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 .
Claims 1-20 are presented for examination.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because “Various embodiments of the present disclosure provide” is used and should be avoided. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1
Step 1: The claim recites “A computer-implemented method comprising”; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
determining… a plurality of first-level variable effects on a target variable based on a plurality of first-level variable changed to a plurality of first-level variables: These limitations recite a mentally performable process of observation to determine… a plurality of first-level variable effects on a target variable based on a plurality of first-level variable changes to a plurality of first-level variables.
determining… one or more first estimated effects on the target variable by configuring a first set of one or more first-level variables of the plurality of first-level variables based on the plurality of first-level variable effects: These limitations recite a mentally performable process of observation to determine… one or more first estimated effects on the target variable by configuring a first set of one or more first-level variables of the plurality of first-level variables based on the plurality of first-level variable effects.
generating… one or more first prediction outputs based on the one or more first estimated effects: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to generate… one or more first prediction outputs based on the one or more first estimated effects.
generating… a first set of combined prediction outputs by combining the one or more first prediction outputs with one or more second prediction outputs that are generated based on one or more second estimated effects on the target variable with respect to one or more quadrant variables: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to generate… a first set of combined prediction outputs by combining the one or more first prediction outputs with one or more second prediction outputs that are generated based on one or more second estimated effects on the target variable with respect to one or more quadrant variables.
initiating… the performance of one or more prediction-based actions based on the first set of combined prediction outputs: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to initiate… the performance of one or more prediction-based actions based on the first set of combined prediction outputs.
Thus, this claim recites a judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
by the one or more processors: These additional elements are recited at a high level of generality and merely recite generic computer components invoked as a tool to carry out the abstract idea. See MPEP 2106.05(f).
by the one or more processors and using a target variable machine learning model: These additional elements are recited at a high level of generality and merely recite generic computer components invoked as a tool to carry out the abstract idea. See MPEP 2106.05(f).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of invoking a computer or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 2
Step 1: a process, as in claim 1.
Step 2A Prong 1: the claim recites, inter alia:
determining a plurality of first-level quadrant variable effects on the target variable based on the plurality of first-level variable changes to the plurality of first-level variables with respect to the one or more quadrant variables: These limitations recite a mentally performable process of observation to determine a plurality of first-level quadrant variable effects on the target variable based on the plurality of first-level variable changed to the plurality of first-level variables with respect to the one or more quadrant variables.
determining the one or more second estimated effects on the target variable by configuring a second set of one or more first-level variables of the plurality of first-level variables based on the plurality of first-level quadrant variable effects: These limitations recite a mentally performable process of observation to determine the one or more second estimated effects on the target variable by configuring a second set of one or more first-level variables of the plurality of first-level variables based on the plurality of first-level quadrant variable effects.
generating… the one or more second prediction outputs based on the one or more second estimated effects: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to generate… the one or more second prediction outputs based on the one or more second estimated effects.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
using one or more first-level quadrant machine learning models: These additional elements amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 3
Step 1: a process, as in claim 1.
Step 2A Prong 1: the claim recites, inter alia:
determining a plurality of second-level variable effects on the plurality of first-level variables based on a plurality of second-level variable changes to a plurality of second-level variables:
These limitations recite a mentally performable process of observation to determine a plurality of second-level variable effects on the plurality of first-level variables based on a plurality of second-level variable changes to a plurality of second-level variables.
determining a plurality of third estimated effects on the plurality of first-level variables by configuring a first set of one or more second-level variables of the plurality of second-level variables based on the plurality of second-level variable effects: These limitations recite a mentally performable process of observation to determine a plurality of third estimated effects on the plurality of first-level variables by configuring a first set of one or more second-level variables of the plurality of second-level variables based on the plurality of second-level variable effects.
generating… a plurality of third prediction outputs based on the plurality of third estimated effects: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to generate… a plurality of third prediction outputs based on the plurality of third estimated effects.
generating a second set of combined prediction outputs by combining the plurality of third prediction outputs with a plurality of fourth prediction outputs that are generated based on one or more fourth estimated effects on the plurality of first-level variables with respect to the one or more quadrant variables: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to generate a second set of combined prediction outputs by combining the plurality of third prediction outputs with a plurality of fourth prediction outputs that are generated based on one or more fourth estimated effects on the plurality of first-level variables with respect to the one or more quadrant variables.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
using a plurality of first-level variable machine learning models: These additional elements amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 4
Step 1: a process, as in claim 3.
Step 2A Prong 1: the claim recites, inter alia:
determining a plurality of second-level quadrant variable effects on the plurality of first-level variables based on the plurality of second-level variable changes to the plurality of second-level variables with respect to the one or more quadrant variables: These limitations recite a mentally performable process of observation to determine a plurality of second-level quadrant variable effects on the plurality of first-level variables based on the plurality of second-level variable changes to the plurality of second-level variables with respect to the one or more quadrant variables.
determining the one or more fourth estimated effects on the plurality of first-level variables by configuring a second set of one or more second-level variables of the plurality of second-level variables based on the plurality of second-level quadrant variable effects: These limitations recite a mentally performable process of observation to determine the one or more fourth estimated effects on the plurality of first-level variables by configuring a second set of one or more second-level variables of the plurality of second-level variables based on the plurality of second-level quadrant variable effects.
generating… the plurality of fourth prediction outputs based on the one or more fourth estimated effects: These limitations recite a mentally performable process with the aid of pen and paper of using judgement and evaluation to generating… the plurality of fourth prediction outputs based on the one or more fourth estimated effects.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
using a plurality of second-level quadrant machine learning models: These additional elements amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 5
Step 1: a process, as in claim 3.
Step 2A Prong 1: the claim recites, inter alia:
generating a simulation data object based on one or more of the first set of combined prediction outputs or the second set of combined prediction outputs: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to generate a simulation data object based on one or more of the first set of combined prediction outputs or the second set of combined prediction outputs.
initiating the performance of the one or more prediction-based actions based on the simulation data object: These limitations recite a mentally performable process with the aid of pen and paper of using judgement or evaluation to initiate the performance of the one or more prediction-based actions based on the simulation data object.
Thus, the claim further recites the judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claim 6
Step 1: a process, as in claim 3.
Step 2A Prong 1: the claim recites, inter alia.
wherein the target variable machine learning model or at least one of the plurality of first-level variable machine learning models comprises a probabilistic structural equation model, a supervised machine learning model, or an unsupervised machine learning model: These limitations recite a mathematical concept of a formula/equation.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claim 7
Step 1: a process, as in claim 1.
Step 2A Prong 1: The claim recites the same abstract ideas as claim 1 as the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the one or more first prediction outputs comprise one or more simulations that are based on the one or more first estimated effects: These additional elements amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 8
Step 1: a process, as in claim 1.
Step 2A Prong 1: The claim recites the same abstract ideas as claim 1 as the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the one or more second prediction outputs comprise one or more simulations that are based on the one or more second estimated effects: These additional elements amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of linking the use of a judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 9
Step 1: a process, as in claim 1.
Step 2A Prong 1: the claim recites, inter alia:
wherein the one or more quadrant variables are associated with one or more data cohorts: These limitations recite a mathematical concept of a mathematical relationship similar to organizing information and manipulating information, e.g. the one or more quadrant variables are associated with one or more data cohorts.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claims 10-15
Step 1: These claims are directed to “A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:”; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: Claims 10-15 recite substantially the same abstract ideas as in claims 1-6, respectively, as the judicial exception.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. The only difference between claims 10-15 and claims 1-6 is that claims 10-15 are directed to “A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:” However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. memory and one or more processors communicatively coupled to the memory, the one or more processors configured to process, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception the analysis at this step mirrors that of claims 1-6, respectively.
Step 2B: These claims do not contain significantly more than the judicial exception. The only difference between claims 10-15 and claims 1-6 is that claims 10-15 are directed to “A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:” However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. memory and one or more processors communicatively coupled to the memory, the one or more processors configured to process, cannot amount to significantly more than the judicial exception. See MPEP 2106.05(f). With that exception the analysis at this step mirrors that of claims 1-6, respectively.
Claims 16-20
Step 1: These claims are directed to “One or more non-transitory computer-readable storage media including instruction that, when executed by one or more processors, cause the one or more processors to:”; therefore, these claims are directed to the statutory category of an article of manufacture.
Step 2A Prong 1: Claims 16-20 recite substantially the same abstract ideas as in claims 1-5. respectively, as the judicial exception.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. The only difference between claims 16-20 and claims 1-5 is that claims 16-20 are directed to “One or more non-transitory computer-readable storage media including instruction that, when executed by one or more processors, cause the one or more processors to:” However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. one or more non-transitory computer-readable storage media including instruction that, when executed by one or more processors, cause the one or more processors to process, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception the analysis at this step mirrors that of claims 1-5, respectively.
Step 2B: These claims do not contain significantly more than the judicial exception. The only difference between claims 16-20 and claims 1-5 is that claims 16-20 are directed to “One or more non-transitory computer-readable storage media including instruction that, when executed by one or more processors, cause the one or more processors to:” However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. one or more non-transitory computer-readable storage media including instruction that, when executed by one or more processors, cause the one or more processors to process, cannot amount to significantly more than the judicial exception. See MPEP 2106.05(f). With that exception the analysis at this step mirrors that of claims 1-5, respectively.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Eidelman et al. (US 11651460 B2, filed 12/07/2020), hereinafter Eidelman, and in view of Gupta et al. (US 20240343259 A1, filed 04/12/2023), hereinafter Gupta.
Regarding claim 1, Eidelman teaches: A computer-implemented method comprising: (Eidelman; [Column 11 lines 57-62], Briefly described, the present disclosure being implemented using a general-purpose computer):
determining, by one or more processors, a plurality of first-level variable effects on a
target variable based on a plurality of first-level variable changes to a plurality of first-level variables: (Eidelman; [Abstract], Briefly described, at least one or more processors; [Column 67 line 57 – Column 68 line 20], Briefly described, assumption data serving the role as first-level variables and representing a value, and desired outcome data serving the role as a target variable reached based on a success probability to measure estimated first-level variable effects; [Column 68 lines 39-48], Briefly described, an outcome alteration module to change the assumption data that affects the success probability to determine if it is achieving the desired outcome data or target variable).
determining, by the one or more processors, one or more first estimated effects on the
target variable by configuring a first set of one or more first-level variables of the plurality of first-level variable based on the plurality of first-level variable effects: (Eidelman; [Abstract], Briefly described, at least one or more processors; [Column 69 line 25-31], Briefly described, a plurality of first-level variables in the form of a set of assumption determining an estimated effect on a desired outcome through predicting an outcome, when convincing Sen. Rob Smith or Sen. Kevin Bloom to cosponsor the bill, then the success probability will increase indicating a further achievement of reaching the desired outcome or target variable):
generating, by the one or more processors and using a target variable machine learning
model, one or more first prediction outputs based on the one or more first estimated effects: (Eidelman; [Abstract], Briefly described, at least one or more processors; [Column 47 line 23-25], Briefly described, a target variable machine learning model operating the estimated effects for prediction outputs; [Column 69 line 21-31], Briefly described, the prediction outputs of the bill succeeding if Sen. Rob Smith or Sen. Kevin Bloom cosponsor the bill being generated as an initial prediction based on the likelihood that the first estimated effects or the success probability will increase):
generated based on one or more second estimated effects on the target variable with
respect to one or more quadrant variables: (Eidelman; [Column 47 line 9-33], Briefly described, a second likelihood of estimated effects being generated with its own success probability of reaching the desired outcome data or target variable; [Column 58 line 7-25], Briefly described, quadrant variables indicating the policymaker information to determine how they will vote based on their political alignment, increasing or decreasing the success probability of achieving a desired outcome data or target variable):
initiating, by the one or more processors, the performance of one or more
prediction-based actions: (Eidelman; [Column 68 line 49-67], Briefly described, an outcome alteration module displaying to a system user a recommendation to take the action in order to increase the likelihood of achieving the desired outcome through motivating the policymaker’s constituents to send letters to the policymaker in support of the policy).
However, Eidelman fails to expressly teach – generating, by the one or more processors, a first set of combined prediction outputs by combining the one or more first prediction outputs with one or more second prediction outputs that are; and – based on the first set of combined prediction outputs.
In the same field of endeavor, Gupta teaches:
generating, by the one or more processors, a first set of combined prediction outputs by
combining the one or more first prediction outputs with one or more second prediction outputs that are: (Gupta, [0050], Briefly described, one or more processors to perform the actions; [0060], Briefly described, two predictions generated, a first prediction capturing eye motion and a second prediction capturing body motion; [0064], Briefly described, generated prediction outputs can be combined into a first prediction output generated by models):
based on the first set of combined prediction outputs: (Gupta, [0064], Briefly described,
generated prediction outputs can be combined into a first prediction output generated by models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated – generating, by the one or more processors, a first set of combined prediction outputs by combining the one or more first prediction outputs with one or more second prediction outputs that are; and – based on the first set of combined prediction outputs as suggested by Eidelman and Gupta. Doing so would be desirable because having a first set of combined prediction outputs from Gupta would help provide a more adequate prediction for the machine learning model to reach a target variable. Without the combination, predictions produced by the machine learning model would not be able to initiate actions with the input of all the predictions at once. The combination of Eidelman and Gupta provides the alterable variables with a prediction outcome while also combining those outcomes based on the alterable variables to achieve a target variable.
Regarding claim 2, the combination of Eidelman and Gupta teaches the invention as claimed in claim 1 above including determining a plurality of first-level quadrant variable effects on the target variable based on the plurality of first-level variable changes to the plurality of first-level variables with respect to the one or more quadrant variables: (Eidelman; [Column 35 line 62 – Column 36 line 7], Briefly described, altering the assumption data so the outcome to achieve of target variable to pass a floor vote as indicated as a label in the quadrant is achieved; [Column 58 line 11-25], Briefly described, a plurality of first-level quadrant variable effects that are determined by the variable placed on the quadrant, where the effects of “vote with you” and “vote against you” are labels intending to the effect of the first-level quadrant variables to reach a target variable):
determining the one or more second estimated effects on the target variable by
configuring a second set of one or more first-level variables of the plurality of first-level variables based on the plurality of first-level quadrant variable effects: (Eidelman; [Column 69 line 25-36], Briefly described, a second set of first-level variables or assumption data with the estimated effects or success probability determining achievement of the target variable increasing by 13%; [Column 58 line 7-25], Briefly described, the first-level quadrant variables of the assumption data placed on a quadrant indicating a weight favored between “vote with you” and “vote against you”, where the weight in either direction corresponds with the success probability of the second estimated effects, and these effects determined by where the first-level quadrant variables are placed on the quadrant; [Column 47 line 9-33], Briefly described, a second likelihood of estimated effects being generated with its own success probability of reaching the desired outcome data or target variable, where a second likelihood also follows the labels of the quadrant to determine that a legislator may vote for a bill that will pass):
generating, using one or more first-level quadrant machine learning models, the one or
more second prediction outputs based on the one or more second estimated effects: (Eidelman; [Column 7 line 15-27], Briefly described, a second prediction output generated based off the second information to increase a likelihood or effect of achieving the desired outcome; [Column 47 line 9-33], Briefly described, a second likelihood of estimated effects being generated with its own success probability of reaching the desired outcome data or target variable, [Column 55 line 47-63], Briefly described, models used to determine the legislator positions to come in tact with their placement on the quadrant).
Regarding claim 3, the combination of Eidelman and Gupta teaches the invention as claimed in claim 1 above including determining a plurality of second-level variable effects on the plurality of first-level variables based on a plurality of second-level variable changes to a plurality of second-level variables: (Eidelman; [Column 47 line 55 – Column 48 line 3], Briefly described, an outlook being applied to change the likelihood using a calculation of second-level variables that can affect the first-level variables by altering “the strength of the bill sponsor”, “the language in the bill”, and “the network of the co-sponsor”, causing the effects onto the plurality of first-level variables to go from “less likely” to “much less likely”):
determining a plurality of third estimated effects on the plurality of first-level variables
by configuring a first set of one or more second-level variables of the plurality of second-level variables based on the plurality of second-level variable effects: (Eidelman; [Column 47 line 50 – Column 48 line 3], Briefly described, a third likelihood of estimated effects based off the second likelihood of second-level variable effects, where the second-level variables of “the strength of the bill sponsor”, “the language in the bill”, and “the network of the co-sponsor” affecting the first-level variables are configured in an Outlook or a first set of these second-level variables):
generating, using a plurality of first-level variable machine learning models, a plurality of
third prediction outputs based on the plurality of third estimated effects: (Eidelman; [Column 47 line 50-65], Briefly described, third prediction outputs of “much less likely” being produced based off the third likelihood of no votes not increasing as much as previously predicted in the second prediction outputs; [Column 55 line 47-63], Briefly described, models used to determine the legislator positions to come in tact with their placement on the quadrant):
generating a second set of combined prediction outputs by combining the plurality of
third prediction outputs with a plurality of fourth prediction outputs that are generated based on one or more fourth estimated effects on the plurality of first-level variables with respect to the one or more quadrant variables: (Eidelman; [Column 15 line 38-49], Briefly described, a plurality of prediction outputs and/or likelihoods or estimated effects that can be outputs by a plurality of models, meaning a fourth prediction output can be generated based on a fourth estimated effect or likelihood on the plurality of first-level variables; [Column 58 line 7-25], Briefly described, quadrants to indicate the position of variables with respect to political alignment and likelihood to vote) (Gupta; [0061], Briefly described, a third prediction output generated by a model; [0062], Briefly described, a fourth prediction output generated by a model; [0064], Briefly described, the third and fourth prediction output can be combined to generate a second combined prediction output generated by models).
Regarding claim 4, the combination of Eidelman and Gupta teaches the invention as claimed in claim 3 above including determining a plurality of second-level quadrant variable effects on the plurality of first-level variables based on the plurality of second-level variable changes to the plurality of second-level variables with respect to the one or more quadrant variables: (Eidelman; [Column 47 line 55 – Column 48 line 3], Briefly described, an outlook being applied to change the likelihood using a calculation of second-level variables that can affect the first-level variables by altering “the strength of the bill sponsor”, “the language in the bill”, and “the network of the co-sponsor”, causing the effects onto the plurality of first-level variables to go from “less likely” to “much less likely” ; [Column 58 line 7-25], Briefly described, a quadrant where second-level variables can affect the plurality of first-level variables through determining where on the quadrant it is placed and if it indicates that a vote is more likely or not to happen and the political alignment):
determining the one or more fourth estimated effects on the plurality of first-level
variables by configuring a second set of one or more second-level variables of the plurality of second-level variables based on the plurality of second-level quadrant variable effects: (Eidelman; [Column 58 line 51 – Column 59 line 4], Briefly described, a second set of one or more second-level variables through an Issue Board, where adjustable weighting for second-level variables that focus on an agenda pertaining to the plurality of first-level variables in order to reach a desired outcome; [Column 15 line 38-49], Briefly described, a plurality of prediction outputs and/or likelihoods or estimated effects that can be outputs by a plurality of models, meaning a fourth estimated effect or likelihood can be determined on the plurality of first-level variables based on the quadrant effects; [Column 58 line 7-25], Briefly described, the second-level variables on a quadrant affecting the outcome based on its location of the political alignment and propensity to vote on the quadrant):
generating, using a plurality of second-level quadrant machine learning models, the
plurality of fourth prediction outputs based on the one or more fourth estimated effects: (Eidelman; [Column 55 line 47-63], Briefly described, models used to determine the legislator positions to come in tact with their placement on the quadrant; [Column 15 line 38-49], Briefly described, a plurality of prediction outputs and/or likelihoods or estimated effects that can be outputs by a plurality of models, meaning plurality of fourth prediction outputs can be generated based on fourth estimated effects or likelihoods through a plurality of first-level variables).
Regarding claim 5, the combination of Eidelman and Gupta teaches the invention as claimed in claim 3 above including generating a simulation data object based on one or more of the first set of combined prediction outputs or the second set of combined prediction outputs; (Gupta; [0167], Briefly described, simulation results generated from the combined prediction outputs; [0059], Briefly described, a first prediction output generated; [0060], Briefly described, a second prediction output generated; [0061], Briefly described, a third prediction output generated; [0062], Briefly described, a fourth prediction output generated; [0063], Briefly described, predictions generated by machine learning models; [0064], Briefly described, predictions can be combined into combined prediction outputs, where a first and a second set of combined prediction outputs can be generated):
initiating the performance of the one or more prediction-based actions based on the
simulation data object: (Eidelman; Column 68 line 49-67], Briefly described, an outcome alteration module displaying to a system user a recommendation to take the action in order to increase the likelihood of achieving the desired outcome through motivating the policymaker’s constituents to send letters to the policymaker in support of the policy) (Gupta; [0167], Briefly described, simulation results generated from the combined prediction outputs; [0059], Briefly described, a first prediction output generated; [0060], Briefly described, a second prediction output generated; [0061], Briefly described, a third prediction output generated; [0062], Briefly described, a fourth prediction output generated; [0063], Briefly described, predictions generated by machine learning models; [0064], Briefly described, predictions can be combined into combined prediction outputs, where a first and a second set of combined prediction outputs can be generated).
Regarding claim 6, the combination of Eidelman and Gupta teaches the invention as claimed in claim 3 above including wherein the target variable machine learning model or at least one of the plurality of first-level variable machine learning models comprises a probabilistic structural equation model, a supervised machine learning model, or an unsupervised machine learning model: (Eidelman; [Column 47 line 23-25], Briefly described, a target variable machine learning model operating the estimated effects for prediction outputs; [Column 15 line 31-59], Briefly described, models using a Bayesian model where a supervised learning, a unsupervised learning, and a probabilistic structural equation model can operate).
Regarding claim 7, the combination of Eidelman and Gupta teaches the invention as claimed in claim 1 above including wherein the one or more first prediction outputs comprise one or more simulations that are based on the one or more first estimated effects: (Eidelman; [Column 69 line 25-31], Briefly described, a first set of assumption data created based on the first predicted outcome of the desired outcome data where there is no contribution from cosponsors; [Column 62 line 43-62], Briefly described, a virtual whipboard referring to a virtual representation to provide a simulation of the outcomes and the likelihoods like the first likelihood of a legislator voting on a specific bill were a certain action to take place from the first estimated effects; [Column 69 line 25-36], Briefly described, a first likelihood or estimated effects of assumption data saying there is a success probability of 37%).
Regarding claim 8, the combination of Eidelman and Gupta teaches the invention as claimed in claim 1 above including wherein the one or more second prediction outputs comprise one or more simulations that are based on the one or more second estimated effects: (Eidelman; Column 7 line 15-27, Briefly described, second information causing the generation of a second prediction output to increase the likelihood of achieving the desired outcome; [Column 62 line 43-62], Briefly described, a virtual whipboard referring to a virtual representation to provide a simulation of the outcomes and the likelihoods like the second likelihood of a legislator voting on a specific bill were a certain action to take place from the second estimated effects [Column 69 line 25-36, Briefly described, a second likelihood where the estimated effects cause an increasing success probability of 13%).
Regarding claim 9, the combination of Eidelman and Gupta teaches the invention as claimed in claim 1 above including wherein the one or more quadrant variables are associated with one or more data cohorts: (Eidelman; [Column 58 line 7-25], Briefly described, variables or assumption data placed on a quadrant where the labels for the quadrant in which the variables fall under are categories of political alignment determining and likelihood of voting).
Regarding claims 10-15, they are apparatus claims that correspond to method claims 1-6. Therefore, they are rejected for the same reason as claims 1-6 above.
Regarding claims 16-20, they are computer-readable storage media claims tat correspond to method claims 1-5. Therefore, they are rejected for the same reason as claims 1-5 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Smith et al. (US 10719744 B2, filed 09/25/2018) teaches in Column 106, line 51-64 quadrant variables indicating a target variable or object as a region of interest and Column 105, line 19-32 where the objects or features in an image are the variables located in a region of interest that the model uses as a target.
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/ROLANDO PATRICK VIRREIRA/Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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