DETAILED ACTION
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 pending.
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 a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claims 1-11 are to a method (i.e., a process), claims 12-15 are to a system (i.e., a machine), and claims 16-20 are to a non-transitory computer-readable medium (i.e., a manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 12, and 16 is (claim 1 being representative):
determining a set of potential donors, each potential donor associated with timeseries data comprising data before an intervention and data after the intervention;
for each potential donor in the set of potential donors:
determining an expected post-intervention value for the potential donor; and
comparing the expected post-intervention value to an actual post-intervention value for the potential donor;
selecting a set of training donors from the set of potential donors based on the comparisons;
present a graphical representation based, at least in part, on an observed outcome and the synthetic control model.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps broadly describe 1) evaluating a set of potential donors, including comparisons; and 2) judging which donors to include in a training set. An individual could easily accomplish these tasks manually (examiner notes that the presentation of this analysis could also be accomplished with pen and paper). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions, communicated between parties, pertaining to selecting a set of training donors, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by [0001]-[0002] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the mathematical calculations pertaining to selecting a set of training donors, which constitutes a process that, under its broadest reasonable interpretation, covers mathematical concepts. This is further supported by [0001]-[0002] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts, including mathematical relationships, mathematical formulas or equations, mathematical calculations, then it falls within the Mathematical Concepts grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: training a synthetic control model on the timeseries data associated with each training donor in the set of training donors; causing a visual output device of a computing device.
Claim 12 recites the following additional elements: one or more processors; one or more computer-readable storage devices storing data instructions; train a synthetic control model on the timeseries data associated with each training donor in the set of training donors; cause a visual output device of a computing device.
Claim 16 recites the following additional elements: [a] non-transitory computer-readable medium having stored thereon data instructions; by one or more processors; train a synthetic control model on the timeseries data associated with each training donor in the set of training donors; cause a visual output device of a computing device.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0048]-[0054] of applicant’s specification as filed, for example.
Examiner interprets the “training” step, first described in para. [0030] of applicant’s specification as filed, as an additional element. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "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. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, the “training” step is claimed and described in a results-oriented manner that lacks any details concerning the mechanisms for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 2-11, 13-15, and 17-20 recite additional abstract steps and information that further narrow the abstract idea. This simple narrowing of the abstract idea doesn’t integrate it into practical application or add significantly more, and one or more of the previously highlighted abstract idea groupings apply.
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4, 9-12, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over “An update on the synthetic control method as a tool to understand state policy” by McClelland et al. (NPL attached; hereinafter McClelland) in view of Elisha (US 20210256420).
Claims 1, 12, and 16
McClelland discloses:
[Claim 1] A method for selecting training donors for a synthetic control model {Page 2: Instead of an actual control unit, the method creates a synthetic control so that the treatment can be evaluated by comparing the outcome of the treated unit with the outcome of the synthetic control unit. The synthetic control is created by selecting and then weighting together a small number of control units drawn from a pool of potential donors (i.e., selecting training donors for a synthetic control model).}, the method comprising:
[Claim 12] A system for selecting training donors for a synthetic control model {See previous citation to page 2.}, the system comprising:
[Claims 1, 12, and 16] determining a set of potential donors, each potential donor associated with timeseries data comprising data before an intervention and data after the intervention {Page 18: Exclude any units that enacted policy treatments of similar or larger size during the selected period (i.e., determining a set of potential donors). Page 6: In addition, outcomes must be available for periods before the treatment and at least one period after treatment for both the treated unit and the pool of potential donor units (i.e., each potential donor associated with timeseries data comprising data before an intervention and data after the intervention).};
for each potential donor in the set of potential donors:
determining an expected post-intervention value for the potential donor {Page 22: In the first method, an analyst would start with some value of and in placebo-type exercise separately apply the SCM to each unit in the donor pool, calculating the MSPE after treatment (i.e., determining an expected post-intervention value for the potential donor).}; and
comparing the expected post-intervention value to an actual post-intervention value for the potential donor {Page 22: Because none of the control units receive treatment, the outcomes of the synthetic and actual control units should track each other after treatment (i.e., comparing the expected post-intervention value to an actual post-intervention value for the potential donor).};
causing a visual output device of a computing device to present a graphical representation based, at least in part, on an observed outcome and the synthetic control model {Page 2: The availability of the software in Matlab, Stata, and R means this approach is easily accessible, not simply an interesting, but unused, empirical tool (i.e., causing a visual output device of a computing device). Page 7: The results are shown in figure 2. Cigarette sales per capita in the synthetic control carefully match cigarette sales in actual California until 1988, the year cigarette taxes were increased. From that point forward, the two paths diverge. Although both the synthetic control and actual California show a continued decline in cigarette sales per capita, the decline is faster in actual California, with the difference providing an estimate of the effect of Proposition 99 (i.e., present a graphical representation based, at least in part, on an observed outcome and the synthetic control model).}.
McClelland, while disclosing donor(s) and a synthetic control model, doesn’t explicitly disclose, however, Elisha, which is directed to a similar field of endeavor (i.e., improving model accuracy), teaches:
[Claim 1] one or more processors and one or more computer-readable storage devices storing data instructions that, when executed by the one or more processors, cause the system to {[0149] For example, data item labeler 102, data fetcher 108, AI engine 110, AI model 114, portal 112, browser 118, data item labeler 200, data fetcher 208, AI engine 210, K-fold validator 216, AI model 214, evaluation model(s) 214a, classifier model(s) 214b, training evaluator 246, matrix generator 218, confusion matrix 228, matrix analyzer 220, action recommender 222, portal 212, confusion matrix 300, confusion matrix 400 (and/or any of the components described therein), and/or flowcharts 500, 900, 1000, 1100 may be implemented as computer program code/instructions configured to be executed in one or more processors and stored in a computer readable storage medium (i.e., one or more processors and one or more computer-readable storage devices storing data instructions that, when executed by the one or more processors, cause the system to).};]
[Claim 16] A non-transitory computer-readable medium having stored thereon data instructions that, when executed by one or more processors, cause the one or more processors to {See previous citation to [0149].};
[Claims 1, 12, and 16] selecting a set of training [data] from the set of potential [data] based on the comparisons {[0134] In step 914, a training sample (e.g., the first training sample) may be identified as an erroneous sample based on both: a determination that the first variance exceeds a first threshold; or a determination that the second variance confidence level exceeds a second threshold. For example, as shown in FIG. 2, training evaluator 246 may identify one or more training samples as erroneous training samples if (i) the vector space variance of a training sample exceeds a first threshold and (ii) a predicted category varies from an assigned category for a training sample (i.e., selecting a set of training [data] from the set of potential [data] based on the comparisons).};
training a model on the timeseries data associated with each training [data] in the set of training [data] {[0137] In step 920, prediction accuracy of the first ML model may be improved by training the first ML model with the revised training set instead of the training set (i.e., training a model on the timeseries data associated with each training [data] in the set of training [data]).}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify McClelland to include the features of Elisha. Given that McClelland describes the necessity of accurately estimating the effect of a treatment, one of ordinary skill in the art would have been motivated to look to Elisha, in order to improve model accuracy by identifying and removing suspect categories {[0003] of Elisha}.
(Examiner notes that data associated with donor(s) is disclosed by McClelland. Elisha, in a similar field of endeavor, is relied upon to teach the generically claimed training features. Thus, while not reciting the exact donor verbiage, Elisha, as pertinent prior art, fills in the gaps, when viewed in combination with McClelland.)
Claim 4
McClelland further discloses: wherein the expected post-intervention value for the potential donor is an expected value at a time of the intervention {Page 19: For the treated unit and each donor, calculate the ratio of the MSPE after treatment to the MSPE before treatment. Evidence of an effective treatment is found if the ratio for the treated unit is much higher than for the donors (i.e., wherein the expected post-intervention value for the potential donor is an expected value at a time of the intervention).
Claim 9
McClelland further discloses: donor(s) {See previous citation to Page 2.}.
Elisha further teaches: wherein selecting the set of training donors from the set of potential donors based on the comparison includes: ranking the potential [data] in the set of potential [data] based on the comparisons {[0105] AI engine 210 may be configured to rank different labels and eliminate or suggest elimination (e.g., for training and/or inference) for labels having relatively low scores (i.e., ranking the potential donors in the set of potential donors based on the comparisons).}; and selecting a predetermined number of training [data] from the set of potential [data] based on the ranking {[0105] A category and/or sample with a score below one or more thresholds may lead to a recommendation or decision to avoid sending the category and/or sample to training or to remove from training (i.e., selecting a predetermined number of training [data] from the set of potential [data] based on the ranking).}.
The motivation and rationale to include the additional features of Elisha is the same as set forth previously.
Claim 10
McClelland further discloses: donor(s) {See previous citation to Page 2.}.
Elisha further teaches: wherein the potential [data] are ranked based on differences between the expected post-intervention values and the actual post-intervention values, wherein a higher rank correlates with a smaller difference between the expected post-intervention values and the actual post-intervention values {[0135] In step 916, a removal list may be created by ordering the erroneous samples (e.g., from a highest to a lowest first variance) followed by suspect samples. For example, as shown in FIG. 2, training evaluator 246 may create a removal list that prioritizes removal of erroneous training samples before suspect training samples (i.e., wherein the potential [data] are ranked based on differences between the expected post-intervention values and the actual post-intervention values, wherein a higher rank correlates with a smaller difference between the expected post-intervention values and the actual post-intervention values).}.
The motivation and rationale to include the additional features of Elisha is the same as set forth previously.
Claim 11
McClelland further discloses: donor(s) {See previous citation to Page 2.}.
Elisha further teaches: wherein the set of training [data] includes one or more potential [data] for which a difference between the expected post-intervention value and the actual post-intervention value for the one or more potential [data] at the time of the intervention is less than a predetermined threshold {See previous citations to [0105], [0135]}.
The motivation and rationale to include the additional features of Elisha is the same as set forth previously.
Claim 15
McClelland further discloses: wherein the graphical representation includes one or more of a table or a line chart {See previous citation to page 7.}.
Claims 2-3, 5-6, and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of McClelland and Elisha, further in view of “Non-parametric identifiability and sensitivity analysis of synthetic control models” by Zeitler et al. (NPL attached; hereinafter Zeitler).
Claim 2
The combination of McClelland and Elisha, while teaching the features above, doesn’t explicitly teach, however, Zeitler, which is directed to a similar field of endeavor (i.e., synthetic control models), teaches: wherein the expected post-intervention value for the potential donor is calculated based on the timeseries data for one or more other potential donors in the set of potential donors and one or more error distributions {Page 5: A synthetic control structural causal model consists of a set of latent variables U and their distributions, a set of observed variables Y, X, I representing the target unit, donor units, and the intervention, and a set of deterministic functions mapping parents to their children in the causal structure in Figure 1(a)(a), represented as a directed acyclic graph (DAG), each indexed by a specific time point t, including error distributions (i.e., wherein the expected post-intervention value for the potential donor is calculated based on the timeseries data for one or more other potential donors in the set of potential donors and one or more error distributions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of McClelland and Elisha to include the features of Zeitler. Given that McClelland describes synthetic control models, one of ordinary skill in the art would have been motivated to look to Zeitler, in order to facilitate sensitivity analysis of said synthetic control models, thereby helping to identify key model inputs {Page 3 of Zeitler}.
Claim 3
Zeitler further teaches: wherein the one or more error distributions include at least one of error distributions for the one or more other potential donors or error distributions for one or more latent variables {See previous citation to Page 5.}.
The motivation and rationale to include the additional features of Zeitler is the same as set forth previously.
Claim 5
The combination of McClelland and Elisha, while teaching the features above, doesn’t explicitly teach, however, Zeitler, in a similar field of endeavor directed to synthetic control models, teaches: computing a bias for a synthetic control unit from the synthetic control model, wherein the graphical representation is further based on the bias {Page 10, Fig. 3: The last row shows the bias as defined by the true untreated outcome subtracted from the synthetic control (i.e., computing a bias for a synthetic control unit from the synthetic control model, wherein the graphical representation is further based on the bias).}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of McClelland and Elisha to include the features of Zeitler. Given that McClelland describes synthetic control models, one of ordinary skill in the art would have been motivated to look to Zeitler, in order to facilitate sensitivity analysis of said synthetic control models, thereby helping to identify key model inputs {Page 3 of Zeitler}.
Claim 6
McClelland further discloses: selecting a weight from the synthetic control model {Pages 13-14: As described above, the SCM chooses weights for the donor units to minimize the MSPE between the predictors of the synthetic control and the predictors of the treated unit (i.e., selecting a weight from the synthetic control model).}.
Zeitler further teaches:
wherein computing the bias for the synthetic control unit from the synthetic control model includes: for each training donor in the set of training donors, computing a difference between an average of the timeseries data before the intervention and an average of the timeseries data after the intervention {Page 10, Fig. 3: The first row shows the outcome of interest, as well as its untreated state Y | do(I = 0), written as Y (0) in the figure and depicted in black. The synthetic control is shown as a dashed orange line contained by the bounds in green. The second row shows the average treatment effect on the treated (ATT) by subtracting the synthetic control from the observed outcome and averaging. The third row shows the cumulative effect over time (i.e., for each training donor in the set of training donors, computing a difference between an average of the timeseries data before the intervention and an average of the timeseries data after the intervention.);
selecting a difference from the computed differences {See previous citation to Page 10, Fig. 3.}; and
computing the bias, wherein the bias is a product of a number of training donors, the selected weight, and the selected difference {Page 10, Fig. 3: Row four shows the progression of all proxies, observed and unobserved as well as the outcome over time. The last row shows the bias as defined by the true untreated outcome subtracted from the synthetic control. (a): The bias (red) is contained by the bounds. Given the bounds do not contain 0 in the ATT plot, the effect measured is still positive even if we had a worst case bias, given our assumptions. Thus, when our plausibility assumptions are satisfied, so too is our bound on the bias. (b): The observed proxy X shifts less during the intervention time (blue) such that the bound is smaller as we measure a smaller change in the proxies. As a consequence, the bias (red) is outside the bounds. Hence, when our plausibility assumptions are violated, so too is our bound on the bias (i.e., computing the bias, wherein the bias is a product of a number of training donors, the selected weight, and the selected difference).
The motivation and rationale to include the additional features of Zeitler is the same as set forth previously.
Claim 13
The combination of McClelland and Elisha, while teaching the features above, doesn’t explicitly teach, however, Zeitler, which is directed to a similar field of endeavor (i.e., synthetic control models), teaches: wherein the graphical representation includes a difference between the observed outcome and a synthetic control unit of the synthetic control model {See previous citation to Page 10, Fig. 3.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of McClelland and Elisha to include the features of Zeitler. Given that McClelland describes synthetic control models, one of ordinary skill in the art would have been motivated to look to Zeitler, in order to facilitate sensitivity analysis of said synthetic control models, thereby helping to identify key model inputs {Page 3 of Zeitler}.
Claim 14
The combination of McClelland and Elisha, while teaching the features above, doesn’t explicitly teach, however, Zeitler, which is directed to a similar field of endeavor (i.e., synthetic control models), teaches: wherein the graphical representation includes a cumulative difference between the observed outcome and a synthetic control unit of the synthetic control model {See previous citation to Page 10, Fig. 3.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of McClelland and Elisha to include the features of Zeitler. Given that McClelland describes synthetic control models, one of ordinary skill in the art would have been motivated to look to Zeitler, in order to facilitate sensitivity analysis of said synthetic control models, thereby helping to identify key model inputs {Page 3 of Zeitler}.
No Prior Art Applied to Claims 7-8 and 17-20
There is no prior art rejection applied on claims 7-8 and 17-20. Examiner notes that claims 18 and 20 are indicated as having no prior art based on their dependency. Any amendment incorporating these claims requires intervening claims 17 and/or 19.
In particular, the prior art does not teach or suggest the following features, when viewed in combination:
Claims 7 and 17 (claim 7 being representative):
wherein computing the bias for the synthetic control unit from the synthetic control model includes:
selecting a weight from the synthetic control model;
determining a set of excluded donors, the set of excluded donors including one or more potential donors that are not included in the set of training donors;
for each excluded donor in the set of excluded donors, computing a difference between an average of the timeseries data before the intervention and an average of the timeseries data after the intervention;
selecting a difference from the computed differences; and
computing the bias, wherein the bias is a product of a number of training donors, the selected weight, and the selected difference[, wherein the graphical representation is further based on the bias].
Claims 8 and 19 (claim 8 being representative):
wherein computing the bias for the synthetic control unit from the synthetic control model includes:
selecting a weight from the synthetic control model;
for each training donor in the set of training donors, estimating a spillover value based on the intervention;
selecting a spillover value from the estimated spillover values; and
computing the bias, wherein the bias is a product of a number of training donors, the selected weight, and the selected spillover value[, wherein the graphical representation is further based on the bias].
Examiner also considered:
Pachisia (US 20170323327), which teaches: The SYNTHETIC CONTROL GENERATION AND CAMPAIGN IMPACT ASSESSMENT APPARATUSES, METHODS AND SYSTEMS (“SCG”) provides a platform that, in various embodiments, is configurable to evaluate efficacy and/or return on investment of advertising and/or other media campaigns and/or to recommend actions for improvement thereof. In some implementations, multi-faceted campaigns of media and/or advertising behavior (e.g., including one or more of: internet advertising, television advertising, radio advertising, print advertising, social media publication, product placement, and/or the like) may be considered as a whole in relation to global metric behaviors and/or patterns in order to evaluate the efficacy and/or return on investment associated with the campaign as a whole.
Lada (US 20170331910), which teaches: Systems, methods, and non-transitory computer-readable media can determine an event that may affect at least one activity being performed by a first group of users through the computing system. A set of first measurements of the at least one activity being performed by the first group of users over a period of time are determined. A set of second measurements of the at least one activity for the first group of users over the period of time are generated, wherein the set of second measurements are predicted based at least in part on a machine learning model. Data describing an impact of the event on the first group of users is generated based at least in part on the set of first measurements and the set of second measurements.
Kok (US 20200357486), which teaches: Systems, methods and computer-readable media storing executable instructions are provided for improving performance of an organism with respect to a phenotype of interest at a second scale based upon measurements at a first scale. First scale performance data based at least in part upon observed first performance of first organisms at a first scale and second scale performance data based at least in part upon observed second performance of second organisms at a second scale larger than the first scale are accessed. A prediction function based at least in part upon the relationship of the second scale performance data to the first scale performance data is generated. The prediction function may be applied to performance data observed for test organisms with respect to the phenotype of interest at the first scale to generate second scale predicted performance data for the test organisms at the second scale.
“Robust Synthetic Control” by Amjad (NPL attached), which teaches: Our experiments, using both synthetic and real-world datasets, demonstrate that our robust generalization yields an improvement over the classical synthetic control method.
Still, the references, whether viewed alone or in combination, do not teach the combination of features found in claims 7-8, 17, and 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Pachisia (US 20170323327), which teaches: The SYNTHETIC CONTROL GENERATION AND CAMPAIGN IMPACT ASSESSMENT APPARATUSES, METHODS AND SYSTEMS (“SCG”) provides a platform that, in various embodiments, is configurable to evaluate efficacy and/or return on investment of advertising and/or other media campaigns and/or to recommend actions for improvement thereof. In some implementations, multi-faceted campaigns of media and/or advertising behavior (e.g., including one or more of: internet advertising, television advertising, radio advertising, print advertising, social media publication, product placement, and/or the like) may be considered as a whole in relation to global metric behaviors and/or patterns in order to evaluate the efficacy and/or return on investment associated with the campaign as a whole.
Lada (US 20170331910), which teaches: Systems, methods, and non-transitory computer-readable media can determine an event that may affect at least one activity being performed by a first group of users through the computing system. A set of first measurements of the at least one activity being performed by the first group of users over a period of time are determined. A set of second measurements of the at least one activity for the first group of users over the period of time are generated, wherein the set of second measurements are predicted based at least in part on a machine learning model. Data describing an impact of the event on the first group of users is generated based at least in part on the set of first measurements and the set of second measurements.
Kok (US 20200357486), which teaches: Systems, methods and computer-readable media storing executable instructions are provided for improving performance of an organism with respect to a phenotype of interest at a second scale based upon measurements at a first scale. First scale performance data based at least in part upon observed first performance of first organisms at a first scale and second scale performance data based at least in part upon observed second performance of second organisms at a second scale larger than the first scale are accessed. A prediction function based at least in part upon the relationship of the second scale performance data to the first scale performance data is generated. The prediction function may be applied to performance data observed for test organisms with respect to the phenotype of interest at the first scale to generate second scale predicted performance data for the test organisms at the second scale.
“Robust Synthetic Control” by Amjad (NPL attached), which teaches: Our experiments, using both synthetic and real-world datasets, demonstrate that our robust generalization yields an improvement over the classical synthetic control method.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629
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