DETAILED ACTION
The following NON-FINAL Office Action is in response to application 19/069830 filed on 03/04/2025.
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 . 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.
Status of Claims
Claims 1-20 are currently pending and have been rejected as follows.
Priority
Examiner has noted that the Applicant has claimed priority from the parent application 16/193547 filed on 11/16/2018.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/06/2025 comply with the provisions of 37 CFR 1.97, 1.98, and MPEP 609 and were considered by the Examiner.
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 an abstract idea without significantly more.
Step 1
Claims 15-20 are directed to software per se. The claimed invention is directed to a non-transitory computer-readable medium embodied with software “when executed” which is claimed without any structural limitations. Therefore, the claims are not directed to a patent eligible category of invention. See MPEP 2106.03(I). Because the broadest reasonable interpretation of the claim covers software per se, the claim is rejected under 35 U.S.C. 101 as covering non-statutory subject matter. For the purpose of compact prosecution, Examiner will continue analysis of the claimed invention as though it has been amended to include structural limitations.
Claims 1-7 are directed to a system (i.e., a machine). Claims 8-14 are directed to a method (i.e., a process). Therefore, the claims all fall within one of the four statutory categories of invention.
Step 2A Prong 1
Independent Claim 1, Claim 8, and Claim 15 recites:
retrieve archived supply chain data directly…;
check the retrieved archived supply chain data for range, sign, and value;
transform the retrieved archived supply chain data to normalize, aggregate, and rescale the retrieved archived supply chain data;
train … using the transformed supply chain data for a snapshot time period;
predict an occurrence of one or more supply chain events subsequent to the snapshot time period; and
calculate the occurrence risk score for one or more predicted supply chain failures.
Organizing Human Activity
The limitations of Claim 1, Claim 8, and Claim 15 stated above are processes that under broadest reasonable interpretation covers “certain methods of organizing human activity” (“commercial or legal interactions”). Specifically, business relations in light of paragraph 2 in Applicant’s specification “supply chain management and specifically to systems and methods for predicting and preventing service level failures in a supply chain”. Therefore, the claims recite an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. The independent claims recite a server, a processor, a memory, a non-transitory computer-readable medium embodied with software, an archiving system, and the prediction model.
The additional elements of a server, a processor, a memory, a non-transitory computer-readable medium embodied with software, and an archiving system are recited at a high-level of generality (generic computer/functions), such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components. See MPEP 2106.05(f) “Mere Instructions to Apply an Exception”.
The prediction model amounts to merely indicating a field of use or technological environment in which to apply a judicial exception which cannot integrate a judicial exception into a practical application. Specifically, by requiring that the abstract idea of business relations be performed using a prediction model. The narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment. See MPEP 2106.05(h).
Thus, the claims as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application as the additional elements are mere instructions to apply the judicial exception using generic computer components or field of use, which does not impose meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a server, a processor, a memory, a non-transitory computer-readable medium embodied with software, and an archiving system to perform the steps/functions recited above amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Again, the prediction model amounts to merely indicating a field of use or technological environment in which to apply a judicial exception which cannot integrate a judicial exception into a practical application. Specifically, by requiring that the abstract idea of business relations be performed using a prediction model. The narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment.
Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
None of the steps/functions of Claim 1, Claim 8, and Claim 15 when evaluated individually or as an ordered combination amount to significantly more than the abstract idea. The additional elements are merely used to perform the limitations directed to organizing human activity, mere instruction to apply an exception using a generic computer, and/or field of use, thus, the analysis does not change when considered as an ordered combination. Even when considered in combination, these additional elements represent mere instructions to apply an exception using a generic computer and/or field of use, which cannot provide an inventive concept. Thus, the additional elements do not meaningfully limit the claim. Accordingly, Claim 1, Claim 8, and Claim 15 are ineligible.
Dependent Claims 2-7, 9-14, and 16-20 when considered both separately and in ordered combination with each dependent claim’s corresponding parent claims do not overcome the above analysis.
Claims 2, 9, and 16 further specify “wherein the transformed supply chain data allows a direct comparison of the archived supply chain data received” which is further organizing human activity. The claims also recite the additional element of planning and execution systems recited at a high-level of generality (generic computer/functions), such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components. See MPEP 2106.05(f) “Mere Instructions to Apply an Exception”.
Claims 3, 10, and 17 further specify “samples of the transformed supply chain data are aggregated at a certain granularity” which is further organizing human activity. The limitation of “wherein the prediction model is trained using gradient boosting” may be considered as requiring that the abstract idea of business relations be performed using a prediction model specifically one trained using gradient boosting. The narrowing limitation is merely an attempt to limit the use of the abstract idea to a particular technological environment. See MPEP 2106.05(h).
Claims 4, 11, and 18 further specify “calculate one or more predictive factors for the calculated occurrence risk score” which is further organizing human activity.
Claims 5, 12, and 19 further specify “wherein the calculated occurrence risk score is calculated as a percentage” which is further organizing human activity.
Claims 6, 13, and 20 further specify “generate a visualization of a contribution of one or more predictive factors to the occurrence risk score” which is further organizing human activity.
Claims 7 and 14 further specify “wherein the visualization comprises a waterfall chart” which is further organizing human activity.
The dependent claims further narrow the identified abstract idea but do not otherwise alter the analysis presented above. Nothing in dependent claims 2-7, 9-14, and 16-20 when viewed alone or as an ordered combination, adds additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-20 are ineligible.
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.
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 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Appel et al. (US2020/0134545) in view of Achin et al. (US2018/0046926) in view of non-patent literature Excel Functions (https://www.techonthenet.com/excel/formulas/sign.php; 2017).
As per independent Claim 1, Claim 8, and Claim 15,
Appel teaches a system for training a prediction model to calculate an occurrence risk score, comprising: a server comprising a processor and a memory, the server configured to: / A method for training a prediction model to calculate an occurrence risk score, comprising: / A non-transitory computer-readable medium embodied with software for training a prediction model to calculate an occurrence risk score, the software when executed configured to: (paragraph 73-75 processor, memory, server; para. 9 where a vulnerability score is determined using a cognitive prediction model which includes a machine learning model; para. 6-7 non-transitory computer readable medium; para. 34 “a cognitive model 315. This cognitive model 315 may be implemented as any supervised machine learning model that can use the network structure over time. One example of a cognitive model 315 would be a neural network”)
retrieve archived supply chain data directly from an archiving system (para. 29-32 and figure 3 where the system collects historical data from each supply chain entity in each time period and for each time interval, the system will aggregate all data; figure 1 and para. 16-23 for the supply chain network and the entities within the network)
transform the retrieved archived supply chain data to aggregate the retrieved archived supply chain data (para. 29-32 where in 32-33 where the system will aggregate the historical data to a certain granularity level (monthly, yearly, or any other time granularity) and the aggregated data is used to build the model; see also para. 50-51)
train the prediction model using the transformed supply chain data for a snapshot time period; (para. 29-32 where in 32-33 where the system will aggregate the historical data to a certain granularity level (monthly, yearly, or any other time granularity) and the aggregated data is used to build the model; para. 34 where the historical data snapshots are used to train the model)
predict an occurrence of one or more supply chain events subsequent to the snapshot time period (para. 33-35 where a Weighted Entity Network model is generated for a time period and based on the Weighted Entity Network model a cognitive model is generated, Examiner specifically noting “This cognitive model may be implemented as any supervised machine learning model that can use the network structure over time”, once the cognitive model is generated it utilizes the aggregated historical data (para. 29-32) to generate predictions for the supply chain specifically a score for each entity in the supply chain; para. 37-38 where the predicted supply chain failure is: a first farmer having difficulties because of rainfall volume in the near future, a strike resulting in ground transportation taking another route, or future high rainfall volume resulting in a farmer harvesting before the rainfall; para. 24-27 where the supply chain event may be a weather event, labor strike, political event etc. that results in a failure of the first ground transport to provide service (of transporting produce to the market) to the first farmer and to the storage provider 235 (as the storage provider will not be able to maximize their storage capacity)
calculate the occurrence risk score for one or more predicted supply chain failures (para. 35-36 where the vulnerability score is “the propensity of an entity to have a vulnerability of a failure in the future. The cognitive model may compute the vulnerability score of each entity of that supply chain for a given period in the future”)
Appel does not teach, but Achin teaches:
check the data for range and value (“range” in para. 246 system performs validation in order to avoid bad predictions (e.g. in cases where an input value is outside a range of values”; para. 143 “if numeric features of the dataset span vastly different magnitude ranges, select or prioritize techniques that provide normalization”; “value” in para. 259-263 where in para. 262 check for missing data, outliers, and other data anomalies; para. 231 “a fitted model may make several independent checks of a particular variable's value.”)
to normalize, and rescale the data (Para. 172 and 204 normalizing variable values; Para. 432-435 pre-processing; para. 109 preprocessing techniques include text mining, feature normalization, dimension reduction; para. 336-339 normalization of the dataset and para. 341-343 forecast range; para. 143 select or prioritize technical that provide normalization; “rescale” in para. 207 automatically normalizing the scale of variables; para. 264 scaling for numerical covariates)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Appel invention with Achin with the motivation of increasing accuracy and efficiency.
See para. 246 “However, the system may perform some validation at prediction time to avoid particularly bad predictions (e.g., in cases where an input value is outside a range of values that it has computed as valid given characteristics of the original training data, modeling technique, and final model fitting state)” and para. 273 “In some embodiments, because the predictive modeling system 100 automates and efficiently implements data pretreatment (e.g., anomaly detection), data partitioning, multiple feature generation, model fitting and model evaluation, the time required to develop models may be much shorter than it is in the traditional development cycle. Further, in some embodiments, because the predictive modeling system automatically includes data pre-treatment procedures to handle both well-known data anomalies like missing data and outliers, and less widely appreciated anomalies like inliers (repeated observations that are consistent with the data distribution, but erroneous) and postdictors (i.e., extremely predictive covariates that arise from information leakage), the resulting models may be more accurate and more useful. In some embodiments, the predictive modeling system 100 is able to explore a vastly wider range of model types, and many more specific models of each type, than is traditionally feasible. This model variety may greatly reduce the likelihood of unsatisfactory results, even when applied to a dataset of compromised quality.”
Appel/Achin does not teach, but Excel Functions teaches:
check the data for sign (sign function returns the sign of data)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include check the data for sign as taught by Excel Functions in the system of Appel/Achin, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (increasing the accuracy of the dataset by checking various features of the data).
As per dependent Claim 2, Claim 9, and Claim 16,
Appel/Achin/Excel Functions teaches the system of Claim 1, the method of Claim 8, and the non-transitory computer-readable medium of Claim 15.
Appel further teaches:
wherein the transformed supply chain data allows a direct comparison of the archived supply chain data received from planning and execution systems (para. 30 collect data from each entity of a supply chain in each time period; para. 31 types of data collected and processed according to the unique data structure for that type of data; para. 32 for each time interval, aggregate all data available for example, aggregate information about the volume of rainfall and temperature ranges; para. 33 once the data is aggregated (“transformed”) generate a weighted entity network model where the nodes are the entities and the edges are the relations among the entities where the edge weights represent the amount of dependency between the two entities (“comparison”); para. 50-51 and 54-57)
As per dependent Claim 3, Claim 10, and Claim 17,
Appel/Achin/Excel Functions teaches the system of Claim 1, the method of Claim 8, and the non-transitory computer-readable medium of Claim 15.
Appel further teaches:
wherein the prediction model is trained (para. 33-35 where a Weighted Entity Network model is generated for a time period and based on the Weighted Entity Network model a cognitive model is generated, and trained; see also para. 41 and 52)
samples of the transformed supply chain data are aggregated at a certain granularity (para. 29-32 where in 32-33 where the system will aggregate the historical data to a certain granularity level (monthly, yearly, or any other time granularity) and the aggregated data is used to build the model)
Appel does not teach, but Achin teaches:
using gradient boosting (para. 183 and 195)
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of Achin prediction model trained using gradient boosting for the Appel prediction model trained. Both are machine learning models used as prediction models; thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
As per dependent Claim 4, Claim 11, and Claim 18,
Appel/Achin/Excel Functions teaches the system of Claim 1, the method of Claim 8, and the non-transitory computer-readable medium of Claim 15.
Appel teaches:
One or more predictive factors for the calculated occurrence risk score (para. 2-3 and para. 15 external factors and elements impact the supply chain; para. 29-35 cognitive model is generated and trained using the aggregated historical data of supply chain features like weather/climate, market data, historical risk data, etc.)
Appel does not teach, but Achin teaches:
wherein the server is further configured to: calculate one or more predictive factors (para. 378-392 where in para. 380 calculate feature importance, para. 382 calculate the importance of any feature given a dataset and modelling technical, para. 384 user interface may display the feature importance values individually for each modelling technique; para. 401-403 where in para. 403 the system may display an evaluation of the dataset to the user to identify the more/less important features, predictive value of features, rank the features; see also para. 406)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Appel invention with Achin with the motivation of increasing accuracy and efficiency.
See para. 388-392 “Using the above-described techniques to provide feature importance values either for a prediction problem in general or for a particular model may have numerous benefits. For example:…(1) If a feature is uninformative in general or at least in all accurate models, collection of the data corresponding to that feature may be halted. In some cases there is a real cost to making a feature available, such as the labor of extracting it from its source location or even paying a vendor for the data.
… In some cases, it may be desirable to produce a model that uses as few features as possible to make predictions…Knowing which features are important help may help the user improve predictive models”.
As per dependent Claim 5, Claim 12, and Claim 19,
Appel/Achin/Excel Functions teaches the system of Claim 1, the method of Claim 8, and the non-transitory computer-readable medium of Claim 15.
Appel further teaches:
wherein the calculated occurrence risk score is calculated as a percentage (para. 35-36 where the vulnerability score is “the propensity of an entity to have a vulnerability of a failure in the future. The cognitive model may compute the vulnerability score of each entity of that supply chain for a given period in the future” and may take a value between 0 to 1, for example 0.8)
As per dependent Claim 6, Claim 13, and Claim 20,
Appel/Achin/Excel Functions teaches the system of Claim 1, the method of Claim 8, and the non-transitory computer-readable medium of Claim 15.
Appel teaches:
One or more predictive factors to the occurrence risk score (para. 2-3 and para. 15 external factors and elements impact the supply chain; para. 29-35 cognitive model is generated and trained using the aggregated historical data of supply chain features like weather/climate, market data, historical risk data, etc.)
Appel does not teach, but Achin teaches:
generate a visualization of a contribution of one or more predictive factors (para. 378-392 where in para. 380 calculate feature importance, para. 382 calculate the importance of any feature given a dataset and modelling technical, para. 384 user interface may display the feature importance values individually for each modelling technique; para. 401-403 where in para. 403 the system may display an evaluation of the dataset to the user to identify the more/less important features, predictive value of features, rank the features; see also para. 406)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Appel invention with Achin with the motivation of increasing accuracy and efficiency.
See para. 388-392 “Using the above-described techniques to provide feature importance values either for a prediction problem in general or for a particular model may have numerous benefits. For example:…(1) If a feature is uninformative in general or at least in all accurate models, collection of the data corresponding to that feature may be halted. In some cases there is a real cost to making a feature available, such as the labor of extracting it from its source location or even paying a vendor for the data.
… In some cases, it may be desirable to produce a model that uses as few features as possible to make predictions…Knowing which features are important help may help the user improve predictive models”.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Appel et al. (US2020/0134545) in view of Achin et al. (US2018/0046926) in view of non-patent literature Excel Functions (https://www.techonthenet.com/excel/formulas/sign.php; captured in 2017) as applied to claims 1 and 8 above, further in view of non-patent literature Microsoft (https://www.microsoft.com/en-us/microsoft-365/blog/2015/08/04/introducing-the-waterfall-chart-a-deep-dive-to-a-more-streamlined-chart/, 2015).
As per dependent Claim 7 and Claim 14,
Appel/Achin/Excel Functions teaches the system of Claim 1 and the method of Claim 8.
Appel/Achin/Excel Functions does not teach, but Microsoft teaches:
wherein the visualization comprises a waterfall chart (page 10 the chart is perfect for highlighting the positive and negative contributions that ultimately derive the outcome of any data)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Appel/Achin/Excel Functions invention with Microsoft with the motivation of improving visualization for the user. See effective visualization (page 11) and customized to help improve effectiveness (page 9).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Niininen et al. (US2018/0150758) teaches a predictive ML model trained to classify alerts.
Carstens et al. (US2018/0197128) teaches generating supply chain graphs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lisa Ma whose telephone number is (571)272-2495. The examiner can normally be reached Monday to Thursday 7 AM - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shannon Campbell can be reached at (571)272-5587. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L.M./Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628
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