erroneous classifications (“Remarks”, Page 15).
However, as discussed in MPEP 2106.05(f), “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Further, even assuming the claims do recite an improvement, it would be in the abstract idea of outputting predictive classifications. 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 similar to 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KURT NICHOLAS PRESSLY/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125