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 .
Information Disclosure Statement
The information disclosure statements filed 05/09/2023 and 07/17/2023 fail to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. The NPL reference titled “22- Debiased/Orthogonal Machine Learning” (as cited in both IDS’s under the ‘Non-Patent Literature Documents’ table) included in the application is completely blurred and unreadable; therefore is not considered. It has been placed in the application file, but the information referred to therein has not been considered.
Drawings
The drawings are objected to because Figures 6A-6B submitted on 12/22/2022 are blurred and the text is unreadable. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Application 17/935,392 in view of Eberhardt III et al (20230142594 - hereinafter Eberhardt, as submitted in IDS dated 01/20/2026), in view of Bagchi et al (US Pub. No. 2008/0255910- hereinafter Bagchi), in view of Chernozhukovy et al (NPL: “Double/Debiased Machine Learning for Treatment and Structural Parameters” - hereinafter Chernozhukovy, as submitted in IDS dated 07/17/2023), and further in view of Bequet et al (US Pub. No. 2021/0200522 - hereinafter Becket); as explained in the table below.
Although the claims at issue are not identical, they are not patentably distinct from each other because the main purpose of both the instant application and the 17935392 application is the same, as they are both directed towards the same inventive concept. Both inventions are directed to the use of directed acyclic graphs to allocate resources using historical data for machine learning models to produce causal effect predictions.
Instant Application
Application 17/935,392
Claim 1 (and claims 8, 15 as being analogous independent claims)
A computer-implemented method comprising:
receiving, by one or more processors, historical data and directed acyclic graph data, wherein: (i) the historical data comprises one or more outcome values associated with one or more resource-receiving entities, (ii) each of the one or more outcome values associated with a causal variable value and a pre-defined time window, and (iii) the directed acyclic graph data comprises expert knowledge data;
generating, by the one or more processors and using a resource allocation machine learning framework, one or more non-linear causal effect predictions of one or more causal variables on an outcome of interest associated with the one or more resource-receiving entities based at least in part on the historical data and the directed acyclic graph data, wherein:
(a) the resource allocation machine learning framework comprises a non-linear causal inference machine learning model,
(b) the non-linear causal inference machine learning model is configured to: (i) determine, for each causal variable value selected from a plurality of causal variable values, an outcome value for one or more resource-receiving entities based at least in part on the historical data, and (ii) determine an optimal causal variable value for each of one or more resource-receiving entity cohorts by applying supervised machine learning regression to a plurality of outcome values associated with one or more resource-receiving entities of a respective resource-receiving entity cohort based at least in part on the directed acyclic graph;
determining, by the one or more processors, an optimum operation configuration based at least in part on one or more optimal causal variable values associated with the one or more non- linear causal effect predictions; and
initiating, via the one or more processors, the performance of one or more prediction- based actions based at least in part on the optimum operation configuration.
Claim 1
A computer-implemented method comprising:
receiving, by one or more processors and using a machine learning framework comprising a first machine learning model and a second machine learning model, historical data comprising a plurality of variables corresponding to (i) a plurality of actions and inactions with respect to a plurality of resource-requesting entities, and (ii) one or more a plurality of outcomes corresponding the plurality of actions and inactions;
inputting, by the one or more processors, the historical data to the machine learning framework to receive one or more causal effect predictions, wherein:
the first machine learning model is configured to generate a predictive risk score associated with an event based at least in part on the historical data,
the second machine learning model is configured to:
receive at least a portion of the historical data, the predictive risk score, and a directed acyclic graph data, and
generate the one or more causal effect predictions on an outcome of interest from an action taken on a given resource-requesting entity of the plurality of resource-requesting entities based at least in part on the predictive risk score, at least the portion of the historical data, and the directed acyclic graph data, and
training the second machine learning model comprises:
determining causal effect values for the plurality of resource-requesting entities based at least in part on the predictive risk score, at least the portion of the historical data, and the directed acyclic graph data,
apportioning the plurality of resource-requesting entities into a plurality of resource-requesting entity subgroups, and
ranking the plurality of resource-requesting entity subgroups by magnitude of the causal effect values, wherein the one or more causal effect predictions are based at least in part on the ranking of the plurality of resource- requesting entity subgroups;
determining, by the one or more processors, a subset of the plurality of resource-requesting entity subgroups based at least in part on the one or more causal effect predictions; and
initiating, by the one or more processors, performance of one or more prediction-based actions based at least in part on the of the subset of the plurality of resource-requesting entity subgroups.
Although the claimed limitations are not identical, they are not patentably distinct from each other because the main purpose of these claims is the same, as they are directed towards the same inventive concept.
However, the US Application 17/935,392 cited above fails to teach the limitations in italic above, however, Eberhardt and Bagchi teaches them (see Eberhardt at [0035, 0062] and Bagchi at [0010, 0061]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Conchuir with the above combination of Eberhardt and Bagchi in order to provide optimal quantitative project planning information which accounts for various risk factors (as suggested by Bagchi at [0010]).
Claim 2 (and claims 9, 16 as being analogous dependent claims)
Claim 1
The US Application 17/935,392 cited above fails to teach the limitation at this claim, however, Chernozhukovy teaches them at the Summary.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Conchuir with the above combination of Eberhardt , Bagchi and Chernozhukovy in order to allow construction of valid confidence statements (as suggested by Chernozhukovy at Summary).
Claim 3 (and claims 10, 17 as being analogous dependent claims)
Claim 1
The US Application 17/935,392 cited above fails to teach the limitations at this claim, however, Eberhardt and Bagchi teaches them (see Bagchi at [0024]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Conchuir with the above combination of Eberhardt and Bagchi in order to allow for learning from the performances of completed activities during project execution by providing a technique that associates risk factors to specific activities and estimates their impact on activity durations and costs (as suggested by Bagchi at [0024]).
Claim 4 (and claims 11, 18 as being analogous dependent claims)
Claim 1
The US Application 17/935,392 cited above fails to teach the limitations at this claim, however, Bagchi teaches them (see Bagchi at [0068]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Conchuir with the above combination of Eberhardt and Bagchi in order to provide optimal quantitative project planning information which accounts for various risk factors (as suggested by Bagchi at [0010]).
Claim 5 (and claim 12 as being analogous dependent claims)
Claim 1
The US Application 17/935,392 cited above fails to teach the limitations at this claim, however, Bequet teaches them (see Bequet at [Asbtract]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Conchuir with the above combination of Eberhardt, Bagchi and Bequet in order to enable an efficient presentation of the job flow on a display of a reviewing device as a DAG; thus, review of aspects of a performance of an analysis may be made easier by such a graphical representation of the analysis as a job flow (as suggested by Bequet at 0164).
Claim 6 (and claims 13, 19 as being analogous dependent claims)
Claim 1
Claim 7 (and claims 14, 20 as being analogous dependent claims)
Claim 1
The US Application 17/935,392 cited above fails to teach the limitations at this claim, however, Eberhardt teaches them (see Eberhardt at [0043]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Conchuir with the above teachings of Eberhardt in order to calculate a prediction error for improving accuracy (as suggested by Eberhardt at [0043]).
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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, 3-4, 6-8, 10-11, 13-15, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Eberhardt III et al (20230142594 - hereinafter Eberhardt, as submitted in IDS dated 01/20/2026) in view of Bagchi et al (US Pub. No. 2008/0255910- hereinafter Bagchi).
Referring to Claim 1, Eberhardt teaches a computer-implemented method comprising:
receiving, by one or more processors, historical data and directed acyclic graph data, wherein: (i) the historical data comprises one or more outcome values associated with one or more resource-receiving entities, (ii) each of the one or more outcome values associated with a causal variable value and a pre-defined time window (see Eberhardt at [0006]: “health insurance claim data for a first group of individuals is obtained to generate a training corpus, including a training set of claim data and a holdout set of claim data. The first group of individuals represents enrollees of one or more health insurance plans and the health insurance claim data represents historic insurance claim information for each individual in the first group. A Bayesian belief network (BBN) model is created by training a BBN classifier using the training set of claim data”. Further, at [0052]: “machine learning algorithms … are used to build Bayesian network models of representative sample of data provided from Thomson-Reuters' MarketScan® consisting of 185,322 enrollees with three full years of insurance claims records. Data sets were prepared and a step-wise learning process was used to train a series of Bayesian Belief Networks (BBNs)”. Therefore, this training data used from previous historical claims during three years is interpreted as the historical data associated with a causal variable and pre-defined time window), and (iii) the directed acyclic graph data comprises expert knowledge data (see Eberhardt at [0062]: “A Bayesian network encodes the joint probability distribution of all the variables in a domain by building a network of conditional probabilities. It uses conditional independence assumptions to make the representation tractable. The networks are directed graphs which incorporate parent-child relationships between nodes. Essentially, they provide a hierarchy of how the knowledge of a priori evidence influences the downstream likelihood of an event”. Therefore, the priori knowledge used is interpreted as the expert knowledge data, as the BBN is a directed acyclic graph (per [0035]: “The conditional independence assumptions are represented as a directed acyclic graph”);
generating, by the one or more processors and using a resource allocation machine learning framework, one or more non-linear causal effect predictions of one or more causal variables on an outcome of interest associated with the one or more resource-receiving entities based at least in part on the historical data and the directed acyclic graph data (see Eberhardt at [0003]: “A Bayesian belief network (BBN) is a directed graph and an associated set of probability tables. The graph consists of nodes and arcs. The nodes represent variables, input data for which can be discrete or continuous; however the BBN must segment continuous data into parameterized ranges. The arcs represent causal or influential relationships between variables. More specifically, a BBN is a probabilistic graphical model that represents a set of random variables and their conditional independencies”. See also [0006], [0052] and [0062] as explained above. However, Eberhardt fails to explicitly teach the use of a resource allocation machine learning framework), wherein:
(a) the resource allocation machine learning framework comprises a non-linear causal inference machine learning model (see Eberhardt at [0071]: “One can observe that in the full BBN, there are multiple non-linear relationships representing conditional dependence between variables that predict the outcome of interest”. See also [0003] as explained above. However, Eberhardt fails to explicitly teach that the machine learning framework is for resource allocation),
(b) the non-linear causal inference machine learning model is configured to: (i) determine, for each causal variable value selected from a plurality of causal variable values, an outcome value for one or more resource-receiving entities based at least in part on the historical data (see Eberhardt at [0003], [0006] and [0071] as explained above),
and (ii) determine an optimal causal variable value for each of one or more resource-receiving entity cohorts by applying supervised machine learning regression to a plurality of outcome values associated with one or more resource-receiving entities of a respective resource-receiving entity cohort based at least in part on the directed acyclic graph (see Eberhardt at [0084]: “the validated cost model was used to estimate the potential savings attributable to intervention. It was sought to estimate the reduction in 2006 total enrollee cost if enrollees with SUD were successfully treated at the end of 2005, making the assumption that successful disease management of SUD would change utilization patterns…The entire cohort was ranked by SUD risk score and then within each scoring group ranked by estimated savings. Average per-enrollee savings was calculated for top fractional cohorts in the holdout set consisting of top-ranked cohorts of 50, 100, 250, and 500 enrollees”. Therefore, the predicting of these savings based on the risks predicted is interpreted as an optimal causal variable value);
determining, by the one or more processors, an optimum operation configuration based at least in part on one or more optimal causal variable values associated with the one or more non- linear causal effect predictions (see Eberhardt at [0076]: “It is also calculated the Positive Predictive Value, or the probability that an enrollee flagged as a high SUD risk has had an SUD claim in 2005, 2006, or either. The optimum cohort appears to be the Top 250 group—in this group, it can successfully identify one out of three enrollees for two-year risk and of this group, and 70% of our estimates are accurate”); and
initiating, via the one or more processors, the performance of one or more prediction- based actions based at least in part on the optimum operation configuration (see Eberhardt at [0085]: “This analysis indicates, for example, that selecting the Top 500 enrollees (out of our 18,623 enrollee test set) produces an expected cost reduction benefit of approximately $2,500 in annual savings. Restricting the set to the Top 250 cases produces an expected savings of approximately $5,000 per enrollee, and by further restricting our interventional population to the Top 100 enrollees, we increase our expected average reduction to over $12,000 per patient. Using this approach, one can stratify an interventional population and tune the marginal benefit to maximize both enrollee benefit and financial return in light of the costs and success rate of a given intervention”).
However, Eberhardt fails to explicitly teach using a resource allocation machine learning framework.
Bagchi teaches, in an analogous system, a resource allocation machine learning framework (see Bagchi at [0010]: “In this exemplary embodiment, the activity durations are estimated based on resource allocations for the activity, and a plurality of risk factors for the completing the projects are mapped to one or a plurality of the activities to provide quantitative project planning information which accounts for various risk factors. Mapping of the risk factors to activities can be structured according to a Bayesian Belief Network (BBN)”. Further at [0061]: “The conditional probability tables underlying the BBN are preferably estimated based on historical data about risk factors present in past projects. However, expert opinion may also be used to estimate risk factors. The top half of FIG. 5 reflects the risk factors that are similar across projects. The bottom half of FIG. 5, by contrast is project specific and includes activity durations (5-6, 5-7, 5-8 and 5-9). The number of activities in any one project can vary considerably. Further, the activity durations (5-6, 5-7, 5-8 and 5-9) vary depending on the resource allocation per activity”. Bagchi, being in the same field of endeavor as Eberhardt by using a Bayesian belief Network trained with historical and expert knowledge data, teaches a resource allocation machine learning framework using also a BBN, which is a directed graph consisting of nodes and arcs, wherein the nodes represent variables and the arcs represent causal or influential relationships between variables, as explained by Eberhardt).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Eberhardt with the above teachings of Bagchi by using a direct acyclic graph with historical and expert data to determine causal relationships, as taught by Eberhardt, in order to provide an optimum resource allocation, as taught by Bagchi. The modification would have been obvious because one of ordinary skill in the art would be motivated to provide optimal quantitative project planning information which accounts for various risk factors (as suggested by Bagchi at [0010]).
Referring to Claim 3, the combination of Eberhardt and Bagchi teaches the computer-implemented method of claim 1, wherein the expert knowledge data comprises one or more relationships between selected causal variables, outcomes, and actions (see Eberhardt at [0062]: “Essentially, they provide a hierarchy of how the knowledge of a priori evidence influences the downstream likelihood of an event (e.g., “I know that enrollee X has hypertension, therefore the probability of kidney disease relative to my overall population is y”)”. Therefore, this likelihood of an event is interpreted as causal variables, outcomes or actions, as they reflect a direct causal relationship. Further, Bagchi also teaches the use of expert knowledge, as it can be seen at [0024]: “A risk analyzer (1-5) allows planning based on historical risk impact data and/or expert opinion data. In the preferred embodiment, the risk analyzer (1-5) allows for learning from the performances of completed activities during project execution by providing a technique that associates risk factors to specific activities and estimates their impact on activity durations and costs”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Eberhardt with the above teachings of Bagchi by using a direct acyclic graph with historical and expert data to determine causal relationships, as taught by Eberhardt, in order to provide an optimum resource allocation, as taught by Bagchi. The modification would have been obvious because one of ordinary skill in the art would be motivated to allow for learning from the performances of completed activities during project execution by providing a technique that associates risk factors to specific activities and estimates their impact on activity durations and costs (as suggested by Bagchi at [0024]).
Referring to Claim 4, the combination of Eberhardt and Bagchi teaches the computer-implemented method of claim 1, wherein the one or more causal variables comprise a data variable associated with a quantity of resource allocations enacted on a resource-receiving entity (see Bagchi at [0068]: “Effective resource scenarios for most activities based on appropriately balancing the quantity, skill level and utilization of resources to the time it takes to complete the activity were defined”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Eberhardt with the above teachings of Bagchi by using a direct acyclic graph with historical and expert data to determine causal relationships, as taught by Eberhardt, in order to provide an optimum resource allocation, as taught by Bagchi. The modification would have been obvious because one of ordinary skill in the art would be motivated to provide optimal quantitative project planning information which accounts for various risk factors (as suggested by Bagchi at [0010]).
Referring to Claim 6, the combination of Eberhardt and Bagchi teaches the computer-implemented method of claim 1, wherein applying the supervised machine learning regression further comprises:
determining a plurality of outcome values and a plurality of causal variable values based at least in part on the historical values and the directed acyclic graph (see Eberhardt at [0003]: “A Bayesian belief network (BBN) is a directed graph and an associated set of probability tables. The graph consists of nodes and arcs. The nodes represent variables, input data for which can be discrete or continuous; however the BBN must segment continuous data into parameterized ranges. The arcs represent causal or influential relationships between variables. More specifically, a BBN is a probabilistic graphical model that represents a set of random variables and their conditional independencies”. Further at [0084]: “the validated cost model was used to estimate the potential savings attributable to intervention. It was sought to estimate the reduction in 2006 total enrollee cost if enrollees with SUD were successfully treated at the end of 2005, making the assumption that successful disease management of SUD would change utilization patterns…The entire cohort was ranked by SUD risk score and then within each scoring group ranked by estimated savings. Average per-enrollee savings was calculated for top fractional cohorts in the holdout set consisting of top-ranked cohorts of 50, 100, 250, and 500 enrollees”. Therefore, the predicting of these savings based on the risks predicted is interpreted as the outcome values and causal variable values); and
generating a causal benefit curve based at least in part on the plurality of outcome values and the plurality of causal variable values (see Eberhardt at [0047]: “An ROC curve can be used to represent quality of an outcome of a test for a given set of data. For example, the accuracy of the test depends on how well the test separates the group being tested into those with and without the disease in question. Accuracy is measured by the area under the ROC curve. The area measures discrimination, that is, the ability of the test to correctly classify those with and without the disease”).
Referring to Claim 7, the combination of Eberhardt and Bagchi teaches the computer-implemented method of claim 1 further comprising:
determining the optimal causal variable value by maximizing the outcome value based at least in part on the causal benefit curve and a causal variable cost threshold associated with the causal variable (see Eberhardt at [0043]: “The ROC curve is used to calculate the area-under-the-curve (AUC), a metric of overall model quality, and positive predictive value (PPV), a measure of the probability that a positive is a true positive given a specified probability threshold for the variable of interest”).
Referring to independent Claim 8 and Claim 15, they are rejected on the same basis as independent claim 1 since they are analogous claims.
Referring to dependent Claim 10 and Claim 17, they are rejected on the same basis as dependent claim 3 since they are analogous claims.
Referring to dependent Claim 11 and Claim 18, they are rejected on the same basis as dependent claim 4 since they are analogous claims.
Referring to dependent Claim 13 and Claim 19, they are rejected on the same basis as dependent claim 6 since they are analogous claims.
Referring to dependent Claim 14 and Claim 20, they are rejected on the same basis as dependent claim 7 since they are analogous claims.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Eberhardt et al (20230142594 - hereinafter Eberhardt, as submitted in IDS dated 01/20/2026) in view of Bagchi et al (US Pub. No. 2008/0255910- hereinafter Bagchi) and further in view of Chernozhukovy et al (NPL: “Double/Debiased Machine Learning for Treatment and Structural Parameters” - hereinafter Chernozhukovy, as submitted in IDS dated 07/17/2023).
Referring to Claim 2, the combination of Eberhardt and Bagchi teaches the computer-implemented method of claim 1, however, fails to teach wherein the non-linear causal inference machine learning model comprises a non-parametric double/debiased machine learning model.
Chernozhukovy teaches, in an analogous system, wherein the non-linear causal inference machine learning model comprises a non-parametric double/debiased machine learning model (see Chernozhukovy at Summary “We call the resulting set of methods double or debiased ML (DML). We verify that DML delivers point estimators that concentrate in a N-1/2 neighborhood of the true parameter values and are approximately unbiased and normally distributed, which allows construction of valid confidence statements”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Eberhardt and Bagchi with the above teachings of Chernozhukovy by using a non-linear causal inference machine learning model to determine causal relationships in order to provide an optimum resource allocation, as taught by Eberhardt and Bagchi, wherein the non-linear causal inference machine learning model comprises a non-parametric double/debiased machine learning model, as taught by Chernozhukovy. The modification would have been obvious because one of ordinary skill in the art would be motivated to allow construction of valid confidence statements (as suggested by Chernozhukovy at Summary).
Referring to dependent Claim 9 and Claim 16, they are rejected on the same basis as dependent claim 2 since they are analogous claims.
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Eberhardt et al (20230142594 - hereinafter Eberhardt, as submitted in IDS dated 01/20/2026) in view of Bagchi et al (US Pub. No. 2008/0255910- hereinafter Bagchi) and further in view of Bequet et al (US Pub. No. 2021/0200522 - hereinafter Bequet).
Referring to Claim 5, the combination of Eberhardt and Bagchi teaches the computer-implemented method of claim 1, however, fails to teach wherein the one or more resource- receiving entities comprise objects, articles, files, programs, services, tasks, operations, or computing units that receive resource allocations from a computing device.
Bequet teaches, in an analogous system, wherein the one or more resource- receiving entities comprise objects, articles, files, programs, services, tasks, operations, or computing units that receive resource allocations from a computing device (see Bequet at Abstract: “An apparatus includes at least one processor to retrieve a job flow definition defining a job flow as a set of tasks and dependencies thereamong, store a job performance request message to perform the job flow within a job queue, and in response to the storage of the job performance request message, execute instructions of a performance routine within a storage container to: based on the dependencies, derive an order of performance of the set of tasks that specifies a first task to perform”. Further, at [0164]: “In some embodiments, a job flow definition may be stored within federated area(s) as a file or other type of data structure in which the job flow definition is represented as a DAG (directed acyclic graph). Alternatively or additionally, a file or other type of data structure may be used that organizes aspects of the job flow definition in a manner that enables a DAG to be directly derived therefrom. Such a file or data structure may directly indicate an order of performance of tasks, or may specify dependencies between inputs and outputs of each task to enable an order of performance to be derived”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Eberhardt and Bagchi with the above teachings of Bequet by using a non-linear causal inference machine learning model to determine causal relationships in order to provide an optimum resource allocation, as taught by Eberhardt and Bagchi, wherein the one or more resource- receiving entities comprise objects, articles, files, programs, services, tasks, operations, or computing units that receive resource allocations from a computing device, as taught by Becket. The modification would have been obvious because one of ordinary skill in the art would be motivated to enable an efficient presentation of the job flow on a display of a reviewing device as a DAG; thus, review of aspects of a performance of an analysis may be made easier by such a graphical representation of the analysis as a job flow (as suggested by Bequet at [0164]).
Referring to dependent Claim 12, it is rejected on the same basis as dependent claim 5 since they are analogous claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS A SITIRICHE whose telephone number is (571)270-1316. The examiner can normally be reached M-F 9am-6pm.
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/LUIS A SITIRICHE/ Primary Examiner, Art Unit 2126
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